diff --git a/.gitattributes b/.gitattributes
index c7d9f3332a950355d5a77d85000f05e6f45435ea..674c654a8495256720a116701fc99c9e108740d7 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -32,3 +32,25 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S2_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S3_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_bs/TMA_bs.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S1.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S2.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/DD3S3.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_data/TMA.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/all_Samples_Set_A.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S1_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S2_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/DD3S3_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_mt/TMA_mt.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S1_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S2_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S3_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_qc_eda/TMA_qc_eda.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S1_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S2_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S3_zscore.csv filter=lfs diff=lfs merge=lfs -text
+wetransfer_data-zip_2024-05-17_1431/test_zscore/TMA_zscore.csv filter=lfs diff=lfs merge=lfs -text
diff --git a/wetransfer_data-zip_2024-05-17_1431/1_qc_eda-Copy1.ipynb b/wetransfer_data-zip_2024-05-17_1431/1_qc_eda-Copy1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47cae6ffb458ab01ef00fe2eaf868bf933026ace
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/1_qc_eda-Copy1.ipynb
@@ -0,0 +1,4904 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5fad9226-7ffa-41fc-a6d8-55dcdd1c9eb5",
+   "metadata": {},
+   "source": [
+    "# CyCIF PYTHON PIPELINE"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8337a11b-813a-4abd-8a5e-45a1dcde430f",
+   "metadata": {},
+   "source": [
+    "This sequence of Jupiter Notebooks is designed to run the CyCIF analysis after the ASHLAR registratiom step.\n",
+    "When a '*' is on a part name (see the table of content to your left), it means the user have an input to give.\n",
+    "The pipeline is composed of 5 Notebooks (see README file)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b047ee-225e-415d-a0ae-17f30c0d5f35",
+   "metadata": {},
+   "source": [
+    "# I. QC/EDA NOTEBOOK"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "fbb817d5-aee1-447a-a8dd-786bbd3cc381",
+   "metadata": {},
+   "source": [
+    "10/01/24\n",
+    "Modifications by Zoé Gerber\n",
+    "from an original code from Marilyne Labrie"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "a48dc9ff-3dd1-4f86-bbb5-68bc6254b787",
+   "metadata": {},
+   "source": [
+    "I.1. PACKAGES IMPORT\n",
+    "I.2. DIRECTORIES\n",
+    "I.3. FILES\n",
+    "    I.3.1. DATA\n",
+    "    I.3.2. NOT_INTENSITIES\n",
+    "I.4. QC CHECKS\n",
+    "I.5. COLUMNS OF INTERESTS\n",
+    "I.6. EXPOSURE TIME\n",
+    "I.7. COLORS WORKFLOW\n",
+    "    I.7.1. CHANNELS COLORS\n",
+    "    I.7.2. ROUNDS COLORS\n",
+    "    I.7.3. SAMPLES COLORS\n",
+    "    I.7.4. CLUSTERS COLORS\n",
+    "I.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46755cfd-7896-4ca0-ba50-6792612e7650",
+   "metadata": {},
+   "source": [
+    "## I.1. PACKAGES IMPORT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d52f4de4-6db2-4590-9574-326ccf5bc97e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "import os\n",
+    "import plotly as plt\n",
+    "import seaborn as sb\n",
+    "\n",
+    "from my_modules import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "da8aaae9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: panel in /opt/anaconda3/lib/python3.11/site-packages (1.3.8)\n",
+      "Requirement already satisfied: watchfiles in /opt/anaconda3/lib/python3.11/site-packages (0.21.0)\n",
+      "Requirement already satisfied: bokeh<3.4.0,>=3.2.0 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (3.3.4)\n",
+      "Requirement already satisfied: param<3.0,>=2.0.0 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.0.2)\n",
+      "Requirement already satisfied: pyviz-comms>=2.0.0 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.3.0)\n",
+      "Requirement already satisfied: xyzservices>=2021.09.1 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2022.9.0)\n",
+      "Requirement already satisfied: markdown in /opt/anaconda3/lib/python3.11/site-packages (from panel) (3.4.1)\n",
+      "Requirement already satisfied: markdown-it-py in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.2.0)\n",
+      "Requirement already satisfied: linkify-it-py in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.0.0)\n",
+      "Requirement already satisfied: mdit-py-plugins in /opt/anaconda3/lib/python3.11/site-packages (from panel) (0.3.0)\n",
+      "Requirement already satisfied: requests in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.31.0)\n",
+      "Requirement already satisfied: tqdm>=4.48.0 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (4.65.0)\n",
+      "Requirement already satisfied: bleach in /opt/anaconda3/lib/python3.11/site-packages (from panel) (4.1.0)\n",
+      "Requirement already satisfied: typing-extensions in /opt/anaconda3/lib/python3.11/site-packages (from panel) (4.9.0)\n",
+      "Requirement already satisfied: pandas>=1.2 in /opt/anaconda3/lib/python3.11/site-packages (from panel) (2.1.4)\n",
+      "Requirement already satisfied: anyio>=3.0.0 in /opt/anaconda3/lib/python3.11/site-packages (from watchfiles) (4.2.0)\n",
+      "Requirement already satisfied: idna>=2.8 in /opt/anaconda3/lib/python3.11/site-packages (from anyio>=3.0.0->watchfiles) (3.4)\n",
+      "Requirement already satisfied: sniffio>=1.1 in /opt/anaconda3/lib/python3.11/site-packages (from anyio>=3.0.0->watchfiles) (1.3.0)\n",
+      "Requirement already satisfied: Jinja2>=2.9 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (3.1.3)\n",
+      "Requirement already satisfied: contourpy>=1 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (1.2.0)\n",
+      "Requirement already satisfied: numpy>=1.16 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (1.26.4)\n",
+      "Requirement already satisfied: packaging>=16.8 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (23.1)\n",
+      "Requirement already satisfied: pillow>=7.1.0 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (10.2.0)\n",
+      "Requirement already satisfied: PyYAML>=3.10 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (6.0.1)\n",
+      "Requirement already satisfied: tornado>=5.1 in /opt/anaconda3/lib/python3.11/site-packages (from bokeh<3.4.0,>=3.2.0->panel) (6.3.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/lib/python3.11/site-packages (from pandas>=1.2->panel) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.11/site-packages (from pandas>=1.2->panel) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /opt/anaconda3/lib/python3.11/site-packages (from pandas>=1.2->panel) (2023.3)\n",
+      "Requirement already satisfied: six>=1.9.0 in /opt/anaconda3/lib/python3.11/site-packages (from bleach->panel) (1.16.0)\n",
+      "Requirement already satisfied: webencodings in /opt/anaconda3/lib/python3.11/site-packages (from bleach->panel) (0.5.1)\n",
+      "Requirement already satisfied: uc-micro-py in /opt/anaconda3/lib/python3.11/site-packages (from linkify-it-py->panel) (1.0.1)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /opt/anaconda3/lib/python3.11/site-packages (from markdown-it-py->panel) (0.1.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/lib/python3.11/site-packages (from requests->panel) (2.0.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/lib/python3.11/site-packages (from requests->panel) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.11/site-packages (from requests->panel) (2024.2.2)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/lib/python3.11/site-packages (from Jinja2>=2.9->bokeh<3.4.0,>=3.2.0->panel) (2.1.3)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install panel watchfiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "efdf3910",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pygments in /opt/anaconda3/lib/python3.11/site-packages (2.15.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install pygments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1e483a15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import panel as pn\n",
+    "import holoviews as hv\n",
+    "import hvplot.pandas\n",
+    "from my_modules import apply_header_changes, apply_df_changes, compare_headers, add_metadata_location, shorten_feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9936cc24-34f7-41b5-b184-94a8f2237b05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Silence FutureWarnings & UserWarnings\n",
+    "warnings.filterwarnings('ignore', category= FutureWarning)\n",
+    "warnings.filterwarnings('ignore', category= UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "73290289",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
+       "  var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+       "  var reloading = true;\n",
+       "  var Bokeh = root.Bokeh;\n",
+       "\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "    if (js_modules == null) js_modules = [];\n",
+       "    if (js_exports == null) js_exports = {};\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (!reloading) {\n",
+       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    }\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "    window._bokeh_on_load = on_load\n",
+       "\n",
+       "    function on_error() {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    var skip = [];\n",
+       "    if (window.requirejs) {\n",
+       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
+       "      require([\"jspanel\"], function(jsPanel) {\n",
+       "\twindow.jsPanel = jsPanel\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-modal\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-tooltip\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-hint\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-layout\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-contextmenu\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"jspanel-dock\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"gridstack\"], function(GridStack) {\n",
+       "\twindow.GridStack = GridStack\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      require([\"notyf\"], function() {\n",
+       "\ton_load()\n",
+       "      })\n",
+       "      root._bokeh_is_loading = css_urls.length + 9;\n",
+       "    } else {\n",
+       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
+       "    }\n",
+       "\n",
+       "    var existing_stylesheets = []\n",
+       "    var links = document.getElementsByTagName('link')\n",
+       "    for (var i = 0; i < links.length; i++) {\n",
+       "      var link = links[i]\n",
+       "      if (link.href != null) {\n",
+       "\texisting_stylesheets.push(link.href)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < css_urls.length; i++) {\n",
+       "      var url = css_urls[i];\n",
+       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
+       "\ton_load()\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
+       "      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
+       "      for (var i = 0; i < urls.length; i++) {\n",
+       "        skip.push(urls[i])\n",
+       "      }\n",
+       "    }    var existing_scripts = []\n",
+       "    var scripts = document.getElementsByTagName('script')\n",
+       "    for (var i = 0; i < scripts.length; i++) {\n",
+       "      var script = scripts[i]\n",
+       "      if (script.src != null) {\n",
+       "\texisting_scripts.push(script.src)\n",
+       "      }\n",
+       "    }\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (var i = 0; i < js_modules.length; i++) {\n",
+       "      var url = js_modules[i];\n",
+       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    for (const name in js_exports) {\n",
+       "      var url = js_exports[name];\n",
+       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
+       "\tif (!window.requirejs) {\n",
+       "\t  on_load();\n",
+       "\t}\n",
+       "\tcontinue;\n",
+       "      }\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.type = \"module\";\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      element.textContent = `\n",
+       "      import ${name} from \"${url}\"\n",
+       "      window.${name} = ${name}\n",
+       "      window._bokeh_on_load()\n",
+       "      `\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "    if (!js_urls.length && !js_modules.length) {\n",
+       "      on_load()\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  var js_urls = [];\n",
+       "  var js_modules = [];\n",
+       "  var js_exports = {};\n",
+       "  var css_urls = [];\n",
+       "  var inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {} // ensure no trailing comma for IE\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
+       "      for (var i = 0; i < inline_js.length; i++) {\n",
+       "\ttry {\n",
+       "          inline_js[i].call(root, root.Bokeh);\n",
+       "\t} catch(e) {\n",
+       "\t  if (!reloading) {\n",
+       "\t    throw e;\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "      // Cache old bokeh versions\n",
+       "      if (Bokeh != undefined && !reloading) {\n",
+       "\tvar NewBokeh = root.Bokeh;\n",
+       "\tif (Bokeh.versions === undefined) {\n",
+       "\t  Bokeh.versions = new Map();\n",
+       "\t}\n",
+       "\tif (NewBokeh.version !== Bokeh.version) {\n",
+       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
+       "\t}\n",
+       "\troot.Bokeh = Bokeh;\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    }\n",
+       "    root._bokeh_is_initializing = false\n",
+       "  }\n",
+       "\n",
+       "  function load_or_wait() {\n",
+       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
+       "    // versions of Bokeh and its dependencies at the same time.\n",
+       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
+       "    // to determine whether there is an ongoing attempt to initialize\n",
+       "    // bokeh, however for backward compatibility we also try to ensure\n",
+       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
+       "    // before older versions are fully initialized.\n",
+       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
+       "      root._bokeh_is_initializing = false;\n",
+       "      root._bokeh_onload_callbacks = undefined;\n",
+       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
+       "      load_or_wait();\n",
+       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
+       "      setTimeout(load_or_wait, 100);\n",
+       "    } else {\n",
+       "      root._bokeh_is_initializing = true\n",
+       "      root._bokeh_onload_callbacks = []\n",
+       "      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+       "      if (!reloading && !bokeh_loaded) {\n",
+       "\troot.Bokeh = undefined;\n",
+       "      }\n",
+       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
+       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "\trun_inline_js();\n",
+       "      });\n",
+       "    }\n",
+       "  }\n",
+       "  // Give older versions of the autoload script a head-start to ensure\n",
+       "  // they initialize before we start loading newer version.\n",
+       "  setTimeout(load_or_wait, 100)\n",
+       "}(window));"
+      ],
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = true;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    function JupyterCommManager() {\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        comm_manager.register_target(comm_id, function(comm) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        });\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        });\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "          function processIteratorResult(result) {\n",
+       "            var message = result.value;\n",
+       "            console.log(message)\n",
+       "            var content = {data: message.data, comm_id};\n",
+       "            var buffers = []\n",
+       "            for (var buffer of message.buffers || []) {\n",
+       "              buffers.push(new DataView(buffer))\n",
+       "            }\n",
+       "            var metadata = message.metadata || {};\n",
+       "            var msg = {content, buffers, metadata}\n",
+       "            msg_handler(msg);\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "          return messages.next().then(processIteratorResult);\n",
+       "        })\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
+       "      if (comm_id in window.PyViz.comms) {\n",
+       "        return window.PyViz.comms[comm_id];\n",
+       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
+       "        if (msg_handler) {\n",
+       "          comm.on_msg(msg_handler);\n",
+       "        }\n",
+       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
+       "        comm.open();\n",
+       "        if (msg_handler) {\n",
+       "          comm.onMsg = msg_handler;\n",
+       "        }\n",
+       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
+       "        comm_promise.then((comm) => {\n",
+       "          window.PyViz.comms[comm_id] = comm;\n",
+       "          if (msg_handler) {\n",
+       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
+       "            function processIteratorResult(result) {\n",
+       "              var message = result.value;\n",
+       "              var content = {data: message.data};\n",
+       "              var metadata = message.metadata || {comm_id};\n",
+       "              var msg = {content, metadata}\n",
+       "              msg_handler(msg);\n",
+       "              return messages.next().then(processIteratorResult);\n",
+       "            }\n",
+       "            return messages.next().then(processIteratorResult);\n",
+       "          }\n",
+       "        }) \n",
+       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+       "          return comm_promise.then((comm) => {\n",
+       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
+       "          });\n",
+       "        };\n",
+       "        var comm = {\n",
+       "          send: sendClosure\n",
+       "        };\n",
+       "      }\n",
+       "      window.PyViz.comms[comm_id] = comm;\n",
+       "      return comm;\n",
+       "    }\n",
+       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
+       "    \n",
+       "\n",
+       "\n",
+       "var JS_MIME_TYPE = 'application/javascript';\n",
+       "var HTML_MIME_TYPE = 'text/html';\n",
+       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+       "var CLASS_NAME = 'output';\n",
+       "\n",
+       "/**\n",
+       " * Render data to the DOM node\n",
+       " */\n",
+       "function render(props, node) {\n",
+       "  var div = document.createElement(\"div\");\n",
+       "  var script = document.createElement(\"script\");\n",
+       "  node.appendChild(div);\n",
+       "  node.appendChild(script);\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when a new output is added\n",
+       " */\n",
+       "function handle_add_output(event, handle) {\n",
+       "  var output_area = handle.output_area;\n",
+       "  var output = handle.output;\n",
+       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "    return\n",
+       "  }\n",
+       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "  if (id !== undefined) {\n",
+       "    var nchildren = toinsert.length;\n",
+       "    var html_node = toinsert[nchildren-1].children[0];\n",
+       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var scripts = [];\n",
+       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
+       "    for (var i in nodelist) {\n",
+       "      if (nodelist.hasOwnProperty(i)) {\n",
+       "        scripts.push(nodelist[i])\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    scripts.forEach( function (oldScript) {\n",
+       "      var newScript = document.createElement(\"script\");\n",
+       "      var attrs = [];\n",
+       "      var nodemap = oldScript.attributes;\n",
+       "      for (var j in nodemap) {\n",
+       "        if (nodemap.hasOwnProperty(j)) {\n",
+       "          attrs.push(nodemap[j])\n",
+       "        }\n",
+       "      }\n",
+       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+       "    });\n",
+       "    if (JS_MIME_TYPE in output.data) {\n",
+       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+       "    }\n",
+       "    output_area._hv_plot_id = id;\n",
+       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+       "    } else {\n",
+       "      window.PyViz.plot_index[id] = null;\n",
+       "    }\n",
+       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "    var bk_div = document.createElement(\"div\");\n",
+       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "    var script_attrs = bk_div.children[0].attributes;\n",
+       "    for (var i = 0; i < script_attrs.length; i++) {\n",
+       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "    }\n",
+       "    // store reference to server id on output_area\n",
+       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle when an output is cleared or removed\n",
+       " */\n",
+       "function handle_clear_output(event, handle) {\n",
+       "  var id = handle.cell.output_area._hv_plot_id;\n",
+       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
+       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+       "  if (server_id !== null) {\n",
+       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
+       "    return;\n",
+       "  } else if (comm !== null) {\n",
+       "    comm.send({event_type: 'delete', 'id': id});\n",
+       "  }\n",
+       "  delete PyViz.plot_index[id];\n",
+       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+       "    var doc = window.Bokeh.index[id].model.document\n",
+       "    doc.clear();\n",
+       "    const i = window.Bokeh.documents.indexOf(doc);\n",
+       "    if (i > -1) {\n",
+       "      window.Bokeh.documents.splice(i, 1);\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle kernel restart event\n",
+       " */\n",
+       "function handle_kernel_cleanup(event, handle) {\n",
+       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
+       "  window.PyViz.plot_index = {}\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Handle update_display_data messages\n",
+       " */\n",
+       "function handle_update_output(event, handle) {\n",
+       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+       "  handle_add_output(event, handle)\n",
+       "}\n",
+       "\n",
+       "function register_renderer(events, OutputArea) {\n",
+       "  function append_mime(data, metadata, element) {\n",
+       "    // create a DOM node to render to\n",
+       "    var toinsert = this.create_output_subarea(\n",
+       "    metadata,\n",
+       "    CLASS_NAME,\n",
+       "    EXEC_MIME_TYPE\n",
+       "    );\n",
+       "    this.keyboard_manager.register_events(toinsert);\n",
+       "    // Render to node\n",
+       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "    render(props, toinsert[0]);\n",
+       "    element.append(toinsert);\n",
+       "    return toinsert\n",
+       "  }\n",
+       "\n",
+       "  events.on('output_added.OutputArea', handle_add_output);\n",
+       "  events.on('output_updated.OutputArea', handle_update_output);\n",
+       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
+       "  events.on('delete.Cell', handle_clear_output);\n",
+       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+       "\n",
+       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "    safe: true,\n",
+       "    index: 0\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "if (window.Jupyter !== undefined) {\n",
+       "  try {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  } catch(err) {\n",
+       "  }\n",
+       "}\n"
+      ],
+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='8e44b995-9262-4ec8-9602-2f43b99d97ac'>\n",
+       "  <div id=\"f6ee7cbc-174c-4ec9-a0fe-7fe26b4b5b3c\" data-root-id=\"8e44b995-9262-4ec8-9602-2f43b99d97ac\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"62bd8ea9-7e67-40a6-82b2-d3bf1c07ff56\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"8e44b995-9262-4ec8-9602-2f43b99d97ac\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"8cdad4ce-6015-4fef-8e47-f84066d225a7\",\"attributes\":{\"plot_id\":\"8e44b995-9262-4ec8-9602-2f43b99d97ac\",\"comm_id\":\"2eed1ec9cedb40fc87d646ebc084efe4\",\"client_comm_id\":\"bd2c8c22e4ea4c5d8bbcbebf99982fbc\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"62bd8ea9-7e67-40a6-82b2-d3bf1c07ff56\",\"roots\":{\"8e44b995-9262-4ec8-9602-2f43b99d97ac\":\"f6ee7cbc-174c-4ec9-a0fe-7fe26b4b5b3c\"},\"root_ids\":[\"8e44b995-9262-4ec8-9602-2f43b99d97ac\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "8e44b995-9262-4ec8-9602-2f43b99d97ac"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Initialize Panel extension\n",
+    "pn.extension()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b179f303-c098-4c30-b4b9-df10db6c485a",
+   "metadata": {},
+   "source": [
+    "## I.2. *DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "849b1dd3-c940-4c7a-8cf6-2fd5b4dc43f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set base directory\n",
+    "\n",
+    "directorio_actual = os.getcwd()\n",
+    "print(directorio_actual)\n",
+    "\n",
+    "##### MAC WORKSTATION #####\n",
+    "#base_dir = r'/Volumes/LaboLabrie/Projets/OC_TMA_Pejovic/Temp/Zoe/CyCIF_pipeline/'\n",
+    "###########################\n",
+    "\n",
+    "##### WINDOWS WORKSTATION #####\n",
+    "#base_dir = r'C:\\Users\\LaboLabrie\\gerz2701\\cyCIF-pipeline\\Set_B'\n",
+    "###############################\n",
+    "\n",
+    "##### LOCAL WORKSTATION #####\n",
+    "base_dir = r'/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/'\n",
+    "#############################\n",
+    "\n",
+    "#set_name = 'Set_A'\n",
+    "set_name = 'test'"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "1f68584f-c1f6-49cd-99f8-5a7cc8aae26e",
+   "metadata": {},
+   "source": [
+    "The project is organized as :\n",
+    "main dir \n",
+    "    code\n",
+    "    proj_data             > all input csv files\n",
+    "    proj_metadata         > exposure time csv file, images dir,...\n",
+    "    proj_qc_eda           > csv after the QC/EDA step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e8a4b389-1136-4470-9898-29fd39baf1f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/ directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images directory already exists !\n"
+     ]
+    }
+   ],
+   "source": [
+    "project_name = set_name              # Project name\n",
+    "step_suffix = 'qc_eda'               # Curent part (here part I)\n",
+    "previous_step_suffix_long = \"\"       # Previous part (here empty)\n",
+    "\n",
+    "# Initial input data directory\n",
+    "input_data_dir = os.path.join(base_dir, project_name + \"_data\")\n",
+    "\n",
+    "# QC/EDA output directories\n",
+    "# global output\n",
+    "output_data_dir = os.path.join(base_dir, project_name + \"_\" + step_suffix)\n",
+    "# images subdirectory\n",
+    "output_images_dir = os.path.join(output_data_dir,\"images\")\n",
+    "\n",
+    "# Data and Metadata directories\n",
+    "# global data\n",
+    "metadata_dir = os.path.join(base_dir, project_name + \"_metadata\")\n",
+    "# images subdirectory\n",
+    "metadata_images_dir = os.path.join(metadata_dir,\"images\")\n",
+    "\n",
+    "# Create directories if they don't already exist\n",
+    "for d in [base_dir, input_data_dir, output_data_dir, output_images_dir, metadata_dir, metadata_images_dir]:\n",
+    "    if not os.path.exists(d):\n",
+    "        print(\"Creation of the\" , d, \"directory...\")\n",
+    "        os.makedirs(d)\n",
+    "    else :\n",
+    "        print(\"The\", d, \"directory already exists !\")\n",
+    "\n",
+    "os.chdir(input_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b6e9df25-588a-41b4-a1f5-45bb81f012ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/\n",
+      "input_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data\n",
+      "output_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda\n",
+      "output_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images\n",
+      "metadata_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata\n",
+      "metadata_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify paths\n",
+    "print('base_dir :', base_dir)\n",
+    "print('input_data_dir :', input_data_dir)\n",
+    "print('output_data_dir :', output_data_dir)\n",
+    "print('output_images_dir :', output_images_dir)\n",
+    "print('metadata_dir :', metadata_dir)\n",
+    "print('metadata_images_dir :', metadata_images_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ebdb24-d428-4948-8d9d-485e4591212b",
+   "metadata": {},
+   "source": [
+    "## I.3. FILES"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "b81abd9c-2501-4b31-8c71-aa191c518b31",
+   "metadata": {},
+   "source": [
+    "Don't forget to put your data in the projname_data directory !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b2569b34-ef84-4af6-836d-befe3bdda706",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CSV files were detected:\n",
+      "\n",
+      " ['DD3S1.csv', 'DD3S2.csv', 'DD3S3.csv', 'TMA.csv'] \n",
+      "\n",
+      "in /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data directory.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Listing all the .csv files in the metadata/data directory\n",
+    "# Don't forget to move the csv files into the proj_data directory\n",
+    "# if the data dir is empty it's not going to work \n",
+    "ls_samples = [sample for sample in os.listdir(input_data_dir) if sample.endswith(\".csv\")]\n",
+    "\n",
+    "print(\"The following CSV files were detected:\\n\\n\",[sample for sample in ls_samples], \"\\n\\nin\", input_data_dir, \"directory.\")\n",
+    "\n",
+    "#print(ls_samples[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "591d76f3-8b5b-4dfc-a71a-0d3ec6eeb93e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df :\n",
+      "          Cell Size        Nuc X     Nuc Y Inv  ROI index  Nucleus Size  \\\n",
+      "Cell Id                                                                  \n",
+      "1@1            339  1484.771729  16632.205078          0           127   \n",
+      "\n",
+      "         Nucleus Roundness  AF488 Cell Intensity Average  \\\n",
+      "Cell Id                                                    \n",
+      "1@1                0.95504                   2385.867188   \n",
+      "\n",
+      "         AF488 Cytoplasm Intensity Average  AF488 Nucleus Intensity Average  \\\n",
+      "Cell Id                                                                       \n",
+      "1@1                              2356.6604                       2434.62207   \n",
+      "\n",
+      "         AF555 Cell Intensity Average  ...  r7c2 Nucleus Intensity Average  \\\n",
+      "Cell Id                                ...                                   \n",
+      "1@1                       1358.528076  ...                      290.582672   \n",
+      "\n",
+      "         r8c2 Cell Intensity Average  r8c2 Cytoplasm Intensity Average  \\\n",
+      "Cell Id                                                                  \n",
+      "1@1                       341.790558                         337.82547   \n",
+      "\n",
+      "         r8c2 Nucleus Intensity Average  Sting Cell Intensity Average  \\\n",
+      "Cell Id                                                                 \n",
+      "1@1                          348.409454                   1567.100342   \n",
+      "\n",
+      "         Sting Cytoplasm Intensity Average  Sting Nucleus Intensity Average  \\\n",
+      "Cell Id                                                                       \n",
+      "1@1                             1533.22168                      1623.653564   \n",
+      "\n",
+      "         Vimentin Cell Intensity Average  \\\n",
+      "Cell Id                                    \n",
+      "1@1                          7279.144531   \n",
+      "\n",
+      "         Vimentin Cytoplasm Intensity Average  \\\n",
+      "Cell Id                                         \n",
+      "1@1                               7040.108398   \n",
+      "\n",
+      "         Vimentin Nucleus Intensity Average  \n",
+      "Cell Id                                      \n",
+      "1@1                             7678.165527  \n",
+      "\n",
+      "[1 rows x 141 columns] \n",
+      "\n",
+      "df's columns :\n",
+      " Index(['Cell Size', 'Nuc X', 'Nuc Y Inv', 'ROI index', 'Nucleus Size',\n",
+      "       'Nucleus Roundness', 'AF488 Cell Intensity Average',\n",
+      "       'AF488 Cytoplasm Intensity Average', 'AF488 Nucleus Intensity Average',\n",
+      "       'AF555 Cell Intensity Average',\n",
+      "       ...\n",
+      "       'r7c2 Nucleus Intensity Average', 'r8c2 Cell Intensity Average',\n",
+      "       'r8c2 Cytoplasm Intensity Average', 'r8c2 Nucleus Intensity Average',\n",
+      "       'Sting Cell Intensity Average', 'Sting Cytoplasm Intensity Average',\n",
+      "       'Sting Nucleus Intensity Average', 'Vimentin Cell Intensity Average',\n",
+      "       'Vimentin Cytoplasm Intensity Average',\n",
+      "       'Vimentin Nucleus Intensity Average'],\n",
+      "      dtype='object', length=141) \n",
+      "\n",
+      "df's index :\n",
+      " Index(['1@1'], dtype='object', name='Cell Id') \n",
+      "\n",
+      "df's index name :\n",
+      " Cell Id\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First gather information on expected headers using first file in ls_samples\n",
+    "# Read in the first row of the file corresponding to the first sample (index = 0) in ls_samples\n",
+    "df = pd.read_csv(os.path.join(input_data_dir, ls_samples[0]) , index_col = 0, nrows = 1)\n",
+    "\n",
+    "\n",
+    "# Make sure the file was imported correctly\n",
+    "print(\"df :\\n\", df.head(), \"\\n\")\n",
+    "print(\"df's columns :\\n\", df.columns, \"\\n\")\n",
+    "print(\"df's index :\\n\", df.index, \"\\n\")\n",
+    "print(\"df's index name :\\n\", df.index.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7caa2335-ea22-4973-b50c-7087349c2dc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected the first column in input file (index_col = 0) to be 'ID'. \n",
+      "This column will be used to set the index names (cell number for each sample). \n",
+      "It appears that the column 'Cell Id' was actually the imported as the index column.\n",
+      "A new index name (first column) will be given ('ID') to replace the current one 'Cell Id'\n",
+      "\n",
+      "['Cell_Size' 'Nuc_X' 'Nuc_Y_Inv' 'ROI_index' 'Nucleus_Size'\n",
+      " 'Nucleus_Roundness' 'AF488_Cell_Intensity_Average'\n",
+      " 'AF488_Cytoplasm_Intensity_Average' 'AF488_Nucleus_Intensity_Average'\n",
+      " 'AF555_Cell_Intensity_Average' 'AF555_Cytoplasm_Intensity_Average'\n",
+      " 'AF555_Nucleus_Intensity_Average' 'AF647_Cell_Intensity_Average'\n",
+      " 'AF647_Cytoplasm_Intensity_Average' 'AF647_Nucleus_Intensity_Average'\n",
+      " 'AF750_Cell_Intensity_Average' 'AF750_Cytoplasm_Intensity_Average'\n",
+      " 'AF750_Nucleus_Intensity_Average' 'aSMA_Cell_Intensity_Average'\n",
+      " 'aSMA_Cytoplasm_Intensity_Average' 'aSMA_Nucleus_Intensity_Average'\n",
+      " 'AXL_Cell_Intensity_Average' 'AXL_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Nucleus_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'B7H4_Cytoplasm_Intensity_Average' 'B7H4_Nucleus_Intensity_Average'\n",
+      " 'CA9_Cell_Intensity_Average' 'CA9_Cytoplasm_Intensity_Average'\n",
+      " 'CA9_Nucleus_Intensity_Average' 'CD4_Cell_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'CD4_Nucleus_Intensity_Average'\n",
+      " 'CD8_Cell_Intensity_Average' 'CD8_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Nucleus_Intensity_Average' 'CD11b_Cell_Intensity_Average'\n",
+      " 'CD11b_Cytoplasm_Intensity_Average' 'CD11b_Nucleus_Intensity_Average'\n",
+      " 'CD11c_Cell_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD11c_Nucleus_Intensity_Average' 'CD20_Cell_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'CD20_Nucleus_Intensity_Average'\n",
+      " 'CD31_Cell_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'CD31_Nucleus_Intensity_Average' 'CD44_Cell_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD44_Nucleus_Intensity_Average'\n",
+      " 'CD45_Cell_Intensity_Average' 'CD45_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Nucleus_Intensity_Average' 'CD68_Cell_Intensity_Average'\n",
+      " 'CD68_Cytoplasm_Intensity_Average' 'CD68_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cell_Intensity_Average' 'CD163_Cytoplasm_Intensity_Average'\n",
+      " 'CD163_Nucleus_Intensity_Average' 'CKs_Cell_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'CKs_Nucleus_Intensity_Average'\n",
+      " 'ColVI_Cell_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'ColVI_Nucleus_Intensity_Average' 'DAPI0_Cell_Intensity_Average'\n",
+      " 'DAPI0_Cytoplasm_Intensity_Average' 'DAPI0_Nucleus_Intensity_Average'\n",
+      " 'DAPI1_Cell_Intensity_Average' 'DAPI1_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI1_Nucleus_Intensity_Average' 'DAPI2_Cell_Intensity_Average'\n",
+      " 'DAPI2_Cytoplasm_Intensity_Average' 'DAPI2_Nucleus_Intensity_Average'\n",
+      " 'DAPI3_Cell_Intensity_Average' 'DAPI3_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI3_Nucleus_Intensity_Average' 'DAPI4_Cell_Intensity_Average'\n",
+      " 'DAPI4_Cytoplasm_Intensity_Average' 'DAPI4_Nucleus_Intensity_Average'\n",
+      " 'DAPI5_Cell_Intensity_Average' 'DAPI5_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI5_Nucleus_Intensity_Average' 'DAPI6_Cell_Intensity_Average'\n",
+      " 'DAPI6_Cytoplasm_Intensity_Average' 'DAPI6_Nucleus_Intensity_Average'\n",
+      " 'DAPI7_Cell_Intensity_Average' 'DAPI7_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI7_Nucleus_Intensity_Average' 'DAPI8_Cell_Intensity_Average'\n",
+      " 'DAPI8_Cytoplasm_Intensity_Average' 'DAPI8_Nucleus_Intensity_Average'\n",
+      " 'Desmin_Cell_Intensity_Average' 'Desmin_Cytoplasm_Intensity_Average'\n",
+      " 'Desmin_Nucleus_Intensity_Average' 'Ecad_Cell_Intensity_Average'\n",
+      " 'Ecad_Cytoplasm_Intensity_Average' 'Ecad_Nucleus_Intensity_Average'\n",
+      " 'Fibronectin_Cell_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Nucleus_Intensity_Average' 'FOXP3_Cell_Intensity_Average'\n",
+      " 'FOXP3_Cytoplasm_Intensity_Average' 'FOXP3_Nucleus_Intensity_Average'\n",
+      " 'GATA3_Cell_Intensity_Average' 'GATA3_Cytoplasm_Intensity_Average'\n",
+      " 'GATA3_Nucleus_Intensity_Average' 'HLA_Cell_Intensity_Average'\n",
+      " 'HLA_Cytoplasm_Intensity_Average' 'HLA_Nucleus_Intensity_Average'\n",
+      " 'Ki67_Cell_Intensity_Average' 'Ki67_Cytoplasm_Intensity_Average'\n",
+      " 'Ki67_Nucleus_Intensity_Average' 'MMP9_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'MMP9_Nucleus_Intensity_Average'\n",
+      " 'PD1_Cell_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'PD1_Nucleus_Intensity_Average' 'PDGFR_Cell_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'PDGFR_Nucleus_Intensity_Average'\n",
+      " 'PDL1_Cell_Intensity_Average' 'PDL1_Cytoplasm_Intensity_Average'\n",
+      " 'PDL1_Nucleus_Intensity_Average' 'r5c2_Cell_Intensity_Average'\n",
+      " 'r5c2_Cytoplasm_Intensity_Average' 'r5c2_Nucleus_Intensity_Average'\n",
+      " 'r7c2_Cell_Intensity_Average' 'r7c2_Cytoplasm_Intensity_Average'\n",
+      " 'r7c2_Nucleus_Intensity_Average' 'r8c2_Cell_Intensity_Average'\n",
+      " 'r8c2_Cytoplasm_Intensity_Average' 'r8c2_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cell_Intensity_Average' 'Sting_Cytoplasm_Intensity_Average'\n",
+      " 'Sting_Nucleus_Intensity_Average' 'Vimentin_Cell_Intensity_Average'\n",
+      " 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'Vimentin_Nucleus_Intensity_Average']\n",
+      "\n",
+      "df :\n",
+      "      Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "ID                                                                   \n",
+      "1@1        339  1484.771729  16632.205078          0           127   \n",
+      "\n",
+      "     Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "ID                                                     \n",
+      "1@1            0.95504                   2385.867188   \n",
+      "\n",
+      "     AF488_Cytoplasm_Intensity_Average  AF488_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                          2356.6604                       2434.62207   \n",
+      "\n",
+      "     AF555_Cell_Intensity_Average  ...  r7c2_Nucleus_Intensity_Average  \\\n",
+      "ID                                 ...                                   \n",
+      "1@1                   1358.528076  ...                      290.582672   \n",
+      "\n",
+      "     r8c2_Cell_Intensity_Average  r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                   \n",
+      "1@1                   341.790558                         337.82547   \n",
+      "\n",
+      "     r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "ID                                                                  \n",
+      "1@1                      348.409454                   1567.100342   \n",
+      "\n",
+      "     Sting_Cytoplasm_Intensity_Average  Sting_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                         1533.22168                      1623.653564   \n",
+      "\n",
+      "     Vimentin_Cell_Intensity_Average  Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                           \n",
+      "1@1                      7279.144531                           7040.108398   \n",
+      "\n",
+      "     Vimentin_Nucleus_Intensity_Average  \n",
+      "ID                                       \n",
+      "1@1                         7678.165527  \n",
+      "\n",
+      "[1 rows x 141 columns] \n",
+      "\n",
+      "df's columns :\n",
+      " Index(['Cell_Size', 'Nuc_X', 'Nuc_Y_Inv', 'ROI_index', 'Nucleus_Size',\n",
+      "       'Nucleus_Roundness', 'AF488_Cell_Intensity_Average',\n",
+      "       'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus_Intensity_Average',\n",
+      "       'AF555_Cell_Intensity_Average',\n",
+      "       ...\n",
+      "       'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average',\n",
+      "       'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus_Intensity_Average',\n",
+      "       'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm_Intensity_Average',\n",
+      "       'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell_Intensity_Average',\n",
+      "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+      "       'Vimentin_Nucleus_Intensity_Average'],\n",
+      "      dtype='object', length=141) \n",
+      "\n",
+      "df's index :\n",
+      " Index(['1@1'], dtype='object', name='ID') \n",
+      "\n",
+      "df's index name :\n",
+      " ID\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify that the ID column in input file became the index\n",
+    "# Verify that the index name column is \"ID\", if not, rename it\n",
+    "if df.index.name != \"ID\":\n",
+    "    print(\"Expected the first column in input file (index_col = 0) to be 'ID'. \\n\"\n",
+    "          \"This column will be used to set the index names (cell number for each sample). \\n\"\n",
+    "          \"It appears that the column '\" + df.index.name + \"' was actually the imported as the index column.\")\n",
+    "    #df.index.name = 'ID'\n",
+    "    print(\"A new index name (first column) will be given ('ID') to replace the current one '\" + df.index.name + \"'\\n\")\n",
+    "\n",
+    "# Apply the changes to the headers as specified with apply_header_changes() function (in my_modules.py)\n",
+    "# Apply the changes to the dataframe rows as specified with apply_df_changes() function (in my_modules.py)\n",
+    "df = apply_header_changes(df)\n",
+    "df = apply_df_changes(df)\n",
+    "\n",
+    "# Set variable to hold default header values\n",
+    "expected_headers = df.columns.values\n",
+    "print(expected_headers)\n",
+    "\n",
+    "# Make sure the file is now formated correctly\n",
+    "print(\"\\ndf :\\n\", df.head(), \"\\n\")\n",
+    "print(\"df's columns :\\n\", df.columns, \"\\n\")\n",
+    "print(\"df's index :\\n\", df.index, \"\\n\")\n",
+    "print(\"df's index name :\\n\", df.index.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1e7448a1-b156-4d5e-9698-e94ebe9ef7b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Used DD3S1.csv to determine the expected and corrected headers for all files.\n",
+      "\n",
+      "These headers are: \n",
+      "Cell_Size, Nuc_X, Nuc_Y_Inv, ROI_index, Nucleus_Size, Nucleus_Roundness, AF488_Cell_Intensity_Average, AF488_Cytoplasm_Intensity_Average, AF488_Nucleus_Intensity_Average, AF555_Cell_Intensity_Average, AF555_Cytoplasm_Intensity_Average, AF555_Nucleus_Intensity_Average, AF647_Cell_Intensity_Average, AF647_Cytoplasm_Intensity_Average, AF647_Nucleus_Intensity_Average, AF750_Cell_Intensity_Average, AF750_Cytoplasm_Intensity_Average, AF750_Nucleus_Intensity_Average, aSMA_Cell_Intensity_Average, aSMA_Cytoplasm_Intensity_Average, aSMA_Nucleus_Intensity_Average, AXL_Cell_Intensity_Average, AXL_Cytoplasm_Intensity_Average, AXL_Nucleus_Intensity_Average, B7H4_Cell_Intensity_Average, B7H4_Cytoplasm_Intensity_Average, B7H4_Nucleus_Intensity_Average, CA9_Cell_Intensity_Average, CA9_Cytoplasm_Intensity_Average, CA9_Nucleus_Intensity_Average, CD4_Cell_Intensity_Average, CD4_Cytoplasm_Intensity_Average, CD4_Nucleus_Intensity_Average, CD8_Cell_Intensity_Average, CD8_Cytoplasm_Intensity_Average, CD8_Nucleus_Intensity_Average, CD11b_Cell_Intensity_Average, CD11b_Cytoplasm_Intensity_Average, CD11b_Nucleus_Intensity_Average, CD11c_Cell_Intensity_Average, CD11c_Cytoplasm_Intensity_Average, CD11c_Nucleus_Intensity_Average, CD20_Cell_Intensity_Average, CD20_Cytoplasm_Intensity_Average, CD20_Nucleus_Intensity_Average, CD31_Cell_Intensity_Average, CD31_Cytoplasm_Intensity_Average, CD31_Nucleus_Intensity_Average, CD44_Cell_Intensity_Average, CD44_Cytoplasm_Intensity_Average, CD44_Nucleus_Intensity_Average, CD45_Cell_Intensity_Average, CD45_Cytoplasm_Intensity_Average, CD45_Nucleus_Intensity_Average, CD68_Cell_Intensity_Average, CD68_Cytoplasm_Intensity_Average, CD68_Nucleus_Intensity_Average, CD163_Cell_Intensity_Average, CD163_Cytoplasm_Intensity_Average, CD163_Nucleus_Intensity_Average, CKs_Cell_Intensity_Average, CKs_Cytoplasm_Intensity_Average, CKs_Nucleus_Intensity_Average, ColVI_Cell_Intensity_Average, ColVI_Cytoplasm_Intensity_Average, ColVI_Nucleus_Intensity_Average, DAPI0_Cell_Intensity_Average, DAPI0_Cytoplasm_Intensity_Average, DAPI0_Nucleus_Intensity_Average, DAPI1_Cell_Intensity_Average, DAPI1_Cytoplasm_Intensity_Average, DAPI1_Nucleus_Intensity_Average, DAPI2_Cell_Intensity_Average, DAPI2_Cytoplasm_Intensity_Average, DAPI2_Nucleus_Intensity_Average, DAPI3_Cell_Intensity_Average, DAPI3_Cytoplasm_Intensity_Average, DAPI3_Nucleus_Intensity_Average, DAPI4_Cell_Intensity_Average, DAPI4_Cytoplasm_Intensity_Average, DAPI4_Nucleus_Intensity_Average, DAPI5_Cell_Intensity_Average, DAPI5_Cytoplasm_Intensity_Average, DAPI5_Nucleus_Intensity_Average, DAPI6_Cell_Intensity_Average, DAPI6_Cytoplasm_Intensity_Average, DAPI6_Nucleus_Intensity_Average, DAPI7_Cell_Intensity_Average, DAPI7_Cytoplasm_Intensity_Average, DAPI7_Nucleus_Intensity_Average, DAPI8_Cell_Intensity_Average, DAPI8_Cytoplasm_Intensity_Average, DAPI8_Nucleus_Intensity_Average, Desmin_Cell_Intensity_Average, Desmin_Cytoplasm_Intensity_Average, Desmin_Nucleus_Intensity_Average, Ecad_Cell_Intensity_Average, Ecad_Cytoplasm_Intensity_Average, Ecad_Nucleus_Intensity_Average, Fibronectin_Cell_Intensity_Average, Fibronectin_Cytoplasm_Intensity_Average, Fibronectin_Nucleus_Intensity_Average, FOXP3_Cell_Intensity_Average, FOXP3_Cytoplasm_Intensity_Average, FOXP3_Nucleus_Intensity_Average, GATA3_Cell_Intensity_Average, GATA3_Cytoplasm_Intensity_Average, GATA3_Nucleus_Intensity_Average, HLA_Cell_Intensity_Average, HLA_Cytoplasm_Intensity_Average, HLA_Nucleus_Intensity_Average, Ki67_Cell_Intensity_Average, Ki67_Cytoplasm_Intensity_Average, Ki67_Nucleus_Intensity_Average, MMP9_Cell_Intensity_Average, MMP9_Cytoplasm_Intensity_Average, MMP9_Nucleus_Intensity_Average, PD1_Cell_Intensity_Average, PD1_Cytoplasm_Intensity_Average, PD1_Nucleus_Intensity_Average, PDGFR_Cell_Intensity_Average, PDGFR_Cytoplasm_Intensity_Average, PDGFR_Nucleus_Intensity_Average, PDL1_Cell_Intensity_Average, PDL1_Cytoplasm_Intensity_Average, PDL1_Nucleus_Intensity_Average, r5c2_Cell_Intensity_Average, r5c2_Cytoplasm_Intensity_Average, r5c2_Nucleus_Intensity_Average, r7c2_Cell_Intensity_Average, r7c2_Cytoplasm_Intensity_Average, r7c2_Nucleus_Intensity_Average, r8c2_Cell_Intensity_Average, r8c2_Cytoplasm_Intensity_Average, r8c2_Nucleus_Intensity_Average, Sting_Cell_Intensity_Average, Sting_Cytoplasm_Intensity_Average, Sting_Nucleus_Intensity_Average, Vimentin_Cell_Intensity_Average, Vimentin_Cytoplasm_Intensity_Average, Vimentin_Nucleus_Intensity_Average\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Used \" + ls_samples[0] + \" to determine the expected and corrected headers for all files.\\n\")\n",
+    "print(\"These headers are: \\n\" + \", \".join([h for h in expected_headers]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d0e4670c-acd0-4183-ad09-4b0442abb2ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DD3S1.csv file is processed !\n",
+      "\n",
+      "DD3S2.csv file is processed !\n",
+      "\n",
+      "DD3S3.csv file is processed !\n",
+      "\n",
+      "TMA.csv file is processed !\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import all the others files\n",
+    "dfs = {}\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "for sample in ls_samples:\n",
+    "    file_path = os.path.join(input_data_dir,sample)\n",
+    "    \n",
+    "    try:\n",
+    "        # Read the CSV file\n",
+    "        df = pd.read_csv(file_path, index_col=0)\n",
+    "        # Check if the DataFrame is empty, if so, don't continue trying to process df and remove it\n",
+    "        \n",
+    "        if not df.empty:\n",
+    "            # Manipulations necessary for concatenation\n",
+    "            df = apply_header_changes(df)\n",
+    "            df = apply_df_changes(df)\n",
+    "            # Reorder the columns to match the expected headers list\n",
+    "            df = df.reindex(columns=expected_headers)\n",
+    "            print(sample, \"file is processed !\\n\")\n",
+    "            #print(df) \n",
+    "    \n",
+    "            # Compare df's header df against what is expected\n",
+    "            compare_headers(expected_headers, df.columns.values, sample)\n",
+    "            #print(df.columns.values)\n",
+    "            # Add a new colunm to identify the csv file (sample) where the df comes from\n",
+    "            df['Sample_ID'] = sample \n",
+    "   \n",
+    "    except pd.errors.EmptyDataError:\n",
+    "        print(f'\\nEmpty data error in {sample} file. Removing from analysis...')\n",
+    "        ls_samples.remove(sample)      \n",
+    "    \n",
+    "    # Add df to dfs \n",
+    "    dfs[sample] = df\n",
+    "\n",
+    "#print(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "69a6106c-8106-427c-8d53-abc26c9db6e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "ID                                                                   \n",
+      "1@1        339  1484.771729  16632.205078          0           127   \n",
+      "2@1        344  1426.250000  16627.384766          0           112   \n",
+      "3@1        422  1531.110474  16622.238281          0           181   \n",
+      "4@1        278  1518.907593  16623.007812          0           119   \n",
+      "5@1        502  1488.051758  16616.375000          0           232   \n",
+      "\n",
+      "     Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "ID                                                     \n",
+      "1@1           0.955040                   2385.867188   \n",
+      "2@1           0.966643                   2818.250000   \n",
+      "3@1           0.721534                   2162.047363   \n",
+      "4@1           0.587196                   2422.715820   \n",
+      "5@1           0.655828                   2265.306885   \n",
+      "\n",
+      "     AF488_Cytoplasm_Intensity_Average  AF488_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                        2356.660400                      2434.622070   \n",
+      "2@1                        2884.366455                      2681.294678   \n",
+      "3@1                        2124.817383                      2211.618896   \n",
+      "4@1                        2411.867920                      2437.210205   \n",
+      "5@1                        2154.796387                      2393.918213   \n",
+      "\n",
+      "     AF555_Cell_Intensity_Average  ...  r8c2_Cell_Intensity_Average  \\\n",
+      "ID                                 ...                                \n",
+      "1@1                   1358.528076  ...                   341.790558   \n",
+      "2@1                   1472.325562  ...                   365.531982   \n",
+      "3@1                   1289.054443  ...                   320.874420   \n",
+      "4@1                   1397.992798  ...                   343.320129   \n",
+      "5@1                   1288.657349  ...                   326.241028   \n",
+      "\n",
+      "     r8c2_Cytoplasm_Intensity_Average  r8c2_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                      \n",
+      "1@1                        337.825470                      348.409454   \n",
+      "2@1                        369.340515                      357.642853   \n",
+      "3@1                        315.605804                      327.889496   \n",
+      "4@1                        338.679260                      349.520996   \n",
+      "5@1                        314.748138                      339.616394   \n",
+      "\n",
+      "     Sting_Cell_Intensity_Average  Sting_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                     \n",
+      "1@1                   1567.100342                        1533.221680   \n",
+      "2@1                   1508.014526                        1565.086182   \n",
+      "3@1                   1841.360229                        1772.647339   \n",
+      "4@1                   1723.863281                        1688.094360   \n",
+      "5@1                   1711.464111                        1629.670410   \n",
+      "\n",
+      "     Sting_Nucleus_Intensity_Average  Vimentin_Cell_Intensity_Average  \\\n",
+      "ID                                                                      \n",
+      "1@1                      1623.653564                      7279.144531   \n",
+      "2@1                      1389.794678                      6123.456543   \n",
+      "3@1                      1932.850830                      4252.185059   \n",
+      "4@1                      1771.655518                      6178.647461   \n",
+      "5@1                      1806.655151                      5208.479980   \n",
+      "\n",
+      "     Vimentin_Cytoplasm_Intensity_Average  Vimentin_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                              \n",
+      "1@1                           7040.108398                         7678.165527   \n",
+      "2@1                           6734.603516                         4857.508789   \n",
+      "3@1                           4473.178223                         3957.933594   \n",
+      "4@1                           5316.924316                         7330.025391   \n",
+      "5@1                           4386.700195                         6164.862305   \n",
+      "\n",
+      "     Sample_ID  \n",
+      "ID              \n",
+      "1@1  DD3S1.csv  \n",
+      "2@1  DD3S1.csv  \n",
+      "3@1  DD3S1.csv  \n",
+      "4@1  DD3S1.csv  \n",
+      "5@1  DD3S1.csv  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge dfs into one df\n",
+    "df = pd.concat(dfs.values(), ignore_index=False , sort = False)\n",
+    "#del dfs\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5c724db9-eb76-4af1-8fe3-8beff43d8940",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "DD3S1_Cell_0        339  1484.771729  16632.205078          0           127   \n",
+      "DD3S1_Cell_1        344  1426.250000  16627.384766          0           112   \n",
+      "DD3S1_Cell_2        422  1531.110474  16622.238281          0           181   \n",
+      "DD3S1_Cell_3        278  1518.907593  16623.007812          0           119   \n",
+      "DD3S1_Cell_4        502  1488.051758  16616.375000          0           232   \n",
+      "\n",
+      "              Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0           0.955040                   2385.867188   \n",
+      "DD3S1_Cell_1           0.966643                   2818.250000   \n",
+      "DD3S1_Cell_2           0.721534                   2162.047363   \n",
+      "DD3S1_Cell_3           0.587196                   2422.715820   \n",
+      "DD3S1_Cell_4           0.655828                   2265.306885   \n",
+      "\n",
+      "              AF488_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        2356.660400   \n",
+      "DD3S1_Cell_1                        2884.366455   \n",
+      "DD3S1_Cell_2                        2124.817383   \n",
+      "DD3S1_Cell_3                        2411.867920   \n",
+      "DD3S1_Cell_4                        2154.796387   \n",
+      "\n",
+      "              AF488_Nucleus_Intensity_Average  AF555_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      2434.622070                   1358.528076   \n",
+      "DD3S1_Cell_1                      2681.294678                   1472.325562   \n",
+      "DD3S1_Cell_2                      2211.618896                   1289.054443   \n",
+      "DD3S1_Cell_3                      2437.210205                   1397.992798   \n",
+      "DD3S1_Cell_4                      2393.918213                   1288.657349   \n",
+      "\n",
+      "              ...  r8c2_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0  ...                   341.790558   \n",
+      "DD3S1_Cell_1  ...                   365.531982   \n",
+      "DD3S1_Cell_2  ...                   320.874420   \n",
+      "DD3S1_Cell_3  ...                   343.320129   \n",
+      "DD3S1_Cell_4  ...                   326.241028   \n",
+      "\n",
+      "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        337.825470   \n",
+      "DD3S1_Cell_1                        369.340515   \n",
+      "DD3S1_Cell_2                        315.605804   \n",
+      "DD3S1_Cell_3                        338.679260   \n",
+      "DD3S1_Cell_4                        314.748138   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      348.409454                   1567.100342   \n",
+      "DD3S1_Cell_1                      357.642853                   1508.014526   \n",
+      "DD3S1_Cell_2                      327.889496                   1841.360229   \n",
+      "DD3S1_Cell_3                      349.520996                   1723.863281   \n",
+      "DD3S1_Cell_4                      339.616394                   1711.464111   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        1533.221680   \n",
+      "DD3S1_Cell_1                        1565.086182   \n",
+      "DD3S1_Cell_2                        1772.647339   \n",
+      "DD3S1_Cell_3                        1688.094360   \n",
+      "DD3S1_Cell_4                        1629.670410   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      1623.653564   \n",
+      "DD3S1_Cell_1                      1389.794678   \n",
+      "DD3S1_Cell_2                      1932.850830   \n",
+      "DD3S1_Cell_3                      1771.655518   \n",
+      "DD3S1_Cell_4                      1806.655151   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      7279.144531   \n",
+      "DD3S1_Cell_1                      6123.456543   \n",
+      "DD3S1_Cell_2                      4252.185059   \n",
+      "DD3S1_Cell_3                      6178.647461   \n",
+      "DD3S1_Cell_4                      5208.479980   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                           7040.108398   \n",
+      "DD3S1_Cell_1                           6734.603516   \n",
+      "DD3S1_Cell_2                           4473.178223   \n",
+      "DD3S1_Cell_3                           5316.924316   \n",
+      "DD3S1_Cell_4                           4386.700195   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  Sample_ID  \n",
+      "DD3S1_Cell_0                         7678.165527  DD3S1.csv  \n",
+      "DD3S1_Cell_1                         4857.508789  DD3S1.csv  \n",
+      "DD3S1_Cell_2                         3957.933594  DD3S1.csv  \n",
+      "DD3S1_Cell_3                         7330.025391  DD3S1.csv  \n",
+      "DD3S1_Cell_4                         6164.862305  DD3S1.csv  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set index to Sample_ID + cell number : \n",
+    "# create a new custom index for df based on the sample names and integer cell numbers, and then remove the temporary columns 'level_0' and 'index' that were introduced during the operations\n",
+    "\n",
+    "# Creates a copy of the DataFrame df and resets its index without creating a new column for the old index\n",
+    "# This essentially removes the old index column and replaces it with a default integer index\n",
+    "df = df.copy().reset_index(drop=True)\n",
+    "\n",
+    "#print(df)\n",
+    "\n",
+    "# Initializing an empty list index to store the new index labels for the DataFrame\n",
+    "index = []\n",
+    "\n",
+    "for sample in ls_samples:\n",
+    "    # Extract a chunk of data from the original df where the 'Sample_ID' column matches the current sample name\n",
+    "    # This chunk is stored in the df_chunk df, which is a subset of the original data for that specific sample\n",
+    "    df_chunk = df.loc[df['Sample_ID'] == sample,:].copy()\n",
+    "    old_index = df_chunk.index\n",
+    "    # Reset the index of the df_chunk df, removing the old index and replacing it with a default integer index\n",
+    "    df_chunk = df_chunk.reset_index(drop=True)\n",
+    "    # A new index is created for the df_chunk df. It combines the sample name with 'Cell_' and the integer index values, converting them to strings\n",
+    "    # This new index will have labels like 'SampleName_Cell_0', 'SampleName_Cell_1', and so on.\n",
+    "    sample = sample.split('.')[0]\n",
+    "    df_chunk = df_chunk.set_index(f'{sample}_Cell_' + df_chunk.index.astype(str))\n",
+    "    # The index values of df_chunk are then added to the index list\n",
+    "    index = index + df_chunk.index.values.tolist()\n",
+    "\n",
+    "# After processing all the samples in the loop, assign the index list as the new index of the original df.\n",
+    "df.index =  index\n",
+    "# Remove the 'level_0' and 'index' columns from df\n",
+    "df = df.loc[:,~df.columns.isin(['level_0','index'])]\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c95a292d-72df-4f1e-8a9d-a3de84d99057",
+   "metadata": {},
+   "source": [
+    "### I.3.2. NOT_INTENSITIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8e4bb227-2a4f-477b-a435-e85178d1003a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not_intensities is the list of the columns unrelated to the markers fluorescence intensities\n",
+    "# Can include items that aren't in a given header.\n",
+    "#not_intensitiehttp://localhost:8888/lab/tree/Downloads/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb\n",
+    "#I.3.2.-NOT_INTENSITIESs = ['Nuc_X', 'Nuc_X_Inv', 'Nuc_Y', 'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nucleus_Size', 'Cell_Size', \n",
+    "#                   'ROI_index', 'Sample_ID', 'replicate_ID', 'Cell_ID','cell_type', 'cell_subtype', 'cluster','ID', \n",
+    "#                  'Cytoplasm_Size', 'immune_checkpoint', 'Unique_ROI_index', 'Patient', 'Primary_chem(1)_vs_surg(0)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3a0b81d8-1fa0-45da-92ab-2ee59ef9d51d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not_intensities is the list of the columns unrelated to the markers fluorescence intensities\n",
+    "# Can include items that aren't in a given header.\n",
+    "not_intensities = ['Nuc_X', 'Nuc_X_Inv', 'Nuc_Y', 'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nucleus_Size', 'Cell_Size', \n",
+    "                   'ROI_index', 'Sample_ID', 'replicate_ID', 'Cell_ID','cell_type', 'cell_subtype', 'cluster','ID', \n",
+    "                   'Cytoplasm_Size', 'immune_checkpoint', 'Unique_ROI_index', 'Patient', 'Primary_chem(1)_vs_surg(0)']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "26669861-4eef-43f5-82f8-532f83bd7f9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'not_intensities.csv' already exists.\n",
+      "Reconciling file and Jupyter notebook lists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_not_intensities = os.path.join(metadata_dir,\"not_intensities.csv\")\n",
+    "\n",
+    "# If this file already exists, add only not_intensities items of the list not already present in file\n",
+    "if os.path.exists(path_not_intensities):\n",
+    "    print(\"'not_intensities.csv' already exists.\")\n",
+    "    print(\"Reconciling file and Jupyter notebook lists.\")\n",
+    "    file_not_intensities = open(path_not_intensities, \"r\")\n",
+    "    file_ni = file_not_intensities.read().splitlines()\n",
+    "    # Set difference to identify items not already in file\n",
+    "    to_add = set(not_intensities) - set(file_ni)\n",
+    "    # We want not_intensities to the a complete list\n",
+    "    not_intensities = list(set(file_ni) | set(not_intensities))\n",
+    "    file_not_intensities.close()\n",
+    "    file_not_intensities = open(path_not_intensities, \"a\")\n",
+    "    for item in to_add:\n",
+    "        file_not_intensities.write(item +\"\\n\")\n",
+    "    file_not_intensities.close()\n",
+    "\n",
+    "else:\n",
+    "    # The file does not yet exist\n",
+    "    print(\"Could not find \" + path_not_intensities + \". Creating now.\")\n",
+    "    file_not_intensities = open(path_not_intensities, \"w\")\n",
+    "    for item in not_intensities:\n",
+    "        file_not_intensities.write(item + \"\\n\")\n",
+    "    file_not_intensities.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "175ef4cd-600f-47d6-8e16-583f4a1a0abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ROI_index',\n",
+       " 'Nuc_Y',\n",
+       " 'Nuc_Y_Inv',\n",
+       " 'Nuc_X_Inv',\n",
+       " 'cell_subtype',\n",
+       " 'Cell_ID',\n",
+       " 'Cytoplasm_Size',\n",
+       " 'ID',\n",
+       " 'replicate_ID',\n",
+       " 'Nucleus_Roundness',\n",
+       " 'Patient',\n",
+       " 'Primary_chem(1)_vs_surg(0)',\n",
+       " 'cell_type',\n",
+       " 'immune_checkpoint',\n",
+       " 'Cell_Size',\n",
+       " 'Sample_ID',\n",
+       " 'Nucleus_Size',\n",
+       " 'Nuc_X',\n",
+       " 'cluster',\n",
+       " 'Unique_ROI_index',\n",
+       " 'AF488_Cell_Intensity_Average',\n",
+       " 'AF488_Cytoplasm_Intensity_Average',\n",
+       " 'AF488_Nucleus_Intensity_Average',\n",
+       " 'AF555_Cell_Intensity_Average',\n",
+       " 'AF555_Cytoplasm_Intensity_Average',\n",
+       " 'AF555_Nucleus_Intensity_Average',\n",
+       " 'AF647_Cell_Intensity_Average',\n",
+       " 'AF647_Cytoplasm_Intensity_Average',\n",
+       " 'AF647_Nucleus_Intensity_Average',\n",
+       " 'AF750_Cell_Intensity_Average',\n",
+       " 'AF750_Cytoplasm_Intensity_Average',\n",
+       " 'AF750_Nucleus_Intensity_Average',\n",
+       " 'aSMA_Cell_Intensity_Average',\n",
+       " 'aSMA_Cytoplasm_Intensity_Average',\n",
+       " 'aSMA_Nucleus_Intensity_Average',\n",
+       " 'AXL_Cell_Intensity_Average',\n",
+       " 'AXL_Cytoplasm_Intensity_Average',\n",
+       " 'AXL_Nucleus_Intensity_Average',\n",
+       " 'B7H4_Cell_Intensity_Average',\n",
+       " 'B7H4_Cytoplasm_Intensity_Average',\n",
+       " 'B7H4_Nucleus_Intensity_Average',\n",
+       " 'CA9_Cell_Intensity_Average',\n",
+       " 'CA9_Cytoplasm_Intensity_Average',\n",
+       " 'CA9_Nucleus_Intensity_Average',\n",
+       " 'CD4_Cell_Intensity_Average',\n",
+       " 'CD4_Cytoplasm_Intensity_Average',\n",
+       " 'CD4_Nucleus_Intensity_Average',\n",
+       " 'CD8_Cell_Intensity_Average',\n",
+       " 'CD8_Cytoplasm_Intensity_Average',\n",
+       " 'CD8_Nucleus_Intensity_Average',\n",
+       " 'CD11b_Cell_Intensity_Average',\n",
+       " 'CD11b_Cytoplasm_Intensity_Average',\n",
+       " 'CD11b_Nucleus_Intensity_Average',\n",
+       " 'CD11c_Cell_Intensity_Average',\n",
+       " 'CD11c_Cytoplasm_Intensity_Average',\n",
+       " 'CD11c_Nucleus_Intensity_Average',\n",
+       " 'CD20_Cell_Intensity_Average',\n",
+       " 'CD20_Cytoplasm_Intensity_Average',\n",
+       " 'CD20_Nucleus_Intensity_Average',\n",
+       " 'CD31_Cell_Intensity_Average',\n",
+       " 'CD31_Cytoplasm_Intensity_Average',\n",
+       " 'CD31_Nucleus_Intensity_Average',\n",
+       " 'CD44_Cell_Intensity_Average',\n",
+       " 'CD44_Cytoplasm_Intensity_Average',\n",
+       " 'CD44_Nucleus_Intensity_Average',\n",
+       " 'CD45_Cell_Intensity_Average',\n",
+       " 'CD45_Cytoplasm_Intensity_Average',\n",
+       " 'CD45_Nucleus_Intensity_Average',\n",
+       " 'CD68_Cell_Intensity_Average',\n",
+       " 'CD68_Cytoplasm_Intensity_Average',\n",
+       " 'CD68_Nucleus_Intensity_Average',\n",
+       " 'CD163_Cell_Intensity_Average',\n",
+       " 'CD163_Cytoplasm_Intensity_Average',\n",
+       " 'CD163_Nucleus_Intensity_Average',\n",
+       " 'CKs_Cell_Intensity_Average',\n",
+       " 'CKs_Cytoplasm_Intensity_Average',\n",
+       " 'CKs_Nucleus_Intensity_Average',\n",
+       " 'ColVI_Cell_Intensity_Average',\n",
+       " 'ColVI_Cytoplasm_Intensity_Average',\n",
+       " 'ColVI_Nucleus_Intensity_Average',\n",
+       " 'DAPI0_Cell_Intensity_Average',\n",
+       " 'DAPI0_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI0_Nucleus_Intensity_Average',\n",
+       " 'DAPI1_Cell_Intensity_Average',\n",
+       " 'DAPI1_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI1_Nucleus_Intensity_Average',\n",
+       " 'DAPI2_Cell_Intensity_Average',\n",
+       " 'DAPI2_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI2_Nucleus_Intensity_Average',\n",
+       " 'DAPI3_Cell_Intensity_Average',\n",
+       " 'DAPI3_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI3_Nucleus_Intensity_Average',\n",
+       " 'DAPI4_Cell_Intensity_Average',\n",
+       " 'DAPI4_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI4_Nucleus_Intensity_Average',\n",
+       " 'DAPI5_Cell_Intensity_Average',\n",
+       " 'DAPI5_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI5_Nucleus_Intensity_Average',\n",
+       " 'DAPI6_Cell_Intensity_Average',\n",
+       " 'DAPI6_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI6_Nucleus_Intensity_Average',\n",
+       " 'DAPI7_Cell_Intensity_Average',\n",
+       " 'DAPI7_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI7_Nucleus_Intensity_Average',\n",
+       " 'DAPI8_Cell_Intensity_Average',\n",
+       " 'DAPI8_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI8_Nucleus_Intensity_Average',\n",
+       " 'Desmin_Cell_Intensity_Average',\n",
+       " 'Desmin_Cytoplasm_Intensity_Average',\n",
+       " 'Desmin_Nucleus_Intensity_Average',\n",
+       " 'Ecad_Cell_Intensity_Average',\n",
+       " 'Ecad_Cytoplasm_Intensity_Average',\n",
+       " 'Ecad_Nucleus_Intensity_Average',\n",
+       " 'Fibronectin_Cell_Intensity_Average',\n",
+       " 'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       " 'Fibronectin_Nucleus_Intensity_Average',\n",
+       " 'FOXP3_Cell_Intensity_Average',\n",
+       " 'FOXP3_Cytoplasm_Intensity_Average',\n",
+       " 'FOXP3_Nucleus_Intensity_Average',\n",
+       " 'GATA3_Cell_Intensity_Average',\n",
+       " 'GATA3_Cytoplasm_Intensity_Average',\n",
+       " 'GATA3_Nucleus_Intensity_Average',\n",
+       " 'HLA_Cell_Intensity_Average',\n",
+       " 'HLA_Cytoplasm_Intensity_Average',\n",
+       " 'HLA_Nucleus_Intensity_Average',\n",
+       " 'Ki67_Cell_Intensity_Average',\n",
+       " 'Ki67_Cytoplasm_Intensity_Average',\n",
+       " 'Ki67_Nucleus_Intensity_Average',\n",
+       " 'MMP9_Cell_Intensity_Average',\n",
+       " 'MMP9_Cytoplasm_Intensity_Average',\n",
+       " 'MMP9_Nucleus_Intensity_Average',\n",
+       " 'PD1_Cell_Intensity_Average',\n",
+       " 'PD1_Cytoplasm_Intensity_Average',\n",
+       " 'PD1_Nucleus_Intensity_Average',\n",
+       " 'PDGFR_Cell_Intensity_Average',\n",
+       " 'PDGFR_Cytoplasm_Intensity_Average',\n",
+       " 'PDGFR_Nucleus_Intensity_Average',\n",
+       " 'PDL1_Cell_Intensity_Average',\n",
+       " 'PDL1_Cytoplasm_Intensity_Average',\n",
+       " 'PDL1_Nucleus_Intensity_Average',\n",
+       " 'r5c2_Cell_Intensity_Average',\n",
+       " 'r5c2_Cytoplasm_Intensity_Average',\n",
+       " 'r5c2_Nucleus_Intensity_Average',\n",
+       " 'r7c2_Cell_Intensity_Average',\n",
+       " 'r7c2_Cytoplasm_Intensity_Average',\n",
+       " 'r7c2_Nucleus_Intensity_Average',\n",
+       " 'r8c2_Cell_Intensity_Average',\n",
+       " 'r8c2_Cytoplasm_Intensity_Average',\n",
+       " 'r8c2_Nucleus_Intensity_Average',\n",
+       " 'Sting_Cell_Intensity_Average',\n",
+       " 'Sting_Cytoplasm_Intensity_Average',\n",
+       " 'Sting_Nucleus_Intensity_Average',\n",
+       " 'Vimentin_Cell_Intensity_Average',\n",
+       " 'Vimentin_Cytoplasm_Intensity_Average',\n",
+       " 'Vimentin_Nucleus_Intensity_Average']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Columns we want to keep: not_intensities, and any intensity column that contains 'Intensity_Average' (drop any intensity marker column that is not a mean intensity)\n",
+    "to_keep = not_intensities + [x for x in df.columns.values[~df.columns.isin(not_intensities)] if 'Intensity_Average' in x]\n",
+    "\n",
+    "to_keep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7ecf0c10-605d-486e-9b9e-f3cbb2a349cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              ROI_index     Nuc_Y_Inv  Nucleus_Roundness  Cell_Size  \\\n",
+      "DD3S1_Cell_0          0  16632.205078           0.955040        339   \n",
+      "DD3S1_Cell_1          0  16627.384766           0.966643        344   \n",
+      "DD3S1_Cell_2          0  16622.238281           0.721534        422   \n",
+      "DD3S1_Cell_3          0  16623.007812           0.587196        278   \n",
+      "DD3S1_Cell_4          0  16616.375000           0.655828        502   \n",
+      "\n",
+      "              Sample_ID  Nucleus_Size        Nuc_X  \\\n",
+      "DD3S1_Cell_0  DD3S1.csv           127  1484.771729   \n",
+      "DD3S1_Cell_1  DD3S1.csv           112  1426.250000   \n",
+      "DD3S1_Cell_2  DD3S1.csv           181  1531.110474   \n",
+      "DD3S1_Cell_3  DD3S1.csv           119  1518.907593   \n",
+      "DD3S1_Cell_4  DD3S1.csv           232  1488.051758   \n",
+      "\n",
+      "              AF488_Cell_Intensity_Average  AF488_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                   2385.867188                        2356.660400   \n",
+      "DD3S1_Cell_1                   2818.250000                        2884.366455   \n",
+      "DD3S1_Cell_2                   2162.047363                        2124.817383   \n",
+      "DD3S1_Cell_3                   2422.715820                        2411.867920   \n",
+      "DD3S1_Cell_4                   2265.306885                        2154.796387   \n",
+      "\n",
+      "              AF488_Nucleus_Intensity_Average  ...  \\\n",
+      "DD3S1_Cell_0                      2434.622070  ...   \n",
+      "DD3S1_Cell_1                      2681.294678  ...   \n",
+      "DD3S1_Cell_2                      2211.618896  ...   \n",
+      "DD3S1_Cell_3                      2437.210205  ...   \n",
+      "DD3S1_Cell_4                      2393.918213  ...   \n",
+      "\n",
+      "              r7c2_Nucleus_Intensity_Average  r8c2_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      290.582672                   341.790558   \n",
+      "DD3S1_Cell_1                      304.133942                   365.531982   \n",
+      "DD3S1_Cell_2                      271.353577                   320.874420   \n",
+      "DD3S1_Cell_3                      292.134460                   343.320129   \n",
+      "DD3S1_Cell_4                      284.642242                   326.241028   \n",
+      "\n",
+      "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        337.825470   \n",
+      "DD3S1_Cell_1                        369.340515   \n",
+      "DD3S1_Cell_2                        315.605804   \n",
+      "DD3S1_Cell_3                        338.679260   \n",
+      "DD3S1_Cell_4                        314.748138   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      348.409454                   1567.100342   \n",
+      "DD3S1_Cell_1                      357.642853                   1508.014526   \n",
+      "DD3S1_Cell_2                      327.889496                   1841.360229   \n",
+      "DD3S1_Cell_3                      349.520996                   1723.863281   \n",
+      "DD3S1_Cell_4                      339.616394                   1711.464111   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        1533.221680   \n",
+      "DD3S1_Cell_1                        1565.086182   \n",
+      "DD3S1_Cell_2                        1772.647339   \n",
+      "DD3S1_Cell_3                        1688.094360   \n",
+      "DD3S1_Cell_4                        1629.670410   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      1623.653564   \n",
+      "DD3S1_Cell_1                      1389.794678   \n",
+      "DD3S1_Cell_2                      1932.850830   \n",
+      "DD3S1_Cell_3                      1771.655518   \n",
+      "DD3S1_Cell_4                      1806.655151   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      7279.144531   \n",
+      "DD3S1_Cell_1                      6123.456543   \n",
+      "DD3S1_Cell_2                      4252.185059   \n",
+      "DD3S1_Cell_3                      6178.647461   \n",
+      "DD3S1_Cell_4                      5208.479980   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                           7040.108398   \n",
+      "DD3S1_Cell_1                           6734.603516   \n",
+      "DD3S1_Cell_2                           4473.178223   \n",
+      "DD3S1_Cell_3                           5316.924316   \n",
+      "DD3S1_Cell_4                           4386.700195   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  \n",
+      "DD3S1_Cell_0                         7678.165527  \n",
+      "DD3S1_Cell_1                         4857.508789  \n",
+      "DD3S1_Cell_2                         3957.933594  \n",
+      "DD3S1_Cell_3                         7330.025391  \n",
+      "DD3S1_Cell_4                         6164.862305  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# However, our to_keep list contains items that might not be in our df headers!\n",
+    "# These items are from our not_intensities list. So let's ask for only those items from to_keep that are actually found in our df\n",
+    "# Retains only the columns from the to_keep list that are found in the df's headers (columns). \n",
+    "# This ensures that we are only keeping the columns that exist in your df, avoiding any potential issues with non-existent column names. \n",
+    "# The result is a df containing only the specified columns.\n",
+    "df = df[[x for x in to_keep if x in df.columns.values]]\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "284c8270-e13e-4f93-839b-5774a7fc9f4d",
+   "metadata": {},
+   "source": [
+    "## I.4. QC CHECKS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "91d9bebf-3c62-408e-923e-00447527ae3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DD3S1_Cell_0', 'DD3S1_Cell_1', 'DD3S1_Cell_2', 'DD3S1_Cell_3',\n",
+       "       'DD3S1_Cell_4', 'DD3S1_Cell_5', 'DD3S1_Cell_6', 'DD3S1_Cell_7',\n",
+       "       'DD3S1_Cell_8', 'DD3S1_Cell_9',\n",
+       "       ...\n",
+       "       'TMA_Cell_115751', 'TMA_Cell_115752', 'TMA_Cell_115753',\n",
+       "       'TMA_Cell_115754', 'TMA_Cell_115755', 'TMA_Cell_115756',\n",
+       "       'TMA_Cell_115757', 'TMA_Cell_115758', 'TMA_Cell_115759',\n",
+       "       'TMA_Cell_115760'],\n",
+       "      dtype='object', length=433976)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's take a look at a few features to make sure our dataframe is as expected\n",
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fe138d0d-7b02-48ac-af9e-caf85c4d3b73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(433976, 142)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e1f30957-ab09-416b-8ed5-41b99a5c0b51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "86e29b98-4758-4014-b976-db155fa9205f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All expected filenames are present in big df Sample_ID column.\n",
+      "DD3S1_Cell_0       DD3S1.csv\n",
+      "DD3S1_Cell_1       DD3S1.csv\n",
+      "DD3S1_Cell_2       DD3S1.csv\n",
+      "DD3S1_Cell_3       DD3S1.csv\n",
+      "DD3S1_Cell_4       DD3S1.csv\n",
+      "                     ...    \n",
+      "TMA_Cell_115756      TMA.csv\n",
+      "TMA_Cell_115757      TMA.csv\n",
+      "TMA_Cell_115758      TMA.csv\n",
+      "TMA_Cell_115759      TMA.csv\n",
+      "TMA_Cell_115760      TMA.csv\n",
+      "Name: Sample_ID, Length: 433976, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check that all expected files were imported into final dataframe\n",
+    "if sorted(df.Sample_ID.unique()) == sorted(ls_samples):\n",
+    "    print(\"All expected filenames are present in big df Sample_ID column.\")\n",
+    "else:\n",
+    "    compare_headers(['no samples'], df.Sample_ID.unique(), \"big df Sample_ID column\")\n",
+    "\n",
+    "print(df.Sample_ID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "66e0487d-8a29-400d-bde6-afde02985fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape before removing 0 mean values:  (433976, 142)\n",
+      "No zero intensity values found in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Delete rows that have 0 value mean intensities for intensity columns\n",
+    "print(\"df.shape before removing 0 mean values: \", df.shape)\n",
+    "\n",
+    "# We use the apply method on df to calculate the mean intensity for each row. It's done this by applying a lambda function to each row. \n",
+    "# The lambda function excludes the columns listed in the not_intensities list (which are not to be considered for mean intensity calculations) \n",
+    "# and calculates the mean of the remaining values in each row.\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "# Calculate mean intensity excluding 'not_intensities' columns\n",
+    "mean_intensity = df.loc[:, ~df.columns.isin(not_intensities)].mean(axis=1)\n",
+    "\n",
+    "# Check if there are any 0 mean intensity values\n",
+    "if (mean_intensity == 0).any():\n",
+    "    df = df.loc[mean_intensity > 0, :]\n",
+    "    print(\"df.shape after removing 0 mean values: \", df.shape)\n",
+    "else:\n",
+    "    print(\"No zero intensity values found in the DataFrame.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "93ff0fac-33ac-4120-b1cf-f3d413489b58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get quantiles (5th, 50th, 95th)\n",
+    "# List of nucleus size percentiles to extract \n",
+    "qs = [0.05,0.50,0.95] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "86b0158b-6cb1-4ff8-815b-26aeed908b4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05     42.0\n",
+       "0.50     88.0\n",
+       "0.95    217.0\n",
+       "Name: Nucleus_Size, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Nucleus_Size\"].quantile(q=qs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d7d6bf19-f83c-47b6-8e3a-a32d78e64a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/Nucleus_Size_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['Nucleus_Size'], bins=30, alpha=0.7, color='skyblue')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['Nucleus_Size'].quantile(0.05)\n",
+    "quantile_95 = df['Nucleus_Size'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of Nucleus_Size column values with horizontal bars at 0.05 and 0.95 quantiles')\n",
+    "ax.set_xlabel('Nucleus_Size Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantiles values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot in the output_images_dir directory using fig.savefig\n",
+    "plot_file_path = os.path.join(output_images_dir, \"Nucleus_Size_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "fe562d2b-5026-4a88-8117-4ec910dd1242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.05    1019.689148\n",
+      "0.50    1346.929932\n",
+      "0.95    2622.449524\n",
+      "Name: AF555_Cell_Intensity_Average, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"AF555_Cell_Intensity_Average\"].quantile(q=qs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8b53dced-802d-4bfd-8e37-de33ffec9c7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/AF555_Cell_Intensity_Average_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['AF555_Cell_Intensity_Average'], bins=30, alpha=0.7, color='skyblue')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['AF555_Cell_Intensity_Average'].quantile(0.05)\n",
+    "quantile_95 = df['AF555_Cell_Intensity_Average'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of AF555_Cell_Intensity_Average column values with horizontal bars at 0.05 and 0.95 quantiles')\n",
+    "ax.set_xlabel('AF555_Cell_Intensity_Average Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantiles values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot in the output_images_dir directory using fig.savefig\n",
+    "plot_file_path = os.path.join(output_images_dir, \"AF555_Cell_Intensity_Average_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "116f1ba5-5a9b-4161-8fab-d5ecb8524179",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/CKs_Cytoplasm_Intensity_Average_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['CKs_Cytoplasm_Intensity_Average'], bins=100, alpha=0.6, color='skyblue')  # Augmented number of bins\n",
+    "\n",
+    "# Calculate mean and median\n",
+    "mean_value = df['CKs_Cytoplasm_Intensity_Average'].mean()\n",
+    "median_value = df['CKs_Cytoplasm_Intensity_Average'].median()\n",
+    "\n",
+    "# Add vertical lines for mean and median\n",
+    "ax.axvline(x=mean_value, color='orange', linestyle='-', label='Mean')\n",
+    "ax.axvline(x=median_value, color='purple', linestyle='-', label='Median')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['CKs_Cytoplasm_Intensity_Average'].quantile(0.05)\n",
+    "quantile_95 = df['CKs_Cytoplasm_Intensity_Average'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of CKs_Cytoplasm_Intensity_Average with Quantiles, Mean, and Median')\n",
+    "ax.set_xlabel('CKs_Cytoplasm_Intensity_Average Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantile values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display mean and median values\n",
+    "ax.text(mean_value, ax.get_ylim()[1]*0.9, f' Mean: {mean_value:.2f}', color='orange', verticalalignment='top')\n",
+    "ax.text(median_value, ax.get_ylim()[1]*0.85, f' Median: {median_value:.2f}', color='purple', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot\n",
+    "plot_file_path = os.path.join(output_images_dir, \"CKs_Cytoplasm_Intensity_Average_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "90c21233-43da-4692-bbf0-c90e037c035c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05     1603.137970\n",
+       "0.50     3559.484741\n",
+       "0.95    10239.075195\n",
+       "Name: CKs_Cytoplasm_Intensity_Average, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"CKs_Cytoplasm_Intensity_Average\"].quantile(q=qs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3e399c-684e-4c40-9963-1e96bb3fcffe",
+   "metadata": {},
+   "source": [
+    "## I.5. COLUMNS OF INTERESTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "fdcf65d1-0d91-4688-9903-25a003d755b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns are now...\n",
+      "['ROI_index', 'Nuc_Y_Inv', 'Nucleus_Roundness', 'Cell_Size', 'Sample_ID', 'Nucleus_Size', 'Nuc_X', 'AF488_Cell_Intensity_Average', 'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus_Intensity_Average', 'AF555_Cell_Intensity_Average', 'AF555_Cytoplasm_Intensity_Average', 'AF555_Nucleus_Intensity_Average', 'AF647_Cell_Intensity_Average', 'AF647_Cytoplasm_Intensity_Average', 'AF647_Nucleus_Intensity_Average', 'AF750_Cell_Intensity_Average', 'AF750_Cytoplasm_Intensity_Average', 'AF750_Nucleus_Intensity_Average', 'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm_Intensity_Average', 'aSMA_Nucleus_Intensity_Average', 'AXL_Cell_Intensity_Average', 'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus_Intensity_Average', 'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm_Intensity_Average', 'B7H4_Nucleus_Intensity_Average', 'CA9_Cell_Intensity_Average', 'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus_Intensity_Average', 'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm_Intensity_Average', 'CD4_Nucleus_Intensity_Average', 'CD8_Cell_Intensity_Average', 'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus_Intensity_Average', 'CD11b_Cell_Intensity_Average', 'CD11b_Cytoplasm_Intensity_Average', 'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell_Intensity_Average', 'CD11c_Cytoplasm_Intensity_Average', 'CD11c_Nucleus_Intensity_Average', 'CD20_Cell_Intensity_Average', 'CD20_Cytoplasm_Intensity_Average', 'CD20_Nucleus_Intensity_Average', 'CD31_Cell_Intensity_Average', 'CD31_Cytoplasm_Intensity_Average', 'CD31_Nucleus_Intensity_Average', 'CD44_Cell_Intensity_Average', 'CD44_Cytoplasm_Intensity_Average', 'CD44_Nucleus_Intensity_Average', 'CD45_Cell_Intensity_Average', 'CD45_Cytoplasm_Intensity_Average', 'CD45_Nucleus_Intensity_Average', 'CD68_Cell_Intensity_Average', 'CD68_Cytoplasm_Intensity_Average', 'CD68_Nucleus_Intensity_Average', 'CD163_Cell_Intensity_Average', 'CD163_Cytoplasm_Intensity_Average', 'CD163_Nucleus_Intensity_Average', 'CKs_Cell_Intensity_Average', 'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus_Intensity_Average', 'ColVI_Cell_Intensity_Average', 'ColVI_Cytoplasm_Intensity_Average', 'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell_Intensity_Average', 'Desmin_Cytoplasm_Intensity_Average', 'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell_Intensity_Average', 'Ecad_Cytoplasm_Intensity_Average', 'Ecad_Nucleus_Intensity_Average', 'Fibronectin_Cell_Intensity_Average', 'Fibronectin_Cytoplasm_Intensity_Average', 'Fibronectin_Nucleus_Intensity_Average', 'FOXP3_Cell_Intensity_Average', 'FOXP3_Cytoplasm_Intensity_Average', 'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell_Intensity_Average', 'GATA3_Cytoplasm_Intensity_Average', 'GATA3_Nucleus_Intensity_Average', 'HLA_Cell_Intensity_Average', 'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus_Intensity_Average', 'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm_Intensity_Average', 'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell_Intensity_Average', 'MMP9_Cytoplasm_Intensity_Average', 'MMP9_Nucleus_Intensity_Average', 'PD1_Cell_Intensity_Average', 'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus_Intensity_Average', 'PDGFR_Cell_Intensity_Average', 'PDGFR_Cytoplasm_Intensity_Average', 'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell_Intensity_Average', 'PDL1_Cytoplasm_Intensity_Average', 'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell_Intensity_Average', 'r5c2_Cytoplasm_Intensity_Average', 'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell_Intensity_Average', 'r7c2_Cytoplasm_Intensity_Average', 'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average', 'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus_Intensity_Average', 'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm_Intensity_Average', 'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell_Intensity_Average', 'Vimentin_Cytoplasm_Intensity_Average', 'Vimentin_Nucleus_Intensity_Average']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove columns containing \"DAPI\"\n",
+    "df = df[[x for x in df.columns.values if 'DAPI' not in x]]\n",
+    "\n",
+    "print(\"Columns are now...\")\n",
+    "print([c for c in df.columns.values])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "17741329-f501-4eec-af23-ed5026b13a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AF488_Cell': 'AF488_Cell_Intensity_Average',\n",
+       " 'AF488_Cytoplasm': 'AF488_Cytoplasm_Intensity_Average',\n",
+       " 'AF488_Nucleus': 'AF488_Nucleus_Intensity_Average',\n",
+       " 'AF555_Cell': 'AF555_Cell_Intensity_Average',\n",
+       " 'AF555_Cytoplasm': 'AF555_Cytoplasm_Intensity_Average',\n",
+       " 'AF555_Nucleus': 'AF555_Nucleus_Intensity_Average',\n",
+       " 'AF647_Cell': 'AF647_Cell_Intensity_Average',\n",
+       " 'AF647_Cytoplasm': 'AF647_Cytoplasm_Intensity_Average',\n",
+       " 'AF647_Nucleus': 'AF647_Nucleus_Intensity_Average',\n",
+       " 'AF750_Cell': 'AF750_Cell_Intensity_Average',\n",
+       " 'AF750_Cytoplasm': 'AF750_Cytoplasm_Intensity_Average',\n",
+       " 'AF750_Nucleus': 'AF750_Nucleus_Intensity_Average',\n",
+       " 'aSMA_Cell': 'aSMA_Cell_Intensity_Average',\n",
+       " 'aSMA_Cytoplasm': 'aSMA_Cytoplasm_Intensity_Average',\n",
+       " 'aSMA_Nucleus': 'aSMA_Nucleus_Intensity_Average',\n",
+       " 'AXL_Cell': 'AXL_Cell_Intensity_Average',\n",
+       " 'AXL_Cytoplasm': 'AXL_Cytoplasm_Intensity_Average',\n",
+       " 'AXL_Nucleus': 'AXL_Nucleus_Intensity_Average',\n",
+       " 'B7H4_Cell': 'B7H4_Cell_Intensity_Average',\n",
+       " 'B7H4_Cytoplasm': 'B7H4_Cytoplasm_Intensity_Average',\n",
+       " 'B7H4_Nucleus': 'B7H4_Nucleus_Intensity_Average',\n",
+       " 'CA9_Cell': 'CA9_Cell_Intensity_Average',\n",
+       " 'CA9_Cytoplasm': 'CA9_Cytoplasm_Intensity_Average',\n",
+       " 'CA9_Nucleus': 'CA9_Nucleus_Intensity_Average',\n",
+       " 'CD4_Cell': 'CD4_Cell_Intensity_Average',\n",
+       " 'CD4_Cytoplasm': 'CD4_Cytoplasm_Intensity_Average',\n",
+       " 'CD4_Nucleus': 'CD4_Nucleus_Intensity_Average',\n",
+       " 'CD8_Cell': 'CD8_Cell_Intensity_Average',\n",
+       " 'CD8_Cytoplasm': 'CD8_Cytoplasm_Intensity_Average',\n",
+       " 'CD8_Nucleus': 'CD8_Nucleus_Intensity_Average',\n",
+       " 'CD11b_Cell': 'CD11b_Cell_Intensity_Average',\n",
+       " 'CD11b_Cytoplasm': 'CD11b_Cytoplasm_Intensity_Average',\n",
+       " 'CD11b_Nucleus': 'CD11b_Nucleus_Intensity_Average',\n",
+       " 'CD11c_Cell': 'CD11c_Cell_Intensity_Average',\n",
+       " 'CD11c_Cytoplasm': 'CD11c_Cytoplasm_Intensity_Average',\n",
+       " 'CD11c_Nucleus': 'CD11c_Nucleus_Intensity_Average',\n",
+       " 'CD20_Cell': 'CD20_Cell_Intensity_Average',\n",
+       " 'CD20_Cytoplasm': 'CD20_Cytoplasm_Intensity_Average',\n",
+       " 'CD20_Nucleus': 'CD20_Nucleus_Intensity_Average',\n",
+       " 'CD31_Cell': 'CD31_Cell_Intensity_Average',\n",
+       " 'CD31_Cytoplasm': 'CD31_Cytoplasm_Intensity_Average',\n",
+       " 'CD31_Nucleus': 'CD31_Nucleus_Intensity_Average',\n",
+       " 'CD44_Cell': 'CD44_Cell_Intensity_Average',\n",
+       " 'CD44_Cytoplasm': 'CD44_Cytoplasm_Intensity_Average',\n",
+       " 'CD44_Nucleus': 'CD44_Nucleus_Intensity_Average',\n",
+       " 'CD45_Cell': 'CD45_Cell_Intensity_Average',\n",
+       " 'CD45_Cytoplasm': 'CD45_Cytoplasm_Intensity_Average',\n",
+       " 'CD45_Nucleus': 'CD45_Nucleus_Intensity_Average',\n",
+       " 'CD68_Cell': 'CD68_Cell_Intensity_Average',\n",
+       " 'CD68_Cytoplasm': 'CD68_Cytoplasm_Intensity_Average',\n",
+       " 'CD68_Nucleus': 'CD68_Nucleus_Intensity_Average',\n",
+       " 'CD163_Cell': 'CD163_Cell_Intensity_Average',\n",
+       " 'CD163_Cytoplasm': 'CD163_Cytoplasm_Intensity_Average',\n",
+       " 'CD163_Nucleus': 'CD163_Nucleus_Intensity_Average',\n",
+       " 'CKs_Cell': 'CKs_Cell_Intensity_Average',\n",
+       " 'CKs_Cytoplasm': 'CKs_Cytoplasm_Intensity_Average',\n",
+       " 'CKs_Nucleus': 'CKs_Nucleus_Intensity_Average',\n",
+       " 'ColVI_Cell': 'ColVI_Cell_Intensity_Average',\n",
+       " 'ColVI_Cytoplasm': 'ColVI_Cytoplasm_Intensity_Average',\n",
+       " 'ColVI_Nucleus': 'ColVI_Nucleus_Intensity_Average',\n",
+       " 'Desmin_Cell': 'Desmin_Cell_Intensity_Average',\n",
+       " 'Desmin_Cytoplasm': 'Desmin_Cytoplasm_Intensity_Average',\n",
+       " 'Desmin_Nucleus': 'Desmin_Nucleus_Intensity_Average',\n",
+       " 'Ecad_Cell': 'Ecad_Cell_Intensity_Average',\n",
+       " 'Ecad_Cytoplasm': 'Ecad_Cytoplasm_Intensity_Average',\n",
+       " 'Ecad_Nucleus': 'Ecad_Nucleus_Intensity_Average',\n",
+       " 'Fibronectin_Cell': 'Fibronectin_Cell_Intensity_Average',\n",
+       " 'Fibronectin_Cytoplasm': 'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       " 'Fibronectin_Nucleus': 'Fibronectin_Nucleus_Intensity_Average',\n",
+       " 'FOXP3_Cell': 'FOXP3_Cell_Intensity_Average',\n",
+       " 'FOXP3_Cytoplasm': 'FOXP3_Cytoplasm_Intensity_Average',\n",
+       " 'FOXP3_Nucleus': 'FOXP3_Nucleus_Intensity_Average',\n",
+       " 'GATA3_Cell': 'GATA3_Cell_Intensity_Average',\n",
+       " 'GATA3_Cytoplasm': 'GATA3_Cytoplasm_Intensity_Average',\n",
+       " 'GATA3_Nucleus': 'GATA3_Nucleus_Intensity_Average',\n",
+       " 'HLA_Cell': 'HLA_Cell_Intensity_Average',\n",
+       " 'HLA_Cytoplasm': 'HLA_Cytoplasm_Intensity_Average',\n",
+       " 'HLA_Nucleus': 'HLA_Nucleus_Intensity_Average',\n",
+       " 'Ki67_Cell': 'Ki67_Cell_Intensity_Average',\n",
+       " 'Ki67_Cytoplasm': 'Ki67_Cytoplasm_Intensity_Average',\n",
+       " 'Ki67_Nucleus': 'Ki67_Nucleus_Intensity_Average',\n",
+       " 'MMP9_Cell': 'MMP9_Cell_Intensity_Average',\n",
+       " 'MMP9_Cytoplasm': 'MMP9_Cytoplasm_Intensity_Average',\n",
+       " 'MMP9_Nucleus': 'MMP9_Nucleus_Intensity_Average',\n",
+       " 'PD1_Cell': 'PD1_Cell_Intensity_Average',\n",
+       " 'PD1_Cytoplasm': 'PD1_Cytoplasm_Intensity_Average',\n",
+       " 'PD1_Nucleus': 'PD1_Nucleus_Intensity_Average',\n",
+       " 'PDGFR_Cell': 'PDGFR_Cell_Intensity_Average',\n",
+       " 'PDGFR_Cytoplasm': 'PDGFR_Cytoplasm_Intensity_Average',\n",
+       " 'PDGFR_Nucleus': 'PDGFR_Nucleus_Intensity_Average',\n",
+       " 'PDL1_Cell': 'PDL1_Cell_Intensity_Average',\n",
+       " 'PDL1_Cytoplasm': 'PDL1_Cytoplasm_Intensity_Average',\n",
+       " 'PDL1_Nucleus': 'PDL1_Nucleus_Intensity_Average',\n",
+       " 'r5c2_Cell': 'r5c2_Cell_Intensity_Average',\n",
+       " 'r5c2_Cytoplasm': 'r5c2_Cytoplasm_Intensity_Average',\n",
+       " 'r5c2_Nucleus': 'r5c2_Nucleus_Intensity_Average',\n",
+       " 'r7c2_Cell': 'r7c2_Cell_Intensity_Average',\n",
+       " 'r7c2_Cytoplasm': 'r7c2_Cytoplasm_Intensity_Average',\n",
+       " 'r7c2_Nucleus': 'r7c2_Nucleus_Intensity_Average',\n",
+       " 'r8c2_Cell': 'r8c2_Cell_Intensity_Average',\n",
+       " 'r8c2_Cytoplasm': 'r8c2_Cytoplasm_Intensity_Average',\n",
+       " 'r8c2_Nucleus': 'r8c2_Nucleus_Intensity_Average',\n",
+       " 'Sting_Cell': 'Sting_Cell_Intensity_Average',\n",
+       " 'Sting_Cytoplasm': 'Sting_Cytoplasm_Intensity_Average',\n",
+       " 'Sting_Nucleus': 'Sting_Nucleus_Intensity_Average',\n",
+       " 'Vimentin_Cell': 'Vimentin_Cell_Intensity_Average',\n",
+       " 'Vimentin_Cytoplasm': 'Vimentin_Cytoplasm_Intensity_Average',\n",
+       " 'Vimentin_Nucleus': 'Vimentin_Nucleus_Intensity_Average'}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create lists of full names and shortened names to use in plotting\n",
+    "full_to_short_names, short_to_full_names =  \\\n",
+    "    shorten_feature_names(df.columns.values[~df.columns.isin(not_intensities)])\n",
+    "\n",
+    "short_to_full_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "dfdd64a5-8705-4ed6-b94f-c42379faefd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The full_to_short_column_names.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data to a metadata file\n",
+    "filename = os.path.join(metadata_dir, \"full_to_short_column_names.csv\")\n",
+    "fh = open(filename, \"w\")\n",
+    "fh.write(\"full_name,short_name\\n\")\n",
+    "for k,v in full_to_short_names.items():\n",
+    "    fh.write(k + \",\" + v + \"\\n\")\n",
+    "    \n",
+    "fh.close()\n",
+    "print(\"The full_to_short_column_names.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b4e26e25-620a-4700-b091-928c6ba08a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The short_to_full_column_names.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data to a metadata file\n",
+    "filename = os.path.join(metadata_dir, \"short_to_full_column_names.csv\")\n",
+    "fh = open(filename, \"w\")\n",
+    "fh.write(\"short_name,full_name\\n\")\n",
+    "for k,v in short_to_full_names.items():\n",
+    "    fh.write(k + \",\" + v + \"\\n\")\n",
+    "    \n",
+    "fh.close()\n",
+    "print(\"The short_to_full_column_names.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c5da27d-39a6-411b-a71f-0cd82ce2b728",
+   "metadata": {},
+   "source": [
+    "## I.6. EXPOSURE TIME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2e0c99c9-b254-41c7-b264-58441252c976",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Round Target   Exp Channel\n",
+      "0    R0  AF488   300      c2\n",
+      "1    R0  AF555  1500      c3\n",
+      "2    R0  AF647  1500      c4\n",
+      "3    R0  AF750  1500      c5\n",
+      "4    R1  ColVI   300      c2\n",
+      "df's shape:  (36, 4)\n",
+      "\n",
+      "No null values detected.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here, we want to end up with a data structure that incorporates metadata on each intensity marker column used in our big dataframe in an easy-to-use format. \n",
+    "# This is going to include the full name of the intensity marker columns in the big data frame, \n",
+    "# the corresponding round and channel, \n",
+    "# the target protein (e.g., CD45), \n",
+    "# and the segmentation localization information (cell, cytoplasm, nucleus)\n",
+    "\n",
+    "# We can use this data structure to assign unique colors to all channels and rounds, for example, for use in later visualizations\n",
+    "# Exposure_time file from ASHLAR analysis\n",
+    "filename = \"Exposure_Time.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "exp_df = pd.read_csv(filename)\n",
+    "\n",
+    "print(exp_df.head())\n",
+    "\n",
+    "# Verify file imported correctly\n",
+    "# File length\n",
+    "print(\"df's shape: \", exp_df.shape)\n",
+    "# Headers\n",
+    "expected_headers =['Round','Target','Exp','Channel']\n",
+    "compare_headers(expected_headers, exp_df.columns.values, \"Imported metadata file\")\n",
+    "\n",
+    "# Missingness\n",
+    "if exp_df.isnull().any().any():\n",
+    "    print(\"\\nexp_df has null value(s) in row(s):\")\n",
+    "    print(exp_df[exp_df.isna().any(axis=1)])\n",
+    "else:\n",
+    "    print(\"\\nNo null values detected.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "17501193-977f-4b30-bbb8-5afdae4a0356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(exp_df['Target']) > len(exp_df['Target'].unique()):\n",
+    "    print(\"One or more non-unique Target values in exp_df. Currently not supported.\")\n",
+    "exp_df = exp_df.drop_duplicates(subset = 'Target').reindex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "90d1feb7-2459-4a02-a526-ddc64082345b",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF647</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF750</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>R4</td>\n",
+       "      <td>AXL</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Round Target   Exp Channel\n",
+       "0     R0  AF488   300      c2\n",
+       "1     R0  AF555  1500      c3\n",
+       "2     R0  AF647  1500      c4\n",
+       "3     R0  AF750  1500      c5\n",
+       "17    R4    AXL  1500      c3"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort exp_df by the values in the 'Target' column in ascending order and then retrieve the first few rows of the sorted df\n",
+    "exp_df.sort_values(by = ['Target']).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b7373851-14bb-4813-be00-7f8c4e1f4ebf",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF647</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>af647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF750</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>af750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R1</td>\n",
+       "      <td>ColVI</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>colvi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Round Target   Exp Channel target_lower\n",
+       "0    R0  AF488   300      c2        af488\n",
+       "1    R0  AF555  1500      c3        af555\n",
+       "2    R0  AF647  1500      c4        af647\n",
+       "3    R0  AF750  1500      c5        af750\n",
+       "4    R1  ColVI   300      c2        colvi"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create lowercase version of target\n",
+    "exp_df['target_lower'] = exp_df['Target'].str.lower()\n",
+    "exp_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "4722fdb1-cd93-45a0-b196-89d50918b2e2",
+   "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>full_column</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column\n",
+       "0       AF488_Cell_Intensity_Average\n",
+       "1  AF488_Cytoplasm_Intensity_Average\n",
+       "2    AF488_Nucleus_Intensity_Average\n",
+       "3       AF555_Cell_Intensity_Average\n",
+       "4  AF555_Cytoplasm_Intensity_Average"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create df that contains marker intensity columns in our df that aren't in not_intensities\n",
+    "intensities = pd.DataFrame({'full_column':df.columns.values[~df.columns.isin(not_intensities)]})\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f88ef6d6-8160-4fa4-a0ed-942e4127414e",
+   "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>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>marker_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column marker marker_lower\n",
+       "0       AF488_Cell_Intensity_Average  AF488        af488\n",
+       "1  AF488_Cytoplasm_Intensity_Average  AF488        af488\n",
+       "2    AF488_Nucleus_Intensity_Average  AF488        af488\n",
+       "3       AF555_Cell_Intensity_Average  AF555        af555\n",
+       "4  AF555_Cytoplasm_Intensity_Average  AF555        af555"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extract the marker information from the `full_column`, which corresponds to full column in big dataframe\n",
+    "# Use regular expressions (regex) to isolate the part of the field that begins (^) with an alphanumeric value (W), and ends with an underscore (_)\n",
+    "# '$' is end of line\n",
+    "intensities['marker'] = intensities['full_column'].str.extract(r'([^\\W_]+)')\n",
+    "# convert to lowercase\n",
+    "intensities['marker_lower'] = intensities['marker'].str.lower()\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "2b9ff02b-a33b-4f42-95c0-4b4132e2aa25",
+   "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>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>marker_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column marker marker_lower\n",
+       "0       AF488_Cell_Intensity_Average  AF488        af488\n",
+       "1  AF488_Cytoplasm_Intensity_Average  AF488        af488\n",
+       "2    AF488_Nucleus_Intensity_Average  AF488        af488\n",
+       "3       AF555_Cell_Intensity_Average  AF555        af555\n",
+       "4  AF555_Cytoplasm_Intensity_Average  AF555        af555"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset the intensities df to exclude any column pertaining to DAPI\n",
+    "intensities = intensities.loc[intensities['marker_lower'] != 'dapi']\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "fa1340aa-1c86-4b0f-82a2-ded1892dea6e",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker  \n",
+       "0         AF488_Cell_Intensity_Average  AF488  \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488  \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488  \n",
+       "3         AF555_Cell_Intensity_Average  AF555  \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555  \n",
+       "..                                 ...    ...  \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting  \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting  \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b  \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b  \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b  \n",
+       "\n",
+       "[108 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Merge the intensities andexp_df together to create metadata\n",
+    "metadata = pd.merge(exp_df, intensities, how = 'left', left_on = 'target_lower',right_on = 'marker_lower')\n",
+    "metadata = metadata.drop(columns = ['marker_lower'])\n",
+    "metadata = metadata.dropna()\n",
+    "\n",
+    "# Target is the capitalization from the Exposure_Time.csv\n",
+    "# target_lower is Target in small caps\n",
+    "# marker is the extracted first component of the full column in segmentation data, with corresponding capitalization\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "75378ca2-3ecb-4c60-a7d4-da997f851066",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker localisation  \n",
+       "0         AF488_Cell_Intensity_Average  AF488         cell  \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488    cytoplasm  \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488      nucleus  \n",
+       "3         AF555_Cell_Intensity_Average  AF555         cell  \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555    cytoplasm  \n",
+       "..                                 ...    ...          ...  \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting    cytoplasm  \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting      nucleus  \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b         cell  \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b    cytoplasm  \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b      nucleus  \n",
+       "\n",
+       "[108 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add a column to signify marker target localisation.\n",
+    "# Use a lambda to determine segmented location of intensity marker column and update metadata accordingly\n",
+    "# Using the add_metadata_location() function in my_modules.py\n",
+    "metadata['localisation'] = metadata.apply(\n",
+    "    lambda row: add_metadata_location(row), axis = 1)\n",
+    "\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "9779567d-bd63-468a-acdb-e30901c71e03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The marker_intensity_metadata.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data structure to the metadata folder\n",
+    "# don't want to add color in because that's better off treating color the same for round, channel, and sample\n",
+    "filename = \"marker_intensity_metadata.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "metadata.to_csv(filename, index = False)\n",
+    "print(\"The marker_intensity_metadata.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d371ebc6-4aee-4b43-914e-933e3f9abbd5",
+   "metadata": {},
+   "source": [
+    "## I.7. COLORS WORKFLOW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4e010e-149b-4fe9-8a58-7f9f872d2be9",
+   "metadata": {},
+   "source": [
+    "### I.7.1. CHANNELS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "fb818967-a0b7-4bd0-9b81-32fd1bfefde1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique channels are: ['c2' 'c3' 'c4' 'c5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are categorical, since Channel is a non-ordered category (yes, they are numbered, but arbitrarily). \n",
+    "# A categorical color palette will have dissimilar colors.\n",
+    "# Get those unique colors\n",
+    "if len(metadata.Channel.unique()) > 10:\n",
+    "    print(\"WARNING: There are more unique channel values than \\\n",
+    "    there are colors to choose from. Select different palette, e.g., \\\n",
+    "    continuous palette 'husl'.\")\n",
+    "channel_color_values = sb.color_palette(\"bright\",n_colors = len(metadata.Channel.unique()))\n",
+    "# chose 'colorblind' because it is categorical and we're unlikely to have > 10\n",
+    "\n",
+    "# You can customize the colors for each channel here\n",
+    "custom_colors = {\n",
+    "    'c2': 'lightgreen',\n",
+    "    'c3': 'tomato',\n",
+    "    'c4': 'pink',\n",
+    "    'c5': 'turquoise'\n",
+    "}\n",
+    "\n",
+    "custom_colors_values = sb.palplot(sb.color_palette([custom_colors.get(ch, 'blue') for ch in metadata.Channel.unique()]))\n",
+    "\n",
+    "# Display those unique customs colors\n",
+    "print(\"Unique channels are:\", metadata.Channel.unique())\n",
+    "sb.palplot(sb.color_palette(channel_color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e7aa9d2e-94b5-461b-859f-fadfa2ccc0c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'c2': array([0.00784314, 0.24313725, 1.        ]),\n",
+       " 'c3': array([1.        , 0.48627451, 0.        ]),\n",
+       " 'c4': array([0.10196078, 0.78823529, 0.21960784]),\n",
+       " 'c5': array([0.90980392, 0.        , 0.04313725])}"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "channel_color_dict = dict(zip(metadata.Channel.unique(), channel_color_values))\n",
+    "channel_color_dict\n",
+    "for k,v in channel_color_dict.items():\n",
+    "    channel_color_dict[k] = np.float64(v)\n",
+    "\n",
+    "channel_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "aa2d6cf5-2126-4df4-a54f-c6926c2c06a1",
+   "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>rgb</th>\n",
+       "      <th>hex</th>\n",
+       "      <th>Channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>c2</th>\n",
+       "      <td>(0.00784313725490196, 0.24313725490196078, 1.0)</td>\n",
+       "      <td>#023eff</td>\n",
+       "      <td>c2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c3</th>\n",
+       "      <td>(1.0, 0.48627450980392156, 0.0)</td>\n",
+       "      <td>#ff7c00</td>\n",
+       "      <td>c3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c4</th>\n",
+       "      <td>(0.10196078431372549, 0.788235294117647, 0.219...</td>\n",
+       "      <td>#1ac938</td>\n",
+       "      <td>c4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c5</th>\n",
+       "      <td>(0.9098039215686274, 0.0, 0.043137254901960784)</td>\n",
+       "      <td>#e8000b</td>\n",
+       "      <td>c5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  rgb      hex Channel\n",
+       "c2    (0.00784313725490196, 0.24313725490196078, 1.0)  #023eff      c2\n",
+       "c3                    (1.0, 0.48627450980392156, 0.0)  #ff7c00      c3\n",
+       "c4  (0.10196078431372549, 0.788235294117647, 0.219...  #1ac938      c4\n",
+       "c5    (0.9098039215686274, 0.0, 0.043137254901960784)  #e8000b      c5"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "color_df_channel = color_dict_to_df(channel_color_dict, \"Channel\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"channel_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_channel.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d8117d2d-c60e-477c-bef4-dc89d18fa6aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of channel info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in channel_color_dict.keys():\n",
+    "        h = g.bar(0,0, color = channel_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Channel'),\n",
+    "                            # bbox_to_anchor=(10,10), \n",
+    "                             #       bbox_transform=plt.gcf().transFigure)\n",
+    "\n",
+    "filename = \"Channel_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d587ec-809c-4312-acda-3747fc63fd83",
+   "metadata": {},
+   "source": [
+    "### I.7.2. ROUNDS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "40bedb40-5740-4956-aa97-a95ac9fb341a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['R0' 'R1' 'R2' 'R3' 'R4' 'R5' 'R6' 'R7' 'R8']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are sequential, since Round is an ordered category. \n",
+    "# We can still generate colors that are easy to distinguish. Also, many of the categorical palettes cap at at about 10 or so unique colors, and repeat from there. \n",
+    "# We do not want any repeats!\n",
+    "round_color_values = sb.cubehelix_palette(\n",
+    "    len(metadata.Round.unique()), start=1, rot= -0.75, dark=0.19, light=.85, reverse=True)\n",
+    "# round_color_values = sb.color_palette(\"cubehelix\",n_colors = len(metadata.Round.unique()))\n",
+    "# chose 'cubehelix' because it is sequential, and round is a continuous process\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "print(metadata.Round.unique())\n",
+    "\n",
+    "sb.palplot(sb.color_palette(round_color_values))\n",
+    "\n",
+    "## TO-DO: write what these parameters mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "4372b4f0-2242-4d76-8881-a78fba5fd814",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'R0': array([0.28685356, 0.13009829, 0.23110332]),\n",
+       " 'R1': array([0.36541462, 0.2025447 , 0.3769331 ]),\n",
+       " 'R2': array([0.40867533, 0.29407612, 0.51667119]),\n",
+       " 'R3': array([0.42890614, 0.40822902, 0.63353489]),\n",
+       " 'R4': array([0.44444629, 0.5264665 , 0.70563219]),\n",
+       " 'R5': array([0.47707206, 0.64270618, 0.74184779]),\n",
+       " 'R6': array([0.54144549, 0.74667592, 0.75729058]),\n",
+       " 'R7': array([0.64147101, 0.83215511, 0.7746773 ]),\n",
+       " 'R8': array([0.76842569, 0.89926671, 0.81713833])}"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "round_color_dict = dict(zip(metadata.Round.unique(), round_color_values))\n",
+    "\n",
+    "for k,v in round_color_dict.items():\n",
+    "    round_color_dict[k] = np.float64(v)\n",
+    "\n",
+    "round_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "3a6053ce-d87c-4137-8c70-a6772208fc37",
+   "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>rgb</th>\n",
+       "      <th>hex</th>\n",
+       "      <th>Round</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>R0</th>\n",
+       "      <td>(0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>#49213b</td>\n",
+       "      <td>R0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R1</th>\n",
+       "      <td>(0.36541462435986094, 0.2025447048359916, 0.37...</td>\n",
+       "      <td>#5d3460</td>\n",
+       "      <td>R1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R2</th>\n",
+       "      <td>(0.40867533458903105, 0.2940761173840091, 0.51...</td>\n",
+       "      <td>#684b84</td>\n",
+       "      <td>R2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R3</th>\n",
+       "      <td>(0.42890613750051265, 0.4082290173220481, 0.63...</td>\n",
+       "      <td>#6d68a2</td>\n",
+       "      <td>R3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R4</th>\n",
+       "      <td>(0.4444462906865238, 0.5264664993764805, 0.705...</td>\n",
+       "      <td>#7186b4</td>\n",
+       "      <td>R4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R5</th>\n",
+       "      <td>(0.47707206309601013, 0.6427061780374552, 0.74...</td>\n",
+       "      <td>#7aa4bd</td>\n",
+       "      <td>R5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R6</th>\n",
+       "      <td>(0.5414454866716836, 0.7466759172596551, 0.757...</td>\n",
+       "      <td>#8abec1</td>\n",
+       "      <td>R6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R7</th>\n",
+       "      <td>(0.6414710091647722, 0.8321551072276492, 0.774...</td>\n",
+       "      <td>#a4d4c6</td>\n",
+       "      <td>R7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R8</th>\n",
+       "      <td>(0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>#c4e5d0</td>\n",
+       "      <td>R8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  rgb      hex Round\n",
+       "R0  (0.28685356234627135, 0.13009829239513535, 0.2...  #49213b    R0\n",
+       "R1  (0.36541462435986094, 0.2025447048359916, 0.37...  #5d3460    R1\n",
+       "R2  (0.40867533458903105, 0.2940761173840091, 0.51...  #684b84    R2\n",
+       "R3  (0.42890613750051265, 0.4082290173220481, 0.63...  #6d68a2    R3\n",
+       "R4  (0.4444462906865238, 0.5264664993764805, 0.705...  #7186b4    R4\n",
+       "R5  (0.47707206309601013, 0.6427061780374552, 0.74...  #7aa4bd    R5\n",
+       "R6  (0.5414454866716836, 0.7466759172596551, 0.757...  #8abec1    R6\n",
+       "R7  (0.6414710091647722, 0.8321551072276492, 0.774...  #a4d4c6    R7\n",
+       "R8  (0.7684256891219349, 0.8992667116749021, 0.817...  #c4e5d0    R8"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "color_df_round = color_dict_to_df(round_color_dict, \"Round\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"round_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_round.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_round"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "977a7e45-64df-4bd6-ab21-cecd44d76fd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of round info only\n",
+    "\n",
+    "round_legend  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "round_legend.axis('off')\n",
+    "handles = []\n",
+    "for item in round_color_dict.keys():\n",
+    "        h = round_legend.bar(0,0, color = round_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Round'),\n",
+    "                            # bbox_to_anchor=(10,10), \n",
+    "                             #       bbox_transform=plt.gcf().transFigure)\n",
+    "\n",
+    "filename = \"Round_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b81c62-58a4-432c-876a-dfa5df0bbe9e",
+   "metadata": {},
+   "source": [
+    "### I.7.3. SAMPLES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3ccb74da-fb1d-4fda-9116-bf6b6a6d868b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are neither sequential nor categorical. \n",
+    "# Categorical would be ideal if we could generate an arbitrary number of colors, but I do not think that we can. \n",
+    "# Hense, we will choose `n` colors from a continuous palette. First we will generate the right number of colors. Later, we will assign TMA samples to gray.\n",
+    "\n",
+    "# Get those unique colors\n",
+    "color_values = sb.color_palette(\"husl\",n_colors = len(ls_samples))#'HLS'\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "\n",
+    "# Display those unique colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "441a57fe-d55a-49e1-8593-0f1939472b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TMA_samples = [s for s in df.Sample_ID.unique() if 'TMA' in s]\n",
+    "TMA_color_values = sb.color_palette(n_colors = len(TMA_samples),palette = \"gray\")\n",
+    "sb.palplot(sb.color_palette(TMA_color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "18dbf741-983e-4652-97d1-d55e87eba4fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DD3S1.csv': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
+       " 'DD3S2.csv': (0.5920891529639701, 0.6418467016378244, 0.1935069134991043),\n",
+       " 'DD3S3.csv': (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
+       " 'TMA.csv': (0.5019607843137255, 0.5019607843137255, 0.5019607843137255)}"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "color_dict = dict()\n",
+    "color_dict = dict(zip(df.Sample_ID.unique(), color_values))\n",
+    "\n",
+    "# Replace all TMA samples' colors with gray\n",
+    "i = 0\n",
+    "for key in color_dict.keys():\n",
+    "    if 'TMA' in key:\n",
+    "        color_dict[key] = TMA_color_values[i]\n",
+    "        i +=1\n",
+    "\n",
+    "color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "807f2bb5-6d19-4086-81c1-98836e850dcd",
+   "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>rgb</th>\n",
+       "      <th>hex</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>(0.9677975592919913, 0.44127456009157356, 0.53...</td>\n",
+       "      <td>#f77189</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>(0.5920891529639701, 0.6418467016378244, 0.193...</td>\n",
+       "      <td>#97a431</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>(0.21044753832183283, 0.6773105080456748, 0.64...</td>\n",
+       "      <td>#36ada4</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA.csv</th>\n",
+       "      <td>(0.5019607843137255, 0.5019607843137255, 0.501...</td>\n",
+       "      <td>#808080</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                         rgb      hex  \\\n",
+       "DD3S1.csv  (0.9677975592919913, 0.44127456009157356, 0.53...  #f77189   \n",
+       "DD3S2.csv  (0.5920891529639701, 0.6418467016378244, 0.193...  #97a431   \n",
+       "DD3S3.csv  (0.21044753832183283, 0.6773105080456748, 0.64...  #36ada4   \n",
+       "TMA.csv    (0.5019607843137255, 0.5019607843137255, 0.501...  #808080   \n",
+       "\n",
+       "           Sample_ID  \n",
+       "DD3S1.csv  DD3S1.csv  \n",
+       "DD3S2.csv  DD3S2.csv  \n",
+       "DD3S3.csv  DD3S3.csv  \n",
+       "TMA.csv      TMA.csv  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "color_df_sample = color_dict_to_df(color_dict, \"Sample_ID\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"sample_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_sample.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "39b6afdb-2e37-471e-86a0-e55ab7667e39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of sample info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in color_dict.keys():\n",
+    "        h = g.bar(0,0, color = color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Sample')\n",
+    "\n",
+    "filename = \"Sample_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3cf0dea-43db-41fa-952e-bbba53e89cdb",
+   "metadata": {},
+   "source": [
+    "### I.7.4. CLUSTERS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "d37c61d7-de64-4b7c-8d01-86ee16ac67c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if 'cluster' in df.columns:\n",
+    "    cluster_color_values = sb.color_palette(\"hls\",n_colors = len(df.cluster.unique()))\n",
+    "\n",
+    "    print(sorted(test_df.cluster.unique()))\n",
+    "    # Display those unique colors\n",
+    "    sb.palplot(sb.color_palette(cluster_color_values))\n",
+    "    \n",
+    "    cluster_color_dict = dict(zip(sorted(test_df.cluster.unique()), cluster_color_values))\n",
+    "    print(cluster_color_dict)\n",
+    "    \n",
+    "    # Create dataframe\n",
+    "    cluster_color_df = color_dict_to_df(cluster_color_dict, \"cluster\")\n",
+    "    cluster_color_df.head()\n",
+    "\n",
+    "    # Save to file in metadatadirectory\n",
+    "    filename = \"cluster_color_data.csv\"\n",
+    "    filename = os.path.join(metadata_dir, filename)\n",
+    "    cluster_color_df.to_csv(filename, index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c9215452-fdb6-4963-9f56-31f16e0483bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Legend of cluster info only\n",
+    "\n",
+    "if 'cluster' in df.columns:\n",
+    "    g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "    g.axis('off')\n",
+    "    handles = []\n",
+    "    for item in sorted(cluster_color_dict.keys()):\n",
+    "            h = g.bar(0,0, color = cluster_color_dict[item],\n",
+    "                      label = item, linewidth =0)\n",
+    "            handles.append(h)\n",
+    "    first_legend = plt.legend(handles=handles, loc='upper right', title = 'Cluster'),\n",
+    "\n",
+    "\n",
+    "    filename = \"Clustertype_legend.png\"\n",
+    "    filename = os.path.join(metadata_images_dir, filename)\n",
+    "    plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "715409aa",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "      <th>round_color</th>\n",
+       "      <th>channel_color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[1.0, 0.48627450980392156, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[1.0, 0.48627450980392156, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.10196078431372549, 0.788235294117647, 0.219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.10196078431372549, 0.788235294117647, 0.219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker localisation  \\\n",
+       "0         AF488_Cell_Intensity_Average  AF488         cell   \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488    cytoplasm   \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488      nucleus   \n",
+       "3         AF555_Cell_Intensity_Average  AF555         cell   \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555    cytoplasm   \n",
+       "..                                 ...    ...          ...   \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting    cytoplasm   \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting      nucleus   \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b         cell   \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b    cytoplasm   \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b      nucleus   \n",
+       "\n",
+       "                                           round_color  \\\n",
+       "0    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "1    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "2    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "3    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "4    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "..                                                 ...   \n",
+       "103  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "104  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "105  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "106  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "107  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "\n",
+       "                                         channel_color  \n",
+       "0      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "1      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "2      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "3                      [1.0, 0.48627450980392156, 0.0]  \n",
+       "4                      [1.0, 0.48627450980392156, 0.0]  \n",
+       "..                                                 ...  \n",
+       "103  [0.10196078431372549, 0.788235294117647, 0.219...  \n",
+       "104  [0.10196078431372549, 0.788235294117647, 0.219...  \n",
+       "105    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "106    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "107    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "\n",
+       "[108 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add in the color information in both RGB (range 0-1) and hex values, for use in visualizations\n",
+    "metadata['round_color'] = metadata.apply(lambda row: round_color_dict[row['Round']], axis = 1)\n",
+    "metadata['channel_color'] = metadata.apply(lambda row: channel_color_dict[row['Channel']], axis = 1)\n",
+    "\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "9cca88fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import panel as pn\n",
+    "\n",
+    "PRIMARY_COLOR = \"#0072B5\"\n",
+    "SECONDARY_COLOR = \"#B54300\"\n",
+    "CSV_FILE = (\n",
+    "    \"https://raw.githubusercontent.com/holoviz/panel/main/examples/assets/occupancy.csv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "3c7402e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform_data(variable, window, sigma):\n",
+    "    \"\"\"Calculates the rolling average and identifies outliers\"\"\"\n",
+    "    avg = metadata[variable].rolling(window=window).mean()\n",
+    "    residual = metadata[variable] - avg\n",
+    "    std = residual.rolling(window=window).std()\n",
+    "    outliers = np.abs(residual) > std * sigma\n",
+    "    return avg, avg[outliers]\n",
+    "\n",
+    "\n",
+    "def get_plot(variable=\"Exp\", window=30, sigma=10):\n",
+    "    \"\"\"Plots the rolling average and the outliers\"\"\"\n",
+    "    avg, highlight = transform_data(variable, window, sigma)\n",
+    "    return avg.hvplot(\n",
+    "        height=300, legend=False, color=PRIMARY_COLOR\n",
+    "    ) * highlight.hvplot.scatter(color=SECONDARY_COLOR, padding=0.1, legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "38636bb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variable_widget = pn.widgets.Select(name=\"Target\", value=\"Exp\", options=list(metadata.columns))\n",
+    "window_widget = pn.widgets.IntSlider(name=\"window\", value=30, start=1, end=60)\n",
+    "sigma_widget = pn.widgets.IntSlider(name=\"sigma\", value=10, start=0, end=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a4785336",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='8c06a97e-378a-4028-a51c-8b8fbb1cf019'>\n",
+       "  <div id=\"bf02f2b0-22a5-43fa-a449-ed0498039d06\" data-root-id=\"8c06a97e-378a-4028-a51c-8b8fbb1cf019\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"eb0d6325-ef7d-438d-a6bb-504fdc0055f9\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"8c06a97e-378a-4028-a51c-8b8fbb1cf019\",\"attributes\":{\"name\":\"Row01048\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"eb0d6325-ef7d-438d-a6bb-504fdc0055f9\",\"roots\":{\"8c06a97e-378a-4028-a51c-8b8fbb1cf019\":\"bf02f2b0-22a5-43fa-a449-ed0498039d06\"},\"root_ids\":[\"8c06a97e-378a-4028-a51c-8b8fbb1cf019\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       ":Overlay\n",
+       "   .Curve.Exp   :Curve   [index]   (Exp)\n",
+       "   .Scatter.Exp :Scatter   [index]   (Exp)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "8c06a97e-378a-4028-a51c-8b8fbb1cf019"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_plot(variable='Exp', window=20, sigma=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "39f7b3e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bound_plot = pn.bind(\n",
+    "    get_plot, variable=variable_widget, window=window_widget, sigma=sigma_widget\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "c58b4fb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='31cb1aa6-32cb-4971-9559-0aecc17d32d4'>\n",
+       "  <div id=\"e438be2e-215d-42e8-8e49-944bb9d04dc4\" data-root-id=\"31cb1aa6-32cb-4971-9559-0aecc17d32d4\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"e896e76d-18f9-4f0a-b686-16616454ef74\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.layout.Column\",\"id\":\"31cb1aa6-32cb-4971-9559-0aecc17d32d4\",\"attributes\":{\"name\":\"Column01277\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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+       "  var render_items = [{\"docid\":\"e896e76d-18f9-4f0a-b686-16616454ef74\",\"roots\":{\"31cb1aa6-32cb-4971-9559-0aecc17d32d4\":\"e438be2e-215d-42e8-8e49-944bb9d04dc4\"},\"root_ids\":[\"31cb1aa6-32cb-4971-9559-0aecc17d32d4\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       "Column\n",
+       "    [0] Column(sizing_mode='fixed', width=300)\n",
+       "        [0] Select(name='Target', options=['Round', 'Target', ...], value='Exp')\n",
+       "        [1] IntSlider(end=60, name='window', start=1, value=30)\n",
+       "        [2] IntSlider(end=20, name='sigma', value=10)\n",
+       "    [1] ParamFunction(function, _pane=HoloViews, defer_load=False)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "31cb1aa6-32cb-4971-9559-0aecc17d32d4"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets = pn.Column(variable_widget, window_widget, sigma_widget, sizing_mode=\"fixed\", width=300)\n",
+    "pn.Column(widgets, bound_plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0642911-7a0b-49f6-9598-c8975b188807",
+   "metadata": {},
+   "source": [
+    "## I.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "67b33926-3ecf-415d-b67e-09d1054eab62",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Do you want to overwrite all existing files without confirmation? (yes/no): \n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "Interrupted by user",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[68], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m filename \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m was overwritten!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 13\u001b[0m     user_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile by name \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m filename \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m already exists. Do you want to overwrite it? (yes/no): \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m user_response\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myes\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m     15\u001b[0m         df_save \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mloc[df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSample_ID\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m sample, :]\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py:1262\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1260\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1261\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_request(\n\u001b[1;32m   1263\u001b[0m     \u001b[38;5;28mstr\u001b[39m(prompt),\n\u001b[1;32m   1264\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent_ident[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshell\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m   1265\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_parent(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshell\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1266\u001b[0m     password\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1267\u001b[0m )\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py:1305\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1302\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1303\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1304\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1305\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1306\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1307\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
+     ]
+    }
+   ],
+   "source": [
+    "overwrite_all = input(\"Do you want to overwrite all existing files without confirmation? (yes/no): \")\n",
+    "overwrite_all = overwrite_all.lower().strip()\n",
+    "\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('.csv')[0]\n",
+    "    filename = os.path.join(output_data_dir, sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "    if os.path.exists(filename):\n",
+    "        if overwrite_all == 'yes':\n",
+    "            df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "            df_save.to_csv(filename, index=True, index_label='ID', mode='w')  # 'mode='w'' overwrites the file\n",
+    "            print(\"File \" + filename + \" was overwritten!\")\n",
+    "        else:\n",
+    "            user_response = input(\"File by name \" + filename + \" already exists. Do you want to overwrite it? (yes/no): \")\n",
+    "            if user_response.lower().strip() == 'yes':\n",
+    "                df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "                df_save.to_csv(filename, index=True, index_label='ID', mode='w')  # 'mode='w'' overwrites the file\n",
+    "                print(\"File \" + filename + \" was overwritten!\")\n",
+    "            else:\n",
+    "                print(\"File was not overwritten. Moving to the next sample.\")\n",
+    "    else:\n",
+    "        df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "        df_save.to_csv(filename, index=True, index_label='ID')  # Save normally if the file doesn't exist\n",
+    "        print(\"File \" + filename + \" was created and saved !\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e41a9fb8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb b/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c756446bc7333b3d6a8ed930a68cd435518db9da
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb
@@ -0,0 +1,3695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5fad9226-7ffa-41fc-a6d8-55dcdd1c9eb5",
+   "metadata": {},
+   "source": [
+    "# CyCIF PYTHON PIPELINE"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8337a11b-813a-4abd-8a5e-45a1dcde430f",
+   "metadata": {},
+   "source": [
+    "This sequence of Jupiter Notebooks is designed to run the CyCIF analysis after the ASHLAR registratiom step.\n",
+    "When a '*' is on a part name (see the table of content to your left), it means the user have an input to give.\n",
+    "The pipeline is composed of 5 Notebooks (see README file)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b047ee-225e-415d-a0ae-17f30c0d5f35",
+   "metadata": {},
+   "source": [
+    "# I. QC/EDA NOTEBOOK"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "fbb817d5-aee1-447a-a8dd-786bbd3cc381",
+   "metadata": {},
+   "source": [
+    "10/01/24\n",
+    "Modifications by Zoé Gerber\n",
+    "from an original code from Marilyne Labrie"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "a48dc9ff-3dd1-4f86-bbb5-68bc6254b787",
+   "metadata": {},
+   "source": [
+    "I.1. PACKAGES IMPORT\n",
+    "I.2. DIRECTORIES\n",
+    "I.3. FILES\n",
+    "    I.3.1. DATA\n",
+    "    I.3.2. NOT_INTENSITIES\n",
+    "I.4. QC CHECKS\n",
+    "I.5. COLUMNS OF INTERESTS\n",
+    "I.6. EXPOSURE TIME\n",
+    "I.7. COLORS WORKFLOW\n",
+    "    I.7.1. CHANNELS COLORS\n",
+    "    I.7.2. ROUNDS COLORS\n",
+    "    I.7.3. SAMPLES COLORS\n",
+    "    I.7.4. CLUSTERS COLORS\n",
+    "I.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46755cfd-7896-4ca0-ba50-6792612e7650",
+   "metadata": {},
+   "source": [
+    "## I.1. PACKAGES IMPORT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d52f4de4-6db2-4590-9574-326ccf5bc97e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "import os\n",
+    "import plotly as plt\n",
+    "import seaborn as sb\n",
+    "\n",
+    "from my_modules import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9936cc24-34f7-41b5-b184-94a8f2237b05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Silence FutureWarnings & UserWarnings\n",
+    "warnings.filterwarnings('ignore', category= FutureWarning)\n",
+    "warnings.filterwarnings('ignore', category= UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b179f303-c098-4c30-b4b9-df10db6c485a",
+   "metadata": {},
+   "source": [
+    "## I.2. *DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "849b1dd3-c940-4c7a-8cf6-2fd5b4dc43f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set base directory\n",
+    "\n",
+    "directorio_actual = os.getcwd()\n",
+    "print(directorio_actual)\n",
+    "\n",
+    "##### MAC WORKSTATION #####\n",
+    "#base_dir = r'/Volumes/LaboLabrie/Projets/OC_TMA_Pejovic/Temp/Zoe/CyCIF_pipeline/'\n",
+    "###########################\n",
+    "\n",
+    "##### WINDOWS WORKSTATION #####\n",
+    "#base_dir = r'C:\\Users\\LaboLabrie\\gerz2701\\cyCIF-pipeline\\Set_B'\n",
+    "###############################\n",
+    "\n",
+    "##### LOCAL WORKSTATION #####\n",
+    "base_dir = r'/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/'\n",
+    "#############################\n",
+    "\n",
+    "#set_name = 'Set_A'\n",
+    "set_name = 'test'"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "1f68584f-c1f6-49cd-99f8-5a7cc8aae26e",
+   "metadata": {},
+   "source": [
+    "The project is organized as :\n",
+    "main dir \n",
+    "    code\n",
+    "    proj_data             > all input csv files\n",
+    "    proj_metadata         > exposure time csv file, images dir,...\n",
+    "    proj_qc_eda           > csv after the QC/EDA step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e8a4b389-1136-4470-9898-29fd39baf1f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/ directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images directory already exists !\n"
+     ]
+    }
+   ],
+   "source": [
+    "project_name = set_name              # Project name\n",
+    "step_suffix = 'qc_eda'               # Curent part (here part I)\n",
+    "previous_step_suffix_long = \"\"       # Previous part (here empty)\n",
+    "\n",
+    "# Initial input data directory\n",
+    "input_data_dir = os.path.join(base_dir, project_name + \"_data\")\n",
+    "\n",
+    "# QC/EDA output directories\n",
+    "# global output\n",
+    "output_data_dir = os.path.join(base_dir, project_name + \"_\" + step_suffix)\n",
+    "# images subdirectory\n",
+    "output_images_dir = os.path.join(output_data_dir,\"images\")\n",
+    "\n",
+    "# Data and Metadata directories\n",
+    "# global data\n",
+    "metadata_dir = os.path.join(base_dir, project_name + \"_metadata\")\n",
+    "# images subdirectory\n",
+    "metadata_images_dir = os.path.join(metadata_dir,\"images\")\n",
+    "\n",
+    "# Create directories if they don't already exist\n",
+    "for d in [base_dir, input_data_dir, output_data_dir, output_images_dir, metadata_dir, metadata_images_dir]:\n",
+    "    if not os.path.exists(d):\n",
+    "        print(\"Creation of the\" , d, \"directory...\")\n",
+    "        os.makedirs(d)\n",
+    "    else :\n",
+    "        print(\"The\", d, \"directory already exists !\")\n",
+    "\n",
+    "os.chdir(input_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b6e9df25-588a-41b4-a1f5-45bb81f012ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/\n",
+      "input_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data\n",
+      "output_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda\n",
+      "output_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images\n",
+      "metadata_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata\n",
+      "metadata_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify paths\n",
+    "print('base_dir :', base_dir)\n",
+    "print('input_data_dir :', input_data_dir)\n",
+    "print('output_data_dir :', output_data_dir)\n",
+    "print('output_images_dir :', output_images_dir)\n",
+    "print('metadata_dir :', metadata_dir)\n",
+    "print('metadata_images_dir :', metadata_images_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ebdb24-d428-4948-8d9d-485e4591212b",
+   "metadata": {},
+   "source": [
+    "## I.3. FILES"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "b81abd9c-2501-4b31-8c71-aa191c518b31",
+   "metadata": {},
+   "source": [
+    "Don't forget to put your data in the projname_data directory !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b2569b34-ef84-4af6-836d-befe3bdda706",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CSV files were detected:\n",
+      "\n",
+      " ['DD3S1.csv', 'DD3S2.csv', 'DD3S3.csv', 'TMA.csv'] \n",
+      "\n",
+      "in /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_data directory.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Listing all the .csv files in the metadata/data directory\n",
+    "# Don't forget to move the csv files into the proj_data directory\n",
+    "# if the data dir is empty it's not going to work \n",
+    "ls_samples = [sample for sample in os.listdir(input_data_dir) if sample.endswith(\".csv\")]\n",
+    "\n",
+    "print(\"The following CSV files were detected:\\n\\n\",[sample for sample in ls_samples], \"\\n\\nin\", input_data_dir, \"directory.\")\n",
+    "\n",
+    "#print(ls_samples[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "591d76f3-8b5b-4dfc-a71a-0d3ec6eeb93e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df :\n",
+      "          Cell Size        Nuc X     Nuc Y Inv  ROI index  Nucleus Size  \\\n",
+      "Cell Id                                                                  \n",
+      "1@1            339  1484.771729  16632.205078          0           127   \n",
+      "\n",
+      "         Nucleus Roundness  AF488 Cell Intensity Average  \\\n",
+      "Cell Id                                                    \n",
+      "1@1                0.95504                   2385.867188   \n",
+      "\n",
+      "         AF488 Cytoplasm Intensity Average  AF488 Nucleus Intensity Average  \\\n",
+      "Cell Id                                                                       \n",
+      "1@1                              2356.6604                       2434.62207   \n",
+      "\n",
+      "         AF555 Cell Intensity Average  ...  r7c2 Nucleus Intensity Average  \\\n",
+      "Cell Id                                ...                                   \n",
+      "1@1                       1358.528076  ...                      290.582672   \n",
+      "\n",
+      "         r8c2 Cell Intensity Average  r8c2 Cytoplasm Intensity Average  \\\n",
+      "Cell Id                                                                  \n",
+      "1@1                       341.790558                         337.82547   \n",
+      "\n",
+      "         r8c2 Nucleus Intensity Average  Sting Cell Intensity Average  \\\n",
+      "Cell Id                                                                 \n",
+      "1@1                          348.409454                   1567.100342   \n",
+      "\n",
+      "         Sting Cytoplasm Intensity Average  Sting Nucleus Intensity Average  \\\n",
+      "Cell Id                                                                       \n",
+      "1@1                             1533.22168                      1623.653564   \n",
+      "\n",
+      "         Vimentin Cell Intensity Average  \\\n",
+      "Cell Id                                    \n",
+      "1@1                          7279.144531   \n",
+      "\n",
+      "         Vimentin Cytoplasm Intensity Average  \\\n",
+      "Cell Id                                         \n",
+      "1@1                               7040.108398   \n",
+      "\n",
+      "         Vimentin Nucleus Intensity Average  \n",
+      "Cell Id                                      \n",
+      "1@1                             7678.165527  \n",
+      "\n",
+      "[1 rows x 141 columns] \n",
+      "\n",
+      "df's columns :\n",
+      " Index(['Cell Size', 'Nuc X', 'Nuc Y Inv', 'ROI index', 'Nucleus Size',\n",
+      "       'Nucleus Roundness', 'AF488 Cell Intensity Average',\n",
+      "       'AF488 Cytoplasm Intensity Average', 'AF488 Nucleus Intensity Average',\n",
+      "       'AF555 Cell Intensity Average',\n",
+      "       ...\n",
+      "       'r7c2 Nucleus Intensity Average', 'r8c2 Cell Intensity Average',\n",
+      "       'r8c2 Cytoplasm Intensity Average', 'r8c2 Nucleus Intensity Average',\n",
+      "       'Sting Cell Intensity Average', 'Sting Cytoplasm Intensity Average',\n",
+      "       'Sting Nucleus Intensity Average', 'Vimentin Cell Intensity Average',\n",
+      "       'Vimentin Cytoplasm Intensity Average',\n",
+      "       'Vimentin Nucleus Intensity Average'],\n",
+      "      dtype='object', length=141) \n",
+      "\n",
+      "df's index :\n",
+      " Index(['1@1'], dtype='object', name='Cell Id') \n",
+      "\n",
+      "df's index name :\n",
+      " Cell Id\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First gather information on expected headers using first file in ls_samples\n",
+    "# Read in the first row of the file corresponding to the first sample (index = 0) in ls_samples\n",
+    "df = pd.read_csv(os.path.join(input_data_dir, ls_samples[0]) , index_col = 0, nrows = 1)\n",
+    "\n",
+    "\n",
+    "# Make sure the file was imported correctly\n",
+    "print(\"df :\\n\", df.head(), \"\\n\")\n",
+    "print(\"df's columns :\\n\", df.columns, \"\\n\")\n",
+    "print(\"df's index :\\n\", df.index, \"\\n\")\n",
+    "print(\"df's index name :\\n\", df.index.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7caa2335-ea22-4973-b50c-7087349c2dc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected the first column in input file (index_col = 0) to be 'ID'. \n",
+      "This column will be used to set the index names (cell number for each sample). \n",
+      "It appears that the column 'Cell Id' was actually the imported as the index column.\n",
+      "A new index name (first column) will be given ('ID') to replace the current one 'Cell Id'\n",
+      "\n",
+      "['Cell_Size' 'Nuc_X' 'Nuc_Y_Inv' 'ROI_index' 'Nucleus_Size'\n",
+      " 'Nucleus_Roundness' 'AF488_Cell_Intensity_Average'\n",
+      " 'AF488_Cytoplasm_Intensity_Average' 'AF488_Nucleus_Intensity_Average'\n",
+      " 'AF555_Cell_Intensity_Average' 'AF555_Cytoplasm_Intensity_Average'\n",
+      " 'AF555_Nucleus_Intensity_Average' 'AF647_Cell_Intensity_Average'\n",
+      " 'AF647_Cytoplasm_Intensity_Average' 'AF647_Nucleus_Intensity_Average'\n",
+      " 'AF750_Cell_Intensity_Average' 'AF750_Cytoplasm_Intensity_Average'\n",
+      " 'AF750_Nucleus_Intensity_Average' 'aSMA_Cell_Intensity_Average'\n",
+      " 'aSMA_Cytoplasm_Intensity_Average' 'aSMA_Nucleus_Intensity_Average'\n",
+      " 'AXL_Cell_Intensity_Average' 'AXL_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Nucleus_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'B7H4_Cytoplasm_Intensity_Average' 'B7H4_Nucleus_Intensity_Average'\n",
+      " 'CA9_Cell_Intensity_Average' 'CA9_Cytoplasm_Intensity_Average'\n",
+      " 'CA9_Nucleus_Intensity_Average' 'CD4_Cell_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'CD4_Nucleus_Intensity_Average'\n",
+      " 'CD8_Cell_Intensity_Average' 'CD8_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Nucleus_Intensity_Average' 'CD11b_Cell_Intensity_Average'\n",
+      " 'CD11b_Cytoplasm_Intensity_Average' 'CD11b_Nucleus_Intensity_Average'\n",
+      " 'CD11c_Cell_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD11c_Nucleus_Intensity_Average' 'CD20_Cell_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'CD20_Nucleus_Intensity_Average'\n",
+      " 'CD31_Cell_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'CD31_Nucleus_Intensity_Average' 'CD44_Cell_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD44_Nucleus_Intensity_Average'\n",
+      " 'CD45_Cell_Intensity_Average' 'CD45_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Nucleus_Intensity_Average' 'CD68_Cell_Intensity_Average'\n",
+      " 'CD68_Cytoplasm_Intensity_Average' 'CD68_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cell_Intensity_Average' 'CD163_Cytoplasm_Intensity_Average'\n",
+      " 'CD163_Nucleus_Intensity_Average' 'CKs_Cell_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'CKs_Nucleus_Intensity_Average'\n",
+      " 'ColVI_Cell_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'ColVI_Nucleus_Intensity_Average' 'DAPI0_Cell_Intensity_Average'\n",
+      " 'DAPI0_Cytoplasm_Intensity_Average' 'DAPI0_Nucleus_Intensity_Average'\n",
+      " 'DAPI1_Cell_Intensity_Average' 'DAPI1_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI1_Nucleus_Intensity_Average' 'DAPI2_Cell_Intensity_Average'\n",
+      " 'DAPI2_Cytoplasm_Intensity_Average' 'DAPI2_Nucleus_Intensity_Average'\n",
+      " 'DAPI3_Cell_Intensity_Average' 'DAPI3_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI3_Nucleus_Intensity_Average' 'DAPI4_Cell_Intensity_Average'\n",
+      " 'DAPI4_Cytoplasm_Intensity_Average' 'DAPI4_Nucleus_Intensity_Average'\n",
+      " 'DAPI5_Cell_Intensity_Average' 'DAPI5_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI5_Nucleus_Intensity_Average' 'DAPI6_Cell_Intensity_Average'\n",
+      " 'DAPI6_Cytoplasm_Intensity_Average' 'DAPI6_Nucleus_Intensity_Average'\n",
+      " 'DAPI7_Cell_Intensity_Average' 'DAPI7_Cytoplasm_Intensity_Average'\n",
+      " 'DAPI7_Nucleus_Intensity_Average' 'DAPI8_Cell_Intensity_Average'\n",
+      " 'DAPI8_Cytoplasm_Intensity_Average' 'DAPI8_Nucleus_Intensity_Average'\n",
+      " 'Desmin_Cell_Intensity_Average' 'Desmin_Cytoplasm_Intensity_Average'\n",
+      " 'Desmin_Nucleus_Intensity_Average' 'Ecad_Cell_Intensity_Average'\n",
+      " 'Ecad_Cytoplasm_Intensity_Average' 'Ecad_Nucleus_Intensity_Average'\n",
+      " 'Fibronectin_Cell_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Nucleus_Intensity_Average' 'FOXP3_Cell_Intensity_Average'\n",
+      " 'FOXP3_Cytoplasm_Intensity_Average' 'FOXP3_Nucleus_Intensity_Average'\n",
+      " 'GATA3_Cell_Intensity_Average' 'GATA3_Cytoplasm_Intensity_Average'\n",
+      " 'GATA3_Nucleus_Intensity_Average' 'HLA_Cell_Intensity_Average'\n",
+      " 'HLA_Cytoplasm_Intensity_Average' 'HLA_Nucleus_Intensity_Average'\n",
+      " 'Ki67_Cell_Intensity_Average' 'Ki67_Cytoplasm_Intensity_Average'\n",
+      " 'Ki67_Nucleus_Intensity_Average' 'MMP9_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'MMP9_Nucleus_Intensity_Average'\n",
+      " 'PD1_Cell_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'PD1_Nucleus_Intensity_Average' 'PDGFR_Cell_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'PDGFR_Nucleus_Intensity_Average'\n",
+      " 'PDL1_Cell_Intensity_Average' 'PDL1_Cytoplasm_Intensity_Average'\n",
+      " 'PDL1_Nucleus_Intensity_Average' 'r5c2_Cell_Intensity_Average'\n",
+      " 'r5c2_Cytoplasm_Intensity_Average' 'r5c2_Nucleus_Intensity_Average'\n",
+      " 'r7c2_Cell_Intensity_Average' 'r7c2_Cytoplasm_Intensity_Average'\n",
+      " 'r7c2_Nucleus_Intensity_Average' 'r8c2_Cell_Intensity_Average'\n",
+      " 'r8c2_Cytoplasm_Intensity_Average' 'r8c2_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cell_Intensity_Average' 'Sting_Cytoplasm_Intensity_Average'\n",
+      " 'Sting_Nucleus_Intensity_Average' 'Vimentin_Cell_Intensity_Average'\n",
+      " 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'Vimentin_Nucleus_Intensity_Average']\n",
+      "\n",
+      "df :\n",
+      "      Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "ID                                                                   \n",
+      "1@1        339  1484.771729  16632.205078          0           127   \n",
+      "\n",
+      "     Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "ID                                                     \n",
+      "1@1            0.95504                   2385.867188   \n",
+      "\n",
+      "     AF488_Cytoplasm_Intensity_Average  AF488_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                          2356.6604                       2434.62207   \n",
+      "\n",
+      "     AF555_Cell_Intensity_Average  ...  r7c2_Nucleus_Intensity_Average  \\\n",
+      "ID                                 ...                                   \n",
+      "1@1                   1358.528076  ...                      290.582672   \n",
+      "\n",
+      "     r8c2_Cell_Intensity_Average  r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                   \n",
+      "1@1                   341.790558                         337.82547   \n",
+      "\n",
+      "     r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "ID                                                                  \n",
+      "1@1                      348.409454                   1567.100342   \n",
+      "\n",
+      "     Sting_Cytoplasm_Intensity_Average  Sting_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                         1533.22168                      1623.653564   \n",
+      "\n",
+      "     Vimentin_Cell_Intensity_Average  Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                           \n",
+      "1@1                      7279.144531                           7040.108398   \n",
+      "\n",
+      "     Vimentin_Nucleus_Intensity_Average  \n",
+      "ID                                       \n",
+      "1@1                         7678.165527  \n",
+      "\n",
+      "[1 rows x 141 columns] \n",
+      "\n",
+      "df's columns :\n",
+      " Index(['Cell_Size', 'Nuc_X', 'Nuc_Y_Inv', 'ROI_index', 'Nucleus_Size',\n",
+      "       'Nucleus_Roundness', 'AF488_Cell_Intensity_Average',\n",
+      "       'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus_Intensity_Average',\n",
+      "       'AF555_Cell_Intensity_Average',\n",
+      "       ...\n",
+      "       'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average',\n",
+      "       'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus_Intensity_Average',\n",
+      "       'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm_Intensity_Average',\n",
+      "       'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell_Intensity_Average',\n",
+      "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+      "       'Vimentin_Nucleus_Intensity_Average'],\n",
+      "      dtype='object', length=141) \n",
+      "\n",
+      "df's index :\n",
+      " Index(['1@1'], dtype='object', name='ID') \n",
+      "\n",
+      "df's index name :\n",
+      " ID\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify that the ID column in input file became the index\n",
+    "# Verify that the index name column is \"ID\", if not, rename it\n",
+    "if df.index.name != \"ID\":\n",
+    "    print(\"Expected the first column in input file (index_col = 0) to be 'ID'. \\n\"\n",
+    "          \"This column will be used to set the index names (cell number for each sample). \\n\"\n",
+    "          \"It appears that the column '\" + df.index.name + \"' was actually the imported as the index column.\")\n",
+    "    #df.index.name = 'ID'\n",
+    "    print(\"A new index name (first column) will be given ('ID') to replace the current one '\" + df.index.name + \"'\\n\")\n",
+    "\n",
+    "# Apply the changes to the headers as specified with apply_header_changes() function (in my_modules.py)\n",
+    "# Apply the changes to the dataframe rows as specified with apply_df_changes() function (in my_modules.py)\n",
+    "df = apply_header_changes(df)\n",
+    "df = apply_df_changes(df)\n",
+    "\n",
+    "# Set variable to hold default header values\n",
+    "expected_headers = df.columns.values\n",
+    "print(expected_headers)\n",
+    "\n",
+    "# Make sure the file is now formated correctly\n",
+    "print(\"\\ndf :\\n\", df.head(), \"\\n\")\n",
+    "print(\"df's columns :\\n\", df.columns, \"\\n\")\n",
+    "print(\"df's index :\\n\", df.index, \"\\n\")\n",
+    "print(\"df's index name :\\n\", df.index.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1e7448a1-b156-4d5e-9698-e94ebe9ef7b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Used DD3S1.csv to determine the expected and corrected headers for all files.\n",
+      "\n",
+      "These headers are: \n",
+      "Cell_Size, Nuc_X, Nuc_Y_Inv, ROI_index, Nucleus_Size, Nucleus_Roundness, AF488_Cell_Intensity_Average, AF488_Cytoplasm_Intensity_Average, AF488_Nucleus_Intensity_Average, AF555_Cell_Intensity_Average, AF555_Cytoplasm_Intensity_Average, AF555_Nucleus_Intensity_Average, AF647_Cell_Intensity_Average, AF647_Cytoplasm_Intensity_Average, AF647_Nucleus_Intensity_Average, AF750_Cell_Intensity_Average, AF750_Cytoplasm_Intensity_Average, AF750_Nucleus_Intensity_Average, aSMA_Cell_Intensity_Average, aSMA_Cytoplasm_Intensity_Average, aSMA_Nucleus_Intensity_Average, AXL_Cell_Intensity_Average, AXL_Cytoplasm_Intensity_Average, AXL_Nucleus_Intensity_Average, B7H4_Cell_Intensity_Average, B7H4_Cytoplasm_Intensity_Average, B7H4_Nucleus_Intensity_Average, CA9_Cell_Intensity_Average, CA9_Cytoplasm_Intensity_Average, CA9_Nucleus_Intensity_Average, CD4_Cell_Intensity_Average, CD4_Cytoplasm_Intensity_Average, CD4_Nucleus_Intensity_Average, CD8_Cell_Intensity_Average, CD8_Cytoplasm_Intensity_Average, CD8_Nucleus_Intensity_Average, CD11b_Cell_Intensity_Average, CD11b_Cytoplasm_Intensity_Average, CD11b_Nucleus_Intensity_Average, CD11c_Cell_Intensity_Average, CD11c_Cytoplasm_Intensity_Average, CD11c_Nucleus_Intensity_Average, CD20_Cell_Intensity_Average, CD20_Cytoplasm_Intensity_Average, CD20_Nucleus_Intensity_Average, CD31_Cell_Intensity_Average, CD31_Cytoplasm_Intensity_Average, CD31_Nucleus_Intensity_Average, CD44_Cell_Intensity_Average, CD44_Cytoplasm_Intensity_Average, CD44_Nucleus_Intensity_Average, CD45_Cell_Intensity_Average, CD45_Cytoplasm_Intensity_Average, CD45_Nucleus_Intensity_Average, CD68_Cell_Intensity_Average, CD68_Cytoplasm_Intensity_Average, CD68_Nucleus_Intensity_Average, CD163_Cell_Intensity_Average, CD163_Cytoplasm_Intensity_Average, CD163_Nucleus_Intensity_Average, CKs_Cell_Intensity_Average, CKs_Cytoplasm_Intensity_Average, CKs_Nucleus_Intensity_Average, ColVI_Cell_Intensity_Average, ColVI_Cytoplasm_Intensity_Average, ColVI_Nucleus_Intensity_Average, DAPI0_Cell_Intensity_Average, DAPI0_Cytoplasm_Intensity_Average, DAPI0_Nucleus_Intensity_Average, DAPI1_Cell_Intensity_Average, DAPI1_Cytoplasm_Intensity_Average, DAPI1_Nucleus_Intensity_Average, DAPI2_Cell_Intensity_Average, DAPI2_Cytoplasm_Intensity_Average, DAPI2_Nucleus_Intensity_Average, DAPI3_Cell_Intensity_Average, DAPI3_Cytoplasm_Intensity_Average, DAPI3_Nucleus_Intensity_Average, DAPI4_Cell_Intensity_Average, DAPI4_Cytoplasm_Intensity_Average, DAPI4_Nucleus_Intensity_Average, DAPI5_Cell_Intensity_Average, DAPI5_Cytoplasm_Intensity_Average, DAPI5_Nucleus_Intensity_Average, DAPI6_Cell_Intensity_Average, DAPI6_Cytoplasm_Intensity_Average, DAPI6_Nucleus_Intensity_Average, DAPI7_Cell_Intensity_Average, DAPI7_Cytoplasm_Intensity_Average, DAPI7_Nucleus_Intensity_Average, DAPI8_Cell_Intensity_Average, DAPI8_Cytoplasm_Intensity_Average, DAPI8_Nucleus_Intensity_Average, Desmin_Cell_Intensity_Average, Desmin_Cytoplasm_Intensity_Average, Desmin_Nucleus_Intensity_Average, Ecad_Cell_Intensity_Average, Ecad_Cytoplasm_Intensity_Average, Ecad_Nucleus_Intensity_Average, Fibronectin_Cell_Intensity_Average, Fibronectin_Cytoplasm_Intensity_Average, Fibronectin_Nucleus_Intensity_Average, FOXP3_Cell_Intensity_Average, FOXP3_Cytoplasm_Intensity_Average, FOXP3_Nucleus_Intensity_Average, GATA3_Cell_Intensity_Average, GATA3_Cytoplasm_Intensity_Average, GATA3_Nucleus_Intensity_Average, HLA_Cell_Intensity_Average, HLA_Cytoplasm_Intensity_Average, HLA_Nucleus_Intensity_Average, Ki67_Cell_Intensity_Average, Ki67_Cytoplasm_Intensity_Average, Ki67_Nucleus_Intensity_Average, MMP9_Cell_Intensity_Average, MMP9_Cytoplasm_Intensity_Average, MMP9_Nucleus_Intensity_Average, PD1_Cell_Intensity_Average, PD1_Cytoplasm_Intensity_Average, PD1_Nucleus_Intensity_Average, PDGFR_Cell_Intensity_Average, PDGFR_Cytoplasm_Intensity_Average, PDGFR_Nucleus_Intensity_Average, PDL1_Cell_Intensity_Average, PDL1_Cytoplasm_Intensity_Average, PDL1_Nucleus_Intensity_Average, r5c2_Cell_Intensity_Average, r5c2_Cytoplasm_Intensity_Average, r5c2_Nucleus_Intensity_Average, r7c2_Cell_Intensity_Average, r7c2_Cytoplasm_Intensity_Average, r7c2_Nucleus_Intensity_Average, r8c2_Cell_Intensity_Average, r8c2_Cytoplasm_Intensity_Average, r8c2_Nucleus_Intensity_Average, Sting_Cell_Intensity_Average, Sting_Cytoplasm_Intensity_Average, Sting_Nucleus_Intensity_Average, Vimentin_Cell_Intensity_Average, Vimentin_Cytoplasm_Intensity_Average, Vimentin_Nucleus_Intensity_Average\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Used \" + ls_samples[0] + \" to determine the expected and corrected headers for all files.\\n\")\n",
+    "print(\"These headers are: \\n\" + \", \".join([h for h in expected_headers]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d0e4670c-acd0-4183-ad09-4b0442abb2ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DD3S1.csv file is processed !\n",
+      "\n",
+      "DD3S2.csv file is processed !\n",
+      "\n",
+      "DD3S3.csv file is processed !\n",
+      "\n",
+      "TMA.csv file is processed !\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import all the others files\n",
+    "dfs = {}\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "for sample in ls_samples:\n",
+    "    file_path = os.path.join(input_data_dir,sample)\n",
+    "    \n",
+    "    try:\n",
+    "        # Read the CSV file\n",
+    "        df = pd.read_csv(file_path, index_col=0)\n",
+    "        # Check if the DataFrame is empty, if so, don't continue trying to process df and remove it\n",
+    "        \n",
+    "        if not df.empty:\n",
+    "            # Manipulations necessary for concatenation\n",
+    "            df = apply_header_changes(df)\n",
+    "            df = apply_df_changes(df)\n",
+    "            # Reorder the columns to match the expected headers list\n",
+    "            df = df.reindex(columns=expected_headers)\n",
+    "            print(sample, \"file is processed !\\n\")\n",
+    "            #print(df) \n",
+    "    \n",
+    "            # Compare df's header df against what is expected\n",
+    "            compare_headers(expected_headers, df.columns.values, sample)\n",
+    "            #print(df.columns.values)\n",
+    "            # Add a new colunm to identify the csv file (sample) where the df comes from\n",
+    "            df['Sample_ID'] = sample \n",
+    "   \n",
+    "    except pd.errors.EmptyDataError:\n",
+    "        print(f'\\nEmpty data error in {sample} file. Removing from analysis...')\n",
+    "        ls_samples.remove(sample)      \n",
+    "    \n",
+    "    # Add df to dfs \n",
+    "    dfs[sample] = df\n",
+    "\n",
+    "#print(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "69a6106c-8106-427c-8d53-abc26c9db6e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "ID                                                                   \n",
+      "1@1        339  1484.771729  16632.205078          0           127   \n",
+      "2@1        344  1426.250000  16627.384766          0           112   \n",
+      "3@1        422  1531.110474  16622.238281          0           181   \n",
+      "4@1        278  1518.907593  16623.007812          0           119   \n",
+      "5@1        502  1488.051758  16616.375000          0           232   \n",
+      "\n",
+      "     Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "ID                                                     \n",
+      "1@1           0.955040                   2385.867188   \n",
+      "2@1           0.966643                   2818.250000   \n",
+      "3@1           0.721534                   2162.047363   \n",
+      "4@1           0.587196                   2422.715820   \n",
+      "5@1           0.655828                   2265.306885   \n",
+      "\n",
+      "     AF488_Cytoplasm_Intensity_Average  AF488_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                        \n",
+      "1@1                        2356.660400                      2434.622070   \n",
+      "2@1                        2884.366455                      2681.294678   \n",
+      "3@1                        2124.817383                      2211.618896   \n",
+      "4@1                        2411.867920                      2437.210205   \n",
+      "5@1                        2154.796387                      2393.918213   \n",
+      "\n",
+      "     AF555_Cell_Intensity_Average  ...  r8c2_Cell_Intensity_Average  \\\n",
+      "ID                                 ...                                \n",
+      "1@1                   1358.528076  ...                   341.790558   \n",
+      "2@1                   1472.325562  ...                   365.531982   \n",
+      "3@1                   1289.054443  ...                   320.874420   \n",
+      "4@1                   1397.992798  ...                   343.320129   \n",
+      "5@1                   1288.657349  ...                   326.241028   \n",
+      "\n",
+      "     r8c2_Cytoplasm_Intensity_Average  r8c2_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                      \n",
+      "1@1                        337.825470                      348.409454   \n",
+      "2@1                        369.340515                      357.642853   \n",
+      "3@1                        315.605804                      327.889496   \n",
+      "4@1                        338.679260                      349.520996   \n",
+      "5@1                        314.748138                      339.616394   \n",
+      "\n",
+      "     Sting_Cell_Intensity_Average  Sting_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                     \n",
+      "1@1                   1567.100342                        1533.221680   \n",
+      "2@1                   1508.014526                        1565.086182   \n",
+      "3@1                   1841.360229                        1772.647339   \n",
+      "4@1                   1723.863281                        1688.094360   \n",
+      "5@1                   1711.464111                        1629.670410   \n",
+      "\n",
+      "     Sting_Nucleus_Intensity_Average  Vimentin_Cell_Intensity_Average  \\\n",
+      "ID                                                                      \n",
+      "1@1                      1623.653564                      7279.144531   \n",
+      "2@1                      1389.794678                      6123.456543   \n",
+      "3@1                      1932.850830                      4252.185059   \n",
+      "4@1                      1771.655518                      6178.647461   \n",
+      "5@1                      1806.655151                      5208.479980   \n",
+      "\n",
+      "     Vimentin_Cytoplasm_Intensity_Average  Vimentin_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                              \n",
+      "1@1                           7040.108398                         7678.165527   \n",
+      "2@1                           6734.603516                         4857.508789   \n",
+      "3@1                           4473.178223                         3957.933594   \n",
+      "4@1                           5316.924316                         7330.025391   \n",
+      "5@1                           4386.700195                         6164.862305   \n",
+      "\n",
+      "     Sample_ID  \n",
+      "ID              \n",
+      "1@1  DD3S1.csv  \n",
+      "2@1  DD3S1.csv  \n",
+      "3@1  DD3S1.csv  \n",
+      "4@1  DD3S1.csv  \n",
+      "5@1  DD3S1.csv  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge dfs into one df\n",
+    "df = pd.concat(dfs.values(), ignore_index=False , sort = False)\n",
+    "#del dfs\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5c724db9-eb76-4af1-8fe3-8beff43d8940",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Cell_Size        Nuc_X     Nuc_Y_Inv  ROI_index  Nucleus_Size  \\\n",
+      "DD3S1_Cell_0        339  1484.771729  16632.205078          0           127   \n",
+      "DD3S1_Cell_1        344  1426.250000  16627.384766          0           112   \n",
+      "DD3S1_Cell_2        422  1531.110474  16622.238281          0           181   \n",
+      "DD3S1_Cell_3        278  1518.907593  16623.007812          0           119   \n",
+      "DD3S1_Cell_4        502  1488.051758  16616.375000          0           232   \n",
+      "\n",
+      "              Nucleus_Roundness  AF488_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0           0.955040                   2385.867188   \n",
+      "DD3S1_Cell_1           0.966643                   2818.250000   \n",
+      "DD3S1_Cell_2           0.721534                   2162.047363   \n",
+      "DD3S1_Cell_3           0.587196                   2422.715820   \n",
+      "DD3S1_Cell_4           0.655828                   2265.306885   \n",
+      "\n",
+      "              AF488_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        2356.660400   \n",
+      "DD3S1_Cell_1                        2884.366455   \n",
+      "DD3S1_Cell_2                        2124.817383   \n",
+      "DD3S1_Cell_3                        2411.867920   \n",
+      "DD3S1_Cell_4                        2154.796387   \n",
+      "\n",
+      "              AF488_Nucleus_Intensity_Average  AF555_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      2434.622070                   1358.528076   \n",
+      "DD3S1_Cell_1                      2681.294678                   1472.325562   \n",
+      "DD3S1_Cell_2                      2211.618896                   1289.054443   \n",
+      "DD3S1_Cell_3                      2437.210205                   1397.992798   \n",
+      "DD3S1_Cell_4                      2393.918213                   1288.657349   \n",
+      "\n",
+      "              ...  r8c2_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0  ...                   341.790558   \n",
+      "DD3S1_Cell_1  ...                   365.531982   \n",
+      "DD3S1_Cell_2  ...                   320.874420   \n",
+      "DD3S1_Cell_3  ...                   343.320129   \n",
+      "DD3S1_Cell_4  ...                   326.241028   \n",
+      "\n",
+      "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        337.825470   \n",
+      "DD3S1_Cell_1                        369.340515   \n",
+      "DD3S1_Cell_2                        315.605804   \n",
+      "DD3S1_Cell_3                        338.679260   \n",
+      "DD3S1_Cell_4                        314.748138   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      348.409454                   1567.100342   \n",
+      "DD3S1_Cell_1                      357.642853                   1508.014526   \n",
+      "DD3S1_Cell_2                      327.889496                   1841.360229   \n",
+      "DD3S1_Cell_3                      349.520996                   1723.863281   \n",
+      "DD3S1_Cell_4                      339.616394                   1711.464111   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        1533.221680   \n",
+      "DD3S1_Cell_1                        1565.086182   \n",
+      "DD3S1_Cell_2                        1772.647339   \n",
+      "DD3S1_Cell_3                        1688.094360   \n",
+      "DD3S1_Cell_4                        1629.670410   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      1623.653564   \n",
+      "DD3S1_Cell_1                      1389.794678   \n",
+      "DD3S1_Cell_2                      1932.850830   \n",
+      "DD3S1_Cell_3                      1771.655518   \n",
+      "DD3S1_Cell_4                      1806.655151   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      7279.144531   \n",
+      "DD3S1_Cell_1                      6123.456543   \n",
+      "DD3S1_Cell_2                      4252.185059   \n",
+      "DD3S1_Cell_3                      6178.647461   \n",
+      "DD3S1_Cell_4                      5208.479980   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                           7040.108398   \n",
+      "DD3S1_Cell_1                           6734.603516   \n",
+      "DD3S1_Cell_2                           4473.178223   \n",
+      "DD3S1_Cell_3                           5316.924316   \n",
+      "DD3S1_Cell_4                           4386.700195   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  Sample_ID  \n",
+      "DD3S1_Cell_0                         7678.165527  DD3S1.csv  \n",
+      "DD3S1_Cell_1                         4857.508789  DD3S1.csv  \n",
+      "DD3S1_Cell_2                         3957.933594  DD3S1.csv  \n",
+      "DD3S1_Cell_3                         7330.025391  DD3S1.csv  \n",
+      "DD3S1_Cell_4                         6164.862305  DD3S1.csv  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set index to Sample_ID + cell number : \n",
+    "# create a new custom index for df based on the sample names and integer cell numbers, and then remove the temporary columns 'level_0' and 'index' that were introduced during the operations\n",
+    "\n",
+    "# Creates a copy of the DataFrame df and resets its index without creating a new column for the old index\n",
+    "# This essentially removes the old index column and replaces it with a default integer index\n",
+    "df = df.copy().reset_index(drop=True)\n",
+    "\n",
+    "#print(df)\n",
+    "\n",
+    "# Initializing an empty list index to store the new index labels for the DataFrame\n",
+    "index = []\n",
+    "\n",
+    "for sample in ls_samples:\n",
+    "    # Extract a chunk of data from the original df where the 'Sample_ID' column matches the current sample name\n",
+    "    # This chunk is stored in the df_chunk df, which is a subset of the original data for that specific sample\n",
+    "    df_chunk = df.loc[df['Sample_ID'] == sample,:].copy()\n",
+    "    old_index = df_chunk.index\n",
+    "    # Reset the index of the df_chunk df, removing the old index and replacing it with a default integer index\n",
+    "    df_chunk = df_chunk.reset_index(drop=True)\n",
+    "    # A new index is created for the df_chunk df. It combines the sample name with 'Cell_' and the integer index values, converting them to strings\n",
+    "    # This new index will have labels like 'SampleName_Cell_0', 'SampleName_Cell_1', and so on.\n",
+    "    sample = sample.split('.')[0]\n",
+    "    df_chunk = df_chunk.set_index(f'{sample}_Cell_' + df_chunk.index.astype(str))\n",
+    "    # The index values of df_chunk are then added to the index list\n",
+    "    index = index + df_chunk.index.values.tolist()\n",
+    "\n",
+    "# After processing all the samples in the loop, assign the index list as the new index of the original df.\n",
+    "df.index =  index\n",
+    "# Remove the 'level_0' and 'index' columns from df\n",
+    "df = df.loc[:,~df.columns.isin(['level_0','index'])]\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c95a292d-72df-4f1e-8a9d-a3de84d99057",
+   "metadata": {},
+   "source": [
+    "### I.3.2. NOT_INTENSITIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8e4bb227-2a4f-477b-a435-e85178d1003a",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (562768646.py, line 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[15], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    not_intensitiehttp://localhost:8888/lab/tree/Downloads/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb\u001b[0m\n\u001b[0m                                                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n"
+     ]
+    }
+   ],
+   "source": [
+    "# not_intensities is the list of the columns unrelated to the markers fluorescence intensities\n",
+    "# Can include items that aren't in a given header.\n",
+    "not_intensitiehttp://localhost:8888/lab/tree/Downloads/wetransfer_data-zip_2024-05-17_1431/1_qc_eda.ipynb\n",
+    "#I.3.2.-NOT_INTENSITIESs = ['Nuc_X', 'Nuc_X_Inv', 'Nuc_Y', 'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nucleus_Size', 'Cell_Size', \n",
+    "#                   'ROI_index', 'Sample_ID', 'replicate_ID', 'Cell_ID','cell_type', 'cell_subtype', 'cluster','ID', \n",
+    "#                  'Cytoplasm_Size', 'immune_checkpoint', 'Unique_ROI_index', 'Patient', 'Primary_chem(1)_vs_surg(0)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3a0b81d8-1fa0-45da-92ab-2ee59ef9d51d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not_intensities is the list of the columns unrelated to the markers fluorescence intensities\n",
+    "# Can include items that aren't in a given header.\n",
+    "not_intensities = ['Nuc_X', 'Nuc_X_Inv', 'Nuc_Y', 'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nucleus_Size', 'Cell_Size', \n",
+    "                   'ROI_index', 'Sample_ID', 'replicate_ID', 'Cell_ID','cell_type', 'cell_subtype', 'cluster','ID', \n",
+    "                   'Cytoplasm_Size', 'immune_checkpoint', 'Unique_ROI_index', 'Patient', 'Primary_chem(1)_vs_surg(0)']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "26669861-4eef-43f5-82f8-532f83bd7f9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'not_intensities.csv' already exists.\n",
+      "Reconciling file and Jupyter notebook lists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_not_intensities = os.path.join(metadata_dir,\"not_intensities.csv\")\n",
+    "\n",
+    "# If this file already exists, add only not_intensities items of the list not already present in file\n",
+    "if os.path.exists(path_not_intensities):\n",
+    "    print(\"'not_intensities.csv' already exists.\")\n",
+    "    print(\"Reconciling file and Jupyter notebook lists.\")\n",
+    "    file_not_intensities = open(path_not_intensities, \"r\")\n",
+    "    file_ni = file_not_intensities.read().splitlines()\n",
+    "    # Set difference to identify items not already in file\n",
+    "    to_add = set(not_intensities) - set(file_ni)\n",
+    "    # We want not_intensities to the a complete list\n",
+    "    not_intensities = list(set(file_ni) | set(not_intensities))\n",
+    "    file_not_intensities.close()\n",
+    "    file_not_intensities = open(path_not_intensities, \"a\")\n",
+    "    for item in to_add:\n",
+    "        file_not_intensities.write(item +\"\\n\")\n",
+    "    file_not_intensities.close()\n",
+    "\n",
+    "else:\n",
+    "    # The file does not yet exist\n",
+    "    print(\"Could not find \" + path_not_intensities + \". Creating now.\")\n",
+    "    file_not_intensities = open(path_not_intensities, \"w\")\n",
+    "    for item in not_intensities:\n",
+    "        file_not_intensities.write(item + \"\\n\")\n",
+    "    file_not_intensities.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "175ef4cd-600f-47d6-8e16-583f4a1a0abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ROI_index',\n",
+       " 'Nuc_X_Inv',\n",
+       " 'Patient',\n",
+       " 'ID',\n",
+       " 'cluster',\n",
+       " 'Sample_ID',\n",
+       " 'replicate_ID',\n",
+       " 'cell_subtype',\n",
+       " 'cell_type',\n",
+       " 'Nucleus_Roundness',\n",
+       " 'Nucleus_Size',\n",
+       " 'Primary_chem(1)_vs_surg(0)',\n",
+       " 'Nuc_X',\n",
+       " 'Nuc_Y',\n",
+       " 'Nuc_Y_Inv',\n",
+       " 'Unique_ROI_index',\n",
+       " 'Cytoplasm_Size',\n",
+       " 'immune_checkpoint',\n",
+       " 'Cell_ID',\n",
+       " 'Cell_Size',\n",
+       " 'AF488_Cell_Intensity_Average',\n",
+       " 'AF488_Cytoplasm_Intensity_Average',\n",
+       " 'AF488_Nucleus_Intensity_Average',\n",
+       " 'AF555_Cell_Intensity_Average',\n",
+       " 'AF555_Cytoplasm_Intensity_Average',\n",
+       " 'AF555_Nucleus_Intensity_Average',\n",
+       " 'AF647_Cell_Intensity_Average',\n",
+       " 'AF647_Cytoplasm_Intensity_Average',\n",
+       " 'AF647_Nucleus_Intensity_Average',\n",
+       " 'AF750_Cell_Intensity_Average',\n",
+       " 'AF750_Cytoplasm_Intensity_Average',\n",
+       " 'AF750_Nucleus_Intensity_Average',\n",
+       " 'aSMA_Cell_Intensity_Average',\n",
+       " 'aSMA_Cytoplasm_Intensity_Average',\n",
+       " 'aSMA_Nucleus_Intensity_Average',\n",
+       " 'AXL_Cell_Intensity_Average',\n",
+       " 'AXL_Cytoplasm_Intensity_Average',\n",
+       " 'AXL_Nucleus_Intensity_Average',\n",
+       " 'B7H4_Cell_Intensity_Average',\n",
+       " 'B7H4_Cytoplasm_Intensity_Average',\n",
+       " 'B7H4_Nucleus_Intensity_Average',\n",
+       " 'CA9_Cell_Intensity_Average',\n",
+       " 'CA9_Cytoplasm_Intensity_Average',\n",
+       " 'CA9_Nucleus_Intensity_Average',\n",
+       " 'CD4_Cell_Intensity_Average',\n",
+       " 'CD4_Cytoplasm_Intensity_Average',\n",
+       " 'CD4_Nucleus_Intensity_Average',\n",
+       " 'CD8_Cell_Intensity_Average',\n",
+       " 'CD8_Cytoplasm_Intensity_Average',\n",
+       " 'CD8_Nucleus_Intensity_Average',\n",
+       " 'CD11b_Cell_Intensity_Average',\n",
+       " 'CD11b_Cytoplasm_Intensity_Average',\n",
+       " 'CD11b_Nucleus_Intensity_Average',\n",
+       " 'CD11c_Cell_Intensity_Average',\n",
+       " 'CD11c_Cytoplasm_Intensity_Average',\n",
+       " 'CD11c_Nucleus_Intensity_Average',\n",
+       " 'CD20_Cell_Intensity_Average',\n",
+       " 'CD20_Cytoplasm_Intensity_Average',\n",
+       " 'CD20_Nucleus_Intensity_Average',\n",
+       " 'CD31_Cell_Intensity_Average',\n",
+       " 'CD31_Cytoplasm_Intensity_Average',\n",
+       " 'CD31_Nucleus_Intensity_Average',\n",
+       " 'CD44_Cell_Intensity_Average',\n",
+       " 'CD44_Cytoplasm_Intensity_Average',\n",
+       " 'CD44_Nucleus_Intensity_Average',\n",
+       " 'CD45_Cell_Intensity_Average',\n",
+       " 'CD45_Cytoplasm_Intensity_Average',\n",
+       " 'CD45_Nucleus_Intensity_Average',\n",
+       " 'CD68_Cell_Intensity_Average',\n",
+       " 'CD68_Cytoplasm_Intensity_Average',\n",
+       " 'CD68_Nucleus_Intensity_Average',\n",
+       " 'CD163_Cell_Intensity_Average',\n",
+       " 'CD163_Cytoplasm_Intensity_Average',\n",
+       " 'CD163_Nucleus_Intensity_Average',\n",
+       " 'CKs_Cell_Intensity_Average',\n",
+       " 'CKs_Cytoplasm_Intensity_Average',\n",
+       " 'CKs_Nucleus_Intensity_Average',\n",
+       " 'ColVI_Cell_Intensity_Average',\n",
+       " 'ColVI_Cytoplasm_Intensity_Average',\n",
+       " 'ColVI_Nucleus_Intensity_Average',\n",
+       " 'DAPI0_Cell_Intensity_Average',\n",
+       " 'DAPI0_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI0_Nucleus_Intensity_Average',\n",
+       " 'DAPI1_Cell_Intensity_Average',\n",
+       " 'DAPI1_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI1_Nucleus_Intensity_Average',\n",
+       " 'DAPI2_Cell_Intensity_Average',\n",
+       " 'DAPI2_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI2_Nucleus_Intensity_Average',\n",
+       " 'DAPI3_Cell_Intensity_Average',\n",
+       " 'DAPI3_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI3_Nucleus_Intensity_Average',\n",
+       " 'DAPI4_Cell_Intensity_Average',\n",
+       " 'DAPI4_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI4_Nucleus_Intensity_Average',\n",
+       " 'DAPI5_Cell_Intensity_Average',\n",
+       " 'DAPI5_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI5_Nucleus_Intensity_Average',\n",
+       " 'DAPI6_Cell_Intensity_Average',\n",
+       " 'DAPI6_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI6_Nucleus_Intensity_Average',\n",
+       " 'DAPI7_Cell_Intensity_Average',\n",
+       " 'DAPI7_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI7_Nucleus_Intensity_Average',\n",
+       " 'DAPI8_Cell_Intensity_Average',\n",
+       " 'DAPI8_Cytoplasm_Intensity_Average',\n",
+       " 'DAPI8_Nucleus_Intensity_Average',\n",
+       " 'Desmin_Cell_Intensity_Average',\n",
+       " 'Desmin_Cytoplasm_Intensity_Average',\n",
+       " 'Desmin_Nucleus_Intensity_Average',\n",
+       " 'Ecad_Cell_Intensity_Average',\n",
+       " 'Ecad_Cytoplasm_Intensity_Average',\n",
+       " 'Ecad_Nucleus_Intensity_Average',\n",
+       " 'Fibronectin_Cell_Intensity_Average',\n",
+       " 'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       " 'Fibronectin_Nucleus_Intensity_Average',\n",
+       " 'FOXP3_Cell_Intensity_Average',\n",
+       " 'FOXP3_Cytoplasm_Intensity_Average',\n",
+       " 'FOXP3_Nucleus_Intensity_Average',\n",
+       " 'GATA3_Cell_Intensity_Average',\n",
+       " 'GATA3_Cytoplasm_Intensity_Average',\n",
+       " 'GATA3_Nucleus_Intensity_Average',\n",
+       " 'HLA_Cell_Intensity_Average',\n",
+       " 'HLA_Cytoplasm_Intensity_Average',\n",
+       " 'HLA_Nucleus_Intensity_Average',\n",
+       " 'Ki67_Cell_Intensity_Average',\n",
+       " 'Ki67_Cytoplasm_Intensity_Average',\n",
+       " 'Ki67_Nucleus_Intensity_Average',\n",
+       " 'MMP9_Cell_Intensity_Average',\n",
+       " 'MMP9_Cytoplasm_Intensity_Average',\n",
+       " 'MMP9_Nucleus_Intensity_Average',\n",
+       " 'PD1_Cell_Intensity_Average',\n",
+       " 'PD1_Cytoplasm_Intensity_Average',\n",
+       " 'PD1_Nucleus_Intensity_Average',\n",
+       " 'PDGFR_Cell_Intensity_Average',\n",
+       " 'PDGFR_Cytoplasm_Intensity_Average',\n",
+       " 'PDGFR_Nucleus_Intensity_Average',\n",
+       " 'PDL1_Cell_Intensity_Average',\n",
+       " 'PDL1_Cytoplasm_Intensity_Average',\n",
+       " 'PDL1_Nucleus_Intensity_Average',\n",
+       " 'r5c2_Cell_Intensity_Average',\n",
+       " 'r5c2_Cytoplasm_Intensity_Average',\n",
+       " 'r5c2_Nucleus_Intensity_Average',\n",
+       " 'r7c2_Cell_Intensity_Average',\n",
+       " 'r7c2_Cytoplasm_Intensity_Average',\n",
+       " 'r7c2_Nucleus_Intensity_Average',\n",
+       " 'r8c2_Cell_Intensity_Average',\n",
+       " 'r8c2_Cytoplasm_Intensity_Average',\n",
+       " 'r8c2_Nucleus_Intensity_Average',\n",
+       " 'Sting_Cell_Intensity_Average',\n",
+       " 'Sting_Cytoplasm_Intensity_Average',\n",
+       " 'Sting_Nucleus_Intensity_Average',\n",
+       " 'Vimentin_Cell_Intensity_Average',\n",
+       " 'Vimentin_Cytoplasm_Intensity_Average',\n",
+       " 'Vimentin_Nucleus_Intensity_Average']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Columns we want to keep: not_intensities, and any intensity column that contains 'Intensity_Average' (drop any intensity marker column that is not a mean intensity)\n",
+    "to_keep = not_intensities + [x for x in df.columns.values[~df.columns.isin(not_intensities)] if 'Intensity_Average' in x]\n",
+    "\n",
+    "to_keep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7ecf0c10-605d-486e-9b9e-f3cbb2a349cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              ROI_index  Sample_ID  Nucleus_Roundness  Nucleus_Size  \\\n",
+      "DD3S1_Cell_0          0  DD3S1.csv           0.955040           127   \n",
+      "DD3S1_Cell_1          0  DD3S1.csv           0.966643           112   \n",
+      "DD3S1_Cell_2          0  DD3S1.csv           0.721534           181   \n",
+      "DD3S1_Cell_3          0  DD3S1.csv           0.587196           119   \n",
+      "DD3S1_Cell_4          0  DD3S1.csv           0.655828           232   \n",
+      "\n",
+      "                    Nuc_X     Nuc_Y_Inv  Cell_Size  \\\n",
+      "DD3S1_Cell_0  1484.771729  16632.205078        339   \n",
+      "DD3S1_Cell_1  1426.250000  16627.384766        344   \n",
+      "DD3S1_Cell_2  1531.110474  16622.238281        422   \n",
+      "DD3S1_Cell_3  1518.907593  16623.007812        278   \n",
+      "DD3S1_Cell_4  1488.051758  16616.375000        502   \n",
+      "\n",
+      "              AF488_Cell_Intensity_Average  AF488_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                   2385.867188                        2356.660400   \n",
+      "DD3S1_Cell_1                   2818.250000                        2884.366455   \n",
+      "DD3S1_Cell_2                   2162.047363                        2124.817383   \n",
+      "DD3S1_Cell_3                   2422.715820                        2411.867920   \n",
+      "DD3S1_Cell_4                   2265.306885                        2154.796387   \n",
+      "\n",
+      "              AF488_Nucleus_Intensity_Average  ...  \\\n",
+      "DD3S1_Cell_0                      2434.622070  ...   \n",
+      "DD3S1_Cell_1                      2681.294678  ...   \n",
+      "DD3S1_Cell_2                      2211.618896  ...   \n",
+      "DD3S1_Cell_3                      2437.210205  ...   \n",
+      "DD3S1_Cell_4                      2393.918213  ...   \n",
+      "\n",
+      "              r7c2_Nucleus_Intensity_Average  r8c2_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      290.582672                   341.790558   \n",
+      "DD3S1_Cell_1                      304.133942                   365.531982   \n",
+      "DD3S1_Cell_2                      271.353577                   320.874420   \n",
+      "DD3S1_Cell_3                      292.134460                   343.320129   \n",
+      "DD3S1_Cell_4                      284.642242                   326.241028   \n",
+      "\n",
+      "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        337.825470   \n",
+      "DD3S1_Cell_1                        369.340515   \n",
+      "DD3S1_Cell_2                        315.605804   \n",
+      "DD3S1_Cell_3                        338.679260   \n",
+      "DD3S1_Cell_4                        314.748138   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      348.409454                   1567.100342   \n",
+      "DD3S1_Cell_1                      357.642853                   1508.014526   \n",
+      "DD3S1_Cell_2                      327.889496                   1841.360229   \n",
+      "DD3S1_Cell_3                      349.520996                   1723.863281   \n",
+      "DD3S1_Cell_4                      339.616394                   1711.464111   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                        1533.221680   \n",
+      "DD3S1_Cell_1                        1565.086182   \n",
+      "DD3S1_Cell_2                        1772.647339   \n",
+      "DD3S1_Cell_3                        1688.094360   \n",
+      "DD3S1_Cell_4                        1629.670410   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      1623.653564   \n",
+      "DD3S1_Cell_1                      1389.794678   \n",
+      "DD3S1_Cell_2                      1932.850830   \n",
+      "DD3S1_Cell_3                      1771.655518   \n",
+      "DD3S1_Cell_4                      1806.655151   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                      7279.144531   \n",
+      "DD3S1_Cell_1                      6123.456543   \n",
+      "DD3S1_Cell_2                      4252.185059   \n",
+      "DD3S1_Cell_3                      6178.647461   \n",
+      "DD3S1_Cell_4                      5208.479980   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "DD3S1_Cell_0                           7040.108398   \n",
+      "DD3S1_Cell_1                           6734.603516   \n",
+      "DD3S1_Cell_2                           4473.178223   \n",
+      "DD3S1_Cell_3                           5316.924316   \n",
+      "DD3S1_Cell_4                           4386.700195   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  \n",
+      "DD3S1_Cell_0                         7678.165527  \n",
+      "DD3S1_Cell_1                         4857.508789  \n",
+      "DD3S1_Cell_2                         3957.933594  \n",
+      "DD3S1_Cell_3                         7330.025391  \n",
+      "DD3S1_Cell_4                         6164.862305  \n",
+      "\n",
+      "[5 rows x 142 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# However, our to_keep list contains items that might not be in our df headers!\n",
+    "# These items are from our not_intensities list. So let's ask for only those items from to_keep that are actually found in our df\n",
+    "# Retains only the columns from the to_keep list that are found in the df's headers (columns). \n",
+    "# This ensures that we are only keeping the columns that exist in your df, avoiding any potential issues with non-existent column names. \n",
+    "# The result is a df containing only the specified columns.\n",
+    "df = df[[x for x in to_keep if x in df.columns.values]]\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "284c8270-e13e-4f93-839b-5774a7fc9f4d",
+   "metadata": {},
+   "source": [
+    "## I.4. QC CHECKS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "91d9bebf-3c62-408e-923e-00447527ae3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DD3S1_Cell_0', 'DD3S1_Cell_1', 'DD3S1_Cell_2', 'DD3S1_Cell_3',\n",
+       "       'DD3S1_Cell_4', 'DD3S1_Cell_5', 'DD3S1_Cell_6', 'DD3S1_Cell_7',\n",
+       "       'DD3S1_Cell_8', 'DD3S1_Cell_9',\n",
+       "       ...\n",
+       "       'TMA_Cell_115751', 'TMA_Cell_115752', 'TMA_Cell_115753',\n",
+       "       'TMA_Cell_115754', 'TMA_Cell_115755', 'TMA_Cell_115756',\n",
+       "       'TMA_Cell_115757', 'TMA_Cell_115758', 'TMA_Cell_115759',\n",
+       "       'TMA_Cell_115760'],\n",
+       "      dtype='object', length=433976)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's take a look at a few features to make sure our dataframe is as expected\n",
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fe138d0d-7b02-48ac-af9e-caf85c4d3b73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(433976, 142)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e1f30957-ab09-416b-8ed5-41b99a5c0b51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "86e29b98-4758-4014-b976-db155fa9205f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All expected filenames are present in big df Sample_ID column.\n",
+      "DD3S1_Cell_0       DD3S1.csv\n",
+      "DD3S1_Cell_1       DD3S1.csv\n",
+      "DD3S1_Cell_2       DD3S1.csv\n",
+      "DD3S1_Cell_3       DD3S1.csv\n",
+      "DD3S1_Cell_4       DD3S1.csv\n",
+      "                     ...    \n",
+      "TMA_Cell_115756      TMA.csv\n",
+      "TMA_Cell_115757      TMA.csv\n",
+      "TMA_Cell_115758      TMA.csv\n",
+      "TMA_Cell_115759      TMA.csv\n",
+      "TMA_Cell_115760      TMA.csv\n",
+      "Name: Sample_ID, Length: 433976, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check that all expected files were imported into final dataframe\n",
+    "if sorted(df.Sample_ID.unique()) == sorted(ls_samples):\n",
+    "    print(\"All expected filenames are present in big df Sample_ID column.\")\n",
+    "else:\n",
+    "    compare_headers(['no samples'], df.Sample_ID.unique(), \"big df Sample_ID column\")\n",
+    "\n",
+    "print(df.Sample_ID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "66e0487d-8a29-400d-bde6-afde02985fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape before removing 0 mean values:  (433976, 142)\n",
+      "No zero intensity values found in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Delete rows that have 0 value mean intensities for intensity columns\n",
+    "print(\"df.shape before removing 0 mean values: \", df.shape)\n",
+    "\n",
+    "# We use the apply method on df to calculate the mean intensity for each row. It's done this by applying a lambda function to each row. \n",
+    "# The lambda function excludes the columns listed in the not_intensities list (which are not to be considered for mean intensity calculations) \n",
+    "# and calculates the mean of the remaining values in each row.\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "# Calculate mean intensity excluding 'not_intensities' columns\n",
+    "mean_intensity = df.loc[:, ~df.columns.isin(not_intensities)].mean(axis=1)\n",
+    "\n",
+    "# Check if there are any 0 mean intensity values\n",
+    "if (mean_intensity == 0).any():\n",
+    "    df = df.loc[mean_intensity > 0, :]\n",
+    "    print(\"df.shape after removing 0 mean values: \", df.shape)\n",
+    "else:\n",
+    "    print(\"No zero intensity values found in the DataFrame.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "93ff0fac-33ac-4120-b1cf-f3d413489b58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get quantiles (5th, 50th, 95th)\n",
+    "# List of nucleus size percentiles to extract \n",
+    "qs = [0.05,0.50,0.95] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "86b0158b-6cb1-4ff8-815b-26aeed908b4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05     42.0\n",
+       "0.50     88.0\n",
+       "0.95    217.0\n",
+       "Name: Nucleus_Size, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Nucleus_Size\"].quantile(q=qs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d7d6bf19-f83c-47b6-8e3a-a32d78e64a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/Nucleus_Size_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['Nucleus_Size'], bins=30, alpha=0.7, color='skyblue')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['Nucleus_Size'].quantile(0.05)\n",
+    "quantile_95 = df['Nucleus_Size'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of Nucleus_Size column values with horizontal bars at 0.05 and 0.95 quantiles')\n",
+    "ax.set_xlabel('Nucleus_Size Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantiles values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot in the output_images_dir directory using fig.savefig\n",
+    "plot_file_path = os.path.join(output_images_dir, \"Nucleus_Size_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "fe562d2b-5026-4a88-8117-4ec910dd1242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.05    1019.689148\n",
+      "0.50    1346.929932\n",
+      "0.95    2622.449524\n",
+      "Name: AF555_Cell_Intensity_Average, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"AF555_Cell_Intensity_Average\"].quantile(q=qs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8b53dced-802d-4bfd-8e37-de33ffec9c7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/AF555_Cell_Intensity_Average_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['AF555_Cell_Intensity_Average'], bins=30, alpha=0.7, color='skyblue')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['AF555_Cell_Intensity_Average'].quantile(0.05)\n",
+    "quantile_95 = df['AF555_Cell_Intensity_Average'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of AF555_Cell_Intensity_Average column values with horizontal bars at 0.05 and 0.95 quantiles')\n",
+    "ax.set_xlabel('AF555_Cell_Intensity_Average Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantiles values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot in the output_images_dir directory using fig.savefig\n",
+    "plot_file_path = os.path.join(output_images_dir, \"AF555_Cell_Intensity_Average_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "116f1ba5-5a9b-4161-8fab-d5ecb8524179",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The plot is saved at: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/images/CKs_Cytoplasm_Intensity_Average_Distribution.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the distribution of the Nucleus_Size column\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.hist(df['CKs_Cytoplasm_Intensity_Average'], bins=100, alpha=0.6, color='skyblue')  # Augmented number of bins\n",
+    "\n",
+    "# Calculate mean and median\n",
+    "mean_value = df['CKs_Cytoplasm_Intensity_Average'].mean()\n",
+    "median_value = df['CKs_Cytoplasm_Intensity_Average'].median()\n",
+    "\n",
+    "# Add vertical lines for mean and median\n",
+    "ax.axvline(x=mean_value, color='orange', linestyle='-', label='Mean')\n",
+    "ax.axvline(x=median_value, color='purple', linestyle='-', label='Median')\n",
+    "\n",
+    "# Add horizontal bars for the 0.05 and 0.95 quantiles\n",
+    "quantile_05 = df['CKs_Cytoplasm_Intensity_Average'].quantile(0.05)\n",
+    "quantile_95 = df['CKs_Cytoplasm_Intensity_Average'].quantile(0.95)\n",
+    "ax.axvline(x=quantile_05, color='r', linestyle='--', label='Quantile 0.05')\n",
+    "ax.axvline(x=quantile_95, color='g', linestyle='--', label='Quantile 0.95')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "ax.set_title('Distribution of CKs_Cytoplasm_Intensity_Average with Quantiles, Mean, and Median')\n",
+    "ax.set_xlabel('CKs_Cytoplasm_Intensity_Average Values')\n",
+    "ax.set_ylabel('Frequency')\n",
+    "ax.legend()\n",
+    "\n",
+    "# Display quantile values\n",
+    "ax.text(quantile_05, ax.get_ylim()[1], f' 5th Quantile: {quantile_05:.2f}', color='r', verticalalignment='top')\n",
+    "ax.text(quantile_95, ax.get_ylim()[1], f' 95th Quantile: {quantile_95:.2f}', color='g', verticalalignment='top')\n",
+    "\n",
+    "# Display mean and median values\n",
+    "ax.text(mean_value, ax.get_ylim()[1]*0.9, f' Mean: {mean_value:.2f}', color='orange', verticalalignment='top')\n",
+    "ax.text(median_value, ax.get_ylim()[1]*0.85, f' Median: {median_value:.2f}', color='purple', verticalalignment='top')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Save the plot\n",
+    "plot_file_path = os.path.join(output_images_dir, \"CKs_Cytoplasm_Intensity_Average_Distribution.png\")\n",
+    "fig.savefig(plot_file_path)\n",
+    "print(f\"The plot is saved at: {plot_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "90c21233-43da-4692-bbf0-c90e037c035c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05     1603.137970\n",
+       "0.50     3559.484741\n",
+       "0.95    10239.075195\n",
+       "Name: CKs_Cytoplasm_Intensity_Average, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"CKs_Cytoplasm_Intensity_Average\"].quantile(q=qs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3e399c-684e-4c40-9963-1e96bb3fcffe",
+   "metadata": {},
+   "source": [
+    "## I.5. COLUMNS OF INTERESTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fdcf65d1-0d91-4688-9903-25a003d755b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns are now...\n",
+      "['ROI_index', 'Sample_ID', 'Nucleus_Roundness', 'Nucleus_Size', 'Nuc_X', 'Nuc_Y_Inv', 'Cell_Size', 'AF488_Cell_Intensity_Average', 'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus_Intensity_Average', 'AF555_Cell_Intensity_Average', 'AF555_Cytoplasm_Intensity_Average', 'AF555_Nucleus_Intensity_Average', 'AF647_Cell_Intensity_Average', 'AF647_Cytoplasm_Intensity_Average', 'AF647_Nucleus_Intensity_Average', 'AF750_Cell_Intensity_Average', 'AF750_Cytoplasm_Intensity_Average', 'AF750_Nucleus_Intensity_Average', 'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm_Intensity_Average', 'aSMA_Nucleus_Intensity_Average', 'AXL_Cell_Intensity_Average', 'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus_Intensity_Average', 'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm_Intensity_Average', 'B7H4_Nucleus_Intensity_Average', 'CA9_Cell_Intensity_Average', 'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus_Intensity_Average', 'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm_Intensity_Average', 'CD4_Nucleus_Intensity_Average', 'CD8_Cell_Intensity_Average', 'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus_Intensity_Average', 'CD11b_Cell_Intensity_Average', 'CD11b_Cytoplasm_Intensity_Average', 'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell_Intensity_Average', 'CD11c_Cytoplasm_Intensity_Average', 'CD11c_Nucleus_Intensity_Average', 'CD20_Cell_Intensity_Average', 'CD20_Cytoplasm_Intensity_Average', 'CD20_Nucleus_Intensity_Average', 'CD31_Cell_Intensity_Average', 'CD31_Cytoplasm_Intensity_Average', 'CD31_Nucleus_Intensity_Average', 'CD44_Cell_Intensity_Average', 'CD44_Cytoplasm_Intensity_Average', 'CD44_Nucleus_Intensity_Average', 'CD45_Cell_Intensity_Average', 'CD45_Cytoplasm_Intensity_Average', 'CD45_Nucleus_Intensity_Average', 'CD68_Cell_Intensity_Average', 'CD68_Cytoplasm_Intensity_Average', 'CD68_Nucleus_Intensity_Average', 'CD163_Cell_Intensity_Average', 'CD163_Cytoplasm_Intensity_Average', 'CD163_Nucleus_Intensity_Average', 'CKs_Cell_Intensity_Average', 'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus_Intensity_Average', 'ColVI_Cell_Intensity_Average', 'ColVI_Cytoplasm_Intensity_Average', 'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell_Intensity_Average', 'Desmin_Cytoplasm_Intensity_Average', 'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell_Intensity_Average', 'Ecad_Cytoplasm_Intensity_Average', 'Ecad_Nucleus_Intensity_Average', 'Fibronectin_Cell_Intensity_Average', 'Fibronectin_Cytoplasm_Intensity_Average', 'Fibronectin_Nucleus_Intensity_Average', 'FOXP3_Cell_Intensity_Average', 'FOXP3_Cytoplasm_Intensity_Average', 'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell_Intensity_Average', 'GATA3_Cytoplasm_Intensity_Average', 'GATA3_Nucleus_Intensity_Average', 'HLA_Cell_Intensity_Average', 'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus_Intensity_Average', 'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm_Intensity_Average', 'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell_Intensity_Average', 'MMP9_Cytoplasm_Intensity_Average', 'MMP9_Nucleus_Intensity_Average', 'PD1_Cell_Intensity_Average', 'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus_Intensity_Average', 'PDGFR_Cell_Intensity_Average', 'PDGFR_Cytoplasm_Intensity_Average', 'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell_Intensity_Average', 'PDL1_Cytoplasm_Intensity_Average', 'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell_Intensity_Average', 'r5c2_Cytoplasm_Intensity_Average', 'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell_Intensity_Average', 'r7c2_Cytoplasm_Intensity_Average', 'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average', 'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus_Intensity_Average', 'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm_Intensity_Average', 'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell_Intensity_Average', 'Vimentin_Cytoplasm_Intensity_Average', 'Vimentin_Nucleus_Intensity_Average']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove columns containing \"DAPI\"\n",
+    "df = df[[x for x in df.columns.values if 'DAPI' not in x]]\n",
+    "\n",
+    "print(\"Columns are now...\")\n",
+    "print([c for c in df.columns.values])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "17741329-f501-4eec-af23-ed5026b13a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AF488_Cell': 'AF488_Cell_Intensity_Average',\n",
+       " 'AF488_Cytoplasm': 'AF488_Cytoplasm_Intensity_Average',\n",
+       " 'AF488_Nucleus': 'AF488_Nucleus_Intensity_Average',\n",
+       " 'AF555_Cell': 'AF555_Cell_Intensity_Average',\n",
+       " 'AF555_Cytoplasm': 'AF555_Cytoplasm_Intensity_Average',\n",
+       " 'AF555_Nucleus': 'AF555_Nucleus_Intensity_Average',\n",
+       " 'AF647_Cell': 'AF647_Cell_Intensity_Average',\n",
+       " 'AF647_Cytoplasm': 'AF647_Cytoplasm_Intensity_Average',\n",
+       " 'AF647_Nucleus': 'AF647_Nucleus_Intensity_Average',\n",
+       " 'AF750_Cell': 'AF750_Cell_Intensity_Average',\n",
+       " 'AF750_Cytoplasm': 'AF750_Cytoplasm_Intensity_Average',\n",
+       " 'AF750_Nucleus': 'AF750_Nucleus_Intensity_Average',\n",
+       " 'aSMA_Cell': 'aSMA_Cell_Intensity_Average',\n",
+       " 'aSMA_Cytoplasm': 'aSMA_Cytoplasm_Intensity_Average',\n",
+       " 'aSMA_Nucleus': 'aSMA_Nucleus_Intensity_Average',\n",
+       " 'AXL_Cell': 'AXL_Cell_Intensity_Average',\n",
+       " 'AXL_Cytoplasm': 'AXL_Cytoplasm_Intensity_Average',\n",
+       " 'AXL_Nucleus': 'AXL_Nucleus_Intensity_Average',\n",
+       " 'B7H4_Cell': 'B7H4_Cell_Intensity_Average',\n",
+       " 'B7H4_Cytoplasm': 'B7H4_Cytoplasm_Intensity_Average',\n",
+       " 'B7H4_Nucleus': 'B7H4_Nucleus_Intensity_Average',\n",
+       " 'CA9_Cell': 'CA9_Cell_Intensity_Average',\n",
+       " 'CA9_Cytoplasm': 'CA9_Cytoplasm_Intensity_Average',\n",
+       " 'CA9_Nucleus': 'CA9_Nucleus_Intensity_Average',\n",
+       " 'CD4_Cell': 'CD4_Cell_Intensity_Average',\n",
+       " 'CD4_Cytoplasm': 'CD4_Cytoplasm_Intensity_Average',\n",
+       " 'CD4_Nucleus': 'CD4_Nucleus_Intensity_Average',\n",
+       " 'CD8_Cell': 'CD8_Cell_Intensity_Average',\n",
+       " 'CD8_Cytoplasm': 'CD8_Cytoplasm_Intensity_Average',\n",
+       " 'CD8_Nucleus': 'CD8_Nucleus_Intensity_Average',\n",
+       " 'CD11b_Cell': 'CD11b_Cell_Intensity_Average',\n",
+       " 'CD11b_Cytoplasm': 'CD11b_Cytoplasm_Intensity_Average',\n",
+       " 'CD11b_Nucleus': 'CD11b_Nucleus_Intensity_Average',\n",
+       " 'CD11c_Cell': 'CD11c_Cell_Intensity_Average',\n",
+       " 'CD11c_Cytoplasm': 'CD11c_Cytoplasm_Intensity_Average',\n",
+       " 'CD11c_Nucleus': 'CD11c_Nucleus_Intensity_Average',\n",
+       " 'CD20_Cell': 'CD20_Cell_Intensity_Average',\n",
+       " 'CD20_Cytoplasm': 'CD20_Cytoplasm_Intensity_Average',\n",
+       " 'CD20_Nucleus': 'CD20_Nucleus_Intensity_Average',\n",
+       " 'CD31_Cell': 'CD31_Cell_Intensity_Average',\n",
+       " 'CD31_Cytoplasm': 'CD31_Cytoplasm_Intensity_Average',\n",
+       " 'CD31_Nucleus': 'CD31_Nucleus_Intensity_Average',\n",
+       " 'CD44_Cell': 'CD44_Cell_Intensity_Average',\n",
+       " 'CD44_Cytoplasm': 'CD44_Cytoplasm_Intensity_Average',\n",
+       " 'CD44_Nucleus': 'CD44_Nucleus_Intensity_Average',\n",
+       " 'CD45_Cell': 'CD45_Cell_Intensity_Average',\n",
+       " 'CD45_Cytoplasm': 'CD45_Cytoplasm_Intensity_Average',\n",
+       " 'CD45_Nucleus': 'CD45_Nucleus_Intensity_Average',\n",
+       " 'CD68_Cell': 'CD68_Cell_Intensity_Average',\n",
+       " 'CD68_Cytoplasm': 'CD68_Cytoplasm_Intensity_Average',\n",
+       " 'CD68_Nucleus': 'CD68_Nucleus_Intensity_Average',\n",
+       " 'CD163_Cell': 'CD163_Cell_Intensity_Average',\n",
+       " 'CD163_Cytoplasm': 'CD163_Cytoplasm_Intensity_Average',\n",
+       " 'CD163_Nucleus': 'CD163_Nucleus_Intensity_Average',\n",
+       " 'CKs_Cell': 'CKs_Cell_Intensity_Average',\n",
+       " 'CKs_Cytoplasm': 'CKs_Cytoplasm_Intensity_Average',\n",
+       " 'CKs_Nucleus': 'CKs_Nucleus_Intensity_Average',\n",
+       " 'ColVI_Cell': 'ColVI_Cell_Intensity_Average',\n",
+       " 'ColVI_Cytoplasm': 'ColVI_Cytoplasm_Intensity_Average',\n",
+       " 'ColVI_Nucleus': 'ColVI_Nucleus_Intensity_Average',\n",
+       " 'Desmin_Cell': 'Desmin_Cell_Intensity_Average',\n",
+       " 'Desmin_Cytoplasm': 'Desmin_Cytoplasm_Intensity_Average',\n",
+       " 'Desmin_Nucleus': 'Desmin_Nucleus_Intensity_Average',\n",
+       " 'Ecad_Cell': 'Ecad_Cell_Intensity_Average',\n",
+       " 'Ecad_Cytoplasm': 'Ecad_Cytoplasm_Intensity_Average',\n",
+       " 'Ecad_Nucleus': 'Ecad_Nucleus_Intensity_Average',\n",
+       " 'Fibronectin_Cell': 'Fibronectin_Cell_Intensity_Average',\n",
+       " 'Fibronectin_Cytoplasm': 'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       " 'Fibronectin_Nucleus': 'Fibronectin_Nucleus_Intensity_Average',\n",
+       " 'FOXP3_Cell': 'FOXP3_Cell_Intensity_Average',\n",
+       " 'FOXP3_Cytoplasm': 'FOXP3_Cytoplasm_Intensity_Average',\n",
+       " 'FOXP3_Nucleus': 'FOXP3_Nucleus_Intensity_Average',\n",
+       " 'GATA3_Cell': 'GATA3_Cell_Intensity_Average',\n",
+       " 'GATA3_Cytoplasm': 'GATA3_Cytoplasm_Intensity_Average',\n",
+       " 'GATA3_Nucleus': 'GATA3_Nucleus_Intensity_Average',\n",
+       " 'HLA_Cell': 'HLA_Cell_Intensity_Average',\n",
+       " 'HLA_Cytoplasm': 'HLA_Cytoplasm_Intensity_Average',\n",
+       " 'HLA_Nucleus': 'HLA_Nucleus_Intensity_Average',\n",
+       " 'Ki67_Cell': 'Ki67_Cell_Intensity_Average',\n",
+       " 'Ki67_Cytoplasm': 'Ki67_Cytoplasm_Intensity_Average',\n",
+       " 'Ki67_Nucleus': 'Ki67_Nucleus_Intensity_Average',\n",
+       " 'MMP9_Cell': 'MMP9_Cell_Intensity_Average',\n",
+       " 'MMP9_Cytoplasm': 'MMP9_Cytoplasm_Intensity_Average',\n",
+       " 'MMP9_Nucleus': 'MMP9_Nucleus_Intensity_Average',\n",
+       " 'PD1_Cell': 'PD1_Cell_Intensity_Average',\n",
+       " 'PD1_Cytoplasm': 'PD1_Cytoplasm_Intensity_Average',\n",
+       " 'PD1_Nucleus': 'PD1_Nucleus_Intensity_Average',\n",
+       " 'PDGFR_Cell': 'PDGFR_Cell_Intensity_Average',\n",
+       " 'PDGFR_Cytoplasm': 'PDGFR_Cytoplasm_Intensity_Average',\n",
+       " 'PDGFR_Nucleus': 'PDGFR_Nucleus_Intensity_Average',\n",
+       " 'PDL1_Cell': 'PDL1_Cell_Intensity_Average',\n",
+       " 'PDL1_Cytoplasm': 'PDL1_Cytoplasm_Intensity_Average',\n",
+       " 'PDL1_Nucleus': 'PDL1_Nucleus_Intensity_Average',\n",
+       " 'r5c2_Cell': 'r5c2_Cell_Intensity_Average',\n",
+       " 'r5c2_Cytoplasm': 'r5c2_Cytoplasm_Intensity_Average',\n",
+       " 'r5c2_Nucleus': 'r5c2_Nucleus_Intensity_Average',\n",
+       " 'r7c2_Cell': 'r7c2_Cell_Intensity_Average',\n",
+       " 'r7c2_Cytoplasm': 'r7c2_Cytoplasm_Intensity_Average',\n",
+       " 'r7c2_Nucleus': 'r7c2_Nucleus_Intensity_Average',\n",
+       " 'r8c2_Cell': 'r8c2_Cell_Intensity_Average',\n",
+       " 'r8c2_Cytoplasm': 'r8c2_Cytoplasm_Intensity_Average',\n",
+       " 'r8c2_Nucleus': 'r8c2_Nucleus_Intensity_Average',\n",
+       " 'Sting_Cell': 'Sting_Cell_Intensity_Average',\n",
+       " 'Sting_Cytoplasm': 'Sting_Cytoplasm_Intensity_Average',\n",
+       " 'Sting_Nucleus': 'Sting_Nucleus_Intensity_Average',\n",
+       " 'Vimentin_Cell': 'Vimentin_Cell_Intensity_Average',\n",
+       " 'Vimentin_Cytoplasm': 'Vimentin_Cytoplasm_Intensity_Average',\n",
+       " 'Vimentin_Nucleus': 'Vimentin_Nucleus_Intensity_Average'}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create lists of full names and shortened names to use in plotting\n",
+    "full_to_short_names, short_to_full_names =  \\\n",
+    "    shorten_feature_names(df.columns.values[~df.columns.isin(not_intensities)])\n",
+    "\n",
+    "short_to_full_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dfdd64a5-8705-4ed6-b94f-c42379faefd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The full_to_short_column_names.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data to a metadata file\n",
+    "filename = os.path.join(metadata_dir, \"full_to_short_column_names.csv\")\n",
+    "fh = open(filename, \"w\")\n",
+    "fh.write(\"full_name,short_name\\n\")\n",
+    "for k,v in full_to_short_names.items():\n",
+    "    fh.write(k + \",\" + v + \"\\n\")\n",
+    "    \n",
+    "fh.close()\n",
+    "print(\"The full_to_short_column_names.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b4e26e25-620a-4700-b091-928c6ba08a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The short_to_full_column_names.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data to a metadata file\n",
+    "filename = os.path.join(metadata_dir, \"short_to_full_column_names.csv\")\n",
+    "fh = open(filename, \"w\")\n",
+    "fh.write(\"short_name,full_name\\n\")\n",
+    "for k,v in short_to_full_names.items():\n",
+    "    fh.write(k + \",\" + v + \"\\n\")\n",
+    "    \n",
+    "fh.close()\n",
+    "print(\"The short_to_full_column_names.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c5da27d-39a6-411b-a71f-0cd82ce2b728",
+   "metadata": {},
+   "source": [
+    "## I.6. EXPOSURE TIME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "2e0c99c9-b254-41c7-b264-58441252c976",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Round Target   Exp Channel\n",
+      "0    R0  AF488   300      c2\n",
+      "1    R0  AF555  1500      c3\n",
+      "2    R0  AF647  1500      c4\n",
+      "3    R0  AF750  1500      c5\n",
+      "4    R1  ColVI   300      c2\n",
+      "df's shape:  (36, 4)\n",
+      "\n",
+      "No null values detected.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here, we want to end up with a data structure that incorporates metadata on each intensity marker column used in our big dataframe in an easy-to-use format. \n",
+    "# This is going to include the full name of the intensity marker columns in the big data frame, \n",
+    "# the corresponding round and channel, \n",
+    "# the target protein (e.g., CD45), \n",
+    "# and the segmentation localization information (cell, cytoplasm, nucleus)\n",
+    "\n",
+    "# We can use this data structure to assign unique colors to all channels and rounds, for example, for use in later visualizations\n",
+    "# Exposure_time file from ASHLAR analysis\n",
+    "filename = \"Exposure_Time.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "exp_df = pd.read_csv(filename)\n",
+    "\n",
+    "print(exp_df.head())\n",
+    "\n",
+    "# Verify file imported correctly\n",
+    "# File length\n",
+    "print(\"df's shape: \", exp_df.shape)\n",
+    "# Headers\n",
+    "expected_headers =['Round','Target','Exp','Channel']\n",
+    "compare_headers(expected_headers, exp_df.columns.values, \"Imported metadata file\")\n",
+    "\n",
+    "# Missingness\n",
+    "if exp_df.isnull().any().any():\n",
+    "    print(\"\\nexp_df has null value(s) in row(s):\")\n",
+    "    print(exp_df[exp_df.isna().any(axis=1)])\n",
+    "else:\n",
+    "    print(\"\\nNo null values detected.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "17501193-977f-4b30-bbb8-5afdae4a0356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(exp_df['Target']) > len(exp_df['Target'].unique()):\n",
+    "    print(\"One or more non-unique Target values in exp_df. Currently not supported.\")\n",
+    "exp_df = exp_df.drop_duplicates(subset = 'Target').reindex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "90d1feb7-2459-4a02-a526-ddc64082345b",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF647</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF750</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>R4</td>\n",
+       "      <td>AXL</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Round Target   Exp Channel\n",
+       "0     R0  AF488   300      c2\n",
+       "1     R0  AF555  1500      c3\n",
+       "2     R0  AF647  1500      c4\n",
+       "3     R0  AF750  1500      c5\n",
+       "17    R4    AXL  1500      c3"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort exp_df by the values in the 'Target' column in ascending order and then retrieve the first few rows of the sorted df\n",
+    "exp_df.sort_values(by = ['Target']).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b7373851-14bb-4813-be00-7f8c4e1f4ebf",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF647</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>af647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF750</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>af750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R1</td>\n",
+       "      <td>ColVI</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>colvi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Round Target   Exp Channel target_lower\n",
+       "0    R0  AF488   300      c2        af488\n",
+       "1    R0  AF555  1500      c3        af555\n",
+       "2    R0  AF647  1500      c4        af647\n",
+       "3    R0  AF750  1500      c5        af750\n",
+       "4    R1  ColVI   300      c2        colvi"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create lowercase version of target\n",
+    "exp_df['target_lower'] = exp_df['Target'].str.lower()\n",
+    "exp_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4722fdb1-cd93-45a0-b196-89d50918b2e2",
+   "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>full_column</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column\n",
+       "0       AF488_Cell_Intensity_Average\n",
+       "1  AF488_Cytoplasm_Intensity_Average\n",
+       "2    AF488_Nucleus_Intensity_Average\n",
+       "3       AF555_Cell_Intensity_Average\n",
+       "4  AF555_Cytoplasm_Intensity_Average"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create df that contains marker intensity columns in our df that aren't in not_intensities\n",
+    "intensities = pd.DataFrame({'full_column':df.columns.values[~df.columns.isin(not_intensities)]})\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f88ef6d6-8160-4fa4-a0ed-942e4127414e",
+   "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>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>marker_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column marker marker_lower\n",
+       "0       AF488_Cell_Intensity_Average  AF488        af488\n",
+       "1  AF488_Cytoplasm_Intensity_Average  AF488        af488\n",
+       "2    AF488_Nucleus_Intensity_Average  AF488        af488\n",
+       "3       AF555_Cell_Intensity_Average  AF555        af555\n",
+       "4  AF555_Cytoplasm_Intensity_Average  AF555        af555"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extract the marker information from the `full_column`, which corresponds to full column in big dataframe\n",
+    "# Use regular expressions (regex) to isolate the part of the field that begins (^) with an alphanumeric value (W), and ends with an underscore (_)\n",
+    "# '$' is end of line\n",
+    "intensities['marker'] = intensities['full_column'].str.extract(r'([^\\W_]+)')\n",
+    "# convert to lowercase\n",
+    "intensities['marker_lower'] = intensities['marker'].str.lower()\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "2b9ff02b-a33b-4f42-95c0-4b4132e2aa25",
+   "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>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>marker_lower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>af488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>af555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         full_column marker marker_lower\n",
+       "0       AF488_Cell_Intensity_Average  AF488        af488\n",
+       "1  AF488_Cytoplasm_Intensity_Average  AF488        af488\n",
+       "2    AF488_Nucleus_Intensity_Average  AF488        af488\n",
+       "3       AF555_Cell_Intensity_Average  AF555        af555\n",
+       "4  AF555_Cytoplasm_Intensity_Average  AF555        af555"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset the intensities df to exclude any column pertaining to DAPI\n",
+    "intensities = intensities.loc[intensities['marker_lower'] != 'dapi']\n",
+    "\n",
+    "intensities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fa1340aa-1c86-4b0f-82a2-ded1892dea6e",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker  \n",
+       "0         AF488_Cell_Intensity_Average  AF488  \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488  \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488  \n",
+       "3         AF555_Cell_Intensity_Average  AF555  \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555  \n",
+       "..                                 ...    ...  \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting  \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting  \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b  \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b  \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b  \n",
+       "\n",
+       "[108 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Merge the intensities andexp_df together to create metadata\n",
+    "metadata = pd.merge(exp_df, intensities, how = 'left', left_on = 'target_lower',right_on = 'marker_lower')\n",
+    "metadata = metadata.drop(columns = ['marker_lower'])\n",
+    "metadata = metadata.dropna()\n",
+    "\n",
+    "# Target is the capitalization from the Exposure_Time.csv\n",
+    "# target_lower is Target in small caps\n",
+    "# marker is the extracted first component of the full column in segmentation data, with corresponding capitalization\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "75378ca2-3ecb-4c60-a7d4-da997f851066",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker localisation  \n",
+       "0         AF488_Cell_Intensity_Average  AF488         cell  \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488    cytoplasm  \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488      nucleus  \n",
+       "3         AF555_Cell_Intensity_Average  AF555         cell  \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555    cytoplasm  \n",
+       "..                                 ...    ...          ...  \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting    cytoplasm  \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting      nucleus  \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b         cell  \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b    cytoplasm  \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b      nucleus  \n",
+       "\n",
+       "[108 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add a column to signify marker target localisation.\n",
+    "# Use a lambda to determine segmented location of intensity marker column and update metadata accordingly\n",
+    "# Using the add_metadata_location() function in my_modules.py\n",
+    "metadata['localisation'] = metadata.apply(\n",
+    "    lambda row: add_metadata_location(row), axis = 1)\n",
+    "\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9779567d-bd63-468a-acdb-e30901c71e03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The marker_intensity_metadata.csv file was created !\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save this data structure to the metadata folder\n",
+    "# don't want to add color in because that's better off treating color the same for round, channel, and sample\n",
+    "filename = \"marker_intensity_metadata.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "metadata.to_csv(filename, index = False)\n",
+    "print(\"The marker_intensity_metadata.csv file was created !\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d371ebc6-4aee-4b43-914e-933e3f9abbd5",
+   "metadata": {},
+   "source": [
+    "## I.7. COLORS WORKFLOW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4e010e-149b-4fe9-8a58-7f9f872d2be9",
+   "metadata": {},
+   "source": [
+    "### I.7.1. CHANNELS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "fb818967-a0b7-4bd0-9b81-32fd1bfefde1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique channels are: ['c2' 'c3' 'c4' 'c5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are categorical, since Channel is a non-ordered category (yes, they are numbered, but arbitrarily). \n",
+    "# A categorical color palette will have dissimilar colors.\n",
+    "# Get those unique colors\n",
+    "if len(metadata.Channel.unique()) > 10:\n",
+    "    print(\"WARNING: There are more unique channel values than \\\n",
+    "    there are colors to choose from. Select different palette, e.g., \\\n",
+    "    continuous palette 'husl'.\")\n",
+    "channel_color_values = sb.color_palette(\"bright\",n_colors = len(metadata.Channel.unique()))\n",
+    "# chose 'colorblind' because it is categorical and we're unlikely to have > 10\n",
+    "\n",
+    "# You can customize the colors for each channel here\n",
+    "custom_colors = {\n",
+    "    'c2': 'lightgreen',\n",
+    "    'c3': 'tomato',\n",
+    "    'c4': 'pink',\n",
+    "    'c5': 'turquoise'\n",
+    "}\n",
+    "\n",
+    "custom_colors_values = sb.palplot(sb.color_palette([custom_colors.get(ch, 'blue') for ch in metadata.Channel.unique()]))\n",
+    "\n",
+    "# Display those unique customs colors\n",
+    "print(\"Unique channels are:\", metadata.Channel.unique())\n",
+    "sb.palplot(sb.color_palette(channel_color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e7aa9d2e-94b5-461b-859f-fadfa2ccc0c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'c2': array([0.00784314, 0.24313725, 1.        ]),\n",
+       " 'c3': array([1.        , 0.48627451, 0.        ]),\n",
+       " 'c4': array([0.10196078, 0.78823529, 0.21960784]),\n",
+       " 'c5': array([0.90980392, 0.        , 0.04313725])}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "channel_color_dict = dict(zip(metadata.Channel.unique(), channel_color_values))\n",
+    "channel_color_dict\n",
+    "for k,v in channel_color_dict.items():\n",
+    "    channel_color_dict[k] = np.float64(v)\n",
+    "\n",
+    "channel_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "aa2d6cf5-2126-4df4-a54f-c6926c2c06a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m color_df_channel \u001b[38;5;241m=\u001b[39m color_dict_to_df(channel_color_dict, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChannel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Save to file in metadatadirectory\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_color_data.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df\u001b[0;34m(cd, column_name)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:10034\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m  10022\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m  10024\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m  10025\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10026\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10032\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m  10033\u001b[0m )\n\u001b[0;32m> 10034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:837\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw()\n\u001b[0;32m--> 837\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:965\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 965\u001b[0m     results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m    967\u001b[0m     \u001b[38;5;66;03m# wrap results\u001b[39;00m\n\u001b[1;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results(results, res_index)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:981\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    979\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m    980\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m    982\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m    983\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m    984\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m    985\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df.<locals>.<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/numpy/__init__.py:324\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    319\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn the future `np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be defined as the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorresponding NumPy scalar.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __former_attrs__:\n\u001b[0;32m--> 324\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr])\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesting\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtesting\u001b[39;00m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
+     ]
+    }
+   ],
+   "source": [
+    "color_df_channel = color_dict_to_df(channel_color_dict, \"Channel\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"channel_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_channel.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8117d2d-c60e-477c-bef4-dc89d18fa6aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Legend of channel info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in channel_color_dict.keys():\n",
+    "        h = g.bar(0,0, color = channel_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Channel'),\n",
+    "                            # bbox_to_anchor=(10,10), \n",
+    "                             #       bbox_transform=plt.gcf().transFigure)\n",
+    "\n",
+    "filename = \"Channel_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d587ec-809c-4312-acda-3747fc63fd83",
+   "metadata": {},
+   "source": [
+    "### I.7.2. ROUNDS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "40bedb40-5740-4956-aa97-a95ac9fb341a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['R0' 'R1' 'R2' 'R3' 'R4' 'R5' 'R6' 'R7' 'R8']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are sequential, since Round is an ordered category. \n",
+    "# We can still generate colors that are easy to distinguish. Also, many of the categorical palettes cap at at about 10 or so unique colors, and repeat from there. \n",
+    "# We do not want any repeats!\n",
+    "round_color_values = sb.cubehelix_palette(\n",
+    "    len(metadata.Round.unique()), start=1, rot= -0.75, dark=0.19, light=.85, reverse=True)\n",
+    "# round_color_values = sb.color_palette(\"cubehelix\",n_colors = len(metadata.Round.unique()))\n",
+    "# chose 'cubehelix' because it is sequential, and round is a continuous process\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "print(metadata.Round.unique())\n",
+    "\n",
+    "sb.palplot(sb.color_palette(round_color_values))\n",
+    "\n",
+    "## TO-DO: write what these parameters mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4372b4f0-2242-4d76-8881-a78fba5fd814",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'R0': array([0.28685356, 0.13009829, 0.23110332]),\n",
+       " 'R1': array([0.36541462, 0.2025447 , 0.3769331 ]),\n",
+       " 'R2': array([0.40867533, 0.29407612, 0.51667119]),\n",
+       " 'R3': array([0.42890614, 0.40822902, 0.63353489]),\n",
+       " 'R4': array([0.44444629, 0.5264665 , 0.70563219]),\n",
+       " 'R5': array([0.47707206, 0.64270618, 0.74184779]),\n",
+       " 'R6': array([0.54144549, 0.74667592, 0.75729058]),\n",
+       " 'R7': array([0.64147101, 0.83215511, 0.7746773 ]),\n",
+       " 'R8': array([0.76842569, 0.89926671, 0.81713833])}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "round_color_dict = dict(zip(metadata.Round.unique(), round_color_values))\n",
+    "\n",
+    "for k,v in round_color_dict.items():\n",
+    "    round_color_dict[k] = np.float64(v)\n",
+    "\n",
+    "round_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3a6053ce-d87c-4137-8c70-a6772208fc37",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m color_df_round \u001b[38;5;241m=\u001b[39m color_dict_to_df(round_color_dict, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRound\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Save to file in metadatadirectory\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mround_color_data.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df\u001b[0;34m(cd, column_name)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:10034\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m  10022\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m  10024\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m  10025\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10026\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10032\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m  10033\u001b[0m )\n\u001b[0;32m> 10034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:837\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw()\n\u001b[0;32m--> 837\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:965\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 965\u001b[0m     results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m    967\u001b[0m     \u001b[38;5;66;03m# wrap results\u001b[39;00m\n\u001b[1;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results(results, res_index)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:981\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    979\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m    980\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m    982\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m    983\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m    984\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m    985\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df.<locals>.<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/numpy/__init__.py:324\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    319\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn the future `np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be defined as the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorresponding NumPy scalar.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __former_attrs__:\n\u001b[0;32m--> 324\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr])\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesting\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtesting\u001b[39;00m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
+     ]
+    }
+   ],
+   "source": [
+    "color_df_round = color_dict_to_df(round_color_dict, \"Round\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"round_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_round.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_round"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "977a7e45-64df-4bd6-ab21-cecd44d76fd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of round info only\n",
+    "\n",
+    "round_legend  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "round_legend.axis('off')\n",
+    "handles = []\n",
+    "for item in round_color_dict.keys():\n",
+    "        h = round_legend.bar(0,0, color = round_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Round'),\n",
+    "                            # bbox_to_anchor=(10,10), \n",
+    "                             #       bbox_transform=plt.gcf().transFigure)\n",
+    "\n",
+    "filename = \"Round_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b81c62-58a4-432c-876a-dfa5df0bbe9e",
+   "metadata": {},
+   "source": [
+    "### I.7.3. SAMPLES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "3ccb74da-fb1d-4fda-9116-bf6b6a6d868b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAABlCAYAAAArpKpSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAACuUlEQVR4nO3ZMWsTcRzG8V9SaVohCXQMydTB2ffhu3BxEFzduvhWuvqGhA49yAvogSmCPQdRJ/NcW8P17Ocz/4eHf+DLXW7SdV1XAPzVdOgBAE+dUAIEQgkQCCVAIJQAgVACBEIJELzoc+ju7q62223N5/OaTCaH3gRwcF3XVdu2tVqtajrd/8zYK5Tb7bY2m80/GQfwlDRNU+v1eu+ZXqGcz+dVVfXl7ceaH588ftkzcfn689ATRuny5P3QE0bn3fWboSeMzu62rQ+fXv3u2z69QvnrdXt+fFKLmVD2dXp6NPSEUTo6fTn0hNE5PVkMPWG0+vyd6GMOQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQCCUAIFQAgRCCRAIJUAglACBUAIEQgkQCCVAIJQAgVACBEIJEAglQPCiz6Gu66qqqv12e9Ax/5vd7vvQE0bpe/d16Amjs7u9GXrC6Oxu26r607d9Jl2PU1dXV3V+fv74ZQBPTNM0tV6v957p9UR5dnZWVVXX19e1XC4fv+yZuLm5qc1mU03T1GKxGHrOKLizh3Fv99d1XbVtW6vVKp7tFcrp9Odfmcvl0o/wAIvFwr3dkzt7GPd2P30f/HzMAQiEEiDoFcrZbFYXFxc1m80Ovee/4t7uz509jHs7rF5fvQGeM6/eAIFQAgRCCRAIJUAglACBUAIEQgkQCCVA8ANJyGd00cXSZwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we want colors that are neither sequential nor categorical. \n",
+    "# Categorical would be ideal if we could generate an arbitrary number of colors, but I do not think that we can. \n",
+    "# Hense, we will choose `n` colors from a continuous palette. First we will generate the right number of colors. Later, we will assign TMA samples to gray.\n",
+    "\n",
+    "# Get those unique colors\n",
+    "color_values = sb.color_palette(\"husl\",n_colors = len(ls_samples))#'HLS'\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "\n",
+    "# Display those unique colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "441a57fe-d55a-49e1-8593-0f1939472b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TMA_samples = [s for s in df.Sample_ID.unique() if 'TMA' in s]\n",
+    "TMA_color_values = sb.color_palette(n_colors = len(TMA_samples),palette = \"gray\")\n",
+    "sb.palplot(sb.color_palette(TMA_color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "18dbf741-983e-4652-97d1-d55e87eba4fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DD3S1.csv': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
+       " 'DD3S2.csv': (0.5920891529639701, 0.6418467016378244, 0.1935069134991043),\n",
+       " 'DD3S3.csv': (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
+       " 'TMA.csv': (0.5019607843137255, 0.5019607843137255, 0.5019607843137255)}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dictionary\n",
+    "color_dict = dict()\n",
+    "color_dict = dict(zip(df.Sample_ID.unique(), color_values))\n",
+    "\n",
+    "# Replace all TMA samples' colors with gray\n",
+    "i = 0\n",
+    "for key in color_dict.keys():\n",
+    "    if 'TMA' in key:\n",
+    "        color_dict[key] = TMA_color_values[i]\n",
+    "        i +=1\n",
+    "\n",
+    "color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "807f2bb5-6d19-4086-81c1-98836e850dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m color_df_sample \u001b[38;5;241m=\u001b[39m color_dict_to_df(color_dict, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSample_ID\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Save to file in metadatadirectory\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_color_data.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df\u001b[0;34m(cd, column_name)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:10034\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m  10022\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m  10024\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m  10025\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10026\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10032\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m  10033\u001b[0m )\n\u001b[0;32m> 10034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:837\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m    835\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw()\n\u001b[0;32m--> 837\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:965\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 965\u001b[0m     results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m    967\u001b[0m     \u001b[38;5;66;03m# wrap results\u001b[39;00m\n\u001b[1;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results(results, res_index)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/pandas/core/apply.py:981\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    979\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m    980\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m    982\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m    983\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m    984\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m    985\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "File \u001b[0;32m~/Downloads/wetransfer_data-zip_2024-05-17_1431/my_modules.py:379\u001b[0m, in \u001b[0;36mcolor_dict_to_df.<locals>.<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcolor_dict_to_df\u001b[39m(cd, column_name):\n\u001b[1;32m    378\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_dict(cd, orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 379\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: (np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat(row[\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mfloat64(row[\u001b[38;5;241m2\u001b[39m])), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    380\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m    381\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: mplc\u001b[38;5;241m.\u001b[39mto_hex(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/numpy/__init__.py:324\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    319\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn the future `np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be defined as the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorresponding NumPy scalar.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __former_attrs__:\n\u001b[0;32m--> 324\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr])\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesting\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtesting\u001b[39;00m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
+     ]
+    }
+   ],
+   "source": [
+    "color_df_sample = color_dict_to_df(color_dict, \"Sample_ID\")\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"sample_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "color_df_sample.to_csv(filename, index = False)\n",
+    "\n",
+    "color_df_sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "39b6afdb-2e37-471e-86a0-e55ab7667e39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of sample info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in color_dict.keys():\n",
+    "        h = g.bar(0,0, color = color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Sample')\n",
+    "\n",
+    "filename = \"Sample_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3cf0dea-43db-41fa-952e-bbba53e89cdb",
+   "metadata": {},
+   "source": [
+    "### I.7.4. CLUSTERS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "d37c61d7-de64-4b7c-8d01-86ee16ac67c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if 'cluster' in df.columns:\n",
+    "    cluster_color_values = sb.color_palette(\"hls\",n_colors = len(df.cluster.unique()))\n",
+    "\n",
+    "    print(sorted(test_df.cluster.unique()))\n",
+    "    # Display those unique colors\n",
+    "    sb.palplot(sb.color_palette(cluster_color_values))\n",
+    "    \n",
+    "    cluster_color_dict = dict(zip(sorted(test_df.cluster.unique()), cluster_color_values))\n",
+    "    print(cluster_color_dict)\n",
+    "    \n",
+    "    # Create dataframe\n",
+    "    cluster_color_df = color_dict_to_df(cluster_color_dict, \"cluster\")\n",
+    "    cluster_color_df.head()\n",
+    "\n",
+    "    # Save to file in metadatadirectory\n",
+    "    filename = \"cluster_color_data.csv\"\n",
+    "    filename = os.path.join(metadata_dir, filename)\n",
+    "    cluster_color_df.to_csv(filename, index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "c9215452-fdb6-4963-9f56-31f16e0483bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Legend of cluster info only\n",
+    "\n",
+    "if 'cluster' in df.columns:\n",
+    "    g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "    g.axis('off')\n",
+    "    handles = []\n",
+    "    for item in sorted(cluster_color_dict.keys()):\n",
+    "            h = g.bar(0,0, color = cluster_color_dict[item],\n",
+    "                      label = item, linewidth =0)\n",
+    "            handles.append(h)\n",
+    "    first_legend = plt.legend(handles=handles, loc='upper right', title = 'Cluster'),\n",
+    "\n",
+    "\n",
+    "    filename = \"Clustertype_legend.png\"\n",
+    "    filename = os.path.join(metadata_images_dir, filename)\n",
+    "    plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d0a7ff0a-98a8-4c58-8646-e50d07314dd9",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "      <th>round_color</th>\n",
+       "      <th>channel_color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[0.00784313725490196, 0.24313725490196078, 1.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[1.0, 0.48627450980392156, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.28685356234627135, 0.13009829239513535, 0.2...</td>\n",
+       "      <td>[1.0, 0.48627450980392156, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.10196078431372549, 0.788235294117647, 0.219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.10196078431372549, 0.788235294117647, 0.219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[0.7684256891219349, 0.8992667116749021, 0.817...</td>\n",
+       "      <td>[0.9098039215686274, 0.0, 0.043137254901960784]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker localisation  \\\n",
+       "0         AF488_Cell_Intensity_Average  AF488         cell   \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488    cytoplasm   \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488      nucleus   \n",
+       "3         AF555_Cell_Intensity_Average  AF555         cell   \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555    cytoplasm   \n",
+       "..                                 ...    ...          ...   \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting    cytoplasm   \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting      nucleus   \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b         cell   \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b    cytoplasm   \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b      nucleus   \n",
+       "\n",
+       "                                           round_color  \\\n",
+       "0    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "1    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "2    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "3    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "4    [0.28685356234627135, 0.13009829239513535, 0.2...   \n",
+       "..                                                 ...   \n",
+       "103  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "104  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "105  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "106  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "107  [0.7684256891219349, 0.8992667116749021, 0.817...   \n",
+       "\n",
+       "                                         channel_color  \n",
+       "0      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "1      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "2      [0.00784313725490196, 0.24313725490196078, 1.0]  \n",
+       "3                      [1.0, 0.48627450980392156, 0.0]  \n",
+       "4                      [1.0, 0.48627450980392156, 0.0]  \n",
+       "..                                                 ...  \n",
+       "103  [0.10196078431372549, 0.788235294117647, 0.219...  \n",
+       "104  [0.10196078431372549, 0.788235294117647, 0.219...  \n",
+       "105    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "106    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "107    [0.9098039215686274, 0.0, 0.043137254901960784]  \n",
+       "\n",
+       "[108 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add in the color information in both RGB (range 0-1) and hex values, for use in visualizations\n",
+    "metadata['round_color'] = metadata.apply(lambda row: round_color_dict[row['Round']], axis = 1)\n",
+    "metadata['channel_color'] = metadata.apply(lambda row: channel_color_dict[row['Channel']], axis = 1)\n",
+    "\n",
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0642911-7a0b-49f6-9598-c8975b188807",
+   "metadata": {},
+   "source": [
+    "## I.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "67b33926-3ecf-415d-b67e-09d1054eab62",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Do you want to overwrite all existing files without confirmation? (yes/no): n\n",
+      "File by name /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S1_qc_eda.csv already exists. Do you want to overwrite it? (yes/no): n\n",
+      "File was not overwritten. Moving to the next sample.\n",
+      "File by name /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S2_qc_eda.csv already exists. Do you want to overwrite it? (yes/no): n\n",
+      "File was not overwritten. Moving to the next sample.\n",
+      "File by name /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/DD3S3_qc_eda.csv already exists. Do you want to overwrite it? (yes/no): n\n",
+      "File was not overwritten. Moving to the next sample.\n",
+      "File by name /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda/TMA_qc_eda.csv already exists. Do you want to overwrite it? (yes/no): n\n",
+      "File was not overwritten. Moving to the next sample.\n"
+     ]
+    }
+   ],
+   "source": [
+    "overwrite_all = input(\"Do you want to overwrite all existing files without confirmation? (yes/no): \")\n",
+    "overwrite_all = overwrite_all.lower().strip()\n",
+    "\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('.csv')[0]\n",
+    "    filename = os.path.join(output_data_dir, sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "    if os.path.exists(filename):\n",
+    "        if overwrite_all == 'yes':\n",
+    "            df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "            df_save.to_csv(filename, index=True, index_label='ID', mode='w')  # 'mode='w'' overwrites the file\n",
+    "            print(\"File \" + filename + \" was overwritten!\")\n",
+    "        else:\n",
+    "            user_response = input(\"File by name \" + filename + \" already exists. Do you want to overwrite it? (yes/no): \")\n",
+    "            if user_response.lower().strip() == 'yes':\n",
+    "                df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "                df_save.to_csv(filename, index=True, index_label='ID', mode='w')  # 'mode='w'' overwrites the file\n",
+    "                print(\"File \" + filename + \" was overwritten!\")\n",
+    "            else:\n",
+    "                print(\"File was not overwritten. Moving to the next sample.\")\n",
+    "    else:\n",
+    "        df_save = df.loc[df['Sample_ID'] == sample, :]\n",
+    "        df_save.to_csv(filename, index=True, index_label='ID')  # Save normally if the file doesn't exist\n",
+    "        print(\"File \" + filename + \" was created and saved !\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e010511b-6128-4870-a365-8d202dead49e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/wetransfer_data-zip_2024-05-17_1431/2_background_substraction.ipynb b/wetransfer_data-zip_2024-05-17_1431/2_background_substraction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bec51650c72f49e2b32b824294bf4cbf137f1997
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/2_background_substraction.ipynb
@@ -0,0 +1,3525 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1107981a-6197-4184-8327-03ed05b31a5e",
+   "metadata": {},
+   "source": [
+    "# II. BACKGROUND SUBSTRACTION NOTEBOOK"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "a0c470f4-c306-4b84-8aa5-d8b3daf9c810",
+   "metadata": {},
+   "source": [
+    "10/01/24\n",
+    "Modifications by Zoé Gerber\n",
+    "from an original code from Marilyne Labrie"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "d53c8eb3-f434-4f16-bbd4-4f757747b501",
+   "metadata": {},
+   "source": [
+    "II.1. PACKAGES IMPORT\n",
+    "II.2. DIRECTORIES\n",
+    "II.3. FILES\n",
+    "    II.3.1. METADATA\n",
+    "    II.3.2. NOT_INTENSITIES\n",
+    "    II.3.3. FULL_TO_SHORT_COLUMN_NAMES\n",
+    "    II.3.4. SHORT_TO_FULL_COLUMN_NAMES\n",
+    "    II.3.5. SAMPLES COLORS\n",
+    "    II.3.6. CHANNELS COLORS\n",
+    "    II.3.7. ROUNDS COLORS\n",
+    "    II.3.8. DATA\n",
+    "II.4. FILTERING\n",
+    "II.5. CELL TYPES COLORS\n",
+    "II.6. CELL SUBTYPES COLORS\n",
+    "\n",
+    "II.7. BACKGROUND SUBSTRACTION\n",
+    "II.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "125cf03e-b740-4daa-9b16-21057959faee",
+   "metadata": {},
+   "source": [
+    "## II.1. PACKAGES IMPORT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b4faaea6-5510-44e5-9e8d-b9160dc4b3b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.29.1.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mplc\n",
+    "import subprocess\n",
+    "import warnings\n",
+    "\n",
+    "from scipy import signal\n",
+    "\n",
+    "import plotly.figure_factory as ff\n",
+    "import plotly\n",
+    "import plotly.graph_objs as go\n",
+    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \n",
+    "import plotly.express as px\n",
+    "init_notebook_mode(connected = True)\n",
+    "\n",
+    "from my_modules import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "41c1e319-4dfb-43a2-b38b-83b789498988",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Silence FutureWarnings & UserWarnings\n",
+    "warnings.filterwarnings('ignore', category= FutureWarning)\n",
+    "warnings.filterwarnings('ignore', category= UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7946a507-14f5-4dd0-b2f7-a8b1ced9c3df",
+   "metadata": {},
+   "source": [
+    "## II.2. *DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "108f1f6d-4cd5-495f-91b7-b826f3d1f772",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set base directory\n",
+    "\n",
+    "##### MAC WORKSTATION #####\n",
+    "#base_dir = r'/Volumes/LaboLabrie/Projets/OC_TMA_Pejovic/Temp/Zoe/CyCIF_pipeline/'\n",
+    "###########################\n",
+    "\n",
+    "##### WINDOWS WORKSTATION #####\n",
+    "#base_dir = r'C:\\Users\\LaboLabrie\\gerz2701\\cyCIF-pipeline\\Set_B'\n",
+    "###############################\n",
+    "\n",
+    "##### LOCAL WORKSTATION #####\n",
+    "base_dir = r'/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/'\n",
+    "#############################\n",
+    "\n",
+    "#set_name = 'Set_A'\n",
+    "set_name = 'test'"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "aa48c54f-12f6-4f27-bb71-edcae686bb2b",
+   "metadata": {},
+   "source": [
+    "The project is organized as :\n",
+    "main dir \n",
+    "    code\n",
+    "    proj_data             > all csv files\n",
+    "    proj_metadata         > exposure time csv file, images dir,...\n",
+    "    proj_qc_eda           > csv after the QC/EDA step\n",
+    "    proj_bs               > csv after the BS step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a64af03a-7a84-4121-8eaa-7f08e6b3b21e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/ directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/images directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images directory already exists !\n"
+     ]
+    }
+   ],
+   "source": [
+    "project_name = set_name               # Project name\n",
+    "step_suffix = 'bs'                    # Curent part (here part II)\n",
+    "previous_step_suffix_long = \"_qc_eda\" # Previous part (here QC/EDA NOTEBOOK)\n",
+    "\n",
+    "# Initial input data directory\n",
+    "input_data_dir = os.path.join(base_dir, project_name + previous_step_suffix_long) \n",
+    "\n",
+    "# BS output directories\n",
+    "output_data_dir = os.path.join(base_dir, project_name + \"_\" + step_suffix)\n",
+    "# BS images subdirectory\n",
+    "output_images_dir = os.path.join(output_data_dir,\"images\")\n",
+    "\n",
+    "# Data and Metadata directories\n",
+    "# Metadata directories\n",
+    "metadata_dir = os.path.join(base_dir, project_name + \"_metadata\")\n",
+    "# images subdirectory\n",
+    "metadata_images_dir = os.path.join(metadata_dir,\"images\")\n",
+    "\n",
+    "# Create directories if they don't already exist\n",
+    "for d in [base_dir, input_data_dir, output_data_dir, output_images_dir, metadata_dir, metadata_images_dir]:\n",
+    "    if not os.path.exists(d):\n",
+    "        print(\"Creation of the\" , d, \"directory...\")\n",
+    "        os.makedirs(d)\n",
+    "    else :\n",
+    "        print(\"The\", d, \"directory already exists !\")\n",
+    "\n",
+    "os.chdir(input_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3396590c-e964-4053-be52-ef079e2d8e46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/\n",
+      "input_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_qc_eda\n",
+      "output_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs\n",
+      "output_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/images\n",
+      "metadata_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata\n",
+      "metadata_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify paths\n",
+    "print('base_dir :', base_dir)\n",
+    "print('input_data_dir :', input_data_dir)\n",
+    "print('output_data_dir :', output_data_dir)\n",
+    "print('output_images_dir :', output_images_dir)\n",
+    "print('metadata_dir :', metadata_dir)\n",
+    "print('metadata_images_dir :', metadata_images_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fce1113-2492-49a1-bb79-2553cb4a4fcd",
+   "metadata": {},
+   "source": [
+    "## II.3. FILES"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "41524daf-bcaa-4407-96aa-7a11a2dff993",
+   "metadata": {},
+   "source": [
+    "Don't forget to put your data in the projname_data directory !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d6665ba-cb34-4e75-bb77-085888c8af8b",
+   "metadata": {},
+   "source": [
+    "### II.3.1. METADATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "558e4ac8-3fd8-45fb-acdc-803baaf8a8a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv file was imported for further analysis!\n",
+      "WARNING: 'Marker metadata file' has the following unexpected item(s): \n",
+      "['Exp']\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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Round Target   Exp Channel target_lower                        full_column  \\\n",
+       "0    R0  AF488   300      c2        af488       AF488_Cell_Intensity_Average   \n",
+       "1    R0  AF488   300      c2        af488  AF488_Cytoplasm_Intensity_Average   \n",
+       "2    R0  AF488   300      c2        af488    AF488_Nucleus_Intensity_Average   \n",
+       "3    R0  AF555  1500      c3        af555       AF555_Cell_Intensity_Average   \n",
+       "4    R0  AF555  1500      c3        af555  AF555_Cytoplasm_Intensity_Average   \n",
+       "\n",
+       "  marker localisation  \n",
+       "0  AF488         cell  \n",
+       "1  AF488    cytoplasm  \n",
+       "2  AF488      nucleus  \n",
+       "3  AF555         cell  \n",
+       "4  AF555    cytoplasm  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import all metadata we need from the QC/EDA chapter\n",
+    "\n",
+    "# METADATA\n",
+    "filename = \"marker_intensity_metadata.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "metadata = pd.read_csv(filename)\n",
+    "\n",
+    "# Verify size with verify_line_no() function in my_modules.py\n",
+    "#verify_line_no(filename, metadata.shape[0] + 1)\n",
+    "\n",
+    "# Verify headers\n",
+    "exp_cols = ['Round','Target','Channel','target_lower','full_column','marker','localisation']\n",
+    "compare_headers(exp_cols, metadata.columns.values, \"Marker metadata file\")\n",
+    "\n",
+    "metadata = metadata.dropna()\n",
+    "metadata.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85e0a921-eeb1-4ece-8252-1df8325bc883",
+   "metadata": {},
+   "source": [
+    "### II.3.2. NOT_INTENSITIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f5965d04-1254-45d8-be20-f207068d25c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "not_intensities =\n",
+      " ['Cytoplasm_Size', 'Nuc_X', 'Primary_chem(1)_vs_surg(0)', 'cluster', 'immune_checkpoint', 'Sample_ID', 'Nucleus_Roundness', 'Unique_ROI_index', 'Nuc_Y', 'Nuc_X_Inv', 'Cell_ID', 'cell_subtype', 'ID', 'Nuc_Y_Inv', 'Patient', 'replicate_ID', 'cell_type', 'ROI_index', 'Cell_Size', 'Nucleus_Size']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NOT_INTENSITIES\n",
+    "filename = \"not_intensities.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "\n",
+    "# Open, read in information\n",
+    "not_intensities = []\n",
+    "with open(filename, 'r') as fh:\n",
+    "    not_intensities = fh.read().strip().split(\"\\n\")\n",
+    "    # take str, strip whitespace, split on new line character\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, len(not_intensities))\n",
+    "\n",
+    "# Print to console\n",
+    "print(\"not_intensities =\\n\", not_intensities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4568f560-eca8-4010-8d61-ef585d0bc5b3",
+   "metadata": {},
+   "source": [
+    "### II.3.3. FULL_TO_SHORT_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4fccb7b8-27e0-47e3-8b86-392ac2dfed8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "full_to_short_names =\n",
+      " {'AF488_Cell_Intensity_Average': 'AF488_Cell', 'AF488_Cytoplasm_Intensity_Average': 'AF488_Cytoplasm', 'AF488_Nucleus_Intensity_Average': 'AF488_Nucleus', 'AF555_Cell_Intensity_Average': 'AF555_Cell', 'AF555_Cytoplasm_Intensity_Average': 'AF555_Cytoplasm', 'AF555_Nucleus_Intensity_Average': 'AF555_Nucleus', 'AF647_Cell_Intensity_Average': 'AF647_Cell', 'AF647_Cytoplasm_Intensity_Average': 'AF647_Cytoplasm', 'AF647_Nucleus_Intensity_Average': 'AF647_Nucleus', 'AF750_Cell_Intensity_Average': 'AF750_Cell', 'AF750_Cytoplasm_Intensity_Average': 'AF750_Cytoplasm', 'AF750_Nucleus_Intensity_Average': 'AF750_Nucleus', 'aSMA_Cell_Intensity_Average': 'aSMA_Cell', 'aSMA_Cytoplasm_Intensity_Average': 'aSMA_Cytoplasm', 'aSMA_Nucleus_Intensity_Average': 'aSMA_Nucleus', 'AXL_Cell_Intensity_Average': 'AXL_Cell', 'AXL_Cytoplasm_Intensity_Average': 'AXL_Cytoplasm', 'AXL_Nucleus_Intensity_Average': 'AXL_Nucleus', 'B7H4_Cell_Intensity_Average': 'B7H4_Cell', 'B7H4_Cytoplasm_Intensity_Average': 'B7H4_Cytoplasm', 'B7H4_Nucleus_Intensity_Average': 'B7H4_Nucleus', 'CA9_Cell_Intensity_Average': 'CA9_Cell', 'CA9_Cytoplasm_Intensity_Average': 'CA9_Cytoplasm', 'CA9_Nucleus_Intensity_Average': 'CA9_Nucleus', 'CD4_Cell_Intensity_Average': 'CD4_Cell', 'CD4_Cytoplasm_Intensity_Average': 'CD4_Cytoplasm', 'CD4_Nucleus_Intensity_Average': 'CD4_Nucleus', 'CD8_Cell_Intensity_Average': 'CD8_Cell', 'CD8_Cytoplasm_Intensity_Average': 'CD8_Cytoplasm', 'CD8_Nucleus_Intensity_Average': 'CD8_Nucleus', 'CD11b_Cell_Intensity_Average': 'CD11b_Cell', 'CD11b_Cytoplasm_Intensity_Average': 'CD11b_Cytoplasm', 'CD11b_Nucleus_Intensity_Average': 'CD11b_Nucleus', 'CD11c_Cell_Intensity_Average': 'CD11c_Cell', 'CD11c_Cytoplasm_Intensity_Average': 'CD11c_Cytoplasm', 'CD11c_Nucleus_Intensity_Average': 'CD11c_Nucleus', 'CD20_Cell_Intensity_Average': 'CD20_Cell', 'CD20_Cytoplasm_Intensity_Average': 'CD20_Cytoplasm', 'CD20_Nucleus_Intensity_Average': 'CD20_Nucleus', 'CD31_Cell_Intensity_Average': 'CD31_Cell', 'CD31_Cytoplasm_Intensity_Average': 'CD31_Cytoplasm', 'CD31_Nucleus_Intensity_Average': 'CD31_Nucleus', 'CD44_Cell_Intensity_Average': 'CD44_Cell', 'CD44_Cytoplasm_Intensity_Average': 'CD44_Cytoplasm', 'CD44_Nucleus_Intensity_Average': 'CD44_Nucleus', 'CD45_Cell_Intensity_Average': 'CD45_Cell', 'CD45_Cytoplasm_Intensity_Average': 'CD45_Cytoplasm', 'CD45_Nucleus_Intensity_Average': 'CD45_Nucleus', 'CD68_Cell_Intensity_Average': 'CD68_Cell', 'CD68_Cytoplasm_Intensity_Average': 'CD68_Cytoplasm', 'CD68_Nucleus_Intensity_Average': 'CD68_Nucleus', 'CD163_Cell_Intensity_Average': 'CD163_Cell', 'CD163_Cytoplasm_Intensity_Average': 'CD163_Cytoplasm', 'CD163_Nucleus_Intensity_Average': 'CD163_Nucleus', 'CKs_Cell_Intensity_Average': 'CKs_Cell', 'CKs_Cytoplasm_Intensity_Average': 'CKs_Cytoplasm', 'CKs_Nucleus_Intensity_Average': 'CKs_Nucleus', 'ColVI_Cell_Intensity_Average': 'ColVI_Cell', 'ColVI_Cytoplasm_Intensity_Average': 'ColVI_Cytoplasm', 'ColVI_Nucleus_Intensity_Average': 'ColVI_Nucleus', 'Desmin_Cell_Intensity_Average': 'Desmin_Cell', 'Desmin_Cytoplasm_Intensity_Average': 'Desmin_Cytoplasm', 'Desmin_Nucleus_Intensity_Average': 'Desmin_Nucleus', 'Ecad_Cell_Intensity_Average': 'Ecad_Cell', 'Ecad_Cytoplasm_Intensity_Average': 'Ecad_Cytoplasm', 'Ecad_Nucleus_Intensity_Average': 'Ecad_Nucleus', 'Fibronectin_Cell_Intensity_Average': 'Fibronectin_Cell', 'Fibronectin_Cytoplasm_Intensity_Average': 'Fibronectin_Cytoplasm', 'Fibronectin_Nucleus_Intensity_Average': 'Fibronectin_Nucleus', 'FOXP3_Cell_Intensity_Average': 'FOXP3_Cell', 'FOXP3_Cytoplasm_Intensity_Average': 'FOXP3_Cytoplasm', 'FOXP3_Nucleus_Intensity_Average': 'FOXP3_Nucleus', 'GATA3_Cell_Intensity_Average': 'GATA3_Cell', 'GATA3_Cytoplasm_Intensity_Average': 'GATA3_Cytoplasm', 'GATA3_Nucleus_Intensity_Average': 'GATA3_Nucleus', 'HLA_Cell_Intensity_Average': 'HLA_Cell', 'HLA_Cytoplasm_Intensity_Average': 'HLA_Cytoplasm', 'HLA_Nucleus_Intensity_Average': 'HLA_Nucleus', 'Ki67_Cell_Intensity_Average': 'Ki67_Cell', 'Ki67_Cytoplasm_Intensity_Average': 'Ki67_Cytoplasm', 'Ki67_Nucleus_Intensity_Average': 'Ki67_Nucleus', 'MMP9_Cell_Intensity_Average': 'MMP9_Cell', 'MMP9_Cytoplasm_Intensity_Average': 'MMP9_Cytoplasm', 'MMP9_Nucleus_Intensity_Average': 'MMP9_Nucleus', 'PD1_Cell_Intensity_Average': 'PD1_Cell', 'PD1_Cytoplasm_Intensity_Average': 'PD1_Cytoplasm', 'PD1_Nucleus_Intensity_Average': 'PD1_Nucleus', 'PDGFR_Cell_Intensity_Average': 'PDGFR_Cell', 'PDGFR_Cytoplasm_Intensity_Average': 'PDGFR_Cytoplasm', 'PDGFR_Nucleus_Intensity_Average': 'PDGFR_Nucleus', 'PDL1_Cell_Intensity_Average': 'PDL1_Cell', 'PDL1_Cytoplasm_Intensity_Average': 'PDL1_Cytoplasm', 'PDL1_Nucleus_Intensity_Average': 'PDL1_Nucleus', 'r5c2_Cell_Intensity_Average': 'r5c2_Cell', 'r5c2_Cytoplasm_Intensity_Average': 'r5c2_Cytoplasm', 'r5c2_Nucleus_Intensity_Average': 'r5c2_Nucleus', 'r7c2_Cell_Intensity_Average': 'r7c2_Cell', 'r7c2_Cytoplasm_Intensity_Average': 'r7c2_Cytoplasm', 'r7c2_Nucleus_Intensity_Average': 'r7c2_Nucleus', 'r8c2_Cell_Intensity_Average': 'r8c2_Cell', 'r8c2_Cytoplasm_Intensity_Average': 'r8c2_Cytoplasm', 'r8c2_Nucleus_Intensity_Average': 'r8c2_Nucleus', 'Sting_Cell_Intensity_Average': 'Sting_Cell', 'Sting_Cytoplasm_Intensity_Average': 'Sting_Cytoplasm', 'Sting_Nucleus_Intensity_Average': 'Sting_Nucleus', 'Vimentin_Cell_Intensity_Average': 'Vimentin_Cell', 'Vimentin_Cytoplasm_Intensity_Average': 'Vimentin_Cytoplasm', 'Vimentin_Nucleus_Intensity_Average': 'Vimentin_Nucleus'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# FULL_TO_SHORT_COLUMN_NAMES\n",
+    "filename = \"full_to_short_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "full_to_short_names = df.set_index('full_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('full_to_short_names =\\n',full_to_short_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5858f81-2f63-44b7-abee-9c00ed6c7aba",
+   "metadata": {},
+   "source": [
+    "### II.3.4. SHORT_TO_FULL_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b47edf92-9401-4d16-b532-0156fb493c0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "short_to_full_names =\n",
+      " {'AF488_Cell': 'AF488_Cell_Intensity_Average', 'AF488_Cytoplasm': 'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus': 'AF488_Nucleus_Intensity_Average', 'AF555_Cell': 'AF555_Cell_Intensity_Average', 'AF555_Cytoplasm': 'AF555_Cytoplasm_Intensity_Average', 'AF555_Nucleus': 'AF555_Nucleus_Intensity_Average', 'AF647_Cell': 'AF647_Cell_Intensity_Average', 'AF647_Cytoplasm': 'AF647_Cytoplasm_Intensity_Average', 'AF647_Nucleus': 'AF647_Nucleus_Intensity_Average', 'AF750_Cell': 'AF750_Cell_Intensity_Average', 'AF750_Cytoplasm': 'AF750_Cytoplasm_Intensity_Average', 'AF750_Nucleus': 'AF750_Nucleus_Intensity_Average', 'aSMA_Cell': 'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm': 'aSMA_Cytoplasm_Intensity_Average', 'aSMA_Nucleus': 'aSMA_Nucleus_Intensity_Average', 'AXL_Cell': 'AXL_Cell_Intensity_Average', 'AXL_Cytoplasm': 'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus': 'AXL_Nucleus_Intensity_Average', 'B7H4_Cell': 'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm': 'B7H4_Cytoplasm_Intensity_Average', 'B7H4_Nucleus': 'B7H4_Nucleus_Intensity_Average', 'CA9_Cell': 'CA9_Cell_Intensity_Average', 'CA9_Cytoplasm': 'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus': 'CA9_Nucleus_Intensity_Average', 'CD4_Cell': 'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm': 'CD4_Cytoplasm_Intensity_Average', 'CD4_Nucleus': 'CD4_Nucleus_Intensity_Average', 'CD8_Cell': 'CD8_Cell_Intensity_Average', 'CD8_Cytoplasm': 'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus': 'CD8_Nucleus_Intensity_Average', 'CD11b_Cell': 'CD11b_Cell_Intensity_Average', 'CD11b_Cytoplasm': 'CD11b_Cytoplasm_Intensity_Average', 'CD11b_Nucleus': 'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell': 'CD11c_Cell_Intensity_Average', 'CD11c_Cytoplasm': 'CD11c_Cytoplasm_Intensity_Average', 'CD11c_Nucleus': 'CD11c_Nucleus_Intensity_Average', 'CD20_Cell': 'CD20_Cell_Intensity_Average', 'CD20_Cytoplasm': 'CD20_Cytoplasm_Intensity_Average', 'CD20_Nucleus': 'CD20_Nucleus_Intensity_Average', 'CD31_Cell': 'CD31_Cell_Intensity_Average', 'CD31_Cytoplasm': 'CD31_Cytoplasm_Intensity_Average', 'CD31_Nucleus': 'CD31_Nucleus_Intensity_Average', 'CD44_Cell': 'CD44_Cell_Intensity_Average', 'CD44_Cytoplasm': 'CD44_Cytoplasm_Intensity_Average', 'CD44_Nucleus': 'CD44_Nucleus_Intensity_Average', 'CD45_Cell': 'CD45_Cell_Intensity_Average', 'CD45_Cytoplasm': 'CD45_Cytoplasm_Intensity_Average', 'CD45_Nucleus': 'CD45_Nucleus_Intensity_Average', 'CD68_Cell': 'CD68_Cell_Intensity_Average', 'CD68_Cytoplasm': 'CD68_Cytoplasm_Intensity_Average', 'CD68_Nucleus': 'CD68_Nucleus_Intensity_Average', 'CD163_Cell': 'CD163_Cell_Intensity_Average', 'CD163_Cytoplasm': 'CD163_Cytoplasm_Intensity_Average', 'CD163_Nucleus': 'CD163_Nucleus_Intensity_Average', 'CKs_Cell': 'CKs_Cell_Intensity_Average', 'CKs_Cytoplasm': 'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus': 'CKs_Nucleus_Intensity_Average', 'ColVI_Cell': 'ColVI_Cell_Intensity_Average', 'ColVI_Cytoplasm': 'ColVI_Cytoplasm_Intensity_Average', 'ColVI_Nucleus': 'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell': 'Desmin_Cell_Intensity_Average', 'Desmin_Cytoplasm': 'Desmin_Cytoplasm_Intensity_Average', 'Desmin_Nucleus': 'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell': 'Ecad_Cell_Intensity_Average', 'Ecad_Cytoplasm': 'Ecad_Cytoplasm_Intensity_Average', 'Ecad_Nucleus': 'Ecad_Nucleus_Intensity_Average', 'Fibronectin_Cell': 'Fibronectin_Cell_Intensity_Average', 'Fibronectin_Cytoplasm': 'Fibronectin_Cytoplasm_Intensity_Average', 'Fibronectin_Nucleus': 'Fibronectin_Nucleus_Intensity_Average', 'FOXP3_Cell': 'FOXP3_Cell_Intensity_Average', 'FOXP3_Cytoplasm': 'FOXP3_Cytoplasm_Intensity_Average', 'FOXP3_Nucleus': 'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell': 'GATA3_Cell_Intensity_Average', 'GATA3_Cytoplasm': 'GATA3_Cytoplasm_Intensity_Average', 'GATA3_Nucleus': 'GATA3_Nucleus_Intensity_Average', 'HLA_Cell': 'HLA_Cell_Intensity_Average', 'HLA_Cytoplasm': 'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus': 'HLA_Nucleus_Intensity_Average', 'Ki67_Cell': 'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm': 'Ki67_Cytoplasm_Intensity_Average', 'Ki67_Nucleus': 'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell': 'MMP9_Cell_Intensity_Average', 'MMP9_Cytoplasm': 'MMP9_Cytoplasm_Intensity_Average', 'MMP9_Nucleus': 'MMP9_Nucleus_Intensity_Average', 'PD1_Cell': 'PD1_Cell_Intensity_Average', 'PD1_Cytoplasm': 'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus': 'PD1_Nucleus_Intensity_Average', 'PDGFR_Cell': 'PDGFR_Cell_Intensity_Average', 'PDGFR_Cytoplasm': 'PDGFR_Cytoplasm_Intensity_Average', 'PDGFR_Nucleus': 'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell': 'PDL1_Cell_Intensity_Average', 'PDL1_Cytoplasm': 'PDL1_Cytoplasm_Intensity_Average', 'PDL1_Nucleus': 'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell': 'r5c2_Cell_Intensity_Average', 'r5c2_Cytoplasm': 'r5c2_Cytoplasm_Intensity_Average', 'r5c2_Nucleus': 'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell': 'r7c2_Cell_Intensity_Average', 'r7c2_Cytoplasm': 'r7c2_Cytoplasm_Intensity_Average', 'r7c2_Nucleus': 'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell': 'r8c2_Cell_Intensity_Average', 'r8c2_Cytoplasm': 'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus': 'r8c2_Nucleus_Intensity_Average', 'Sting_Cell': 'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm': 'Sting_Cytoplasm_Intensity_Average', 'Sting_Nucleus': 'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell': 'Vimentin_Cell_Intensity_Average', 'Vimentin_Cytoplasm': 'Vimentin_Cytoplasm_Intensity_Average', 'Vimentin_Nucleus': 'Vimentin_Nucleus_Intensity_Average'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SHORT_TO_FULL_COLUMN_NAMES\n",
+    "filename = \"short_to_full_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "\n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "short_to_full_names = df.set_index('short_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('short_to_full_names =\\n',short_to_full_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "450d6f35-64de-47da-a359-7125a677cfd6",
+   "metadata": {},
+   "source": [
+    "### II.3.5. SAMPLES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "4a72b8c0-bdc2-4d2a-8549-2b24cefba020",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "sample_color_dict =\n",
+      " {'DD3S1.csv': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701), 'DD3S2.csv': (0.5920891529639701, 0.6418467016378244, 0.1935069134991043), 'DD3S3.csv': (0.21044753832183283, 0.6773105080456748, 0.6433941168468681), 'TMA.csv': (0.5019607843137255, 0.5019607843137255, 0.5019607843137255)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# COLORS INFORMATION\n",
+    "filename = \"sample_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "sample_color_dict = df.set_index('Sample_ID').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('sample_color_dict =\\n',sample_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6249335-5aef-4d35-9c80-163d68ae1432",
+   "metadata": {},
+   "source": [
+    "### II.3.6. CHANNELS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e460924e-82b4-4542-b297-399cd8fd6e68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "channel_color_dict =\n",
+      " {'c2': (0.00784313725490196, 0.24313725490196078, 1.0), 'c3': (1.0, 0.48627450980392156, 0.0), 'c4': (0.10196078431372549, 0.788235294117647, 0.2196078431372549), 'c5': (0.9098039215686274, 0.0, 0.043137254901960784)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CHANNELS\n",
+    "filename = \"channel_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "\n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "channel_color_dict = df.set_index('Channel').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('channel_color_dict =\\n',channel_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "051455b8-048b-4392-b5b5-9145d03391a8",
+   "metadata": {},
+   "source": [
+    "### II.3.7. ROUNDS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "636ec133-d4e8-4f61-9893-f369d1538c83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "round_color_dict =\n",
+      " {'R0': (0.28685356234627135, 0.13009829239513535, 0.23110332132624437), 'R1': (0.36541462435986094, 0.2025447048359916, 0.37693310021636883), 'R2': (0.40867533458903105, 0.2940761173840091, 0.5166711878800253), 'R3': (0.42890613750051265, 0.4082290173220481, 0.6335348887063806), 'R4': (0.4444462906865238, 0.5264664993764805, 0.7056321892616532), 'R5': (0.47707206309601013, 0.6427061780374552, 0.7418477948908153), 'R6': (0.5414454866716836, 0.7466759172596551, 0.7572905778378964), 'R7': (0.6414710091647722, 0.8321551072276492, 0.7746773027952071), 'R8': (0.7684256891219349, 0.8992667116749021, 0.8171383269422353)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ROUND\n",
+    "filename = \"round_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "round_color_dict = df.set_index('Round').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('round_color_dict =\\n',round_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05ad70c3-4525-4e98-ac85-2398bde990ab",
+   "metadata": {},
+   "source": [
+    "### II.3.8. DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "eb2392ef-dd07-44cb-b94e-0a044abd0a47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CSV files were detected:\n",
+      "['DD3S3_qc_eda.csv', 'TMA_qc_eda.csv', 'DD3S1_qc_eda.csv', 'DD3S2_qc_eda.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# DATA\n",
+    "# List files in the directory\n",
+    "# Check if the directory exists\n",
+    "if os.path.exists(input_data_dir):\n",
+    "    ls_samples = [sample for sample in os.listdir(input_data_dir) if sample.endswith(\"_qc_eda.csv\")]\n",
+    "\n",
+    "    print(\"The following CSV files were detected:\")\n",
+    "    print([sample for sample in ls_samples])\n",
+    "else:\n",
+    "    print(f\"The directory {input_data_dir} does not exist.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "99e5b1af-1b1a-432e-8e75-e31e0c70e7c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Nuc_X' 'Sample_ID' 'Nucleus_Roundness' 'Nuc_Y_Inv' 'ROI_index'\n",
+      " 'Cell_Size' 'Nucleus_Size' 'AF488_Cell_Intensity_Average'\n",
+      " 'AF488_Cytoplasm_Intensity_Average' 'AF488_Nucleus_Intensity_Average'\n",
+      " 'AF555_Cell_Intensity_Average' 'AF555_Cytoplasm_Intensity_Average'\n",
+      " 'AF555_Nucleus_Intensity_Average' 'AF647_Cell_Intensity_Average'\n",
+      " 'AF647_Cytoplasm_Intensity_Average' 'AF647_Nucleus_Intensity_Average'\n",
+      " 'AF750_Cell_Intensity_Average' 'AF750_Cytoplasm_Intensity_Average'\n",
+      " 'AF750_Nucleus_Intensity_Average' 'aSMA_Cell_Intensity_Average'\n",
+      " 'aSMA_Cytoplasm_Intensity_Average' 'aSMA_Nucleus_Intensity_Average'\n",
+      " 'AXL_Cell_Intensity_Average' 'AXL_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Nucleus_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'B7H4_Cytoplasm_Intensity_Average' 'B7H4_Nucleus_Intensity_Average'\n",
+      " 'CA9_Cell_Intensity_Average' 'CA9_Cytoplasm_Intensity_Average'\n",
+      " 'CA9_Nucleus_Intensity_Average' 'CD4_Cell_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'CD4_Nucleus_Intensity_Average'\n",
+      " 'CD8_Cell_Intensity_Average' 'CD8_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Nucleus_Intensity_Average' 'CD11b_Cell_Intensity_Average'\n",
+      " 'CD11b_Cytoplasm_Intensity_Average' 'CD11b_Nucleus_Intensity_Average'\n",
+      " 'CD11c_Cell_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD11c_Nucleus_Intensity_Average' 'CD20_Cell_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'CD20_Nucleus_Intensity_Average'\n",
+      " 'CD31_Cell_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'CD31_Nucleus_Intensity_Average' 'CD44_Cell_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD44_Nucleus_Intensity_Average'\n",
+      " 'CD45_Cell_Intensity_Average' 'CD45_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Nucleus_Intensity_Average' 'CD68_Cell_Intensity_Average'\n",
+      " 'CD68_Cytoplasm_Intensity_Average' 'CD68_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cell_Intensity_Average' 'CD163_Cytoplasm_Intensity_Average'\n",
+      " 'CD163_Nucleus_Intensity_Average' 'CKs_Cell_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'CKs_Nucleus_Intensity_Average'\n",
+      " 'ColVI_Cell_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'ColVI_Nucleus_Intensity_Average' 'Desmin_Cell_Intensity_Average'\n",
+      " 'Desmin_Cytoplasm_Intensity_Average' 'Desmin_Nucleus_Intensity_Average'\n",
+      " 'Ecad_Cell_Intensity_Average' 'Ecad_Cytoplasm_Intensity_Average'\n",
+      " 'Ecad_Nucleus_Intensity_Average' 'Fibronectin_Cell_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Nucleus_Intensity_Average' 'FOXP3_Cell_Intensity_Average'\n",
+      " 'FOXP3_Cytoplasm_Intensity_Average' 'FOXP3_Nucleus_Intensity_Average'\n",
+      " 'GATA3_Cell_Intensity_Average' 'GATA3_Cytoplasm_Intensity_Average'\n",
+      " 'GATA3_Nucleus_Intensity_Average' 'HLA_Cell_Intensity_Average'\n",
+      " 'HLA_Cytoplasm_Intensity_Average' 'HLA_Nucleus_Intensity_Average'\n",
+      " 'Ki67_Cell_Intensity_Average' 'Ki67_Cytoplasm_Intensity_Average'\n",
+      " 'Ki67_Nucleus_Intensity_Average' 'MMP9_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'MMP9_Nucleus_Intensity_Average'\n",
+      " 'PD1_Cell_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'PD1_Nucleus_Intensity_Average' 'PDGFR_Cell_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'PDGFR_Nucleus_Intensity_Average'\n",
+      " 'PDL1_Cell_Intensity_Average' 'PDL1_Cytoplasm_Intensity_Average'\n",
+      " 'PDL1_Nucleus_Intensity_Average' 'r5c2_Cell_Intensity_Average'\n",
+      " 'r5c2_Cytoplasm_Intensity_Average' 'r5c2_Nucleus_Intensity_Average'\n",
+      " 'r7c2_Cell_Intensity_Average' 'r7c2_Cytoplasm_Intensity_Average'\n",
+      " 'r7c2_Nucleus_Intensity_Average' 'r8c2_Cell_Intensity_Average'\n",
+      " 'r8c2_Cytoplasm_Intensity_Average' 'r8c2_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cell_Intensity_Average' 'Sting_Cytoplasm_Intensity_Average'\n",
+      " 'Sting_Nucleus_Intensity_Average' 'Vimentin_Cell_Intensity_Average'\n",
+      " 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'Vimentin_Nucleus_Intensity_Average']\n",
+      "DD3S3_qc_eda.csv file is processed !\n",
+      "\n",
+      "TMA_qc_eda.csv file is processed !\n",
+      "\n",
+      "DD3S1_qc_eda.csv file is processed !\n",
+      "\n",
+      "DD3S2_qc_eda.csv file is processed !\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import all the others files\n",
+    "dfs = {}\n",
+    "\n",
+    "# Set variable to hold default header values\n",
+    "# First gather information on expected headers using first file in ls_samples\n",
+    "# Read in the first row of the file corresponding to the first sample (index = 0) in ls_samples\n",
+    "df = pd.read_csv(os.path.join(input_data_dir, ls_samples[0]) , index_col = 0, nrows = 1)\n",
+    "expected_headers = df.columns.values\n",
+    "print(expected_headers)\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "for sample in ls_samples:\n",
+    "    file_path = os.path.join(input_data_dir,sample)\n",
+    "   \n",
+    "    try:\n",
+    "        # Read the CSV file\n",
+    "        df = pd.read_csv(file_path, index_col=0)\n",
+    "        # Check if the DataFrame is empty, if so, don't continue trying to process df and remove it\n",
+    "        \n",
+    "        if not df.empty:\n",
+    "            # Reorder the columns to match the expected headers list\n",
+    "            df = df.reindex(columns=expected_headers)\n",
+    "            print(sample, \"file is processed !\\n\")\n",
+    "            #print(df) \n",
+    "   \n",
+    "    except pd.errors.EmptyDataError:\n",
+    "        print(f'\\nEmpty data error in {sample} file. Removing from analysis...')\n",
+    "        ls_samples.remove(sample)      \n",
+    "    \n",
+    "    # Add df to dfs \n",
+    "    dfs[sample] = df\n",
+    "\n",
+    "#print(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "2e2def98-8ea5-46ee-bad3-10d19feb56db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+      "ID                                                                     \n",
+      "DD3S3_Cell_0  823.567871  DD3S3.csv           0.835324  15699.382812   \n",
+      "DD3S3_Cell_1  852.840027  DD3S3.csv           0.523421  15690.533203   \n",
+      "DD3S3_Cell_2  868.272705  DD3S3.csv           0.686147  15682.994141   \n",
+      "DD3S3_Cell_3  842.131592  DD3S3.csv           0.881136  15688.894531   \n",
+      "DD3S3_Cell_4  704.337280  DD3S3.csv           0.757623  15683.059570   \n",
+      "\n",
+      "              ROI_index  Cell_Size  Nucleus_Size  \\\n",
+      "ID                                                 \n",
+      "DD3S3_Cell_0          0        281            81   \n",
+      "DD3S3_Cell_1          0        200            75   \n",
+      "DD3S3_Cell_2          0        425           165   \n",
+      "DD3S3_Cell_3          0        114            38   \n",
+      "DD3S3_Cell_4          0        418           169   \n",
+      "\n",
+      "              AF488_Cell_Intensity_Average  AF488_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                                              \n",
+      "DD3S3_Cell_0                   1996.348755                        1883.150024   \n",
+      "DD3S3_Cell_1                   2523.199951                        2450.087891   \n",
+      "DD3S3_Cell_2                   2864.345947                        2765.046143   \n",
+      "DD3S3_Cell_3                   2328.473633                        2267.855225   \n",
+      "DD3S3_Cell_4                   3349.023926                        3018.883545   \n",
+      "\n",
+      "              AF488_Nucleus_Intensity_Average  ...  \\\n",
+      "ID                                             ...   \n",
+      "DD3S3_Cell_0                      2275.851807  ...   \n",
+      "DD3S3_Cell_1                      2645.053223  ...   \n",
+      "DD3S3_Cell_2                      3020.818115  ...   \n",
+      "DD3S3_Cell_3                      2449.710449  ...   \n",
+      "DD3S3_Cell_4                      3835.443848  ...   \n",
+      "\n",
+      "              r7c2_Nucleus_Intensity_Average  r8c2_Cell_Intensity_Average  \\\n",
+      "ID                                                                          \n",
+      "DD3S3_Cell_0                      252.555557                   268.523132   \n",
+      "DD3S3_Cell_1                      271.226654                   310.339996   \n",
+      "DD3S3_Cell_2                      333.078796                   349.395294   \n",
+      "DD3S3_Cell_3                      263.500000                   291.289459   \n",
+      "DD3S3_Cell_4                      375.662720                   384.011963   \n",
+      "\n",
+      "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                               \n",
+      "DD3S3_Cell_0                        257.945007   \n",
+      "DD3S3_Cell_1                        301.056000   \n",
+      "DD3S3_Cell_2                        339.603851   \n",
+      "DD3S3_Cell_3                        282.789459   \n",
+      "DD3S3_Cell_4                        350.662659   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "ID                                                                           \n",
+      "DD3S3_Cell_0                      294.641968                   1358.562256   \n",
+      "DD3S3_Cell_1                      325.813324                   1856.005005   \n",
+      "DD3S3_Cell_2                      364.824249                   1969.552979   \n",
+      "DD3S3_Cell_3                      308.289459                   1319.236816   \n",
+      "DD3S3_Cell_4                      433.147919                   1398.476074   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                \n",
+      "DD3S3_Cell_0                        1318.834961   \n",
+      "DD3S3_Cell_1                        1954.375977   \n",
+      "DD3S3_Cell_2                        2171.303955   \n",
+      "DD3S3_Cell_3                        1249.684204   \n",
+      "DD3S3_Cell_4                        1301.931763   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S3_Cell_0                      1456.654297   \n",
+      "DD3S3_Cell_1                      1692.053345   \n",
+      "DD3S3_Cell_2                      1651.642456   \n",
+      "DD3S3_Cell_3                      1458.342163   \n",
+      "DD3S3_Cell_4                      1540.721924   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S3_Cell_0                      2862.252686   \n",
+      "DD3S3_Cell_1                      3007.534912   \n",
+      "DD3S3_Cell_2                      1955.188232   \n",
+      "DD3S3_Cell_3                      3467.263184   \n",
+      "DD3S3_Cell_4                      1861.600464   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                   \n",
+      "DD3S3_Cell_0                           2464.334961   \n",
+      "DD3S3_Cell_1                           3135.496094   \n",
+      "DD3S3_Cell_2                           2099.076904   \n",
+      "DD3S3_Cell_3                           3773.605225   \n",
+      "DD3S3_Cell_4                           1678.666626   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  \n",
+      "ID                                                \n",
+      "DD3S3_Cell_0                         3844.765381  \n",
+      "DD3S3_Cell_1                         2794.266602  \n",
+      "DD3S3_Cell_2                         1728.454590  \n",
+      "DD3S3_Cell_3                         2854.578857  \n",
+      "DD3S3_Cell_4                         2131.130127  \n",
+      "\n",
+      "[5 rows x 115 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge dfs into one df\n",
+    "df = pd.concat(dfs.values(), ignore_index=False , sort = False)\n",
+    "del dfs\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "bbe27b0f-c7a0-489a-b845-74c42289f7bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(433976, 115)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "949edaa7-22ec-4544-a250-4705759ca1bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "657ba08b-cb75-437a-9557-97e8acb46dcb",
+   "metadata": {},
+   "source": [
+    "## II.4. *FILTERING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "db040ebb-6447-4e0a-a904-21e62eb3e1c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of cells before filtering : 433976\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of cells before filtering :\", df.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d571eb73-5e76-4c16-b7ba-37a55553d454",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0b66a96e-32e2-4682-ae20-ebd6fc41a9c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of cells after filtering on nucleus size: 389617\n",
+      "Number of cells after filtering on AF555A ___ intensity: 350554\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Delete small cells and objects w/high AF555 Signal (RBCs) \n",
+    "# We usually use the 95th percentile calculated during QC_EDA\n",
+    "df = df.loc[(df['Nucleus_Size'] > 42 )]\n",
+    "df = df.loc[(df['Nucleus_Size'] < 216)]\n",
+    "print(\"Number of cells after filtering on nucleus size:\", df.shape[0])\n",
+    "\n",
+    "df = df.loc[(df['AF555_Cell_Intensity_Average'] < 2000)]\n",
+    "print(\"Number of cells after filtering on AF555A ___ intensity:\", df.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "3b3ecc8d-66ad-4153-8289-6e4935cf7fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Assign cell type\n",
+    "# Assign tumor cells at each row at first (random assigning here just for development purposes)\n",
+    "# Generate random values for cell_type column\n",
+    "random_values = np.random.randint(0, 10, size=len(df))\n",
+    "\n",
+    "# Assign cell type based on random values\n",
+    "def assign_cell_type(n):\n",
+    "    return 'none'\n",
+    "\n",
+    "df['cell_type'] = np.vectorize(assign_cell_type)(random_values)\n",
+    "df['cell_subtype'] = df['cell_type'].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "15dd4c6e-87d3-419e-b824-0a39fce7faa9",
+   "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>Nuc_X</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>AF488_Cell_Intensity_Average</th>\n",
+       "      <th>AF488_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AF488_Nucleus_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r8c2_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>r8c2_Nucleus_Intensity_Average</th>\n",
+       "      <th>Sting_Cell_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Sting_Nucleus_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cell_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Vimentin_Nucleus_Intensity_Average</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_0</th>\n",
+       "      <td>823.567871</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>15699.382812</td>\n",
+       "      <td>0</td>\n",
+       "      <td>281</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1996.348755</td>\n",
+       "      <td>1883.150024</td>\n",
+       "      <td>2275.851807</td>\n",
+       "      <td>...</td>\n",
+       "      <td>257.945007</td>\n",
+       "      <td>294.641968</td>\n",
+       "      <td>1358.562256</td>\n",
+       "      <td>1318.834961</td>\n",
+       "      <td>1456.654297</td>\n",
+       "      <td>2862.252686</td>\n",
+       "      <td>2464.334961</td>\n",
+       "      <td>3844.765381</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_1</th>\n",
+       "      <td>852.840027</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.523421</td>\n",
+       "      <td>15690.533203</td>\n",
+       "      <td>0</td>\n",
+       "      <td>200</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2523.199951</td>\n",
+       "      <td>2450.087891</td>\n",
+       "      <td>2645.053223</td>\n",
+       "      <td>...</td>\n",
+       "      <td>301.056000</td>\n",
+       "      <td>325.813324</td>\n",
+       "      <td>1856.005005</td>\n",
+       "      <td>1954.375977</td>\n",
+       "      <td>1692.053345</td>\n",
+       "      <td>3007.534912</td>\n",
+       "      <td>3135.496094</td>\n",
+       "      <td>2794.266602</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_2</th>\n",
+       "      <td>868.272705</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.686147</td>\n",
+       "      <td>15682.994141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>425</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2864.345947</td>\n",
+       "      <td>2765.046143</td>\n",
+       "      <td>3020.818115</td>\n",
+       "      <td>...</td>\n",
+       "      <td>339.603851</td>\n",
+       "      <td>364.824249</td>\n",
+       "      <td>1969.552979</td>\n",
+       "      <td>2171.303955</td>\n",
+       "      <td>1651.642456</td>\n",
+       "      <td>1955.188232</td>\n",
+       "      <td>2099.076904</td>\n",
+       "      <td>1728.454590</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_4</th>\n",
+       "      <td>704.337280</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.757623</td>\n",
+       "      <td>15683.059570</td>\n",
+       "      <td>0</td>\n",
+       "      <td>418</td>\n",
+       "      <td>169</td>\n",
+       "      <td>3349.023926</td>\n",
+       "      <td>3018.883545</td>\n",
+       "      <td>3835.443848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>350.662659</td>\n",
+       "      <td>433.147919</td>\n",
+       "      <td>1398.476074</td>\n",
+       "      <td>1301.931763</td>\n",
+       "      <td>1540.721924</td>\n",
+       "      <td>1861.600464</td>\n",
+       "      <td>1678.666626</td>\n",
+       "      <td>2131.130127</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_5</th>\n",
+       "      <td>852.893799</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.714651</td>\n",
+       "      <td>15683.017578</td>\n",
+       "      <td>0</td>\n",
+       "      <td>201</td>\n",
+       "      <td>113</td>\n",
+       "      <td>2366.134277</td>\n",
+       "      <td>2088.715820</td>\n",
+       "      <td>2582.177002</td>\n",
+       "      <td>...</td>\n",
+       "      <td>276.897736</td>\n",
+       "      <td>326.592926</td>\n",
+       "      <td>1328.602051</td>\n",
+       "      <td>1144.681763</td>\n",
+       "      <td>1471.831909</td>\n",
+       "      <td>1457.726318</td>\n",
+       "      <td>1205.852295</td>\n",
+       "      <td>1653.876099</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 117 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+       "ID                                                                     \n",
+       "DD3S3_Cell_0  823.567871  DD3S3.csv           0.835324  15699.382812   \n",
+       "DD3S3_Cell_1  852.840027  DD3S3.csv           0.523421  15690.533203   \n",
+       "DD3S3_Cell_2  868.272705  DD3S3.csv           0.686147  15682.994141   \n",
+       "DD3S3_Cell_4  704.337280  DD3S3.csv           0.757623  15683.059570   \n",
+       "DD3S3_Cell_5  852.893799  DD3S3.csv           0.714651  15683.017578   \n",
+       "\n",
+       "              ROI_index  Cell_Size  Nucleus_Size  \\\n",
+       "ID                                                 \n",
+       "DD3S3_Cell_0          0        281            81   \n",
+       "DD3S3_Cell_1          0        200            75   \n",
+       "DD3S3_Cell_2          0        425           165   \n",
+       "DD3S3_Cell_4          0        418           169   \n",
+       "DD3S3_Cell_5          0        201           113   \n",
+       "\n",
+       "              AF488_Cell_Intensity_Average  AF488_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                                              \n",
+       "DD3S3_Cell_0                   1996.348755                        1883.150024   \n",
+       "DD3S3_Cell_1                   2523.199951                        2450.087891   \n",
+       "DD3S3_Cell_2                   2864.345947                        2765.046143   \n",
+       "DD3S3_Cell_4                   3349.023926                        3018.883545   \n",
+       "DD3S3_Cell_5                   2366.134277                        2088.715820   \n",
+       "\n",
+       "              AF488_Nucleus_Intensity_Average  ...  \\\n",
+       "ID                                             ...   \n",
+       "DD3S3_Cell_0                      2275.851807  ...   \n",
+       "DD3S3_Cell_1                      2645.053223  ...   \n",
+       "DD3S3_Cell_2                      3020.818115  ...   \n",
+       "DD3S3_Cell_4                      3835.443848  ...   \n",
+       "DD3S3_Cell_5                      2582.177002  ...   \n",
+       "\n",
+       "              r8c2_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S3_Cell_0                        257.945007   \n",
+       "DD3S3_Cell_1                        301.056000   \n",
+       "DD3S3_Cell_2                        339.603851   \n",
+       "DD3S3_Cell_4                        350.662659   \n",
+       "DD3S3_Cell_5                        276.897736   \n",
+       "\n",
+       "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+       "ID                                                                           \n",
+       "DD3S3_Cell_0                      294.641968                   1358.562256   \n",
+       "DD3S3_Cell_1                      325.813324                   1856.005005   \n",
+       "DD3S3_Cell_2                      364.824249                   1969.552979   \n",
+       "DD3S3_Cell_4                      433.147919                   1398.476074   \n",
+       "DD3S3_Cell_5                      326.592926                   1328.602051   \n",
+       "\n",
+       "              Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S3_Cell_0                        1318.834961   \n",
+       "DD3S3_Cell_1                        1954.375977   \n",
+       "DD3S3_Cell_2                        2171.303955   \n",
+       "DD3S3_Cell_4                        1301.931763   \n",
+       "DD3S3_Cell_5                        1144.681763   \n",
+       "\n",
+       "              Sting_Nucleus_Intensity_Average  \\\n",
+       "ID                                              \n",
+       "DD3S3_Cell_0                      1456.654297   \n",
+       "DD3S3_Cell_1                      1692.053345   \n",
+       "DD3S3_Cell_2                      1651.642456   \n",
+       "DD3S3_Cell_4                      1540.721924   \n",
+       "DD3S3_Cell_5                      1471.831909   \n",
+       "\n",
+       "              Vimentin_Cell_Intensity_Average  \\\n",
+       "ID                                              \n",
+       "DD3S3_Cell_0                      2862.252686   \n",
+       "DD3S3_Cell_1                      3007.534912   \n",
+       "DD3S3_Cell_2                      1955.188232   \n",
+       "DD3S3_Cell_4                      1861.600464   \n",
+       "DD3S3_Cell_5                      1457.726318   \n",
+       "\n",
+       "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S3_Cell_0                           2464.334961   \n",
+       "DD3S3_Cell_1                           3135.496094   \n",
+       "DD3S3_Cell_2                           2099.076904   \n",
+       "DD3S3_Cell_4                           1678.666626   \n",
+       "DD3S3_Cell_5                           1205.852295   \n",
+       "\n",
+       "              Vimentin_Nucleus_Intensity_Average  cell_type  cell_subtype  \n",
+       "ID                                                                         \n",
+       "DD3S3_Cell_0                         3844.765381       none          none  \n",
+       "DD3S3_Cell_1                         2794.266602       none          none  \n",
+       "DD3S3_Cell_2                         1728.454590       none          none  \n",
+       "DD3S3_Cell_4                         2131.130127       none          none  \n",
+       "DD3S3_Cell_5                         1653.876099       none          none  \n",
+       "\n",
+       "[5 rows x 117 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de4deb80-bdbe-430b-9991-019406d99ae2",
+   "metadata": {},
+   "source": [
+    "##  II.5. CELL TYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "9214d297-2e4b-45eb-b664-43f748c4bd6d",
+   "metadata": {},
+   "source": [
+    "# Establish colors to use throughout workflow\n",
+    "\n",
+    "# we want colors that are categorical, since Cell Type is a non-ordered category. \n",
+    "# A categorical color palette will have dissimilar colors.\n",
+    "# Get those unique colors\n",
+    "cell_types = ['STROMA','CANCER','IMMUNE','ENDOTHELIAL']\n",
+    "color_values = sb.color_palette(\"hls\", n_colors = len(cell_types))\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "\n",
+    "print(\"Unique cell types are:\",df.cell_type.unique())\n",
+    "# Display those unique colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "72a0ac93-964a-4541-91e0-5e21c46154f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define your custom colors for each cell type\n",
+    "custom_colors = {\n",
+    "    'CANCER': (0.1333, 0.5451, 0.1333),\n",
+    "    'STROMA': (0.4, 0.4, 0.4),\n",
+    "    'IMMUNE': (1, 1, 0),\n",
+    "    'ENDOTHELIAL': (0.502, 0, 0.502)\n",
+    "}\n",
+    "\n",
+    "# Retrieve the list of cell types\n",
+    "cell_types = list(custom_colors.keys())\n",
+    "\n",
+    "# Extract the corresponding colors from the dictionary\n",
+    "color_values = [custom_colors[cell] for cell in cell_types]\n",
+    "\n",
+    "# Display the colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "704674b9-be53-414b-a03a-4ad1465cfb23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CANCER': (0.1333, 0.5451, 0.1333),\n",
+       " 'STROMA': (0.4, 0.4, 0.4),\n",
+       " 'IMMUNE': (1, 1, 0),\n",
+       " 'ENDOTHELIAL': (0.502, 0, 0.502)}"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dctionnary\n",
+    "celltype_color_dict = dict(zip(cell_types, color_values))\n",
+    "celltype_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "eeeb576b-aea8-4c37-ada1-7f324ddde99a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv was created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save color information (mapping and legend) to metadata directory\n",
+    "# Create dataframe\n",
+    "celltype_color_df = color_dict_to_df(celltype_color_dict, \"cell_type\")\n",
+    "celltype_color_df.head()\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"celltype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "celltype_color_df.to_csv(filename, index = False)\n",
+    "print(\"File\" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e5ba634f-bbc8-4662-9c8b-48bf3c649cc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of cell type info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in celltype_color_dict.keys():\n",
+    "        h = g.bar(0,0, color = celltype_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Cell type'),\n",
+    "\n",
+    "\n",
+    "filename = \"Celltype_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "4b3305bb-688b-4540-bcf8-32c7c0ff69fe",
+   "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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>c4</td>\n",
+       "      <td>sting</td>\n",
+       "      <td>Sting_Nucleus_Intensity_Average</td>\n",
+       "      <td>Sting</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cell_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>R8</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c5</td>\n",
+       "      <td>cd11b</td>\n",
+       "      <td>CD11b_Nucleus_Intensity_Average</td>\n",
+       "      <td>CD11b</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Round Target   Exp Channel target_lower  \\\n",
+       "0      R0  AF488   300      c2        af488   \n",
+       "1      R0  AF488   300      c2        af488   \n",
+       "2      R0  AF488   300      c2        af488   \n",
+       "3      R0  AF555  1500      c3        af555   \n",
+       "4      R0  AF555  1500      c3        af555   \n",
+       "..    ...    ...   ...     ...          ...   \n",
+       "103    R8  Sting  1000      c4        sting   \n",
+       "104    R8  Sting  1000      c4        sting   \n",
+       "105    R8  CD11b  1500      c5        cd11b   \n",
+       "106    R8  CD11b  1500      c5        cd11b   \n",
+       "107    R8  CD11b  1500      c5        cd11b   \n",
+       "\n",
+       "                           full_column marker localisation  \n",
+       "0         AF488_Cell_Intensity_Average  AF488         cell  \n",
+       "1    AF488_Cytoplasm_Intensity_Average  AF488    cytoplasm  \n",
+       "2      AF488_Nucleus_Intensity_Average  AF488      nucleus  \n",
+       "3         AF555_Cell_Intensity_Average  AF555         cell  \n",
+       "4    AF555_Cytoplasm_Intensity_Average  AF555    cytoplasm  \n",
+       "..                                 ...    ...          ...  \n",
+       "103  Sting_Cytoplasm_Intensity_Average  Sting    cytoplasm  \n",
+       "104    Sting_Nucleus_Intensity_Average  Sting      nucleus  \n",
+       "105       CD11b_Cell_Intensity_Average  CD11b         cell  \n",
+       "106  CD11b_Cytoplasm_Intensity_Average  CD11b    cytoplasm  \n",
+       "107    CD11b_Nucleus_Intensity_Average  CD11b      nucleus  \n",
+       "\n",
+       "[108 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a54e644d-00a8-409a-9f62-12e6cd30e669",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Nuc_X', 'Sample_ID', 'Nucleus_Roundness', 'Nuc_Y_Inv',\n",
+       "       'ROI_index', 'Cell_Size', 'Nucleus_Size',\n",
+       "       'AF488_Cell_Intensity_Average',\n",
+       "       'AF488_Cytoplasm_Intensity_Average',\n",
+       "       'AF488_Nucleus_Intensity_Average', 'AF555_Cell_Intensity_Average',\n",
+       "       'AF555_Cytoplasm_Intensity_Average',\n",
+       "       'AF555_Nucleus_Intensity_Average', 'AF647_Cell_Intensity_Average',\n",
+       "       'AF647_Cytoplasm_Intensity_Average',\n",
+       "       'AF647_Nucleus_Intensity_Average', 'AF750_Cell_Intensity_Average',\n",
+       "       'AF750_Cytoplasm_Intensity_Average',\n",
+       "       'AF750_Nucleus_Intensity_Average', 'aSMA_Cell_Intensity_Average',\n",
+       "       'aSMA_Cytoplasm_Intensity_Average',\n",
+       "       'aSMA_Nucleus_Intensity_Average', 'AXL_Cell_Intensity_Average',\n",
+       "       'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus_Intensity_Average',\n",
+       "       'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm_Intensity_Average',\n",
+       "       'B7H4_Nucleus_Intensity_Average', 'CA9_Cell_Intensity_Average',\n",
+       "       'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus_Intensity_Average',\n",
+       "       'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm_Intensity_Average',\n",
+       "       'CD4_Nucleus_Intensity_Average', 'CD8_Cell_Intensity_Average',\n",
+       "       'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus_Intensity_Average',\n",
+       "       'CD11b_Cell_Intensity_Average',\n",
+       "       'CD11b_Cytoplasm_Intensity_Average',\n",
+       "       'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell_Intensity_Average',\n",
+       "       'CD11c_Cytoplasm_Intensity_Average',\n",
+       "       'CD11c_Nucleus_Intensity_Average', 'CD20_Cell_Intensity_Average',\n",
+       "       'CD20_Cytoplasm_Intensity_Average',\n",
+       "       'CD20_Nucleus_Intensity_Average', 'CD31_Cell_Intensity_Average',\n",
+       "       'CD31_Cytoplasm_Intensity_Average',\n",
+       "       'CD31_Nucleus_Intensity_Average', 'CD44_Cell_Intensity_Average',\n",
+       "       'CD44_Cytoplasm_Intensity_Average',\n",
+       "       'CD44_Nucleus_Intensity_Average', 'CD45_Cell_Intensity_Average',\n",
+       "       'CD45_Cytoplasm_Intensity_Average',\n",
+       "       'CD45_Nucleus_Intensity_Average', 'CD68_Cell_Intensity_Average',\n",
+       "       'CD68_Cytoplasm_Intensity_Average',\n",
+       "       'CD68_Nucleus_Intensity_Average', 'CD163_Cell_Intensity_Average',\n",
+       "       'CD163_Cytoplasm_Intensity_Average',\n",
+       "       'CD163_Nucleus_Intensity_Average', 'CKs_Cell_Intensity_Average',\n",
+       "       'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus_Intensity_Average',\n",
+       "       'ColVI_Cell_Intensity_Average',\n",
+       "       'ColVI_Cytoplasm_Intensity_Average',\n",
+       "       'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell_Intensity_Average',\n",
+       "       'Desmin_Cytoplasm_Intensity_Average',\n",
+       "       'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell_Intensity_Average',\n",
+       "       'Ecad_Cytoplasm_Intensity_Average',\n",
+       "       'Ecad_Nucleus_Intensity_Average',\n",
+       "       'Fibronectin_Cell_Intensity_Average',\n",
+       "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       "       'Fibronectin_Nucleus_Intensity_Average',\n",
+       "       'FOXP3_Cell_Intensity_Average',\n",
+       "       'FOXP3_Cytoplasm_Intensity_Average',\n",
+       "       'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell_Intensity_Average',\n",
+       "       'GATA3_Cytoplasm_Intensity_Average',\n",
+       "       'GATA3_Nucleus_Intensity_Average', 'HLA_Cell_Intensity_Average',\n",
+       "       'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus_Intensity_Average',\n",
+       "       'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm_Intensity_Average',\n",
+       "       'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell_Intensity_Average',\n",
+       "       'MMP9_Cytoplasm_Intensity_Average',\n",
+       "       'MMP9_Nucleus_Intensity_Average', 'PD1_Cell_Intensity_Average',\n",
+       "       'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus_Intensity_Average',\n",
+       "       'PDGFR_Cell_Intensity_Average',\n",
+       "       'PDGFR_Cytoplasm_Intensity_Average',\n",
+       "       'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell_Intensity_Average',\n",
+       "       'PDL1_Cytoplasm_Intensity_Average',\n",
+       "       'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell_Intensity_Average',\n",
+       "       'r5c2_Cytoplasm_Intensity_Average',\n",
+       "       'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell_Intensity_Average',\n",
+       "       'r7c2_Cytoplasm_Intensity_Average',\n",
+       "       'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average',\n",
+       "       'r8c2_Cytoplasm_Intensity_Average',\n",
+       "       'r8c2_Nucleus_Intensity_Average', 'Sting_Cell_Intensity_Average',\n",
+       "       'Sting_Cytoplasm_Intensity_Average',\n",
+       "       'Sting_Nucleus_Intensity_Average',\n",
+       "       'Vimentin_Cell_Intensity_Average',\n",
+       "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+       "       'Vimentin_Nucleus_Intensity_Average', 'cell_type', 'cell_subtype'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "23fc3f34-ae00-4e5b-9003-bbdb69442dc6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(350554, 117)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f0434951-c5b4-4bc3-bbb8-329ed86603bb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(108, 8)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "8fdee906-95a9-4e44-8990-5ee8ab25458a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divide each marker (and its localisation) by the right exposure setting for each group of samples\n",
+    "df.loc[:, ~df.columns.isin(not_intensities)] = \\\n",
+    "    df.loc[:, ~df.columns.isin(not_intensities)].apply(lambda column: divide_exp_time(column, 'Exp', metadata), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23ac4db9-d5e4-47ca-a8d8-3250ee3a9a03",
+   "metadata": {},
+   "source": [
+    "##  II.6. *CELL SUBTYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "8f77d2ca-4282-4ffd-987d-9ab547b59b96",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique cell types are: ['none']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Establish colors to use throughout workflow\n",
+    "\n",
+    "# we want colors that are categorical, since Cell Type is a non-ordered category. \n",
+    "# A categorical color palette will have dissimilar colors.\n",
+    "# Get those unique colors\n",
+    "cell_subtypes = ['DC','B', 'TCD4','TCD8','M1','M2','Treg', \\\n",
+    "                 'IMMUNE_OTHER', 'CANCER', 'αSMA_myCAF',\\\n",
+    "                 'STROMA_OTHER', 'ENDOTHELIAL']\n",
+    "color_values = sb.color_palette(\"Paired\",n_colors = len(cell_subtypes))\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "\n",
+    "print(\"Unique cell types are:\",df.cell_subtype.unique())\n",
+    "# Display those unique colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "bf75f91b-2bc2-4d4f-bc94-861c6ccf27da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DC': (0.6509803921568628, 0.807843137254902, 0.8901960784313725),\n",
+       " 'B': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765),\n",
+       " 'TCD4': (0.6980392156862745, 0.8745098039215686, 0.5411764705882353),\n",
+       " 'TCD8': (0.2, 0.6274509803921569, 0.17254901960784313),\n",
+       " 'M1': (0.984313725490196, 0.6039215686274509, 0.6),\n",
+       " 'M2': (0.8901960784313725, 0.10196078431372549, 0.10980392156862745),\n",
+       " 'Treg': (0.9921568627450981, 0.7490196078431373, 0.43529411764705883),\n",
+       " 'IMMUNE_OTHER': (1.0, 0.4980392156862745, 0.0),\n",
+       " 'CANCER': (0.792156862745098, 0.6980392156862745, 0.8392156862745098),\n",
+       " 'αSMA_myCAF': (0.41568627450980394, 0.23921568627450981, 0.6039215686274509),\n",
+       " 'STROMA_OTHER': (1.0, 1.0, 0.6),\n",
+       " 'ENDOTHELIAL': (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dctionnary\n",
+    "cellsubtype_color_dict = dict(zip(cell_subtypes, color_values))\n",
+    "cellsubtype_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "b24374ef-ea1c-4910-a343-364fa7567497",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv was created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save color information (mapping and legend) to metadata directory\n",
+    "# Create dataframe\n",
+    "cellsubtype_color_df = color_dict_to_df(cellsubtype_color_dict, \"cell_subtype\")\n",
+    "cellsubtype_color_df.head()\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"cellsubtype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "cellsubtype_color_df.to_csv(filename, index = False)\n",
+    "print(\"File\" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "90df1f96-7934-42d9-948f-69fdbbc284df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of cell type info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in cellsubtype_color_dict.keys():\n",
+    "        h = g.bar(0,0, color = cellsubtype_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Cell subtype'),\n",
+    "\n",
+    "\n",
+    "filename = \"Cellsubtype_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c0e92c5-e026-4868-a5f3-20cee185d893",
+   "metadata": {},
+   "source": [
+    "## II.7. IMMUNE CHECKPOINT COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "e4b41aab-bdd0-41b0-a39e-0fe3e74fe830",
+   "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>Nuc_X</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>AF488_Cell_Intensity_Average</th>\n",
+       "      <th>AF488_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AF488_Nucleus_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r8c2_Nucleus_Intensity_Average</th>\n",
+       "      <th>Sting_Cell_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Sting_Nucleus_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cell_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Vimentin_Nucleus_Intensity_Average</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_0</th>\n",
+       "      <td>823.567871</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>15699.382812</td>\n",
+       "      <td>0</td>\n",
+       "      <td>281</td>\n",
+       "      <td>81</td>\n",
+       "      <td>6.654496</td>\n",
+       "      <td>6.277167</td>\n",
+       "      <td>7.586173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.785679</td>\n",
+       "      <td>1.358562</td>\n",
+       "      <td>1.318835</td>\n",
+       "      <td>1.456654</td>\n",
+       "      <td>19.081685</td>\n",
+       "      <td>16.428900</td>\n",
+       "      <td>25.631769</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_1</th>\n",
+       "      <td>852.840027</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.523421</td>\n",
+       "      <td>15690.533203</td>\n",
+       "      <td>0</td>\n",
+       "      <td>200</td>\n",
+       "      <td>75</td>\n",
+       "      <td>8.410667</td>\n",
+       "      <td>8.166960</td>\n",
+       "      <td>8.816844</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.032533</td>\n",
+       "      <td>1.856005</td>\n",
+       "      <td>1.954376</td>\n",
+       "      <td>1.692053</td>\n",
+       "      <td>20.050233</td>\n",
+       "      <td>20.903307</td>\n",
+       "      <td>18.628444</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_2</th>\n",
+       "      <td>868.272705</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.686147</td>\n",
+       "      <td>15682.994141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>425</td>\n",
+       "      <td>165</td>\n",
+       "      <td>9.547820</td>\n",
+       "      <td>9.216820</td>\n",
+       "      <td>10.069394</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.592970</td>\n",
+       "      <td>1.969553</td>\n",
+       "      <td>2.171304</td>\n",
+       "      <td>1.651642</td>\n",
+       "      <td>13.034588</td>\n",
+       "      <td>13.993846</td>\n",
+       "      <td>11.523031</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_4</th>\n",
+       "      <td>704.337280</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.757623</td>\n",
+       "      <td>15683.059570</td>\n",
+       "      <td>0</td>\n",
+       "      <td>418</td>\n",
+       "      <td>169</td>\n",
+       "      <td>11.163413</td>\n",
+       "      <td>10.062945</td>\n",
+       "      <td>12.784813</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.325917</td>\n",
+       "      <td>1.398476</td>\n",
+       "      <td>1.301932</td>\n",
+       "      <td>1.540722</td>\n",
+       "      <td>12.410670</td>\n",
+       "      <td>11.191111</td>\n",
+       "      <td>14.207534</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_5</th>\n",
+       "      <td>852.893799</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.714651</td>\n",
+       "      <td>15683.017578</td>\n",
+       "      <td>0</td>\n",
+       "      <td>201</td>\n",
+       "      <td>113</td>\n",
+       "      <td>7.887114</td>\n",
+       "      <td>6.962386</td>\n",
+       "      <td>8.607257</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.063717</td>\n",
+       "      <td>1.328602</td>\n",
+       "      <td>1.144682</td>\n",
+       "      <td>1.471832</td>\n",
+       "      <td>9.718175</td>\n",
+       "      <td>8.039015</td>\n",
+       "      <td>11.025841</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91241</th>\n",
+       "      <td>11890.440430</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.654341</td>\n",
+       "      <td>1231.699951</td>\n",
+       "      <td>45</td>\n",
+       "      <td>185</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11.222882</td>\n",
+       "      <td>11.690247</td>\n",
+       "      <td>9.961000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.310400</td>\n",
+       "      <td>2.154859</td>\n",
+       "      <td>2.332244</td>\n",
+       "      <td>1.675920</td>\n",
+       "      <td>11.001081</td>\n",
+       "      <td>11.442271</td>\n",
+       "      <td>9.809867</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91243</th>\n",
+       "      <td>11915.123047</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.854257</td>\n",
+       "      <td>1228.222168</td>\n",
+       "      <td>45</td>\n",
+       "      <td>203</td>\n",
+       "      <td>81</td>\n",
+       "      <td>10.506076</td>\n",
+       "      <td>10.907295</td>\n",
+       "      <td>9.901769</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.592593</td>\n",
+       "      <td>2.356256</td>\n",
+       "      <td>2.432205</td>\n",
+       "      <td>2.241864</td>\n",
+       "      <td>10.545419</td>\n",
+       "      <td>10.825956</td>\n",
+       "      <td>10.122881</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91244</th>\n",
+       "      <td>11961.339844</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.651563</td>\n",
+       "      <td>1230.939941</td>\n",
+       "      <td>45</td>\n",
+       "      <td>157</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11.337304</td>\n",
+       "      <td>11.769533</td>\n",
+       "      <td>10.412333</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.680000</td>\n",
+       "      <td>2.176707</td>\n",
+       "      <td>2.105561</td>\n",
+       "      <td>2.328960</td>\n",
+       "      <td>11.364076</td>\n",
+       "      <td>11.828598</td>\n",
+       "      <td>10.370000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91245</th>\n",
+       "      <td>11969.869141</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.764502</td>\n",
+       "      <td>1230.717407</td>\n",
+       "      <td>45</td>\n",
+       "      <td>115</td>\n",
+       "      <td>46</td>\n",
+       "      <td>10.039797</td>\n",
+       "      <td>10.513092</td>\n",
+       "      <td>9.329855</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.852174</td>\n",
+       "      <td>2.346356</td>\n",
+       "      <td>2.526551</td>\n",
+       "      <td>2.076065</td>\n",
+       "      <td>11.523826</td>\n",
+       "      <td>12.672657</td>\n",
+       "      <td>9.800579</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91247</th>\n",
+       "      <td>11965.208984</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.873094</td>\n",
+       "      <td>1224.179077</td>\n",
+       "      <td>45</td>\n",
+       "      <td>146</td>\n",
+       "      <td>67</td>\n",
+       "      <td>10.225981</td>\n",
+       "      <td>10.388186</td>\n",
+       "      <td>10.034727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.613732</td>\n",
+       "      <td>2.212938</td>\n",
+       "      <td>2.229595</td>\n",
+       "      <td>2.193299</td>\n",
+       "      <td>16.504383</td>\n",
+       "      <td>21.886076</td>\n",
+       "      <td>10.158806</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>350554 rows × 118 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+       "ID                                                                           \n",
+       "DD3S3_Cell_0        823.567871  DD3S3.csv           0.835324  15699.382812   \n",
+       "DD3S3_Cell_1        852.840027  DD3S3.csv           0.523421  15690.533203   \n",
+       "DD3S3_Cell_2        868.272705  DD3S3.csv           0.686147  15682.994141   \n",
+       "DD3S3_Cell_4        704.337280  DD3S3.csv           0.757623  15683.059570   \n",
+       "DD3S3_Cell_5        852.893799  DD3S3.csv           0.714651  15683.017578   \n",
+       "...                        ...        ...                ...           ...   \n",
+       "DD3S2_Cell_91241  11890.440430  DD3S2.csv           0.654341   1231.699951   \n",
+       "DD3S2_Cell_91243  11915.123047  DD3S2.csv           0.854257   1228.222168   \n",
+       "DD3S2_Cell_91244  11961.339844  DD3S2.csv           0.651563   1230.939941   \n",
+       "DD3S2_Cell_91245  11969.869141  DD3S2.csv           0.764502   1230.717407   \n",
+       "DD3S2_Cell_91247  11965.208984  DD3S2.csv           0.873094   1224.179077   \n",
+       "\n",
+       "                  ROI_index  Cell_Size  Nucleus_Size  \\\n",
+       "ID                                                     \n",
+       "DD3S3_Cell_0              0        281            81   \n",
+       "DD3S3_Cell_1              0        200            75   \n",
+       "DD3S3_Cell_2              0        425           165   \n",
+       "DD3S3_Cell_4              0        418           169   \n",
+       "DD3S3_Cell_5              0        201           113   \n",
+       "...                     ...        ...           ...   \n",
+       "DD3S2_Cell_91241         45        185            50   \n",
+       "DD3S2_Cell_91243         45        203            81   \n",
+       "DD3S2_Cell_91244         45        157            50   \n",
+       "DD3S2_Cell_91245         45        115            46   \n",
+       "DD3S2_Cell_91247         45        146            67   \n",
+       "\n",
+       "                  AF488_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S3_Cell_0                          6.654496   \n",
+       "DD3S3_Cell_1                          8.410667   \n",
+       "DD3S3_Cell_2                          9.547820   \n",
+       "DD3S3_Cell_4                         11.163413   \n",
+       "DD3S3_Cell_5                          7.887114   \n",
+       "...                                        ...   \n",
+       "DD3S2_Cell_91241                     11.222882   \n",
+       "DD3S2_Cell_91243                     10.506076   \n",
+       "DD3S2_Cell_91244                     11.337304   \n",
+       "DD3S2_Cell_91245                     10.039797   \n",
+       "DD3S2_Cell_91247                     10.225981   \n",
+       "\n",
+       "                  AF488_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S3_Cell_0                               6.277167   \n",
+       "DD3S3_Cell_1                               8.166960   \n",
+       "DD3S3_Cell_2                               9.216820   \n",
+       "DD3S3_Cell_4                              10.062945   \n",
+       "DD3S3_Cell_5                               6.962386   \n",
+       "...                                             ...   \n",
+       "DD3S2_Cell_91241                          11.690247   \n",
+       "DD3S2_Cell_91243                          10.907295   \n",
+       "DD3S2_Cell_91244                          11.769533   \n",
+       "DD3S2_Cell_91245                          10.513092   \n",
+       "DD3S2_Cell_91247                          10.388186   \n",
+       "\n",
+       "                  AF488_Nucleus_Intensity_Average  ...  \\\n",
+       "ID                                                 ...   \n",
+       "DD3S3_Cell_0                             7.586173  ...   \n",
+       "DD3S3_Cell_1                             8.816844  ...   \n",
+       "DD3S3_Cell_2                            10.069394  ...   \n",
+       "DD3S3_Cell_4                            12.784813  ...   \n",
+       "DD3S3_Cell_5                             8.607257  ...   \n",
+       "...                                           ...  ...   \n",
+       "DD3S2_Cell_91241                         9.961000  ...   \n",
+       "DD3S2_Cell_91243                         9.901769  ...   \n",
+       "DD3S2_Cell_91244                        10.412333  ...   \n",
+       "DD3S2_Cell_91245                         9.329855  ...   \n",
+       "DD3S2_Cell_91247                        10.034727  ...   \n",
+       "\n",
+       "                  r8c2_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S3_Cell_0                           11.785679   \n",
+       "DD3S3_Cell_1                           13.032533   \n",
+       "DD3S3_Cell_2                           14.592970   \n",
+       "DD3S3_Cell_4                           17.325917   \n",
+       "DD3S3_Cell_5                           13.063717   \n",
+       "...                                          ...   \n",
+       "DD3S2_Cell_91241                       13.310400   \n",
+       "DD3S2_Cell_91243                       13.592593   \n",
+       "DD3S2_Cell_91244                       13.680000   \n",
+       "DD3S2_Cell_91245                       12.852174   \n",
+       "DD3S2_Cell_91247                       13.613732   \n",
+       "\n",
+       "                  Sting_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S3_Cell_0                          1.358562   \n",
+       "DD3S3_Cell_1                          1.856005   \n",
+       "DD3S3_Cell_2                          1.969553   \n",
+       "DD3S3_Cell_4                          1.398476   \n",
+       "DD3S3_Cell_5                          1.328602   \n",
+       "...                                        ...   \n",
+       "DD3S2_Cell_91241                      2.154859   \n",
+       "DD3S2_Cell_91243                      2.356256   \n",
+       "DD3S2_Cell_91244                      2.176707   \n",
+       "DD3S2_Cell_91245                      2.346356   \n",
+       "DD3S2_Cell_91247                      2.212938   \n",
+       "\n",
+       "                  Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S3_Cell_0                               1.318835   \n",
+       "DD3S3_Cell_1                               1.954376   \n",
+       "DD3S3_Cell_2                               2.171304   \n",
+       "DD3S3_Cell_4                               1.301932   \n",
+       "DD3S3_Cell_5                               1.144682   \n",
+       "...                                             ...   \n",
+       "DD3S2_Cell_91241                           2.332244   \n",
+       "DD3S2_Cell_91243                           2.432205   \n",
+       "DD3S2_Cell_91244                           2.105561   \n",
+       "DD3S2_Cell_91245                           2.526551   \n",
+       "DD3S2_Cell_91247                           2.229595   \n",
+       "\n",
+       "                  Sting_Nucleus_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S3_Cell_0                             1.456654   \n",
+       "DD3S3_Cell_1                             1.692053   \n",
+       "DD3S3_Cell_2                             1.651642   \n",
+       "DD3S3_Cell_4                             1.540722   \n",
+       "DD3S3_Cell_5                             1.471832   \n",
+       "...                                           ...   \n",
+       "DD3S2_Cell_91241                         1.675920   \n",
+       "DD3S2_Cell_91243                         2.241864   \n",
+       "DD3S2_Cell_91244                         2.328960   \n",
+       "DD3S2_Cell_91245                         2.076065   \n",
+       "DD3S2_Cell_91247                         2.193299   \n",
+       "\n",
+       "                  Vimentin_Cell_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S3_Cell_0                            19.081685   \n",
+       "DD3S3_Cell_1                            20.050233   \n",
+       "DD3S3_Cell_2                            13.034588   \n",
+       "DD3S3_Cell_4                            12.410670   \n",
+       "DD3S3_Cell_5                             9.718175   \n",
+       "...                                           ...   \n",
+       "DD3S2_Cell_91241                        11.001081   \n",
+       "DD3S2_Cell_91243                        10.545419   \n",
+       "DD3S2_Cell_91244                        11.364076   \n",
+       "DD3S2_Cell_91245                        11.523826   \n",
+       "DD3S2_Cell_91247                        16.504383   \n",
+       "\n",
+       "                  Vimentin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                       \n",
+       "DD3S3_Cell_0                                 16.428900   \n",
+       "DD3S3_Cell_1                                 20.903307   \n",
+       "DD3S3_Cell_2                                 13.993846   \n",
+       "DD3S3_Cell_4                                 11.191111   \n",
+       "DD3S3_Cell_5                                  8.039015   \n",
+       "...                                                ...   \n",
+       "DD3S2_Cell_91241                             11.442271   \n",
+       "DD3S2_Cell_91243                             10.825956   \n",
+       "DD3S2_Cell_91244                             11.828598   \n",
+       "DD3S2_Cell_91245                             12.672657   \n",
+       "DD3S2_Cell_91247                             21.886076   \n",
+       "\n",
+       "                  Vimentin_Nucleus_Intensity_Average  cell_type  cell_subtype  \\\n",
+       "ID                                                                              \n",
+       "DD3S3_Cell_0                               25.631769       none          none   \n",
+       "DD3S3_Cell_1                               18.628444       none          none   \n",
+       "DD3S3_Cell_2                               11.523031       none          none   \n",
+       "DD3S3_Cell_4                               14.207534       none          none   \n",
+       "DD3S3_Cell_5                               11.025841       none          none   \n",
+       "...                                              ...        ...           ...   \n",
+       "DD3S2_Cell_91241                            9.809867       none          none   \n",
+       "DD3S2_Cell_91243                           10.122881       none          none   \n",
+       "DD3S2_Cell_91244                           10.370000       none          none   \n",
+       "DD3S2_Cell_91245                            9.800579       none          none   \n",
+       "DD3S2_Cell_91247                           10.158806       none          none   \n",
+       "\n",
+       "                  immune_checkpoint  \n",
+       "ID                                   \n",
+       "DD3S3_Cell_0                   none  \n",
+       "DD3S3_Cell_1                   none  \n",
+       "DD3S3_Cell_2                   none  \n",
+       "DD3S3_Cell_4                   none  \n",
+       "DD3S3_Cell_5                   none  \n",
+       "...                             ...  \n",
+       "DD3S2_Cell_91241               none  \n",
+       "DD3S2_Cell_91243               none  \n",
+       "DD3S2_Cell_91244               none  \n",
+       "DD3S2_Cell_91245               none  \n",
+       "DD3S2_Cell_91247               none  \n",
+       "\n",
+       "[350554 rows x 118 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Assign IMMUNE SUBTYPES\n",
+    "df['cell_subtype'] = df['cell_type'].copy()\n",
+    "df['immune_checkpoint'] = 'none'\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "984f9fe2-41d1-4c00-be20-e2d6ca9d2394",
+   "metadata": {},
+   "source": [
+    "immune_checkpoint = ['B7H4', 'PDL1', 'PD1', 'None']\n",
+    "color_values = sb.color_palette(\"husl\",n_colors=len(immune_checkpoint))\n",
+    "# each color value is a tuple of three values: (R, G, B)\n",
+    "\n",
+    "print(\"Unique immune checkpoint are:\",df.immune_checkpoint.unique())\n",
+    "# Display those unique colors\n",
+    "sb.palplot(sb.color_palette(color_values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "3b593828-a016-4d52-b722-9908aac4e2d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique immune checkpoint combinations are: ['B7H4', 'PDL1', 'PD1', 'B7H4_PDL1', 'None']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "immune_checkpoint = ['B7H4', 'PDL1', 'PD1', 'B7H4_PDL1', 'None']\n",
+    "\n",
+    "# Base colors for the primary checkpoints\n",
+    "base_colors = sb.color_palette(\"husl\", n_colors=3)  # Three distinct colors\n",
+    "\n",
+    "# Function to mix two RGB colors\n",
+    "def mix_colors(color1, color2):\n",
+    "    return tuple((c1 + c2) / 2 for c1, c2 in zip(color1, color2))\n",
+    "\n",
+    "# Generate mixed colors for the combinations of checkpoints\n",
+    "mixed_colors = [\n",
+    "    mix_colors(base_colors[0], base_colors[1]),  # Mix B7H4 and PDL1\n",
+    "#    mix_colors(base_colors[0], base_colors[2]),  # Mix B7H4 and PD1\n",
+    "#    mix_colors(base_colors[1], base_colors[2]),  # Mix PDL1 and PD1\n",
+    "    tuple(np.mean(base_colors, axis=0))  # Mix B7H4, PDL1, and PD1\n",
+    "]\n",
+    "\n",
+    "# Adding the color for 'None'\n",
+    "#none_color = [(0.8, 0.8, 0.8)]  # A shade of gray\n",
+    "\n",
+    "# Combine all colors into one list\n",
+    "color_values = base_colors + mixed_colors #+ none_color\n",
+    "\n",
+    "# Display unique immune checkpoint combinations\n",
+    "print(\"Unique immune checkpoint combinations are:\", immune_checkpoint)\n",
+    "# Display the unique colors\n",
+    "sb.palplot(color_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "ce2c7c21-f90b-4d06-a93f-418d9aae4304",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'B7H4': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
+       " 'PDL1': (0.3126890019504329, 0.6928754610296064, 0.1923704830330379),\n",
+       " 'PD1': (0.23299120924703914, 0.639586552066035, 0.9260706093977744),\n",
+       " 'B7H4_PDL1': (0.6402432806212122, 0.56707501056059, 0.36409039926945397),\n",
+       " 'None': (0.5044925901631545, 0.5912455243957383, 0.5514171359788941)}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store in a dctionnary\n",
+    "immunecheckpoint_color_dict = dict(zip(immune_checkpoint, color_values))\n",
+    "immunecheckpoint_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "2f38cc65-50f3-463d-be1b-9fc1f8c90aa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv was created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save color information (mapping and legend) to metadata directory\n",
+    "# Create dataframe\n",
+    "immunecheckpoint_color_df = color_dict_to_df(immunecheckpoint_color_dict, \"immune_checkpoint\")\n",
+    "immunecheckpoint_color_df.head()\n",
+    "\n",
+    "# Save to file in metadatadirectory\n",
+    "filename = \"immunecheckpoint_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "immunecheckpoint_color_df.to_csv(filename, index = False)\n",
+    "print(\"File \" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "9e2ae256-bbc3-4e93-93bb-560ffa02fb71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 100x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legend of cell type info only\n",
+    "g  = plt.figure(figsize = (1,1)).add_subplot(111)\n",
+    "g.axis('off')\n",
+    "handles = []\n",
+    "for item in immunecheckpoint_color_dict.keys():\n",
+    "        h = g.bar(0,0, color = immunecheckpoint_color_dict[item],\n",
+    "                  label = item, linewidth =0)\n",
+    "        handles.append(h)\n",
+    "first_legend = plt.legend(handles=handles, loc='upper right', title = 'Immune checkpoint'),\n",
+    "\n",
+    "\n",
+    "filename = \"Cellsubtype_legend.png\"\n",
+    "filename = os.path.join(metadata_images_dir, filename)\n",
+    "plt.savefig(filename, bbox_inches = 'tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92332958-9ac4-4415-81b3-b3ae27354da3",
+   "metadata": {},
+   "source": [
+    "## II.7. BACKGROUND SUBSTRACTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "30fb1c46-e5f9-4a8e-91eb-010c878a8785",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Do background subtraction\n",
+    "# this uses a df (metadata) outside of \n",
+    "# the scope of the lambda...\n",
+    "# careful that this might break inside of a script...\n",
+    "df.loc[:,~df.columns.isin(not_intensities)] = \\\n",
+    "    df.loc[:,~df.columns.isin(not_intensities)].apply(lambda column: do_background_sub(column, df, metadata), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "b47e45ac-deed-447b-b630-1ccdaa85d195",
+   "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>Nuc_X</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>AF488_Cell_Intensity_Average</th>\n",
+       "      <th>AF488_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AF488_Nucleus_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r8c2_Nucleus_Intensity_Average</th>\n",
+       "      <th>Sting_Cell_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Sting_Nucleus_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cell_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Vimentin_Nucleus_Intensity_Average</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_0</th>\n",
+       "      <td>823.567871</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>15699.382812</td>\n",
+       "      <td>0</td>\n",
+       "      <td>281</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.199506</td>\n",
+       "      <td>0.663407</td>\n",
+       "      <td>0.636738</td>\n",
+       "      <td>0.729255</td>\n",
+       "      <td>12.427189</td>\n",
+       "      <td>10.151733</td>\n",
+       "      <td>18.045597</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_1</th>\n",
+       "      <td>852.840027</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.523421</td>\n",
+       "      <td>15690.533203</td>\n",
+       "      <td>0</td>\n",
+       "      <td>200</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.215689</td>\n",
+       "      <td>1.107392</td>\n",
+       "      <td>1.215437</td>\n",
+       "      <td>0.927316</td>\n",
+       "      <td>11.639566</td>\n",
+       "      <td>12.736348</td>\n",
+       "      <td>9.811600</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_2</th>\n",
+       "      <td>868.272705</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.686147</td>\n",
+       "      <td>15682.994141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>425</td>\n",
+       "      <td>165</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.523576</td>\n",
+       "      <td>1.151564</td>\n",
+       "      <td>1.370796</td>\n",
+       "      <td>0.806107</td>\n",
+       "      <td>3.486768</td>\n",
+       "      <td>4.777026</td>\n",
+       "      <td>1.453637</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_4</th>\n",
+       "      <td>704.337280</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.757623</td>\n",
+       "      <td>15683.059570</td>\n",
+       "      <td>0</td>\n",
+       "      <td>418</td>\n",
+       "      <td>169</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.541104</td>\n",
+       "      <td>0.516323</td>\n",
+       "      <td>0.459996</td>\n",
+       "      <td>0.599314</td>\n",
+       "      <td>1.247257</td>\n",
+       "      <td>1.128166</td>\n",
+       "      <td>1.422721</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_5</th>\n",
+       "      <td>852.893799</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.714651</td>\n",
+       "      <td>15683.017578</td>\n",
+       "      <td>0</td>\n",
+       "      <td>201</td>\n",
+       "      <td>113</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.456460</td>\n",
+       "      <td>0.591222</td>\n",
+       "      <td>0.442303</td>\n",
+       "      <td>0.707195</td>\n",
+       "      <td>1.831061</td>\n",
+       "      <td>1.076629</td>\n",
+       "      <td>2.418584</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91241</th>\n",
+       "      <td>11890.440430</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.654341</td>\n",
+       "      <td>1231.699951</td>\n",
+       "      <td>45</td>\n",
+       "      <td>185</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.349400</td>\n",
+       "      <td>1.266964</td>\n",
+       "      <td>1.422560</td>\n",
+       "      <td>0.846853</td>\n",
+       "      <td>-0.221802</td>\n",
+       "      <td>-0.247975</td>\n",
+       "      <td>-0.151134</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91243</th>\n",
+       "      <td>11915.123047</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.854257</td>\n",
+       "      <td>1228.222168</td>\n",
+       "      <td>45</td>\n",
+       "      <td>203</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.690824</td>\n",
+       "      <td>1.506736</td>\n",
+       "      <td>1.552194</td>\n",
+       "      <td>1.438268</td>\n",
+       "      <td>0.039343</td>\n",
+       "      <td>-0.081339</td>\n",
+       "      <td>0.221112</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91244</th>\n",
+       "      <td>11961.339844</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.651563</td>\n",
+       "      <td>1230.939941</td>\n",
+       "      <td>45</td>\n",
+       "      <td>157</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.267667</td>\n",
+       "      <td>1.252032</td>\n",
+       "      <td>1.162981</td>\n",
+       "      <td>1.442600</td>\n",
+       "      <td>0.026772</td>\n",
+       "      <td>0.059065</td>\n",
+       "      <td>-0.042333</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91245</th>\n",
+       "      <td>11969.869141</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.764502</td>\n",
+       "      <td>1230.717407</td>\n",
+       "      <td>45</td>\n",
+       "      <td>115</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.522319</td>\n",
+       "      <td>1.508861</td>\n",
+       "      <td>1.657759</td>\n",
+       "      <td>1.285514</td>\n",
+       "      <td>1.484028</td>\n",
+       "      <td>2.159565</td>\n",
+       "      <td>0.470724</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_91247</th>\n",
+       "      <td>11965.208984</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.873094</td>\n",
+       "      <td>1224.179077</td>\n",
+       "      <td>45</td>\n",
+       "      <td>146</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.579005</td>\n",
+       "      <td>1.367290</td>\n",
+       "      <td>1.387004</td>\n",
+       "      <td>1.344045</td>\n",
+       "      <td>6.278402</td>\n",
+       "      <td>11.497890</td>\n",
+       "      <td>0.124080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>350554 rows × 118 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+       "ID                                                                           \n",
+       "DD3S3_Cell_0        823.567871  DD3S3.csv           0.835324  15699.382812   \n",
+       "DD3S3_Cell_1        852.840027  DD3S3.csv           0.523421  15690.533203   \n",
+       "DD3S3_Cell_2        868.272705  DD3S3.csv           0.686147  15682.994141   \n",
+       "DD3S3_Cell_4        704.337280  DD3S3.csv           0.757623  15683.059570   \n",
+       "DD3S3_Cell_5        852.893799  DD3S3.csv           0.714651  15683.017578   \n",
+       "...                        ...        ...                ...           ...   \n",
+       "DD3S2_Cell_91241  11890.440430  DD3S2.csv           0.654341   1231.699951   \n",
+       "DD3S2_Cell_91243  11915.123047  DD3S2.csv           0.854257   1228.222168   \n",
+       "DD3S2_Cell_91244  11961.339844  DD3S2.csv           0.651563   1230.939941   \n",
+       "DD3S2_Cell_91245  11969.869141  DD3S2.csv           0.764502   1230.717407   \n",
+       "DD3S2_Cell_91247  11965.208984  DD3S2.csv           0.873094   1224.179077   \n",
+       "\n",
+       "                  ROI_index  Cell_Size  Nucleus_Size  \\\n",
+       "ID                                                     \n",
+       "DD3S3_Cell_0              0        281            81   \n",
+       "DD3S3_Cell_1              0        200            75   \n",
+       "DD3S3_Cell_2              0        425           165   \n",
+       "DD3S3_Cell_4              0        418           169   \n",
+       "DD3S3_Cell_5              0        201           113   \n",
+       "...                     ...        ...           ...   \n",
+       "DD3S2_Cell_91241         45        185            50   \n",
+       "DD3S2_Cell_91243         45        203            81   \n",
+       "DD3S2_Cell_91244         45        157            50   \n",
+       "DD3S2_Cell_91245         45        115            46   \n",
+       "DD3S2_Cell_91247         45        146            67   \n",
+       "\n",
+       "                  AF488_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S3_Cell_0                               0.0   \n",
+       "DD3S3_Cell_1                               0.0   \n",
+       "DD3S3_Cell_2                               0.0   \n",
+       "DD3S3_Cell_4                               0.0   \n",
+       "DD3S3_Cell_5                               0.0   \n",
+       "...                                        ...   \n",
+       "DD3S2_Cell_91241                           0.0   \n",
+       "DD3S2_Cell_91243                           0.0   \n",
+       "DD3S2_Cell_91244                           0.0   \n",
+       "DD3S2_Cell_91245                           0.0   \n",
+       "DD3S2_Cell_91247                           0.0   \n",
+       "\n",
+       "                  AF488_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S3_Cell_0                                    0.0   \n",
+       "DD3S3_Cell_1                                    0.0   \n",
+       "DD3S3_Cell_2                                    0.0   \n",
+       "DD3S3_Cell_4                                    0.0   \n",
+       "DD3S3_Cell_5                                    0.0   \n",
+       "...                                             ...   \n",
+       "DD3S2_Cell_91241                                0.0   \n",
+       "DD3S2_Cell_91243                                0.0   \n",
+       "DD3S2_Cell_91244                                0.0   \n",
+       "DD3S2_Cell_91245                                0.0   \n",
+       "DD3S2_Cell_91247                                0.0   \n",
+       "\n",
+       "                  AF488_Nucleus_Intensity_Average  ...  \\\n",
+       "ID                                                 ...   \n",
+       "DD3S3_Cell_0                                  0.0  ...   \n",
+       "DD3S3_Cell_1                                  0.0  ...   \n",
+       "DD3S3_Cell_2                                  0.0  ...   \n",
+       "DD3S3_Cell_4                                  0.0  ...   \n",
+       "DD3S3_Cell_5                                  0.0  ...   \n",
+       "...                                           ...  ...   \n",
+       "DD3S2_Cell_91241                              0.0  ...   \n",
+       "DD3S2_Cell_91243                              0.0  ...   \n",
+       "DD3S2_Cell_91244                              0.0  ...   \n",
+       "DD3S2_Cell_91245                              0.0  ...   \n",
+       "DD3S2_Cell_91247                              0.0  ...   \n",
+       "\n",
+       "                  r8c2_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S3_Cell_0                            4.199506   \n",
+       "DD3S3_Cell_1                            4.215689   \n",
+       "DD3S3_Cell_2                            4.523576   \n",
+       "DD3S3_Cell_4                            4.541104   \n",
+       "DD3S3_Cell_5                            4.456460   \n",
+       "...                                          ...   \n",
+       "DD3S2_Cell_91241                        3.349400   \n",
+       "DD3S2_Cell_91243                        3.690824   \n",
+       "DD3S2_Cell_91244                        3.267667   \n",
+       "DD3S2_Cell_91245                        3.522319   \n",
+       "DD3S2_Cell_91247                        3.579005   \n",
+       "\n",
+       "                  Sting_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S3_Cell_0                          0.663407   \n",
+       "DD3S3_Cell_1                          1.107392   \n",
+       "DD3S3_Cell_2                          1.151564   \n",
+       "DD3S3_Cell_4                          0.516323   \n",
+       "DD3S3_Cell_5                          0.591222   \n",
+       "...                                        ...   \n",
+       "DD3S2_Cell_91241                      1.266964   \n",
+       "DD3S2_Cell_91243                      1.506736   \n",
+       "DD3S2_Cell_91244                      1.252032   \n",
+       "DD3S2_Cell_91245                      1.508861   \n",
+       "DD3S2_Cell_91247                      1.367290   \n",
+       "\n",
+       "                  Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S3_Cell_0                               0.636738   \n",
+       "DD3S3_Cell_1                               1.215437   \n",
+       "DD3S3_Cell_2                               1.370796   \n",
+       "DD3S3_Cell_4                               0.459996   \n",
+       "DD3S3_Cell_5                               0.442303   \n",
+       "...                                             ...   \n",
+       "DD3S2_Cell_91241                           1.422560   \n",
+       "DD3S2_Cell_91243                           1.552194   \n",
+       "DD3S2_Cell_91244                           1.162981   \n",
+       "DD3S2_Cell_91245                           1.657759   \n",
+       "DD3S2_Cell_91247                           1.387004   \n",
+       "\n",
+       "                  Sting_Nucleus_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S3_Cell_0                             0.729255   \n",
+       "DD3S3_Cell_1                             0.927316   \n",
+       "DD3S3_Cell_2                             0.806107   \n",
+       "DD3S3_Cell_4                             0.599314   \n",
+       "DD3S3_Cell_5                             0.707195   \n",
+       "...                                           ...   \n",
+       "DD3S2_Cell_91241                         0.846853   \n",
+       "DD3S2_Cell_91243                         1.438268   \n",
+       "DD3S2_Cell_91244                         1.442600   \n",
+       "DD3S2_Cell_91245                         1.285514   \n",
+       "DD3S2_Cell_91247                         1.344045   \n",
+       "\n",
+       "                  Vimentin_Cell_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S3_Cell_0                            12.427189   \n",
+       "DD3S3_Cell_1                            11.639566   \n",
+       "DD3S3_Cell_2                             3.486768   \n",
+       "DD3S3_Cell_4                             1.247257   \n",
+       "DD3S3_Cell_5                             1.831061   \n",
+       "...                                           ...   \n",
+       "DD3S2_Cell_91241                        -0.221802   \n",
+       "DD3S2_Cell_91243                         0.039343   \n",
+       "DD3S2_Cell_91244                         0.026772   \n",
+       "DD3S2_Cell_91245                         1.484028   \n",
+       "DD3S2_Cell_91247                         6.278402   \n",
+       "\n",
+       "                  Vimentin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                       \n",
+       "DD3S3_Cell_0                                 10.151733   \n",
+       "DD3S3_Cell_1                                 12.736348   \n",
+       "DD3S3_Cell_2                                  4.777026   \n",
+       "DD3S3_Cell_4                                  1.128166   \n",
+       "DD3S3_Cell_5                                  1.076629   \n",
+       "...                                                ...   \n",
+       "DD3S2_Cell_91241                             -0.247975   \n",
+       "DD3S2_Cell_91243                             -0.081339   \n",
+       "DD3S2_Cell_91244                              0.059065   \n",
+       "DD3S2_Cell_91245                              2.159565   \n",
+       "DD3S2_Cell_91247                             11.497890   \n",
+       "\n",
+       "                  Vimentin_Nucleus_Intensity_Average  cell_type  cell_subtype  \\\n",
+       "ID                                                                              \n",
+       "DD3S3_Cell_0                               18.045597       none          none   \n",
+       "DD3S3_Cell_1                                9.811600       none          none   \n",
+       "DD3S3_Cell_2                                1.453637       none          none   \n",
+       "DD3S3_Cell_4                                1.422721       none          none   \n",
+       "DD3S3_Cell_5                                2.418584       none          none   \n",
+       "...                                              ...        ...           ...   \n",
+       "DD3S2_Cell_91241                           -0.151134       none          none   \n",
+       "DD3S2_Cell_91243                            0.221112       none          none   \n",
+       "DD3S2_Cell_91244                           -0.042333       none          none   \n",
+       "DD3S2_Cell_91245                            0.470724       none          none   \n",
+       "DD3S2_Cell_91247                            0.124080       none          none   \n",
+       "\n",
+       "                  immune_checkpoint  \n",
+       "ID                                   \n",
+       "DD3S3_Cell_0                   none  \n",
+       "DD3S3_Cell_1                   none  \n",
+       "DD3S3_Cell_2                   none  \n",
+       "DD3S3_Cell_4                   none  \n",
+       "DD3S3_Cell_5                   none  \n",
+       "...                             ...  \n",
+       "DD3S2_Cell_91241               none  \n",
+       "DD3S2_Cell_91243               none  \n",
+       "DD3S2_Cell_91244               none  \n",
+       "DD3S2_Cell_91245               none  \n",
+       "DD3S2_Cell_91247               none  \n",
+       "\n",
+       "[350554 rows x 118 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "729f24de-6494-4eae-91d5-d3eb399f1e56",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Nuc_X' 'Sample_ID' 'Nucleus_Roundness' 'Nuc_Y_Inv' 'ROI_index'\n",
+      " 'Cell_Size' 'Nucleus_Size' 'aSMA_Cell_Intensity_Average'\n",
+      " 'aSMA_Cytoplasm_Intensity_Average' 'aSMA_Nucleus_Intensity_Average'\n",
+      " 'AXL_Cell_Intensity_Average' 'AXL_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Nucleus_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'B7H4_Cytoplasm_Intensity_Average' 'B7H4_Nucleus_Intensity_Average'\n",
+      " 'CA9_Cell_Intensity_Average' 'CA9_Cytoplasm_Intensity_Average'\n",
+      " 'CA9_Nucleus_Intensity_Average' 'CD4_Cell_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'CD4_Nucleus_Intensity_Average'\n",
+      " 'CD8_Cell_Intensity_Average' 'CD8_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Nucleus_Intensity_Average' 'CD11b_Cell_Intensity_Average'\n",
+      " 'CD11b_Cytoplasm_Intensity_Average' 'CD11b_Nucleus_Intensity_Average'\n",
+      " 'CD11c_Cell_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD11c_Nucleus_Intensity_Average' 'CD20_Cell_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'CD20_Nucleus_Intensity_Average'\n",
+      " 'CD31_Cell_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'CD31_Nucleus_Intensity_Average' 'CD44_Cell_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD44_Nucleus_Intensity_Average'\n",
+      " 'CD45_Cell_Intensity_Average' 'CD45_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Nucleus_Intensity_Average' 'CD68_Cell_Intensity_Average'\n",
+      " 'CD68_Cytoplasm_Intensity_Average' 'CD68_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cell_Intensity_Average' 'CD163_Cytoplasm_Intensity_Average'\n",
+      " 'CD163_Nucleus_Intensity_Average' 'CKs_Cell_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'CKs_Nucleus_Intensity_Average'\n",
+      " 'ColVI_Cell_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'ColVI_Nucleus_Intensity_Average' 'Desmin_Cell_Intensity_Average'\n",
+      " 'Desmin_Cytoplasm_Intensity_Average' 'Desmin_Nucleus_Intensity_Average'\n",
+      " 'Ecad_Cell_Intensity_Average' 'Ecad_Cytoplasm_Intensity_Average'\n",
+      " 'Ecad_Nucleus_Intensity_Average' 'Fibronectin_Cell_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Nucleus_Intensity_Average' 'FOXP3_Cell_Intensity_Average'\n",
+      " 'FOXP3_Cytoplasm_Intensity_Average' 'FOXP3_Nucleus_Intensity_Average'\n",
+      " 'GATA3_Cell_Intensity_Average' 'GATA3_Cytoplasm_Intensity_Average'\n",
+      " 'GATA3_Nucleus_Intensity_Average' 'HLA_Cell_Intensity_Average'\n",
+      " 'HLA_Cytoplasm_Intensity_Average' 'HLA_Nucleus_Intensity_Average'\n",
+      " 'Ki67_Cell_Intensity_Average' 'Ki67_Cytoplasm_Intensity_Average'\n",
+      " 'Ki67_Nucleus_Intensity_Average' 'MMP9_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'MMP9_Nucleus_Intensity_Average'\n",
+      " 'PD1_Cell_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'PD1_Nucleus_Intensity_Average' 'PDGFR_Cell_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'PDGFR_Nucleus_Intensity_Average'\n",
+      " 'PDL1_Cell_Intensity_Average' 'PDL1_Cytoplasm_Intensity_Average'\n",
+      " 'PDL1_Nucleus_Intensity_Average' 'r5c2_Cell_Intensity_Average'\n",
+      " 'r5c2_Cytoplasm_Intensity_Average' 'r5c2_Nucleus_Intensity_Average'\n",
+      " 'r7c2_Cell_Intensity_Average' 'r7c2_Cytoplasm_Intensity_Average'\n",
+      " 'r7c2_Nucleus_Intensity_Average' 'r8c2_Cell_Intensity_Average'\n",
+      " 'r8c2_Cytoplasm_Intensity_Average' 'r8c2_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cell_Intensity_Average' 'Sting_Cytoplasm_Intensity_Average'\n",
+      " 'Sting_Nucleus_Intensity_Average' 'Vimentin_Cell_Intensity_Average'\n",
+      " 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'Vimentin_Nucleus_Intensity_Average' 'cell_type' 'cell_subtype'\n",
+      " 'immune_checkpoint']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Drop AF columns\n",
+    "df = df.filter(regex='^(?!AF\\d{3}).*')\n",
+    "print(df.columns.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c23ca0a-2d0e-4f30-b358-54acc69ac3d0",
+   "metadata": {},
+   "source": [
+    "## II.8. SAVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "8c0c8c62-9b55-451f-8c2f-bdc9a33b9fff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S3_bs.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/TMA_bs.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S2_bs.csv was created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the data by Sample_ID\n",
+    "# Check for the existence of the output file first\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0]\n",
+    "    filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "    if os.path.exists(filename):\n",
+    "        print(\"File by name \"+filename+\" already exists.\")\n",
+    "    else:\n",
+    "        sample_id_csv = sample_id + '.csv'\n",
+    "        df_save = df.loc[df['Sample_ID'] == sample_id_csv, :]\n",
+    "        #print(df_save)\n",
+    "        filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "        df_save.to_csv(filename, index=True, index_label='ID')  # Set index parameter to True to retain the index column\n",
+    "        print(\"File \" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ef5b66d-fcc6-4677-aab7-f5a748196295",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a2991f1-7ef3-430e-82f1-810ad70e769d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/wetransfer_data-zip_2024-05-17_1431/3_z_scores.ipynb b/wetransfer_data-zip_2024-05-17_1431/3_z_scores.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1eab4634bda99a6405846c1f2b28cc72cc81be3b
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/3_z_scores.ipynb
@@ -0,0 +1,7067 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8a154f88-1c05-497c-b407-87aab0ec0713",
+   "metadata": {},
+   "source": [
+    "# III. Z-SCORES NOTEBOOK"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "0b3b5aa2-e248-402e-a96f-38797d83f3ed",
+   "metadata": {},
+   "source": [
+    "10/01/24\n",
+    "Modifications by Zoé Gerber\n",
+    "from an original code from Marilyne Labrie"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "5a98ebd2-51b0-45d4-b9eb-fa1206d151a8",
+   "metadata": {},
+   "source": [
+    "III.1. PACKAGES IMPORT\n",
+    "III.2. DIRECTORIES\n",
+    "III.3. FILES\n",
+    "    III.3.1. METADATA\n",
+    "    III.3.2. NOT_INTENSITIES\n",
+    "    III.3.3. FULL_TO_SHORT_COLUMN_NAMES\n",
+    "    III.3.4. SHORT_TO_FULL_COLUMN_NAMES\n",
+    "    III.3.5. SAMPLES COLORS\n",
+    "    III.3.6. CHANNELS COLORS\n",
+    "    III.3.7. ROUND COLORS\n",
+    "    III.3.8. CELL TYPES COLORS\n",
+    "    III.3.9. CELL SUBTYPES COLORS\n",
+    "    III.3.10. DATA\n",
+    "III.4. MARKERS\n",
+    "III.5. Z-SCORE TRANSFORMATION\n",
+    "III.6. VISUALISATIONS\n",
+    "III.7. SAVE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bedcd47-92af-4b16-b7ec-db9c315c4e4e",
+   "metadata": {},
+   "source": [
+    "## III.1. PACKAGES IMPORT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7ac57cd3-ce6a-4cf9-bfc0-f78726aafb05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/5g/xq32h5h56rj2qzygbnhjg65h0000gn/T/ipykernel_18885/364558441.py:14: DeprecationWarning:\n",
+      "\n",
+      "Please import `pearsonr` from the `scipy.stats` namespace; the `scipy.stats.stats` namespace is deprecated and will be removed in SciPy 2.0.0.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-2.29.1.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Cannot change to a different GUI toolkit: widget. Using notebook instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mplc\n",
+    "import subprocess\n",
+    "import warnings\n",
+    "\n",
+    "\n",
+    "from scipy import signal\n",
+    "from scipy.stats.stats import pearsonr\n",
+    "\n",
+    "import plotly.figure_factory as ff\n",
+    "import plotly\n",
+    "import plotly.graph_objs as go\n",
+    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \n",
+    "import plotly.express as px\n",
+    "init_notebook_mode(connected = True)\n",
+    "\n",
+    "%matplotlib notebook\n",
+    "%matplotlib widget\n",
+    "\n",
+    "from my_modules import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "84449f59-6d25-4b0c-8ae0-718d94d892b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Silence FutureWarnings & UserWarnings\n",
+    "warnings.filterwarnings('ignore', category= FutureWarning)\n",
+    "warnings.filterwarnings('ignore', category= UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "089a79ee-bab1-47c3-90ae-d2bbc0b12491",
+   "metadata": {},
+   "source": [
+    "## III.2. *DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2da0047f-5a18-48fb-9fe9-cf94c0186e57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set base directory\n",
+    "\n",
+    "##### MAC WORKSTATION #####\n",
+    "#base_dir = r'/Volumes/LaboLabrie/Projets/OC_TMA_Pejovic/Temp/Zoe/CyCIF_pipeline/'\n",
+    "###########################\n",
+    "\n",
+    "##### WINDOWS WORKSTATION #####\n",
+    "#base_dir = r'C:\\Users\\LaboLabrie\\gerz2701\\cyCIF-pipeline\\Set_B'\n",
+    "###############################\n",
+    "\n",
+    "##### LOCAL WORKSTATION #####\n",
+    "base_dir = r'/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431'\n",
+    "#############################\n",
+    "\n",
+    "#set_name = 'Set_A'\n",
+    "set_name = 'test'"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "446dec49-f5d7-4606-b107-864eeb799273",
+   "metadata": {},
+   "source": [
+    "The project is organized as :\n",
+    "main dir \n",
+    "    code\n",
+    "    proj_data             > all csv files\n",
+    "    proj_metadata         > exposure time csv file, images dir,...\n",
+    "    proj_qc_eda           > csv after the QC/EDA step\n",
+    "    proj_bs               > csv after the BS step\n",
+    "    proj_zscore           > csv after the ZCORES step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "99a4320f-d3a2-480e-970d-5a2bcd456c15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431 directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/images directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images directory already exists !\n"
+     ]
+    }
+   ],
+   "source": [
+    "project_name = set_name            # Project name\n",
+    "step_suffix = 'zscore'              # Curent part (here part III)\n",
+    "previous_step_suffix_long = \"_bs\"   # Previous part (here BS NOTEBOOK)\n",
+    "\n",
+    "# Initial input data directory\n",
+    "input_data_dir = os.path.join(base_dir, project_name + previous_step_suffix_long) \n",
+    "\n",
+    "# ZSCORE/LOG2 output directories\n",
+    "output_data_dir = os.path.join(base_dir, project_name + \"_\" + step_suffix)\n",
+    "# ZSCORE/LOG2 images subdirectory\n",
+    "output_images_dir = os.path.join(output_data_dir,\"images\")\n",
+    "\n",
+    "# Data and Metadata directories\n",
+    "# Metadata directories\n",
+    "metadata_dir = os.path.join(base_dir, project_name + \"_metadata\")\n",
+    "# images subdirectory\n",
+    "metadata_images_dir = os.path.join(metadata_dir,\"images\")\n",
+    "\n",
+    "# Create directories if they don't already exist\n",
+    "for d in [base_dir, input_data_dir, output_data_dir, output_images_dir, metadata_dir, metadata_images_dir]:\n",
+    "    if not os.path.exists(d):\n",
+    "        print(\"Creation of the\" , d, \"directory...\")\n",
+    "        os.makedirs(d)\n",
+    "    else :\n",
+    "        print(\"The\", d, \"directory already exists !\")\n",
+    "\n",
+    "os.chdir(input_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9d54e9d7-465b-4f08-92e1-5c05b0471175",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431\n",
+      "input_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_bs\n",
+      "output_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore\n",
+      "output_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/images\n",
+      "metadata_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata\n",
+      "metadata_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify paths\n",
+    "print('base_dir :', base_dir)\n",
+    "print('input_data_dir :', input_data_dir)\n",
+    "print('output_data_dir :', output_data_dir)\n",
+    "print('output_images_dir :', output_images_dir)\n",
+    "print('metadata_dir :', metadata_dir)\n",
+    "print('metadata_images_dir :', metadata_images_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab2c6c2d-0747-41d1-939c-259c53ea95bf",
+   "metadata": {},
+   "source": [
+    "## III.3. FILES"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "0d7cca3f-05a5-4fe2-a207-731f354307fa",
+   "metadata": {},
+   "source": [
+    "Don't forget to put your data in the projname_data directory !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a5879e-ef18-4dd8-a526-fa24f7ba2147",
+   "metadata": {},
+   "source": [
+    "### III.3.1. METADATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "84f7e435-b9f7-421b-8339-b326b869ebca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv file was imported for further analysis!\n",
+      "WARNING: 'Marker metadata file' has the following unexpected item(s): \n",
+      "['Exp']\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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Round Target   Exp Channel target_lower                        full_column  \\\n",
+       "0    R0  AF488   300      c2        af488       AF488_Cell_Intensity_Average   \n",
+       "1    R0  AF488   300      c2        af488  AF488_Cytoplasm_Intensity_Average   \n",
+       "2    R0  AF488   300      c2        af488    AF488_Nucleus_Intensity_Average   \n",
+       "3    R0  AF555  1500      c3        af555       AF555_Cell_Intensity_Average   \n",
+       "4    R0  AF555  1500      c3        af555  AF555_Cytoplasm_Intensity_Average   \n",
+       "\n",
+       "  marker localisation  \n",
+       "0  AF488         cell  \n",
+       "1  AF488    cytoplasm  \n",
+       "2  AF488      nucleus  \n",
+       "3  AF555         cell  \n",
+       "4  AF555    cytoplasm  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import all metadata we need from the BS chapter\n",
+    "\n",
+    "# METADATA\n",
+    "filename = \"marker_intensity_metadata.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "\n",
+    "# Open, read in information\n",
+    "metadata = pd.read_csv(filename)\n",
+    "\n",
+    "# Verify size with verify_line_no() function in my_modules.py\n",
+    "#verify_line_no(filename, metadata.shape[0] + 1)\n",
+    "\n",
+    "# Verify headers\n",
+    "exp_cols = ['Round','Target','Channel','target_lower','full_column','marker','localisation']\n",
+    "compare_headers(exp_cols, metadata.columns.values, \"Marker metadata file\")\n",
+    "\n",
+    "metadata = metadata.dropna()\n",
+    "metadata.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c4c0a65-4d6b-4c9a-96f3-04d920f77c1c",
+   "metadata": {},
+   "source": [
+    "### III.3.2. NOT_INTENSITIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8cd5efc8-dc94-4f33-ace4-2866159b2b94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "not_intensities =\n",
+      " ['Cytoplasm_Size', 'Nuc_X', 'Primary_chem(1)_vs_surg(0)', 'cluster', 'immune_checkpoint', 'Sample_ID', 'Nucleus_Roundness', 'Unique_ROI_index', 'Nuc_Y', 'Nuc_X_Inv', 'Cell_ID', 'cell_subtype', 'ID', 'Nuc_Y_Inv', 'Patient', 'replicate_ID', 'cell_type', 'ROI_index', 'Cell_Size', 'Nucleus_Size']\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"not_intensities.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "not_intensities = []\n",
+    "with open(filename, 'r') as fh:\n",
+    "    not_intensities = fh.read().strip().split(\"\\n\")\n",
+    "    # take str, strip whitespace, split on new line character\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, len(not_intensities))\n",
+    "\n",
+    "# Print to console\n",
+    "print(\"not_intensities =\\n\", not_intensities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f786a938-6722-4b4d-909d-9587a8d1c7d4",
+   "metadata": {},
+   "source": [
+    "### III.3.3. FULL_TO_SHORT_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dc7ab2e8-53c8-458f-be7f-c4da02f92c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "full_to_short_names =\n",
+      " {'AF488_Cell_Intensity_Average': 'AF488_Cell', 'AF488_Cytoplasm_Intensity_Average': 'AF488_Cytoplasm', 'AF488_Nucleus_Intensity_Average': 'AF488_Nucleus', 'AF555_Cell_Intensity_Average': 'AF555_Cell', 'AF555_Cytoplasm_Intensity_Average': 'AF555_Cytoplasm', 'AF555_Nucleus_Intensity_Average': 'AF555_Nucleus', 'AF647_Cell_Intensity_Average': 'AF647_Cell', 'AF647_Cytoplasm_Intensity_Average': 'AF647_Cytoplasm', 'AF647_Nucleus_Intensity_Average': 'AF647_Nucleus', 'AF750_Cell_Intensity_Average': 'AF750_Cell', 'AF750_Cytoplasm_Intensity_Average': 'AF750_Cytoplasm', 'AF750_Nucleus_Intensity_Average': 'AF750_Nucleus', 'aSMA_Cell_Intensity_Average': 'aSMA_Cell', 'aSMA_Cytoplasm_Intensity_Average': 'aSMA_Cytoplasm', 'aSMA_Nucleus_Intensity_Average': 'aSMA_Nucleus', 'AXL_Cell_Intensity_Average': 'AXL_Cell', 'AXL_Cytoplasm_Intensity_Average': 'AXL_Cytoplasm', 'AXL_Nucleus_Intensity_Average': 'AXL_Nucleus', 'B7H4_Cell_Intensity_Average': 'B7H4_Cell', 'B7H4_Cytoplasm_Intensity_Average': 'B7H4_Cytoplasm', 'B7H4_Nucleus_Intensity_Average': 'B7H4_Nucleus', 'CA9_Cell_Intensity_Average': 'CA9_Cell', 'CA9_Cytoplasm_Intensity_Average': 'CA9_Cytoplasm', 'CA9_Nucleus_Intensity_Average': 'CA9_Nucleus', 'CD4_Cell_Intensity_Average': 'CD4_Cell', 'CD4_Cytoplasm_Intensity_Average': 'CD4_Cytoplasm', 'CD4_Nucleus_Intensity_Average': 'CD4_Nucleus', 'CD8_Cell_Intensity_Average': 'CD8_Cell', 'CD8_Cytoplasm_Intensity_Average': 'CD8_Cytoplasm', 'CD8_Nucleus_Intensity_Average': 'CD8_Nucleus', 'CD11b_Cell_Intensity_Average': 'CD11b_Cell', 'CD11b_Cytoplasm_Intensity_Average': 'CD11b_Cytoplasm', 'CD11b_Nucleus_Intensity_Average': 'CD11b_Nucleus', 'CD11c_Cell_Intensity_Average': 'CD11c_Cell', 'CD11c_Cytoplasm_Intensity_Average': 'CD11c_Cytoplasm', 'CD11c_Nucleus_Intensity_Average': 'CD11c_Nucleus', 'CD20_Cell_Intensity_Average': 'CD20_Cell', 'CD20_Cytoplasm_Intensity_Average': 'CD20_Cytoplasm', 'CD20_Nucleus_Intensity_Average': 'CD20_Nucleus', 'CD31_Cell_Intensity_Average': 'CD31_Cell', 'CD31_Cytoplasm_Intensity_Average': 'CD31_Cytoplasm', 'CD31_Nucleus_Intensity_Average': 'CD31_Nucleus', 'CD44_Cell_Intensity_Average': 'CD44_Cell', 'CD44_Cytoplasm_Intensity_Average': 'CD44_Cytoplasm', 'CD44_Nucleus_Intensity_Average': 'CD44_Nucleus', 'CD45_Cell_Intensity_Average': 'CD45_Cell', 'CD45_Cytoplasm_Intensity_Average': 'CD45_Cytoplasm', 'CD45_Nucleus_Intensity_Average': 'CD45_Nucleus', 'CD68_Cell_Intensity_Average': 'CD68_Cell', 'CD68_Cytoplasm_Intensity_Average': 'CD68_Cytoplasm', 'CD68_Nucleus_Intensity_Average': 'CD68_Nucleus', 'CD163_Cell_Intensity_Average': 'CD163_Cell', 'CD163_Cytoplasm_Intensity_Average': 'CD163_Cytoplasm', 'CD163_Nucleus_Intensity_Average': 'CD163_Nucleus', 'CKs_Cell_Intensity_Average': 'CKs_Cell', 'CKs_Cytoplasm_Intensity_Average': 'CKs_Cytoplasm', 'CKs_Nucleus_Intensity_Average': 'CKs_Nucleus', 'ColVI_Cell_Intensity_Average': 'ColVI_Cell', 'ColVI_Cytoplasm_Intensity_Average': 'ColVI_Cytoplasm', 'ColVI_Nucleus_Intensity_Average': 'ColVI_Nucleus', 'Desmin_Cell_Intensity_Average': 'Desmin_Cell', 'Desmin_Cytoplasm_Intensity_Average': 'Desmin_Cytoplasm', 'Desmin_Nucleus_Intensity_Average': 'Desmin_Nucleus', 'Ecad_Cell_Intensity_Average': 'Ecad_Cell', 'Ecad_Cytoplasm_Intensity_Average': 'Ecad_Cytoplasm', 'Ecad_Nucleus_Intensity_Average': 'Ecad_Nucleus', 'Fibronectin_Cell_Intensity_Average': 'Fibronectin_Cell', 'Fibronectin_Cytoplasm_Intensity_Average': 'Fibronectin_Cytoplasm', 'Fibronectin_Nucleus_Intensity_Average': 'Fibronectin_Nucleus', 'FOXP3_Cell_Intensity_Average': 'FOXP3_Cell', 'FOXP3_Cytoplasm_Intensity_Average': 'FOXP3_Cytoplasm', 'FOXP3_Nucleus_Intensity_Average': 'FOXP3_Nucleus', 'GATA3_Cell_Intensity_Average': 'GATA3_Cell', 'GATA3_Cytoplasm_Intensity_Average': 'GATA3_Cytoplasm', 'GATA3_Nucleus_Intensity_Average': 'GATA3_Nucleus', 'HLA_Cell_Intensity_Average': 'HLA_Cell', 'HLA_Cytoplasm_Intensity_Average': 'HLA_Cytoplasm', 'HLA_Nucleus_Intensity_Average': 'HLA_Nucleus', 'Ki67_Cell_Intensity_Average': 'Ki67_Cell', 'Ki67_Cytoplasm_Intensity_Average': 'Ki67_Cytoplasm', 'Ki67_Nucleus_Intensity_Average': 'Ki67_Nucleus', 'MMP9_Cell_Intensity_Average': 'MMP9_Cell', 'MMP9_Cytoplasm_Intensity_Average': 'MMP9_Cytoplasm', 'MMP9_Nucleus_Intensity_Average': 'MMP9_Nucleus', 'PD1_Cell_Intensity_Average': 'PD1_Cell', 'PD1_Cytoplasm_Intensity_Average': 'PD1_Cytoplasm', 'PD1_Nucleus_Intensity_Average': 'PD1_Nucleus', 'PDGFR_Cell_Intensity_Average': 'PDGFR_Cell', 'PDGFR_Cytoplasm_Intensity_Average': 'PDGFR_Cytoplasm', 'PDGFR_Nucleus_Intensity_Average': 'PDGFR_Nucleus', 'PDL1_Cell_Intensity_Average': 'PDL1_Cell', 'PDL1_Cytoplasm_Intensity_Average': 'PDL1_Cytoplasm', 'PDL1_Nucleus_Intensity_Average': 'PDL1_Nucleus', 'r5c2_Cell_Intensity_Average': 'r5c2_Cell', 'r5c2_Cytoplasm_Intensity_Average': 'r5c2_Cytoplasm', 'r5c2_Nucleus_Intensity_Average': 'r5c2_Nucleus', 'r7c2_Cell_Intensity_Average': 'r7c2_Cell', 'r7c2_Cytoplasm_Intensity_Average': 'r7c2_Cytoplasm', 'r7c2_Nucleus_Intensity_Average': 'r7c2_Nucleus', 'r8c2_Cell_Intensity_Average': 'r8c2_Cell', 'r8c2_Cytoplasm_Intensity_Average': 'r8c2_Cytoplasm', 'r8c2_Nucleus_Intensity_Average': 'r8c2_Nucleus', 'Sting_Cell_Intensity_Average': 'Sting_Cell', 'Sting_Cytoplasm_Intensity_Average': 'Sting_Cytoplasm', 'Sting_Nucleus_Intensity_Average': 'Sting_Nucleus', 'Vimentin_Cell_Intensity_Average': 'Vimentin_Cell', 'Vimentin_Cytoplasm_Intensity_Average': 'Vimentin_Cytoplasm', 'Vimentin_Nucleus_Intensity_Average': 'Vimentin_Nucleus'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"full_to_short_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "full_to_short_names = df.set_index('full_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# CD45 instead of CD45b\n",
+    "if project_name == 'Slide_A' :\n",
+    "    full_to_short_names['CD45_Cytoplasm_Intensity_Average'] = full_to_short_names.pop('CD45b_Cytoplasm_Intensity_Average')\n",
+    "    full_to_short_names['CD45_Cytoplasm_Intensity_Average'] = 'CD45_Cytoplasm'\n",
+    "\n",
+    "# Print information\n",
+    "print('full_to_short_names =\\n',full_to_short_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b99332-be87-4ba5-9254-2d9f87668f7b",
+   "metadata": {},
+   "source": [
+    "### III.3.4. SHORT_TO_FULL_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "920169e1-8dad-4682-9b56-12d2d8f7b984",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "short_to_full_names =\n",
+      " {'AF488_Cell': 'AF488_Cell_Intensity_Average', 'AF488_Cytoplasm': 'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus': 'AF488_Nucleus_Intensity_Average', 'AF555_Cell': 'AF555_Cell_Intensity_Average', 'AF555_Cytoplasm': 'AF555_Cytoplasm_Intensity_Average', 'AF555_Nucleus': 'AF555_Nucleus_Intensity_Average', 'AF647_Cell': 'AF647_Cell_Intensity_Average', 'AF647_Cytoplasm': 'AF647_Cytoplasm_Intensity_Average', 'AF647_Nucleus': 'AF647_Nucleus_Intensity_Average', 'AF750_Cell': 'AF750_Cell_Intensity_Average', 'AF750_Cytoplasm': 'AF750_Cytoplasm_Intensity_Average', 'AF750_Nucleus': 'AF750_Nucleus_Intensity_Average', 'aSMA_Cell': 'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm': 'aSMA_Cytoplasm_Intensity_Average', 'aSMA_Nucleus': 'aSMA_Nucleus_Intensity_Average', 'AXL_Cell': 'AXL_Cell_Intensity_Average', 'AXL_Cytoplasm': 'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus': 'AXL_Nucleus_Intensity_Average', 'B7H4_Cell': 'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm': 'B7H4_Cytoplasm_Intensity_Average', 'B7H4_Nucleus': 'B7H4_Nucleus_Intensity_Average', 'CA9_Cell': 'CA9_Cell_Intensity_Average', 'CA9_Cytoplasm': 'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus': 'CA9_Nucleus_Intensity_Average', 'CD4_Cell': 'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm': 'CD4_Cytoplasm_Intensity_Average', 'CD4_Nucleus': 'CD4_Nucleus_Intensity_Average', 'CD8_Cell': 'CD8_Cell_Intensity_Average', 'CD8_Cytoplasm': 'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus': 'CD8_Nucleus_Intensity_Average', 'CD11b_Cell': 'CD11b_Cell_Intensity_Average', 'CD11b_Cytoplasm': 'CD11b_Cytoplasm_Intensity_Average', 'CD11b_Nucleus': 'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell': 'CD11c_Cell_Intensity_Average', 'CD11c_Cytoplasm': 'CD11c_Cytoplasm_Intensity_Average', 'CD11c_Nucleus': 'CD11c_Nucleus_Intensity_Average', 'CD20_Cell': 'CD20_Cell_Intensity_Average', 'CD20_Cytoplasm': 'CD20_Cytoplasm_Intensity_Average', 'CD20_Nucleus': 'CD20_Nucleus_Intensity_Average', 'CD31_Cell': 'CD31_Cell_Intensity_Average', 'CD31_Cytoplasm': 'CD31_Cytoplasm_Intensity_Average', 'CD31_Nucleus': 'CD31_Nucleus_Intensity_Average', 'CD44_Cell': 'CD44_Cell_Intensity_Average', 'CD44_Cytoplasm': 'CD44_Cytoplasm_Intensity_Average', 'CD44_Nucleus': 'CD44_Nucleus_Intensity_Average', 'CD45_Cell': 'CD45_Cell_Intensity_Average', 'CD45_Cytoplasm': 'CD45_Cytoplasm_Intensity_Average', 'CD45_Nucleus': 'CD45_Nucleus_Intensity_Average', 'CD68_Cell': 'CD68_Cell_Intensity_Average', 'CD68_Cytoplasm': 'CD68_Cytoplasm_Intensity_Average', 'CD68_Nucleus': 'CD68_Nucleus_Intensity_Average', 'CD163_Cell': 'CD163_Cell_Intensity_Average', 'CD163_Cytoplasm': 'CD163_Cytoplasm_Intensity_Average', 'CD163_Nucleus': 'CD163_Nucleus_Intensity_Average', 'CKs_Cell': 'CKs_Cell_Intensity_Average', 'CKs_Cytoplasm': 'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus': 'CKs_Nucleus_Intensity_Average', 'ColVI_Cell': 'ColVI_Cell_Intensity_Average', 'ColVI_Cytoplasm': 'ColVI_Cytoplasm_Intensity_Average', 'ColVI_Nucleus': 'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell': 'Desmin_Cell_Intensity_Average', 'Desmin_Cytoplasm': 'Desmin_Cytoplasm_Intensity_Average', 'Desmin_Nucleus': 'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell': 'Ecad_Cell_Intensity_Average', 'Ecad_Cytoplasm': 'Ecad_Cytoplasm_Intensity_Average', 'Ecad_Nucleus': 'Ecad_Nucleus_Intensity_Average', 'Fibronectin_Cell': 'Fibronectin_Cell_Intensity_Average', 'Fibronectin_Cytoplasm': 'Fibronectin_Cytoplasm_Intensity_Average', 'Fibronectin_Nucleus': 'Fibronectin_Nucleus_Intensity_Average', 'FOXP3_Cell': 'FOXP3_Cell_Intensity_Average', 'FOXP3_Cytoplasm': 'FOXP3_Cytoplasm_Intensity_Average', 'FOXP3_Nucleus': 'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell': 'GATA3_Cell_Intensity_Average', 'GATA3_Cytoplasm': 'GATA3_Cytoplasm_Intensity_Average', 'GATA3_Nucleus': 'GATA3_Nucleus_Intensity_Average', 'HLA_Cell': 'HLA_Cell_Intensity_Average', 'HLA_Cytoplasm': 'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus': 'HLA_Nucleus_Intensity_Average', 'Ki67_Cell': 'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm': 'Ki67_Cytoplasm_Intensity_Average', 'Ki67_Nucleus': 'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell': 'MMP9_Cell_Intensity_Average', 'MMP9_Cytoplasm': 'MMP9_Cytoplasm_Intensity_Average', 'MMP9_Nucleus': 'MMP9_Nucleus_Intensity_Average', 'PD1_Cell': 'PD1_Cell_Intensity_Average', 'PD1_Cytoplasm': 'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus': 'PD1_Nucleus_Intensity_Average', 'PDGFR_Cell': 'PDGFR_Cell_Intensity_Average', 'PDGFR_Cytoplasm': 'PDGFR_Cytoplasm_Intensity_Average', 'PDGFR_Nucleus': 'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell': 'PDL1_Cell_Intensity_Average', 'PDL1_Cytoplasm': 'PDL1_Cytoplasm_Intensity_Average', 'PDL1_Nucleus': 'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell': 'r5c2_Cell_Intensity_Average', 'r5c2_Cytoplasm': 'r5c2_Cytoplasm_Intensity_Average', 'r5c2_Nucleus': 'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell': 'r7c2_Cell_Intensity_Average', 'r7c2_Cytoplasm': 'r7c2_Cytoplasm_Intensity_Average', 'r7c2_Nucleus': 'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell': 'r8c2_Cell_Intensity_Average', 'r8c2_Cytoplasm': 'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus': 'r8c2_Nucleus_Intensity_Average', 'Sting_Cell': 'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm': 'Sting_Cytoplasm_Intensity_Average', 'Sting_Nucleus': 'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell': 'Vimentin_Cell_Intensity_Average', 'Vimentin_Cytoplasm': 'Vimentin_Cytoplasm_Intensity_Average', 'Vimentin_Nucleus': 'Vimentin_Nucleus_Intensity_Average'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"short_to_full_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "short_to_full_names = df.set_index('short_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# CD45 instead of CD45b\n",
+    "if project_name == 'Slide_A' :\n",
+    "    short_to_full_names['CD45_Cytoplasm'] = short_to_full_names.pop('CD45b_Cytoplasm')\n",
+    "    short_to_full_names['CD45_Cytoplasm'] = 'CD45_Cytoplasm_Intensity_Average'\n",
+    "\n",
+    "# Print information\n",
+    "print('short_to_full_names =\\n',short_to_full_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f34deee2-da48-455d-a9c3-c85c19b44af6",
+   "metadata": {},
+   "source": [
+    "### III.3.5. SAMPLES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e37ab86f-1ffe-4a13-8c06-10de2bfd6ca3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "sample_color_dict =\n",
+      " {'DD3S1.csv': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701), 'DD3S2.csv': (0.5920891529639701, 0.6418467016378244, 0.1935069134991043), 'DD3S3.csv': (0.21044753832183283, 0.6773105080456748, 0.6433941168468681), 'TMA.csv': (0.5019607843137255, 0.5019607843137255, 0.5019607843137255)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"sample_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "sample_color_dict = df.set_index('Sample_ID').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('sample_color_dict =\\n',sample_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab0a8169-0607-446f-9fd0-589a1cd71013",
+   "metadata": {},
+   "source": [
+    "### III.3.6. CHANNELS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "99baf121-de7b-4158-97a3-2d3a1d550ce0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "channel_color_dict =\n",
+      " {'c2': (0.00784313725490196, 0.24313725490196078, 1.0), 'c3': (1.0, 0.48627450980392156, 0.0), 'c4': (0.10196078431372549, 0.788235294117647, 0.2196078431372549), 'c5': (0.9098039215686274, 0.0, 0.043137254901960784)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"channel_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "channel_color_dict = df.set_index('Channel').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('channel_color_dict =\\n',channel_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03ba7a15-762d-4535-93b2-a95b88b48a80",
+   "metadata": {},
+   "source": [
+    "### III.3.7. ROUNDS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "15d9630d-9e68-47bf-9d09-b57b6ac76dce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "round_color_dict =\n",
+      " {'R0': (0.28685356234627135, 0.13009829239513535, 0.23110332132624437), 'R1': (0.36541462435986094, 0.2025447048359916, 0.37693310021636883), 'R2': (0.40867533458903105, 0.2940761173840091, 0.5166711878800253), 'R3': (0.42890613750051265, 0.4082290173220481, 0.6335348887063806), 'R4': (0.4444462906865238, 0.5264664993764805, 0.7056321892616532), 'R5': (0.47707206309601013, 0.6427061780374552, 0.7418477948908153), 'R6': (0.5414454866716836, 0.7466759172596551, 0.7572905778378964), 'R7': (0.6414710091647722, 0.8321551072276492, 0.7746773027952071), 'R8': (0.7684256891219349, 0.8992667116749021, 0.8171383269422353)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ROUND\n",
+    "filename = \"round_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "round_color_dict = df.set_index('Round').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('round_color_dict =\\n',round_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf75afa-7681-455e-a752-478993946a4c",
+   "metadata": {},
+   "source": [
+    "### III.3.8. CELL TYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e53d9ea2-204f-44c0-88c2-871c5b106f2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "cell_type_color_dict =\n",
+      " {'CANCER': (0.1333, 0.5451, 0.1333), 'STROMA': (0.4, 0.4, 0.4), 'IMMUNE': (1.0, 1.0, 0.0), 'ENDOTHELIAL': (0.502, 0.0, 0.502)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"celltype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "cell_type_color_dict = df.set_index('cell_type').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('cell_type_color_dict =\\n',cell_type_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8092e80-bed7-4e73-a8d6-76c5a9d60e18",
+   "metadata": {},
+   "source": [
+    "### III.3.9. CELL SUBTYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9377b7d3-2761-49b2-9b34-32f9ffe9ae83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "cell_subtype_color_dict =\n",
+      " {'DC': (0.6509803921568628, 0.807843137254902, 0.8901960784313725), 'B': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), 'TCD4': (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), 'TCD8': (0.2, 0.6274509803921569, 0.17254901960784313), 'M1': (0.984313725490196, 0.6039215686274509, 0.6), 'M2': (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), 'Treg': (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), 'IMMUNE_OTHER': (1.0, 0.4980392156862745, 0.0), 'CANCER': (0.792156862745098, 0.6980392156862745, 0.8392156862745098), 'αSMA_myCAF': (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), 'STROMA_OTHER': (1.0, 1.0, 0.6), 'ENDOTHELIAL': (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"cellsubtype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "cell_subtype_color_dict = df.set_index('cell_subtype').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('cell_subtype_color_dict =\\n',cell_subtype_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a9b59e2-f9d7-49e1-96ea-85353638ed5c",
+   "metadata": {},
+   "source": [
+    "### III.3.10. IMMUNE CHECKPOINT COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7a204d2b-ead7-488a-b629-32dd10731bd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "immune_checkpoint_color_dict =\n",
+      " {'B7H4': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701), 'PDL1': (0.3126890019504329, 0.6928754610296064, 0.1923704830330379), 'PD1': (0.23299120924703914, 0.639586552066035, 0.9260706093977744), 'B7H4_PDL1': (0.6402432806212122, 0.56707501056059, 0.36409039926945397), 'None': (0.5044925901631545, 0.5912455243957383, 0.5514171359788941)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"immunecheckpoint_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "immune_checkpoint_color_dict = df.set_index('immune_checkpoint').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('immune_checkpoint_color_dict =\\n',immune_checkpoint_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19d63518-7ae4-4b1d-b517-2c9149f2df9a",
+   "metadata": {},
+   "source": [
+    "### III.3.10. DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "52ec0eca-b36a-440e-b9ec-340f4a8e66ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CSV files were detected:\n",
+      "['DD3S2_bs.csv', 'TMA_bs.csv', 'DD3S1_bs.csv', 'DD3S3_bs.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# DATA\n",
+    "# List files in the directory\n",
+    "# Check if the directory exists\n",
+    "if os.path.exists(input_data_dir):\n",
+    "    # List files in the directory\n",
+    "    ls_samples = [sample for sample in os.listdir(input_data_dir) if sample.endswith(\"_bs.csv\")]\n",
+    "    print(\"The following CSV files were detected:\")\n",
+    "    print([sample for sample in ls_samples])\n",
+    "else:\n",
+    "    print(f\"The directory {input_data_dir} does not exist.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3d10d406-ff16-49db-8144-06577a45e1d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Nuc_X' 'Sample_ID' 'Nucleus_Roundness' 'Nuc_Y_Inv' 'ROI_index'\n",
+      " 'Cell_Size' 'Nucleus_Size' 'aSMA_Cell_Intensity_Average'\n",
+      " 'aSMA_Cytoplasm_Intensity_Average' 'aSMA_Nucleus_Intensity_Average'\n",
+      " 'AXL_Cell_Intensity_Average' 'AXL_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Nucleus_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'B7H4_Cytoplasm_Intensity_Average' 'B7H4_Nucleus_Intensity_Average'\n",
+      " 'CA9_Cell_Intensity_Average' 'CA9_Cytoplasm_Intensity_Average'\n",
+      " 'CA9_Nucleus_Intensity_Average' 'CD4_Cell_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'CD4_Nucleus_Intensity_Average'\n",
+      " 'CD8_Cell_Intensity_Average' 'CD8_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Nucleus_Intensity_Average' 'CD11b_Cell_Intensity_Average'\n",
+      " 'CD11b_Cytoplasm_Intensity_Average' 'CD11b_Nucleus_Intensity_Average'\n",
+      " 'CD11c_Cell_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD11c_Nucleus_Intensity_Average' 'CD20_Cell_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'CD20_Nucleus_Intensity_Average'\n",
+      " 'CD31_Cell_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'CD31_Nucleus_Intensity_Average' 'CD44_Cell_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD44_Nucleus_Intensity_Average'\n",
+      " 'CD45_Cell_Intensity_Average' 'CD45_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Nucleus_Intensity_Average' 'CD68_Cell_Intensity_Average'\n",
+      " 'CD68_Cytoplasm_Intensity_Average' 'CD68_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cell_Intensity_Average' 'CD163_Cytoplasm_Intensity_Average'\n",
+      " 'CD163_Nucleus_Intensity_Average' 'CKs_Cell_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'CKs_Nucleus_Intensity_Average'\n",
+      " 'ColVI_Cell_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'ColVI_Nucleus_Intensity_Average' 'Desmin_Cell_Intensity_Average'\n",
+      " 'Desmin_Cytoplasm_Intensity_Average' 'Desmin_Nucleus_Intensity_Average'\n",
+      " 'Ecad_Cell_Intensity_Average' 'Ecad_Cytoplasm_Intensity_Average'\n",
+      " 'Ecad_Nucleus_Intensity_Average' 'Fibronectin_Cell_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Nucleus_Intensity_Average' 'FOXP3_Cell_Intensity_Average'\n",
+      " 'FOXP3_Cytoplasm_Intensity_Average' 'FOXP3_Nucleus_Intensity_Average'\n",
+      " 'GATA3_Cell_Intensity_Average' 'GATA3_Cytoplasm_Intensity_Average'\n",
+      " 'GATA3_Nucleus_Intensity_Average' 'HLA_Cell_Intensity_Average'\n",
+      " 'HLA_Cytoplasm_Intensity_Average' 'HLA_Nucleus_Intensity_Average'\n",
+      " 'Ki67_Cell_Intensity_Average' 'Ki67_Cytoplasm_Intensity_Average'\n",
+      " 'Ki67_Nucleus_Intensity_Average' 'MMP9_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'MMP9_Nucleus_Intensity_Average'\n",
+      " 'PD1_Cell_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'PD1_Nucleus_Intensity_Average' 'PDGFR_Cell_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'PDGFR_Nucleus_Intensity_Average'\n",
+      " 'PDL1_Cell_Intensity_Average' 'PDL1_Cytoplasm_Intensity_Average'\n",
+      " 'PDL1_Nucleus_Intensity_Average' 'r5c2_Cell_Intensity_Average'\n",
+      " 'r5c2_Cytoplasm_Intensity_Average' 'r5c2_Nucleus_Intensity_Average'\n",
+      " 'r7c2_Cell_Intensity_Average' 'r7c2_Cytoplasm_Intensity_Average'\n",
+      " 'r7c2_Nucleus_Intensity_Average' 'r8c2_Cell_Intensity_Average'\n",
+      " 'r8c2_Cytoplasm_Intensity_Average' 'r8c2_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cell_Intensity_Average' 'Sting_Cytoplasm_Intensity_Average'\n",
+      " 'Sting_Nucleus_Intensity_Average' 'Vimentin_Cell_Intensity_Average'\n",
+      " 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'Vimentin_Nucleus_Intensity_Average' 'cell_type' 'cell_subtype'\n",
+      " 'immune_checkpoint']\n",
+      "DD3S2_bs.csv file is processed !\n",
+      "\n",
+      "TMA_bs.csv file is processed !\n",
+      "\n",
+      "DD3S1_bs.csv file is processed !\n",
+      "\n",
+      "DD3S3_bs.csv file is processed !\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import all the others files\n",
+    "dfs = {}\n",
+    "\n",
+    "# Set variable to hold default header values\n",
+    "# First gather information on expected headers using first file in ls_samples\n",
+    "# Read in the first row of the file corresponding to the first sample (index = 0) in ls_samples\n",
+    "df = pd.read_csv(os.path.join(input_data_dir, ls_samples[0]) , index_col = 0, nrows = 1)\n",
+    "expected_headers = df.columns.values\n",
+    "print(expected_headers)\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "for sample in ls_samples:\n",
+    "    file_path = os.path.join(input_data_dir,sample)\n",
+    "   \n",
+    "    try:\n",
+    "        # Read the CSV file\n",
+    "        df = pd.read_csv(file_path, index_col=0)\n",
+    "        # Check if the DataFrame is empty, if so, don't continue trying to process df and remove it\n",
+    "        \n",
+    "        if not df.empty:\n",
+    "            # Reorder the columns to match the expected headers list\n",
+    "            df = df.reindex(columns=expected_headers)\n",
+    "            print(sample, \"file is processed !\\n\")\n",
+    "            #print(df) \n",
+    "   \n",
+    "    except pd.errors.EmptyDataError:\n",
+    "        print(f'\\nEmpty data error in {sample} file. Removing from analysis...')\n",
+    "        ls_samples.remove(sample)      \n",
+    "    \n",
+    "    # Add df to dfs \n",
+    "    dfs[sample] = df\n",
+    "\n",
+    "#print(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "de221729-777e-4985-9206-97ae97f1253b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+      "ID                                                                      \n",
+      "DD3S2_Cell_0  2033.197510  DD3S2.csv           0.792312  15494.777344   \n",
+      "DD3S2_Cell_1  2101.638184  DD3S2.csv           0.749236  15495.552734   \n",
+      "DD3S2_Cell_2  2008.148193  DD3S2.csv           0.835324  15492.370117   \n",
+      "DD3S2_Cell_3  1992.441162  DD3S2.csv           0.671957  15489.808594   \n",
+      "DD3S2_Cell_4  2092.007080  DD3S2.csv           0.710829  15488.482422   \n",
+      "\n",
+      "              ROI_index  Cell_Size  Nucleus_Size  aSMA_Cell_Intensity_Average  \\\n",
+      "ID                                                                              \n",
+      "DD3S2_Cell_0          0        307            81                    25.086319   \n",
+      "DD3S2_Cell_1          0        159            47                     3.587149   \n",
+      "DD3S2_Cell_2          0        267            81                    32.531833   \n",
+      "DD3S2_Cell_3          0        293            68                    40.645667   \n",
+      "DD3S2_Cell_4          0        225           141                     5.189259   \n",
+      "\n",
+      "              aSMA_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                               \n",
+      "DD3S2_Cell_0                         23.127993   \n",
+      "DD3S2_Cell_1                          3.733333   \n",
+      "DD3S2_Cell_2                         28.868405   \n",
+      "DD3S2_Cell_3                         41.192237   \n",
+      "DD3S2_Cell_4                          6.561389   \n",
+      "\n",
+      "              aSMA_Nucleus_Intensity_Average  ...  \\\n",
+      "ID                                            ...   \n",
+      "DD3S2_Cell_0                       30.550290  ...   \n",
+      "DD3S2_Cell_1                        3.238795  ...   \n",
+      "DD3S2_Cell_2                       40.944156  ...   \n",
+      "DD3S2_Cell_3                       38.837158  ...   \n",
+      "DD3S2_Cell_4                        4.371820  ...   \n",
+      "\n",
+      "              r8c2_Nucleus_Intensity_Average  Sting_Cell_Intensity_Average  \\\n",
+      "ID                                                                           \n",
+      "DD3S2_Cell_0                        3.244609                      1.549813   \n",
+      "DD3S2_Cell_1                        3.793689                      2.063667   \n",
+      "DD3S2_Cell_2                        3.794774                      2.317112   \n",
+      "DD3S2_Cell_3                        3.287451                      0.956001   \n",
+      "DD3S2_Cell_4                        3.533523                      1.756521   \n",
+      "\n",
+      "              Sting_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                \n",
+      "DD3S2_Cell_0                           1.505413   \n",
+      "DD3S2_Cell_1                           2.211345   \n",
+      "DD3S2_Cell_2                           2.198314   \n",
+      "DD3S2_Cell_3                           0.951068   \n",
+      "DD3S2_Cell_4                           1.922095   \n",
+      "\n",
+      "              Sting_Nucleus_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S2_Cell_0                         1.673696   \n",
+      "DD3S2_Cell_1                         1.711752   \n",
+      "DD3S2_Cell_2                         2.589909   \n",
+      "DD3S2_Cell_3                         0.972323   \n",
+      "DD3S2_Cell_4                         1.657882   \n",
+      "\n",
+      "              Vimentin_Cell_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S2_Cell_0                         8.406796   \n",
+      "DD3S2_Cell_1                         0.159812   \n",
+      "DD3S2_Cell_2                        21.313608   \n",
+      "DD3S2_Cell_3                         8.170421   \n",
+      "DD3S2_Cell_4                         1.829408   \n",
+      "\n",
+      "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                   \n",
+      "DD3S2_Cell_0                              8.064129   \n",
+      "DD3S2_Cell_1                              0.133691   \n",
+      "DD3S2_Cell_2                             22.438442   \n",
+      "DD3S2_Cell_3                              7.536326   \n",
+      "DD3S2_Cell_4                              3.737342   \n",
+      "\n",
+      "              Vimentin_Nucleus_Intensity_Average  cell_type  cell_subtype  \\\n",
+      "ID                                                                          \n",
+      "DD3S2_Cell_0                            9.362881       none          none   \n",
+      "DD3S2_Cell_1                            0.222056       none          none   \n",
+      "DD3S2_Cell_2                           18.730657       none          none   \n",
+      "DD3S2_Cell_3                           10.268529       none          none   \n",
+      "DD3S2_Cell_4                            0.692766       none          none   \n",
+      "\n",
+      "              immune_checkpoint  \n",
+      "ID                               \n",
+      "DD3S2_Cell_0               none  \n",
+      "DD3S2_Cell_1               none  \n",
+      "DD3S2_Cell_2               none  \n",
+      "DD3S2_Cell_3               none  \n",
+      "DD3S2_Cell_4               none  \n",
+      "\n",
+      "[5 rows x 106 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge dfs into one df\n",
+    "df = pd.concat(dfs.values(), ignore_index=False , sort = False)\n",
+    "del dfs\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a750b9a2-7cdf-4e6d-9571-756e0aed5e73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341938, 106)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ad2bc997-52dc-470d-a96a-290ceb7987a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DD3S2_Cell_0', 'DD3S2_Cell_1', 'DD3S2_Cell_2', 'DD3S2_Cell_3',\n",
+       "       'DD3S2_Cell_4', 'DD3S2_Cell_5', 'DD3S2_Cell_6', 'DD3S2_Cell_7',\n",
+       "       'DD3S2_Cell_9', 'DD3S2_Cell_10',\n",
+       "       ...\n",
+       "       'DD3S3_Cell_138961', 'DD3S3_Cell_138962', 'DD3S3_Cell_138963',\n",
+       "       'DD3S3_Cell_138964', 'DD3S3_Cell_138966', 'DD3S3_Cell_138967',\n",
+       "       'DD3S3_Cell_138968', 'DD3S3_Cell_138969', 'DD3S3_Cell_138970',\n",
+       "       'DD3S3_Cell_138971'],\n",
+       "      dtype='object', name='ID', length=341938)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5b8d1763-f600-4206-ac7a-14e8a598659d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Nuc_X', 'Sample_ID', 'Nucleus_Roundness', 'Nuc_Y_Inv',\n",
+       "       'ROI_index', 'Cell_Size', 'Nucleus_Size',\n",
+       "       'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm_Intensity_Average',\n",
+       "       'aSMA_Nucleus_Intensity_Average', 'AXL_Cell_Intensity_Average',\n",
+       "       'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus_Intensity_Average',\n",
+       "       'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm_Intensity_Average',\n",
+       "       'B7H4_Nucleus_Intensity_Average', 'CA9_Cell_Intensity_Average',\n",
+       "       'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus_Intensity_Average',\n",
+       "       'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm_Intensity_Average',\n",
+       "       'CD4_Nucleus_Intensity_Average', 'CD8_Cell_Intensity_Average',\n",
+       "       'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus_Intensity_Average',\n",
+       "       'CD11b_Cell_Intensity_Average',\n",
+       "       'CD11b_Cytoplasm_Intensity_Average',\n",
+       "       'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell_Intensity_Average',\n",
+       "       'CD11c_Cytoplasm_Intensity_Average',\n",
+       "       'CD11c_Nucleus_Intensity_Average', 'CD20_Cell_Intensity_Average',\n",
+       "       'CD20_Cytoplasm_Intensity_Average',\n",
+       "       'CD20_Nucleus_Intensity_Average', 'CD31_Cell_Intensity_Average',\n",
+       "       'CD31_Cytoplasm_Intensity_Average',\n",
+       "       'CD31_Nucleus_Intensity_Average', 'CD44_Cell_Intensity_Average',\n",
+       "       'CD44_Cytoplasm_Intensity_Average',\n",
+       "       'CD44_Nucleus_Intensity_Average', 'CD45_Cell_Intensity_Average',\n",
+       "       'CD45_Cytoplasm_Intensity_Average',\n",
+       "       'CD45_Nucleus_Intensity_Average', 'CD68_Cell_Intensity_Average',\n",
+       "       'CD68_Cytoplasm_Intensity_Average',\n",
+       "       'CD68_Nucleus_Intensity_Average', 'CD163_Cell_Intensity_Average',\n",
+       "       'CD163_Cytoplasm_Intensity_Average',\n",
+       "       'CD163_Nucleus_Intensity_Average', 'CKs_Cell_Intensity_Average',\n",
+       "       'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus_Intensity_Average',\n",
+       "       'ColVI_Cell_Intensity_Average',\n",
+       "       'ColVI_Cytoplasm_Intensity_Average',\n",
+       "       'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell_Intensity_Average',\n",
+       "       'Desmin_Cytoplasm_Intensity_Average',\n",
+       "       'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell_Intensity_Average',\n",
+       "       'Ecad_Cytoplasm_Intensity_Average',\n",
+       "       'Ecad_Nucleus_Intensity_Average',\n",
+       "       'Fibronectin_Cell_Intensity_Average',\n",
+       "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       "       'Fibronectin_Nucleus_Intensity_Average',\n",
+       "       'FOXP3_Cell_Intensity_Average',\n",
+       "       'FOXP3_Cytoplasm_Intensity_Average',\n",
+       "       'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell_Intensity_Average',\n",
+       "       'GATA3_Cytoplasm_Intensity_Average',\n",
+       "       'GATA3_Nucleus_Intensity_Average', 'HLA_Cell_Intensity_Average',\n",
+       "       'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus_Intensity_Average',\n",
+       "       'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm_Intensity_Average',\n",
+       "       'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell_Intensity_Average',\n",
+       "       'MMP9_Cytoplasm_Intensity_Average',\n",
+       "       'MMP9_Nucleus_Intensity_Average', 'PD1_Cell_Intensity_Average',\n",
+       "       'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus_Intensity_Average',\n",
+       "       'PDGFR_Cell_Intensity_Average',\n",
+       "       'PDGFR_Cytoplasm_Intensity_Average',\n",
+       "       'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell_Intensity_Average',\n",
+       "       'PDL1_Cytoplasm_Intensity_Average',\n",
+       "       'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell_Intensity_Average',\n",
+       "       'r5c2_Cytoplasm_Intensity_Average',\n",
+       "       'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell_Intensity_Average',\n",
+       "       'r7c2_Cytoplasm_Intensity_Average',\n",
+       "       'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell_Intensity_Average',\n",
+       "       'r8c2_Cytoplasm_Intensity_Average',\n",
+       "       'r8c2_Nucleus_Intensity_Average', 'Sting_Cell_Intensity_Average',\n",
+       "       'Sting_Cytoplasm_Intensity_Average',\n",
+       "       'Sting_Nucleus_Intensity_Average',\n",
+       "       'Vimentin_Cell_Intensity_Average',\n",
+       "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+       "       'Vimentin_Nucleus_Intensity_Average', 'cell_type', 'cell_subtype',\n",
+       "       'immune_checkpoint'], dtype=object)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2bc3ca56-ff3e-41c0-9f96-7915611485cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e62d125a-1293-4720-8803-0d66d3ee2f76",
+   "metadata": {},
+   "source": [
+    "## III.4. MARKERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "86c821eb-a70c-4959-86eb-01f87b915656",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Listing all the markers of interest for downstream analyses\n",
+    "# !!TODO WITH MARILYNE!!\n",
+    "markers = [\n",
+    "    '53BP1_Nucleus_Intensity_Average',\n",
+    "    'AR_Nucleus_Intensity_Average',\n",
+    "    'CCNB1_Cell_Intensity_Average',\n",
+    "    'CCND1_Nucleus_Intensity_Average',\n",
+    "    'CCNE_Nucleus_Intensity_Average',\n",
+    "    'CD31_Cytoplasm_Intensity_Average',\n",
+    "    'CKs_Cytoplasm_Intensity_Average',\n",
+    "    'ERa_Nucleus_Intensity_Average',\n",
+    "    'Ecad_Cytoplasm_Intensity_Average',\n",
+    "    'GATA3_Nucleus_Intensity_Average',\n",
+    "    'H3K27_Nucleus_Intensity_Average',\n",
+    "    'H3K4me3_Nucleus_Intensity_Average',\n",
+    "    'HER2_Cytoplasm_Intensity_Average',\n",
+    "    'HSP90_Cell_Intensity_Average',\n",
+    "    'Ki67_Nucleus_Intensity_Average',\n",
+    "    'PAX8_Nucleus_Intensity_Average',\n",
+    "    'PCNA_Nucleus_Intensity_Average',\n",
+    "    'PRg_Nucleus_Intensity_Average',\n",
+    "    'S100b_Cytoplasm_Intensity_Average',\n",
+    "    'TP53_Cell_Intensity_Average',\n",
+    "    'Vimentin_Cytoplasm_Intensity_Average',\n",
+    "    'pAKT_Cytoplasm_Intensity_Average',\n",
+    "    'pATM_Nucleus_Intensity_Average',\n",
+    "    'pATR_Nucleus_Intensity_Average',\n",
+    "    'pERK_Cell_Intensity_Average',\n",
+    "    'pRB_Nucleus_Intensity_Average',\n",
+    "    'pS6_Cytoplasm_Intensity_Average',\n",
+    "    'AXL_Cytoplasm_Intensity_Average',\n",
+    "    'B7H4_Cell_Intensity_Average',\n",
+    "    'CD11c_Cytoplasm_Intensity_Average',\n",
+    "    'CD163_Cytoplasm_Intensity_Average',\n",
+    "    'CD20_Cytoplasm_Intensity_Average',\n",
+    "    'CD31_Cytoplasm_Intensity_Average',\n",
+    "    'CD44_Cytoplasm_Intensity_Average',\n",
+    "    'CD45_Cytoplasm_Intensity_Average',\n",
+    "    'CD45b_Cytoplasm_Intensity_Average',    \n",
+    "    'CD4_Cytoplasm_Intensity_Average',\n",
+    "    'CD68_Cytoplasm_Intensity_Average',\n",
+    "    'CD8_Cytoplasm_Intensity_Average',\n",
+    "    'CKs_Cytoplasm_Intensity_Average',\n",
+    "    'ColVI_Cytoplasm_Intensity_Average',\n",
+    "    'Desmin_Cytoplasm_Intensity_Average',\n",
+    "    'Ecad_Cytoplasm_Intensity_Average',\n",
+    "    'FOXP3_Nucleus_Intensity_Average',\n",
+    "    'Fibronectin_Cytoplasm_Intensity_Average',\n",
+    "    'GATA3_Nucleus_Intensity_Average',\n",
+    "    'HLA_Cytoplasm_Intensity_Average',\n",
+    "    'Ki67_Nucleus_Intensity_Average',\n",
+    "    'MMP9_Cytoplasm_Intensity_Average',\n",
+    "    'PD1_Cytoplasm_Intensity_Average',\n",
+    "    'PDGFR_Cytoplasm_Intensity_Average',\n",
+    "    'PDL1_Cytoplasm_Intensity_Average',\n",
+    "    'Sting_Cytoplasm_Intensity_Average',\n",
+    "    'Vimentin_Cytoplasm_Intensity_Average',\n",
+    "    'aSMA_Cytoplasm_Intensity_Average'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3fb24fb3-32e5-4a21-9913-7783a357f73e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following columns are not present in the DataFrame (22 columns missing): \n",
+      "['53BP1_Nucleus_Intensity_Average', 'AR_Nucleus_Intensity_Average', 'CCNB1_Cell_Intensity_Average', 'CCND1_Nucleus_Intensity_Average', 'CCNE_Nucleus_Intensity_Average', 'ERa_Nucleus_Intensity_Average', 'H3K27_Nucleus_Intensity_Average', 'H3K4me3_Nucleus_Intensity_Average', 'HER2_Cytoplasm_Intensity_Average', 'HSP90_Cell_Intensity_Average', 'PAX8_Nucleus_Intensity_Average', 'PCNA_Nucleus_Intensity_Average', 'PRg_Nucleus_Intensity_Average', 'S100b_Cytoplasm_Intensity_Average', 'TP53_Cell_Intensity_Average', 'pAKT_Cytoplasm_Intensity_Average', 'pATM_Nucleus_Intensity_Average', 'pATR_Nucleus_Intensity_Average', 'pERK_Cell_Intensity_Average', 'pRB_Nucleus_Intensity_Average', 'pS6_Cytoplasm_Intensity_Average', 'CD45b_Cytoplasm_Intensity_Average']\n",
+      "\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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Desmin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>GATA3_Nucleus_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>0.183086</td>\n",
+       "      <td>-0.109646</td>\n",
+       "      <td>-0.007868</td>\n",
+       "      <td>-0.060652</td>\n",
+       "      <td>0.181378</td>\n",
+       "      <td>0.179445</td>\n",
+       "      <td>0.225944</td>\n",
+       "      <td>23.127993</td>\n",
+       "      <td>-0.016142</td>\n",
+       "      <td>-0.002348</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.569737</td>\n",
+       "      <td>1.505413</td>\n",
+       "      <td>-0.710457</td>\n",
+       "      <td>0.064117</td>\n",
+       "      <td>-0.042649</td>\n",
+       "      <td>-0.285973</td>\n",
+       "      <td>-0.016453</td>\n",
+       "      <td>8.064129</td>\n",
+       "      <td>6.736799</td>\n",
+       "      <td>-0.172625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>0.300446</td>\n",
+       "      <td>0.467911</td>\n",
+       "      <td>0.078235</td>\n",
+       "      <td>0.014423</td>\n",
+       "      <td>0.157720</td>\n",
+       "      <td>0.076214</td>\n",
+       "      <td>0.101970</td>\n",
+       "      <td>3.733333</td>\n",
+       "      <td>0.080869</td>\n",
+       "      <td>0.200440</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.711191</td>\n",
+       "      <td>2.211345</td>\n",
+       "      <td>-0.259137</td>\n",
+       "      <td>0.095387</td>\n",
+       "      <td>0.024518</td>\n",
+       "      <td>-0.089077</td>\n",
+       "      <td>0.161915</td>\n",
+       "      <td>0.133691</td>\n",
+       "      <td>12.466549</td>\n",
+       "      <td>-0.104625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>0.261423</td>\n",
+       "      <td>0.130480</td>\n",
+       "      <td>0.040497</td>\n",
+       "      <td>0.050348</td>\n",
+       "      <td>0.387416</td>\n",
+       "      <td>0.329509</td>\n",
+       "      <td>0.466484</td>\n",
+       "      <td>28.868405</td>\n",
+       "      <td>0.075090</td>\n",
+       "      <td>0.119287</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.520292</td>\n",
+       "      <td>2.198314</td>\n",
+       "      <td>-0.548943</td>\n",
+       "      <td>0.174529</td>\n",
+       "      <td>0.010638</td>\n",
+       "      <td>0.164462</td>\n",
+       "      <td>0.045070</td>\n",
+       "      <td>22.438442</td>\n",
+       "      <td>1.152088</td>\n",
+       "      <td>-0.028749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>0.161573</td>\n",
+       "      <td>0.064504</td>\n",
+       "      <td>-0.010307</td>\n",
+       "      <td>0.002116</td>\n",
+       "      <td>0.285289</td>\n",
+       "      <td>0.291479</td>\n",
+       "      <td>0.318115</td>\n",
+       "      <td>41.192237</td>\n",
+       "      <td>0.032175</td>\n",
+       "      <td>0.063606</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.417573</td>\n",
+       "      <td>0.951068</td>\n",
+       "      <td>-0.819126</td>\n",
+       "      <td>0.118600</td>\n",
+       "      <td>-0.039049</td>\n",
+       "      <td>-0.124460</td>\n",
+       "      <td>-0.010618</td>\n",
+       "      <td>7.536326</td>\n",
+       "      <td>2.129703</td>\n",
+       "      <td>-0.145493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>0.302397</td>\n",
+       "      <td>-0.161730</td>\n",
+       "      <td>0.052735</td>\n",
+       "      <td>-0.030714</td>\n",
+       "      <td>0.146556</td>\n",
+       "      <td>0.062800</td>\n",
+       "      <td>0.161722</td>\n",
+       "      <td>6.561389</td>\n",
+       "      <td>0.043548</td>\n",
+       "      <td>0.187722</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.699875</td>\n",
+       "      <td>1.922095</td>\n",
+       "      <td>-0.430555</td>\n",
+       "      <td>0.077284</td>\n",
+       "      <td>-0.003968</td>\n",
+       "      <td>-0.158928</td>\n",
+       "      <td>0.179002</td>\n",
+       "      <td>3.737342</td>\n",
+       "      <td>9.608294</td>\n",
+       "      <td>-0.140294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          0.183086   \n",
+       "DD3S2_Cell_1                          0.300446   \n",
+       "DD3S2_Cell_2                          0.261423   \n",
+       "DD3S2_Cell_3                          0.161573   \n",
+       "DD3S2_Cell_4                          0.302397   \n",
+       "\n",
+       "              CD11c_Cytoplasm_Intensity_Average  B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                                                             \n",
+       "DD3S2_Cell_0                          -0.109646                    -0.007868   \n",
+       "DD3S2_Cell_1                           0.467911                     0.078235   \n",
+       "DD3S2_Cell_2                           0.130480                     0.040497   \n",
+       "DD3S2_Cell_3                           0.064504                    -0.010307   \n",
+       "DD3S2_Cell_4                          -0.161730                     0.052735   \n",
+       "\n",
+       "              CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                         -0.060652   \n",
+       "DD3S2_Cell_1                          0.014423   \n",
+       "DD3S2_Cell_2                          0.050348   \n",
+       "DD3S2_Cell_3                          0.002116   \n",
+       "DD3S2_Cell_4                         -0.030714   \n",
+       "\n",
+       "              CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                              \n",
+       "DD3S2_Cell_0                         0.181378   \n",
+       "DD3S2_Cell_1                         0.157720   \n",
+       "DD3S2_Cell_2                         0.387416   \n",
+       "DD3S2_Cell_3                         0.285289   \n",
+       "DD3S2_Cell_4                         0.146556   \n",
+       "\n",
+       "              CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          0.179445   \n",
+       "DD3S2_Cell_1                          0.076214   \n",
+       "DD3S2_Cell_2                          0.329509   \n",
+       "DD3S2_Cell_3                          0.291479   \n",
+       "DD3S2_Cell_4                          0.062800   \n",
+       "\n",
+       "              PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S2_Cell_0                           0.225944   \n",
+       "DD3S2_Cell_1                           0.101970   \n",
+       "DD3S2_Cell_2                           0.466484   \n",
+       "DD3S2_Cell_3                           0.318115   \n",
+       "DD3S2_Cell_4                           0.161722   \n",
+       "\n",
+       "              aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                         23.127993   \n",
+       "DD3S2_Cell_1                          3.733333   \n",
+       "DD3S2_Cell_2                         28.868405   \n",
+       "DD3S2_Cell_3                         41.192237   \n",
+       "DD3S2_Cell_4                          6.561389   \n",
+       "\n",
+       "              CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S2_Cell_0                          -0.016142   \n",
+       "DD3S2_Cell_1                           0.080869   \n",
+       "DD3S2_Cell_2                           0.075090   \n",
+       "DD3S2_Cell_3                           0.032175   \n",
+       "DD3S2_Cell_4                           0.043548   \n",
+       "\n",
+       "              Fibronectin_Cytoplasm_Intensity_Average  ...  \\\n",
+       "ID                                                     ...   \n",
+       "DD3S2_Cell_0                                -0.002348  ...   \n",
+       "DD3S2_Cell_1                                 0.200440  ...   \n",
+       "DD3S2_Cell_2                                 0.119287  ...   \n",
+       "DD3S2_Cell_3                                 0.063606  ...   \n",
+       "DD3S2_Cell_4                                 0.187722  ...   \n",
+       "\n",
+       "              Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                             \n",
+       "DD3S2_Cell_0                        0.569737   \n",
+       "DD3S2_Cell_1                        0.711191   \n",
+       "DD3S2_Cell_2                        0.520292   \n",
+       "DD3S2_Cell_3                        0.417573   \n",
+       "DD3S2_Cell_4                        0.699875   \n",
+       "\n",
+       "              Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S2_Cell_0                           1.505413   \n",
+       "DD3S2_Cell_1                           2.211345   \n",
+       "DD3S2_Cell_2                           2.198314   \n",
+       "DD3S2_Cell_3                           0.951068   \n",
+       "DD3S2_Cell_4                           1.922095   \n",
+       "\n",
+       "              Desmin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S2_Cell_0                           -0.710457   \n",
+       "DD3S2_Cell_1                           -0.259137   \n",
+       "DD3S2_Cell_2                           -0.548943   \n",
+       "DD3S2_Cell_3                           -0.819126   \n",
+       "DD3S2_Cell_4                           -0.430555   \n",
+       "\n",
+       "              CD31_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          0.064117   \n",
+       "DD3S2_Cell_1                          0.095387   \n",
+       "DD3S2_Cell_2                          0.174529   \n",
+       "DD3S2_Cell_3                          0.118600   \n",
+       "DD3S2_Cell_4                          0.077284   \n",
+       "\n",
+       "              PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                              \n",
+       "DD3S2_Cell_0                        -0.042649   \n",
+       "DD3S2_Cell_1                         0.024518   \n",
+       "DD3S2_Cell_2                         0.010638   \n",
+       "DD3S2_Cell_3                        -0.039049   \n",
+       "DD3S2_Cell_4                        -0.003968   \n",
+       "\n",
+       "              ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S2_Cell_0                          -0.285973   \n",
+       "DD3S2_Cell_1                          -0.089077   \n",
+       "DD3S2_Cell_2                           0.164462   \n",
+       "DD3S2_Cell_3                          -0.124460   \n",
+       "DD3S2_Cell_4                          -0.158928   \n",
+       "\n",
+       "              GATA3_Nucleus_Intensity_Average  \\\n",
+       "ID                                              \n",
+       "DD3S2_Cell_0                        -0.016453   \n",
+       "DD3S2_Cell_1                         0.161915   \n",
+       "DD3S2_Cell_2                         0.045070   \n",
+       "DD3S2_Cell_3                        -0.010618   \n",
+       "DD3S2_Cell_4                         0.179002   \n",
+       "\n",
+       "              Vimentin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              8.064129   \n",
+       "DD3S2_Cell_1                              0.133691   \n",
+       "DD3S2_Cell_2                             22.438442   \n",
+       "DD3S2_Cell_3                              7.536326   \n",
+       "DD3S2_Cell_4                              3.737342   \n",
+       "\n",
+       "              CKs_Cytoplasm_Intensity_Average  AXL_Cytoplasm_Intensity_Average  \n",
+       "ID                                                                              \n",
+       "DD3S2_Cell_0                         6.736799                        -0.172625  \n",
+       "DD3S2_Cell_1                        12.466549                        -0.104625  \n",
+       "DD3S2_Cell_2                         1.152088                        -0.028749  \n",
+       "DD3S2_Cell_3                         2.129703                        -0.145493  \n",
+       "DD3S2_Cell_4                         9.608294                        -0.140294  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if all columns in the markers list are present in the DataFrame\n",
+    "missing_columns = [col for col in markers if col not in df.columns]\n",
+    "if missing_columns:\n",
+    "    # If columns are missing that can be because the markers may be present in the other slide\n",
+    "    print(f\"The following columns are not present in the DataFrame ({len(missing_columns)} columns missing): \\n{missing_columns}\\n\")\n",
+    "    # Filter the DataFrame to keep only the columns that are in the markers list and also exist in the DataFrame\n",
+    "    intersected_columns = list(set(markers).intersection(df.columns))\n",
+    "    df_markers = df[intersected_columns]\n",
+    "else:\n",
+    "    # Filter the DataFrame to keep only the columns in the markers list\n",
+    "    df_markers = df[markers]\n",
+    "\n",
+    "df_markers.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4d63bf5f-3bc6-4d19-8e8f-2cdf639c3bd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PDL1_Cytoplasm_Intensity_Average',\n",
+       "       'CD11c_Cytoplasm_Intensity_Average', 'B7H4_Cell_Intensity_Average',\n",
+       "       'CD68_Cytoplasm_Intensity_Average',\n",
+       "       'CD4_Cytoplasm_Intensity_Average',\n",
+       "       'CD44_Cytoplasm_Intensity_Average',\n",
+       "       'PDGFR_Cytoplasm_Intensity_Average',\n",
+       "       'aSMA_Cytoplasm_Intensity_Average',\n",
+       "       'CD163_Cytoplasm_Intensity_Average',\n",
+       "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       "       'CD8_Cytoplasm_Intensity_Average',\n",
+       "       'Ecad_Cytoplasm_Intensity_Average',\n",
+       "       'FOXP3_Nucleus_Intensity_Average',\n",
+       "       'CD20_Cytoplasm_Intensity_Average',\n",
+       "       'HLA_Cytoplasm_Intensity_Average',\n",
+       "       'CD45_Cytoplasm_Intensity_Average',\n",
+       "       'MMP9_Cytoplasm_Intensity_Average',\n",
+       "       'Ki67_Nucleus_Intensity_Average',\n",
+       "       'Sting_Cytoplasm_Intensity_Average',\n",
+       "       'Desmin_Cytoplasm_Intensity_Average',\n",
+       "       'CD31_Cytoplasm_Intensity_Average',\n",
+       "       'PD1_Cytoplasm_Intensity_Average',\n",
+       "       'ColVI_Cytoplasm_Intensity_Average',\n",
+       "       'GATA3_Nucleus_Intensity_Average',\n",
+       "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+       "       'CKs_Cytoplasm_Intensity_Average',\n",
+       "       'AXL_Cytoplasm_Intensity_Average'], dtype=object)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Rename CD45b into CD45 (Slide A!)\n",
+    "if project_name == 'Slide_A' :\n",
+    "    df_markers.rename(columns={\"CD45b_Cytoplasm_Intensity_Average\": \"CD45_Cytoplasm_Intensity_Average\"}, inplace=True)\n",
+    "df_markers.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "0e0ea1a5-8e6f-4475-a75f-c3f2fbe6faa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341938, 27)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_markers.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4351a81a-30f6-4878-be4d-d2a1436bb18c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.9338211263020836,\n",
+       " -0.8475121256510417,\n",
+       " -0.5234746093749999,\n",
+       " -0.7297129720052083,\n",
+       " -1.2201360677083333,\n",
+       " -1.0585365397135418,\n",
+       " -0.7113063151041668,\n",
+       " -8.014408569335938,\n",
+       " -1.4501634114583335,\n",
+       " -0.910650634765625,\n",
+       " -1.238843587239583,\n",
+       " -0.0288550618489584,\n",
+       " -0.9764022623697918,\n",
+       " -0.1649319661458335,\n",
+       " -1.9483092447916663,\n",
+       " -0.2165327148437499,\n",
+       " -1.4945714518229165,\n",
+       " 0.0681667887369792,\n",
+       " -1.1650885416666663,\n",
+       " -12.347664388020831,\n",
+       " -0.7537534790039063,\n",
+       " -1.570408203125,\n",
+       " -10.08868896484375,\n",
+       " -0.2946448567708331,\n",
+       " -10.01797119140625,\n",
+       " -11.341695353190104,\n",
+       " -0.90746337890625]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_values = df_markers.min().tolist()\n",
+    "min_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a7d19ce3-0770-4987-9af5-03a3dcb62b2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Keep not_intensities and markers columns\n",
+    "# Combine both lists\n",
+    "combined_columns = list(set(markers) | set(not_intensities))\n",
+    "\n",
+    "# Filter the DataFrame to keep only the combined columns present in both df and combined_columns\n",
+    "df_markers_not_intensities = df[df.columns.intersection(combined_columns)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "cfc45faf-f38a-4366-86e7-979f3837b6e8",
+   "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>Nuc_X</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>MMP9_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0</td>\n",
+       "      <td>307</td>\n",
+       "      <td>81</td>\n",
+       "      <td>23.127993</td>\n",
+       "      <td>-0.172625</td>\n",
+       "      <td>-0.007868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.569737</td>\n",
+       "      <td>-0.054124</td>\n",
+       "      <td>-0.042649</td>\n",
+       "      <td>0.225944</td>\n",
+       "      <td>0.183086</td>\n",
+       "      <td>1.505413</td>\n",
+       "      <td>8.064129</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>47</td>\n",
+       "      <td>3.733333</td>\n",
+       "      <td>-0.104625</td>\n",
+       "      <td>0.078235</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.711191</td>\n",
+       "      <td>0.031119</td>\n",
+       "      <td>0.024518</td>\n",
+       "      <td>0.101970</td>\n",
+       "      <td>0.300446</td>\n",
+       "      <td>2.211345</td>\n",
+       "      <td>0.133691</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>267</td>\n",
+       "      <td>81</td>\n",
+       "      <td>28.868405</td>\n",
+       "      <td>-0.028749</td>\n",
+       "      <td>0.040497</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.520292</td>\n",
+       "      <td>0.013989</td>\n",
+       "      <td>0.010638</td>\n",
+       "      <td>0.466484</td>\n",
+       "      <td>0.261423</td>\n",
+       "      <td>2.198314</td>\n",
+       "      <td>22.438442</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0</td>\n",
+       "      <td>293</td>\n",
+       "      <td>68</td>\n",
+       "      <td>41.192237</td>\n",
+       "      <td>-0.145493</td>\n",
+       "      <td>-0.010307</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.417573</td>\n",
+       "      <td>-0.017410</td>\n",
+       "      <td>-0.039049</td>\n",
+       "      <td>0.318115</td>\n",
+       "      <td>0.161573</td>\n",
+       "      <td>0.951068</td>\n",
+       "      <td>7.536326</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0</td>\n",
+       "      <td>225</td>\n",
+       "      <td>141</td>\n",
+       "      <td>6.561389</td>\n",
+       "      <td>-0.140294</td>\n",
+       "      <td>0.052735</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.699875</td>\n",
+       "      <td>0.001389</td>\n",
+       "      <td>-0.003968</td>\n",
+       "      <td>0.161722</td>\n",
+       "      <td>0.302397</td>\n",
+       "      <td>1.922095</td>\n",
+       "      <td>3.737342</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>63</td>\n",
+       "      <td>318</td>\n",
+       "      <td>72</td>\n",
+       "      <td>-2.829689</td>\n",
+       "      <td>-0.316496</td>\n",
+       "      <td>-0.291786</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.802662</td>\n",
+       "      <td>-0.068195</td>\n",
+       "      <td>-0.087062</td>\n",
+       "      <td>-0.201317</td>\n",
+       "      <td>0.114778</td>\n",
+       "      <td>0.673324</td>\n",
+       "      <td>-3.891558</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>63</td>\n",
+       "      <td>309</td>\n",
+       "      <td>130</td>\n",
+       "      <td>10.666499</td>\n",
+       "      <td>0.079829</td>\n",
+       "      <td>0.158183</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.596646</td>\n",
+       "      <td>0.102268</td>\n",
+       "      <td>0.131940</td>\n",
+       "      <td>0.800220</td>\n",
+       "      <td>1.348149</td>\n",
+       "      <td>5.238821</td>\n",
+       "      <td>22.863111</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>63</td>\n",
+       "      <td>155</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8.390907</td>\n",
+       "      <td>0.051903</td>\n",
+       "      <td>0.158396</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.006776</td>\n",
+       "      <td>0.098123</td>\n",
+       "      <td>0.203340</td>\n",
+       "      <td>0.624563</td>\n",
+       "      <td>1.102123</td>\n",
+       "      <td>5.399366</td>\n",
+       "      <td>26.146441</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>63</td>\n",
+       "      <td>243</td>\n",
+       "      <td>65</td>\n",
+       "      <td>16.200505</td>\n",
+       "      <td>0.180491</td>\n",
+       "      <td>0.051547</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.706959</td>\n",
+       "      <td>0.033674</td>\n",
+       "      <td>0.068232</td>\n",
+       "      <td>0.742682</td>\n",
+       "      <td>0.888573</td>\n",
+       "      <td>5.667290</td>\n",
+       "      <td>33.745450</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>63</td>\n",
+       "      <td>193</td>\n",
+       "      <td>72</td>\n",
+       "      <td>13.525318</td>\n",
+       "      <td>0.097702</td>\n",
+       "      <td>0.165548</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.402329</td>\n",
+       "      <td>0.085251</td>\n",
+       "      <td>0.201504</td>\n",
+       "      <td>1.100634</td>\n",
+       "      <td>1.284419</td>\n",
+       "      <td>4.288573</td>\n",
+       "      <td>14.974078</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "      <td>none</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Nuc_X  Sample_ID  Nucleus_Roundness     Nuc_Y_Inv  \\\n",
+       "ID                                                                            \n",
+       "DD3S2_Cell_0        2033.197510  DD3S2.csv           0.792312  15494.777344   \n",
+       "DD3S2_Cell_1        2101.638184  DD3S2.csv           0.749236  15495.552734   \n",
+       "DD3S2_Cell_2        2008.148193  DD3S2.csv           0.835324  15492.370117   \n",
+       "DD3S2_Cell_3        1992.441162  DD3S2.csv           0.671957  15489.808594   \n",
+       "DD3S2_Cell_4        2092.007080  DD3S2.csv           0.710829  15488.482422   \n",
+       "...                         ...        ...                ...           ...   \n",
+       "DD3S3_Cell_138967  14827.791992  DD3S3.csv           0.921498   1648.513916   \n",
+       "DD3S3_Cell_138968  14520.892578  DD3S3.csv           0.740617   1642.884644   \n",
+       "DD3S3_Cell_138969  14528.000000  DD3S3.csv           0.694157   1640.038452   \n",
+       "DD3S3_Cell_138970  14553.461914  DD3S3.csv           0.821441   1629.507690   \n",
+       "DD3S3_Cell_138971  14543.513672  DD3S3.csv           0.816589   1628.541626   \n",
+       "\n",
+       "                   ROI_index  Cell_Size  Nucleus_Size  \\\n",
+       "ID                                                      \n",
+       "DD3S2_Cell_0               0        307            81   \n",
+       "DD3S2_Cell_1               0        159            47   \n",
+       "DD3S2_Cell_2               0        267            81   \n",
+       "DD3S2_Cell_3               0        293            68   \n",
+       "DD3S2_Cell_4               0        225           141   \n",
+       "...                      ...        ...           ...   \n",
+       "DD3S3_Cell_138967         63        318            72   \n",
+       "DD3S3_Cell_138968         63        309           130   \n",
+       "DD3S3_Cell_138969         63        155            52   \n",
+       "DD3S3_Cell_138970         63        243            65   \n",
+       "DD3S3_Cell_138971         63        193            72   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              23.127993   \n",
+       "DD3S2_Cell_1                               3.733333   \n",
+       "DD3S2_Cell_2                              28.868405   \n",
+       "DD3S2_Cell_3                              41.192237   \n",
+       "DD3S2_Cell_4                               6.561389   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -2.829689   \n",
+       "DD3S3_Cell_138968                         10.666499   \n",
+       "DD3S3_Cell_138969                          8.390907   \n",
+       "DD3S3_Cell_138970                         16.200505   \n",
+       "DD3S3_Cell_138971                         13.525318   \n",
+       "\n",
+       "                   AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                             -0.172625   \n",
+       "DD3S2_Cell_1                             -0.104625   \n",
+       "DD3S2_Cell_2                             -0.028749   \n",
+       "DD3S2_Cell_3                             -0.145493   \n",
+       "DD3S2_Cell_4                             -0.140294   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                        -0.316496   \n",
+       "DD3S3_Cell_138968                         0.079829   \n",
+       "DD3S3_Cell_138969                         0.051903   \n",
+       "DD3S3_Cell_138970                         0.180491   \n",
+       "DD3S3_Cell_138971                         0.097702   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  ...  \\\n",
+       "ID                                              ...   \n",
+       "DD3S2_Cell_0                         -0.007868  ...   \n",
+       "DD3S2_Cell_1                          0.078235  ...   \n",
+       "DD3S2_Cell_2                          0.040497  ...   \n",
+       "DD3S2_Cell_3                         -0.010307  ...   \n",
+       "DD3S2_Cell_4                          0.052735  ...   \n",
+       "...                                        ...  ...   \n",
+       "DD3S3_Cell_138967                    -0.291786  ...   \n",
+       "DD3S3_Cell_138968                     0.158183  ...   \n",
+       "DD3S3_Cell_138969                     0.158396  ...   \n",
+       "DD3S3_Cell_138970                     0.051547  ...   \n",
+       "DD3S3_Cell_138971                     0.165548  ...   \n",
+       "\n",
+       "                   Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S2_Cell_0                             0.569737   \n",
+       "DD3S2_Cell_1                             0.711191   \n",
+       "DD3S2_Cell_2                             0.520292   \n",
+       "DD3S2_Cell_3                             0.417573   \n",
+       "DD3S2_Cell_4                             0.699875   \n",
+       "...                                           ...   \n",
+       "DD3S3_Cell_138967                        0.802662   \n",
+       "DD3S3_Cell_138968                        3.596646   \n",
+       "DD3S3_Cell_138969                        3.006776   \n",
+       "DD3S3_Cell_138970                        1.706959   \n",
+       "DD3S3_Cell_138971                        5.402329   \n",
+       "\n",
+       "                   MMP9_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.054124   \n",
+       "DD3S2_Cell_1                               0.031119   \n",
+       "DD3S2_Cell_2                               0.013989   \n",
+       "DD3S2_Cell_3                              -0.017410   \n",
+       "DD3S2_Cell_4                               0.001389   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.068195   \n",
+       "DD3S3_Cell_138968                          0.102268   \n",
+       "DD3S3_Cell_138969                          0.098123   \n",
+       "DD3S3_Cell_138970                          0.033674   \n",
+       "DD3S3_Cell_138971                          0.085251   \n",
+       "\n",
+       "                   PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                             -0.042649   \n",
+       "DD3S2_Cell_1                              0.024518   \n",
+       "DD3S2_Cell_2                              0.010638   \n",
+       "DD3S2_Cell_3                             -0.039049   \n",
+       "DD3S2_Cell_4                             -0.003968   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                        -0.087062   \n",
+       "DD3S3_Cell_138968                         0.131940   \n",
+       "DD3S3_Cell_138969                         0.203340   \n",
+       "DD3S3_Cell_138970                         0.068232   \n",
+       "DD3S3_Cell_138971                         0.201504   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.225944   \n",
+       "DD3S2_Cell_1                                0.101970   \n",
+       "DD3S2_Cell_2                                0.466484   \n",
+       "DD3S2_Cell_3                                0.318115   \n",
+       "DD3S2_Cell_4                                0.161722   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                          -0.201317   \n",
+       "DD3S3_Cell_138968                           0.800220   \n",
+       "DD3S3_Cell_138969                           0.624563   \n",
+       "DD3S3_Cell_138970                           0.742682   \n",
+       "DD3S3_Cell_138971                           1.100634   \n",
+       "\n",
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               0.183086   \n",
+       "DD3S2_Cell_1                               0.300446   \n",
+       "DD3S2_Cell_2                               0.261423   \n",
+       "DD3S2_Cell_3                               0.161573   \n",
+       "DD3S2_Cell_4                               0.302397   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          0.114778   \n",
+       "DD3S3_Cell_138968                          1.348149   \n",
+       "DD3S3_Cell_138969                          1.102123   \n",
+       "DD3S3_Cell_138970                          0.888573   \n",
+       "DD3S3_Cell_138971                          1.284419   \n",
+       "\n",
+       "                   Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                1.505413   \n",
+       "DD3S2_Cell_1                                2.211345   \n",
+       "DD3S2_Cell_2                                2.198314   \n",
+       "DD3S2_Cell_3                                0.951068   \n",
+       "DD3S2_Cell_4                                1.922095   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           0.673324   \n",
+       "DD3S3_Cell_138968                           5.238821   \n",
+       "DD3S3_Cell_138969                           5.399366   \n",
+       "DD3S3_Cell_138970                           5.667290   \n",
+       "DD3S3_Cell_138971                           4.288573   \n",
+       "\n",
+       "                   Vimentin_Cytoplasm_Intensity_Average  cell_type  \\\n",
+       "ID                                                                   \n",
+       "DD3S2_Cell_0                                   8.064129       none   \n",
+       "DD3S2_Cell_1                                   0.133691       none   \n",
+       "DD3S2_Cell_2                                  22.438442       none   \n",
+       "DD3S2_Cell_3                                   7.536326       none   \n",
+       "DD3S2_Cell_4                                   3.737342       none   \n",
+       "...                                                 ...        ...   \n",
+       "DD3S3_Cell_138967                             -3.891558       none   \n",
+       "DD3S3_Cell_138968                             22.863111       none   \n",
+       "DD3S3_Cell_138969                             26.146441       none   \n",
+       "DD3S3_Cell_138970                             33.745450       none   \n",
+       "DD3S3_Cell_138971                             14.974078       none   \n",
+       "\n",
+       "                   cell_subtype  immune_checkpoint  \n",
+       "ID                                                  \n",
+       "DD3S2_Cell_0               none               none  \n",
+       "DD3S2_Cell_1               none               none  \n",
+       "DD3S2_Cell_2               none               none  \n",
+       "DD3S2_Cell_3               none               none  \n",
+       "DD3S2_Cell_4               none               none  \n",
+       "...                         ...                ...  \n",
+       "DD3S3_Cell_138967          none               none  \n",
+       "DD3S3_Cell_138968          none               none  \n",
+       "DD3S3_Cell_138969          none               none  \n",
+       "DD3S3_Cell_138970          none               none  \n",
+       "DD3S3_Cell_138971          none               none  \n",
+       "\n",
+       "[341938 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_markers_not_intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "45daa61f-0e4e-46eb-93d5-886ded05cb1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341938, 37)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_markers_not_intensities.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "808eda4e-a18b-4d78-b6cc-9f9210faab82",
+   "metadata": {},
+   "source": [
+    "## III.5. NORMALISATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "386ae099-83fd-47a6-b3ff-342fcf913d7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.9338211263020836,\n",
+       " -0.8475121256510417,\n",
+       " -0.5234746093749999,\n",
+       " -0.7297129720052083,\n",
+       " -1.2201360677083333,\n",
+       " -1.0585365397135418,\n",
+       " -0.7113063151041668,\n",
+       " -8.014408569335938,\n",
+       " -1.4501634114583335,\n",
+       " -0.910650634765625,\n",
+       " -1.238843587239583,\n",
+       " -0.0288550618489584,\n",
+       " -0.9764022623697918,\n",
+       " -0.1649319661458335,\n",
+       " -1.9483092447916663,\n",
+       " -0.2165327148437499,\n",
+       " -1.4945714518229165,\n",
+       " 0.0681667887369792,\n",
+       " -1.1650885416666663,\n",
+       " -12.347664388020831,\n",
+       " -0.7537534790039063,\n",
+       " -1.570408203125,\n",
+       " -10.08868896484375,\n",
+       " -0.2946448567708331,\n",
+       " -10.01797119140625,\n",
+       " -11.341695353190104,\n",
+       " -0.90746337890625]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_markers.min().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1a6e062c-b27a-4c16-b796-9e26c58a7ec7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df_marker.shape before normalisation:  (341938, 27)\n",
+      "min value =  -12.347664388020831\n",
+      "df_markers.shape after normalisation:  (341938, 27)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[12.413843261718748,\n",
+       " 12.50015226236979,\n",
+       " 12.824189778645831,\n",
+       " 12.617951416015623,\n",
+       " 12.127528320312498,\n",
+       " 12.28912784830729,\n",
+       " 12.636358072916664,\n",
+       " 5.3332558186848935,\n",
+       " 11.897500976562497,\n",
+       " 12.437013753255206,\n",
+       " 12.108820800781249,\n",
+       " 13.318809326171873,\n",
+       " 12.37126212565104,\n",
+       " 13.182732421874999,\n",
+       " 11.399355143229165,\n",
+       " 13.131131673177082,\n",
+       " 11.853092936197914,\n",
+       " 13.415831176757811,\n",
+       " 12.182575846354165,\n",
+       " 1.0,\n",
+       " 12.593910909016925,\n",
+       " 11.777256184895831,\n",
+       " 3.258975423177082,\n",
+       " 13.053019531249998,\n",
+       " 3.3296931966145813,\n",
+       " 2.005969034830727,\n",
+       " 12.440201009114581]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# LOG2 TRANFORMATION\n",
+    "#Values need to be higher than 0 for Log2 transformation.\n",
+    "\n",
+    "print(\"df_marker.shape before normalisation: \", df_markers.shape)\n",
+    "\n",
+    "# Option 1\n",
+    "# This step might not be the best approach because in creates pattern in the data.\n",
+    "# set anything that is below 0 to 0, so that we can do the log transform, +1 to all columns\n",
+    "#for f in df_markers.columns[~df_markers.columns.isin(not_intensities)]:\n",
+    "    #df_markers.loc[df_markers[f] < 0,f] = 0\n",
+    "    \n",
+    "\n",
+    "# Option 2\n",
+    "# Add the min from min values (from above) +1 to all columns\n",
+    "#df_markers.loc[:, ~df_markers.columns.isin(not_intensities)] = \\\n",
+    "    #df_markers.loc[:,~df_markers.columns.isin(not_intensities)].copy() + 1 \n",
+    "# Add the minimum value + 1 to each column\n",
+    "# OR\n",
+    "min_value = df_markers.min().min()\n",
+    "print(\"min value = \", min_value)\n",
+    "df_markers = df_markers + (np.abs(min_value))\n",
+    "\n",
+    "\n",
+    "# +1\n",
+    "df_markers = df_markers + 1\n",
+    "print(\"df_markers.shape after normalisation: \", df_markers.shape)\n",
+    "df_markers.min().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "25493174-665c-406c-acb2-cd27b9a4b025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log2 transform finished\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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>Sting_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Desmin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>GATA3_Nucleus_Intensity_Average</th>\n",
+       "      <th>Vimentin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>3.758170</td>\n",
+       "      <td>3.726615</td>\n",
+       "      <td>3.737665</td>\n",
+       "      <td>3.731945</td>\n",
+       "      <td>3.757988</td>\n",
+       "      <td>3.757782</td>\n",
+       "      <td>3.762732</td>\n",
+       "      <td>5.188862</td>\n",
+       "      <td>3.736770</td>\n",
+       "      <td>3.738262</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.798818</td>\n",
+       "      <td>3.892690</td>\n",
+       "      <td>3.659606</td>\n",
+       "      <td>3.745429</td>\n",
+       "      <td>3.733898</td>\n",
+       "      <td>3.707270</td>\n",
+       "      <td>3.736736</td>\n",
+       "      <td>4.420334</td>\n",
+       "      <td>4.328008</td>\n",
+       "      <td>3.719735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>3.770629</td>\n",
+       "      <td>3.788224</td>\n",
+       "      <td>3.746947</td>\n",
+       "      <td>3.740073</td>\n",
+       "      <td>3.755463</td>\n",
+       "      <td>3.746730</td>\n",
+       "      <td>3.749495</td>\n",
+       "      <td>4.094320</td>\n",
+       "      <td>3.747230</td>\n",
+       "      <td>3.760019</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.813407</td>\n",
+       "      <td>3.959678</td>\n",
+       "      <td>3.710231</td>\n",
+       "      <td>3.748789</td>\n",
+       "      <td>3.741163</td>\n",
+       "      <td>3.728855</td>\n",
+       "      <td>3.755911</td>\n",
+       "      <td>3.752894</td>\n",
+       "      <td>4.690094</td>\n",
+       "      <td>3.727162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>3.766498</td>\n",
+       "      <td>3.752550</td>\n",
+       "      <td>3.742886</td>\n",
+       "      <td>3.743947</td>\n",
+       "      <td>3.779793</td>\n",
+       "      <td>3.773698</td>\n",
+       "      <td>3.788075</td>\n",
+       "      <td>5.399720</td>\n",
+       "      <td>3.746609</td>\n",
+       "      <td>3.751351</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.793683</td>\n",
+       "      <td>3.958469</td>\n",
+       "      <td>3.677928</td>\n",
+       "      <td>3.757257</td>\n",
+       "      <td>3.739665</td>\n",
+       "      <td>3.756183</td>\n",
+       "      <td>3.743379</td>\n",
+       "      <td>5.161328</td>\n",
+       "      <td>3.857956</td>\n",
+       "      <td>3.735405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>3.755874</td>\n",
+       "      <td>3.745471</td>\n",
+       "      <td>3.737401</td>\n",
+       "      <td>3.738744</td>\n",
+       "      <td>3.769026</td>\n",
+       "      <td>3.769681</td>\n",
+       "      <td>3.772496</td>\n",
+       "      <td>5.769240</td>\n",
+       "      <td>3.741989</td>\n",
+       "      <td>3.745374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.782958</td>\n",
+       "      <td>3.837815</td>\n",
+       "      <td>3.647146</td>\n",
+       "      <td>3.751278</td>\n",
+       "      <td>3.734289</td>\n",
+       "      <td>3.725000</td>\n",
+       "      <td>3.737367</td>\n",
+       "      <td>4.384325</td>\n",
+       "      <td>3.952088</td>\n",
+       "      <td>3.722703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>3.770836</td>\n",
+       "      <td>3.720928</td>\n",
+       "      <td>3.744204</td>\n",
+       "      <td>3.735192</td>\n",
+       "      <td>3.754270</td>\n",
+       "      <td>3.745287</td>\n",
+       "      <td>3.755890</td>\n",
+       "      <td>4.315353</td>\n",
+       "      <td>3.743215</td>\n",
+       "      <td>3.758664</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.812246</td>\n",
+       "      <td>3.932605</td>\n",
+       "      <td>3.691211</td>\n",
+       "      <td>3.746845</td>\n",
+       "      <td>3.738086</td>\n",
+       "      <td>3.721234</td>\n",
+       "      <td>3.757734</td>\n",
+       "      <td>4.094659</td>\n",
+       "      <td>4.520797</td>\n",
+       "      <td>3.723271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>3.750868</td>\n",
+       "      <td>3.707101</td>\n",
+       "      <td>3.706628</td>\n",
+       "      <td>3.710848</td>\n",
+       "      <td>4.302837</td>\n",
+       "      <td>3.743908</td>\n",
+       "      <td>3.716590</td>\n",
+       "      <td>3.394785</td>\n",
+       "      <td>3.737397</td>\n",
+       "      <td>3.733490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.822763</td>\n",
+       "      <td>3.809516</td>\n",
+       "      <td>3.104181</td>\n",
+       "      <td>3.841043</td>\n",
+       "      <td>3.729074</td>\n",
+       "      <td>4.236746</td>\n",
+       "      <td>3.760684</td>\n",
+       "      <td>3.241246</td>\n",
+       "      <td>3.210896</td>\n",
+       "      <td>3.703895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>3.877333</td>\n",
+       "      <td>3.785992</td>\n",
+       "      <td>3.755512</td>\n",
+       "      <td>3.801541</td>\n",
+       "      <td>3.920946</td>\n",
+       "      <td>3.893987</td>\n",
+       "      <td>3.822514</td>\n",
+       "      <td>4.585814</td>\n",
+       "      <td>3.871388</td>\n",
+       "      <td>3.813215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.082729</td>\n",
+       "      <td>4.216182</td>\n",
+       "      <td>3.907466</td>\n",
+       "      <td>3.794135</td>\n",
+       "      <td>3.752706</td>\n",
+       "      <td>3.730219</td>\n",
+       "      <td>3.776056</td>\n",
+       "      <td>5.178347</td>\n",
+       "      <td>4.137659</td>\n",
+       "      <td>3.747118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>3.852976</td>\n",
+       "      <td>3.783950</td>\n",
+       "      <td>3.755535</td>\n",
+       "      <td>3.809775</td>\n",
+       "      <td>3.898005</td>\n",
+       "      <td>3.802692</td>\n",
+       "      <td>3.804490</td>\n",
+       "      <td>4.442185</td>\n",
+       "      <td>3.823960</td>\n",
+       "      <td>3.811463</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.031610</td>\n",
+       "      <td>4.228590</td>\n",
+       "      <td>3.874268</td>\n",
+       "      <td>3.778591</td>\n",
+       "      <td>3.760328</td>\n",
+       "      <td>3.734028</td>\n",
+       "      <td>3.780450</td>\n",
+       "      <td>5.303565</td>\n",
+       "      <td>4.063099</td>\n",
+       "      <td>3.744115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>3.831496</td>\n",
+       "      <td>3.758385</td>\n",
+       "      <td>3.744076</td>\n",
+       "      <td>3.751376</td>\n",
+       "      <td>3.871842</td>\n",
+       "      <td>3.842041</td>\n",
+       "      <td>3.816635</td>\n",
+       "      <td>4.884997</td>\n",
+       "      <td>3.785072</td>\n",
+       "      <td>3.799217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.912135</td>\n",
+       "      <td>4.249063</td>\n",
+       "      <td>3.743587</td>\n",
+       "      <td>3.939331</td>\n",
+       "      <td>3.745872</td>\n",
+       "      <td>3.857894</td>\n",
+       "      <td>3.776723</td>\n",
+       "      <td>5.557444</td>\n",
+       "      <td>3.923955</td>\n",
+       "      <td>3.757893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>3.871063</td>\n",
+       "      <td>3.786428</td>\n",
+       "      <td>3.756299</td>\n",
+       "      <td>3.774249</td>\n",
+       "      <td>3.976772</td>\n",
+       "      <td>3.927563</td>\n",
+       "      <td>3.852828</td>\n",
+       "      <td>4.748085</td>\n",
+       "      <td>3.799142</td>\n",
+       "      <td>3.836915</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.228818</td>\n",
+       "      <td>4.140471</td>\n",
+       "      <td>3.917669</td>\n",
+       "      <td>3.798172</td>\n",
+       "      <td>3.760132</td>\n",
+       "      <td>3.816358</td>\n",
+       "      <td>3.800749</td>\n",
+       "      <td>4.823838</td>\n",
+       "      <td>4.040701</td>\n",
+       "      <td>3.749037</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.758170   \n",
+       "DD3S2_Cell_1                               3.770629   \n",
+       "DD3S2_Cell_2                               3.766498   \n",
+       "DD3S2_Cell_3                               3.755874   \n",
+       "DD3S2_Cell_4                               3.770836   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.750868   \n",
+       "DD3S3_Cell_138968                          3.877333   \n",
+       "DD3S3_Cell_138969                          3.852976   \n",
+       "DD3S3_Cell_138970                          3.831496   \n",
+       "DD3S3_Cell_138971                          3.871063   \n",
+       "\n",
+       "                   CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.726615   \n",
+       "DD3S2_Cell_1                                3.788224   \n",
+       "DD3S2_Cell_2                                3.752550   \n",
+       "DD3S2_Cell_3                                3.745471   \n",
+       "DD3S2_Cell_4                                3.720928   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.707101   \n",
+       "DD3S3_Cell_138968                           3.785992   \n",
+       "DD3S3_Cell_138969                           3.783950   \n",
+       "DD3S3_Cell_138970                           3.758385   \n",
+       "DD3S3_Cell_138971                           3.786428   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          3.737665   \n",
+       "DD3S2_Cell_1                          3.746947   \n",
+       "DD3S2_Cell_2                          3.742886   \n",
+       "DD3S2_Cell_3                          3.737401   \n",
+       "DD3S2_Cell_4                          3.744204   \n",
+       "...                                        ...   \n",
+       "DD3S3_Cell_138967                     3.706628   \n",
+       "DD3S3_Cell_138968                     3.755512   \n",
+       "DD3S3_Cell_138969                     3.755535   \n",
+       "DD3S3_Cell_138970                     3.744076   \n",
+       "DD3S3_Cell_138971                     3.756299   \n",
+       "\n",
+       "                   CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.731945   \n",
+       "DD3S2_Cell_1                               3.740073   \n",
+       "DD3S2_Cell_2                               3.743947   \n",
+       "DD3S2_Cell_3                               3.738744   \n",
+       "DD3S2_Cell_4                               3.735192   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.710848   \n",
+       "DD3S3_Cell_138968                          3.801541   \n",
+       "DD3S3_Cell_138969                          3.809775   \n",
+       "DD3S3_Cell_138970                          3.751376   \n",
+       "DD3S3_Cell_138971                          3.774249   \n",
+       "\n",
+       "                   CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              3.757988   \n",
+       "DD3S2_Cell_1                              3.755463   \n",
+       "DD3S2_Cell_2                              3.779793   \n",
+       "DD3S2_Cell_3                              3.769026   \n",
+       "DD3S2_Cell_4                              3.754270   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         4.302837   \n",
+       "DD3S3_Cell_138968                         3.920946   \n",
+       "DD3S3_Cell_138969                         3.898005   \n",
+       "DD3S3_Cell_138970                         3.871842   \n",
+       "DD3S3_Cell_138971                         3.976772   \n",
+       "\n",
+       "                   CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.757782   \n",
+       "DD3S2_Cell_1                               3.746730   \n",
+       "DD3S2_Cell_2                               3.773698   \n",
+       "DD3S2_Cell_3                               3.769681   \n",
+       "DD3S2_Cell_4                               3.745287   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.743908   \n",
+       "DD3S3_Cell_138968                          3.893987   \n",
+       "DD3S3_Cell_138969                          3.802692   \n",
+       "DD3S3_Cell_138970                          3.842041   \n",
+       "DD3S3_Cell_138971                          3.927563   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.762732   \n",
+       "DD3S2_Cell_1                                3.749495   \n",
+       "DD3S2_Cell_2                                3.788075   \n",
+       "DD3S2_Cell_3                                3.772496   \n",
+       "DD3S2_Cell_4                                3.755890   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.716590   \n",
+       "DD3S3_Cell_138968                           3.822514   \n",
+       "DD3S3_Cell_138969                           3.804490   \n",
+       "DD3S3_Cell_138970                           3.816635   \n",
+       "DD3S3_Cell_138971                           3.852828   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               5.188862   \n",
+       "DD3S2_Cell_1                               4.094320   \n",
+       "DD3S2_Cell_2                               5.399720   \n",
+       "DD3S2_Cell_3                               5.769240   \n",
+       "DD3S2_Cell_4                               4.315353   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.394785   \n",
+       "DD3S3_Cell_138968                          4.585814   \n",
+       "DD3S3_Cell_138969                          4.442185   \n",
+       "DD3S3_Cell_138970                          4.884997   \n",
+       "DD3S3_Cell_138971                          4.748085   \n",
+       "\n",
+       "                   CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.736770   \n",
+       "DD3S2_Cell_1                                3.747230   \n",
+       "DD3S2_Cell_2                                3.746609   \n",
+       "DD3S2_Cell_3                                3.741989   \n",
+       "DD3S2_Cell_4                                3.743215   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.737397   \n",
+       "DD3S3_Cell_138968                           3.871388   \n",
+       "DD3S3_Cell_138969                           3.823960   \n",
+       "DD3S3_Cell_138970                           3.785072   \n",
+       "DD3S3_Cell_138971                           3.799142   \n",
+       "\n",
+       "                   Fibronectin_Cytoplasm_Intensity_Average  ...  \\\n",
+       "ID                                                          ...   \n",
+       "DD3S2_Cell_0                                      3.738262  ...   \n",
+       "DD3S2_Cell_1                                      3.760019  ...   \n",
+       "DD3S2_Cell_2                                      3.751351  ...   \n",
+       "DD3S2_Cell_3                                      3.745374  ...   \n",
+       "DD3S2_Cell_4                                      3.758664  ...   \n",
+       "...                                                    ...  ...   \n",
+       "DD3S3_Cell_138967                                 3.733490  ...   \n",
+       "DD3S3_Cell_138968                                 3.813215  ...   \n",
+       "DD3S3_Cell_138969                                 3.811463  ...   \n",
+       "DD3S3_Cell_138970                                 3.799217  ...   \n",
+       "DD3S3_Cell_138971                                 3.836915  ...   \n",
+       "\n",
+       "                   Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S2_Cell_0                             3.798818   \n",
+       "DD3S2_Cell_1                             3.813407   \n",
+       "DD3S2_Cell_2                             3.793683   \n",
+       "DD3S2_Cell_3                             3.782958   \n",
+       "DD3S2_Cell_4                             3.812246   \n",
+       "...                                           ...   \n",
+       "DD3S3_Cell_138967                        3.822763   \n",
+       "DD3S3_Cell_138968                        4.082729   \n",
+       "DD3S3_Cell_138969                        4.031610   \n",
+       "DD3S3_Cell_138970                        3.912135   \n",
+       "DD3S3_Cell_138971                        4.228818   \n",
+       "\n",
+       "                   Sting_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.892690   \n",
+       "DD3S2_Cell_1                                3.959678   \n",
+       "DD3S2_Cell_2                                3.958469   \n",
+       "DD3S2_Cell_3                                3.837815   \n",
+       "DD3S2_Cell_4                                3.932605   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.809516   \n",
+       "DD3S3_Cell_138968                           4.216182   \n",
+       "DD3S3_Cell_138969                           4.228590   \n",
+       "DD3S3_Cell_138970                           4.249063   \n",
+       "DD3S3_Cell_138971                           4.140471   \n",
+       "\n",
+       "                   Desmin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                      \n",
+       "DD3S2_Cell_0                                 3.659606   \n",
+       "DD3S2_Cell_1                                 3.710231   \n",
+       "DD3S2_Cell_2                                 3.677928   \n",
+       "DD3S2_Cell_3                                 3.647146   \n",
+       "DD3S2_Cell_4                                 3.691211   \n",
+       "...                                               ...   \n",
+       "DD3S3_Cell_138967                            3.104181   \n",
+       "DD3S3_Cell_138968                            3.907466   \n",
+       "DD3S3_Cell_138969                            3.874268   \n",
+       "DD3S3_Cell_138970                            3.743587   \n",
+       "DD3S3_Cell_138971                            3.917669   \n",
+       "\n",
+       "                   CD31_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.745429   \n",
+       "DD3S2_Cell_1                               3.748789   \n",
+       "DD3S2_Cell_2                               3.757257   \n",
+       "DD3S2_Cell_3                               3.751278   \n",
+       "DD3S2_Cell_4                               3.746845   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.841043   \n",
+       "DD3S3_Cell_138968                          3.794135   \n",
+       "DD3S3_Cell_138969                          3.778591   \n",
+       "DD3S3_Cell_138970                          3.939331   \n",
+       "DD3S3_Cell_138971                          3.798172   \n",
+       "\n",
+       "                   PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              3.733898   \n",
+       "DD3S2_Cell_1                              3.741163   \n",
+       "DD3S2_Cell_2                              3.739665   \n",
+       "DD3S2_Cell_3                              3.734289   \n",
+       "DD3S2_Cell_4                              3.738086   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         3.729074   \n",
+       "DD3S3_Cell_138968                         3.752706   \n",
+       "DD3S3_Cell_138969                         3.760328   \n",
+       "DD3S3_Cell_138970                         3.745872   \n",
+       "DD3S3_Cell_138971                         3.760132   \n",
+       "\n",
+       "                   ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.707270   \n",
+       "DD3S2_Cell_1                                3.728855   \n",
+       "DD3S2_Cell_2                                3.756183   \n",
+       "DD3S2_Cell_3                                3.725000   \n",
+       "DD3S2_Cell_4                                3.721234   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           4.236746   \n",
+       "DD3S3_Cell_138968                           3.730219   \n",
+       "DD3S3_Cell_138969                           3.734028   \n",
+       "DD3S3_Cell_138970                           3.857894   \n",
+       "DD3S3_Cell_138971                           3.816358   \n",
+       "\n",
+       "                   GATA3_Nucleus_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              3.736736   \n",
+       "DD3S2_Cell_1                              3.755911   \n",
+       "DD3S2_Cell_2                              3.743379   \n",
+       "DD3S2_Cell_3                              3.737367   \n",
+       "DD3S2_Cell_4                              3.757734   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         3.760684   \n",
+       "DD3S3_Cell_138968                         3.776056   \n",
+       "DD3S3_Cell_138969                         3.780450   \n",
+       "DD3S3_Cell_138970                         3.776723   \n",
+       "DD3S3_Cell_138971                         3.800749   \n",
+       "\n",
+       "                   Vimentin_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                        \n",
+       "DD3S2_Cell_0                                   4.420334   \n",
+       "DD3S2_Cell_1                                   3.752894   \n",
+       "DD3S2_Cell_2                                   5.161328   \n",
+       "DD3S2_Cell_3                                   4.384325   \n",
+       "DD3S2_Cell_4                                   4.094659   \n",
+       "...                                                 ...   \n",
+       "DD3S3_Cell_138967                              3.241246   \n",
+       "DD3S3_Cell_138968                              5.178347   \n",
+       "DD3S3_Cell_138969                              5.303565   \n",
+       "DD3S3_Cell_138970                              5.557444   \n",
+       "DD3S3_Cell_138971                              4.823838   \n",
+       "\n",
+       "                   CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              4.328008   \n",
+       "DD3S2_Cell_1                              4.690094   \n",
+       "DD3S2_Cell_2                              3.857956   \n",
+       "DD3S2_Cell_3                              3.952088   \n",
+       "DD3S2_Cell_4                              4.520797   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         3.210896   \n",
+       "DD3S3_Cell_138968                         4.137659   \n",
+       "DD3S3_Cell_138969                         4.063099   \n",
+       "DD3S3_Cell_138970                         3.923955   \n",
+       "DD3S3_Cell_138971                         4.040701   \n",
+       "\n",
+       "                   AXL_Cytoplasm_Intensity_Average  \n",
+       "ID                                                  \n",
+       "DD3S2_Cell_0                              3.719735  \n",
+       "DD3S2_Cell_1                              3.727162  \n",
+       "DD3S2_Cell_2                              3.735405  \n",
+       "DD3S2_Cell_3                              3.722703  \n",
+       "DD3S2_Cell_4                              3.723271  \n",
+       "...                                            ...  \n",
+       "DD3S3_Cell_138967                         3.703895  \n",
+       "DD3S3_Cell_138968                         3.747118  \n",
+       "DD3S3_Cell_138969                         3.744115  \n",
+       "DD3S3_Cell_138970                         3.757893  \n",
+       "DD3S3_Cell_138971                         3.749037  \n",
+       "\n",
+       "[341938 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply log2\n",
+    "df_markers.loc[:,~df_markers.columns.isin(not_intensities)] = \\\n",
+    "    np.log2(df_markers.loc[:, ~df_markers.columns.isin(not_intensities)])\n",
+    "print('log2 transform finished')\n",
+    "\n",
+    "df_markers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c0019e7a-6dc7-4eab-b9b4-935deb5cb277",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['PDL1_Cytoplasm_Intensity_Average',\n",
+       " 'CD11c_Cytoplasm_Intensity_Average',\n",
+       " 'B7H4_Cell_Intensity_Average',\n",
+       " 'CD68_Cytoplasm_Intensity_Average',\n",
+       " 'CD4_Cytoplasm_Intensity_Average',\n",
+       " 'CD44_Cytoplasm_Intensity_Average',\n",
+       " 'PDGFR_Cytoplasm_Intensity_Average',\n",
+       " 'aSMA_Cytoplasm_Intensity_Average',\n",
+       " 'CD163_Cytoplasm_Intensity_Average',\n",
+       " 'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       " 'CD8_Cytoplasm_Intensity_Average',\n",
+       " 'Ecad_Cytoplasm_Intensity_Average',\n",
+       " 'FOXP3_Nucleus_Intensity_Average',\n",
+       " 'CD20_Cytoplasm_Intensity_Average',\n",
+       " 'HLA_Cytoplasm_Intensity_Average',\n",
+       " 'CD45_Cytoplasm_Intensity_Average',\n",
+       " 'MMP9_Cytoplasm_Intensity_Average',\n",
+       " 'Ki67_Nucleus_Intensity_Average',\n",
+       " 'Sting_Cytoplasm_Intensity_Average',\n",
+       " 'Desmin_Cytoplasm_Intensity_Average',\n",
+       " 'CD31_Cytoplasm_Intensity_Average',\n",
+       " 'PD1_Cytoplasm_Intensity_Average',\n",
+       " 'ColVI_Cytoplasm_Intensity_Average',\n",
+       " 'GATA3_Nucleus_Intensity_Average',\n",
+       " 'Vimentin_Cytoplasm_Intensity_Average',\n",
+       " 'CKs_Cytoplasm_Intensity_Average',\n",
+       " 'AXL_Cytoplasm_Intensity_Average']"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_markers.columns.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f9d0b2c8-63ee-4d29-9af8-eb5ab2730908",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df_markers.isnull().any().any()\n",
+    "\n",
+    "count_nan_in_df_markers = df_markers.isnull().sum().sum()\n",
+    "print(count_nan_in_df_markers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad54027-9314-491e-b4b5-14e7fc6fb801",
+   "metadata": {},
+   "source": [
+    "## III.6. Z-SCORE TRANSFORMATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "23b53d3b-2aaa-4497-ad41-6361a1593e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following columns are not present in the DataFrame (10 columns missing):         \n",
+      "['Cytoplasm_Size', 'Primary_chem(1)_vs_surg(0)', 'cluster', 'Unique_ROI_index', 'Nuc_Y', 'Nuc_X_Inv', 'Cell_ID', 'ID', 'Patient', 'replicate_ID']\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>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</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>DD3S2_Cell_0</th>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>307</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>159</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>267</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>293</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>225</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>318</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>309</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>155</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>243</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>193</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Nuc_X cell_subtype  ROI_index immune_checkpoint  \\\n",
+       "ID                                                                          \n",
+       "DD3S2_Cell_0        2033.197510         none          0              none   \n",
+       "DD3S2_Cell_1        2101.638184         none          0              none   \n",
+       "DD3S2_Cell_2        2008.148193         none          0              none   \n",
+       "DD3S2_Cell_3        1992.441162         none          0              none   \n",
+       "DD3S2_Cell_4        2092.007080         none          0              none   \n",
+       "...                         ...          ...        ...               ...   \n",
+       "DD3S3_Cell_138967  14827.791992         none         63              none   \n",
+       "DD3S3_Cell_138968  14520.892578         none         63              none   \n",
+       "DD3S3_Cell_138969  14528.000000         none         63              none   \n",
+       "DD3S3_Cell_138970  14553.461914         none         63              none   \n",
+       "DD3S3_Cell_138971  14543.513672         none         63              none   \n",
+       "\n",
+       "                      Nuc_Y_Inv  Nucleus_Roundness  Cell_Size  Sample_ID  \\\n",
+       "ID                                                                         \n",
+       "DD3S2_Cell_0       15494.777344           0.792312        307  DD3S2.csv   \n",
+       "DD3S2_Cell_1       15495.552734           0.749236        159  DD3S2.csv   \n",
+       "DD3S2_Cell_2       15492.370117           0.835324        267  DD3S2.csv   \n",
+       "DD3S2_Cell_3       15489.808594           0.671957        293  DD3S2.csv   \n",
+       "DD3S2_Cell_4       15488.482422           0.710829        225  DD3S2.csv   \n",
+       "...                         ...                ...        ...        ...   \n",
+       "DD3S3_Cell_138967   1648.513916           0.921498        318  DD3S3.csv   \n",
+       "DD3S3_Cell_138968   1642.884644           0.740617        309  DD3S3.csv   \n",
+       "DD3S3_Cell_138969   1640.038452           0.694157        155  DD3S3.csv   \n",
+       "DD3S3_Cell_138970   1629.507690           0.821441        243  DD3S3.csv   \n",
+       "DD3S3_Cell_138971   1628.541626           0.816589        193  DD3S3.csv   \n",
+       "\n",
+       "                  cell_type  Nucleus_Size  \n",
+       "ID                                         \n",
+       "DD3S2_Cell_0           none            81  \n",
+       "DD3S2_Cell_1           none            47  \n",
+       "DD3S2_Cell_2           none            81  \n",
+       "DD3S2_Cell_3           none            68  \n",
+       "DD3S2_Cell_4           none           141  \n",
+       "...                     ...           ...  \n",
+       "DD3S3_Cell_138967      none            72  \n",
+       "DD3S3_Cell_138968      none           130  \n",
+       "DD3S3_Cell_138969      none            52  \n",
+       "DD3S3_Cell_138970      none            65  \n",
+       "DD3S3_Cell_138971      none            72  \n",
+       "\n",
+       "[341938 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter the DataFrame df to keep only the columns specified in the not_intensities list\n",
+    "#df = df.loc[:, not_intensities]\n",
+    "#df\n",
+    "\n",
+    "# Check if all columns in the markers list are present in the DataFrame\n",
+    "missing_columns = [col for col in not_intensities if col not in df.columns]\n",
+    "if missing_columns:\n",
+    "    print(f\"The following columns are not present in the DataFrame ({len(missing_columns)} columns missing): \\\n",
+    "        \\n{missing_columns}\")\n",
+    "    # Filter the DataFrame to keep only the columns that are in the markers list and also exist in the DataFrame\n",
+    "    intersected_columns = list(set(not_intensities).intersection(df.columns))\n",
+    "    df = df[intersected_columns]\n",
+    "else:\n",
+    "    # Filter the DataFrame to keep only the columns in the markers list\n",
+    "    df.loc[:, not_intensities]\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "93744a35-1e3d-41ee-89d9-be8f7ca3c412",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>3.758170</td>\n",
+       "      <td>3.726615</td>\n",
+       "      <td>3.737665</td>\n",
+       "      <td>3.731945</td>\n",
+       "      <td>3.757988</td>\n",
+       "      <td>3.757782</td>\n",
+       "      <td>3.762732</td>\n",
+       "      <td>5.188862</td>\n",
+       "      <td>3.736770</td>\n",
+       "      <td>3.738262</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>307</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>3.770629</td>\n",
+       "      <td>3.788224</td>\n",
+       "      <td>3.746947</td>\n",
+       "      <td>3.740073</td>\n",
+       "      <td>3.755463</td>\n",
+       "      <td>3.746730</td>\n",
+       "      <td>3.749495</td>\n",
+       "      <td>4.094320</td>\n",
+       "      <td>3.747230</td>\n",
+       "      <td>3.760019</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>159</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>3.766498</td>\n",
+       "      <td>3.752550</td>\n",
+       "      <td>3.742886</td>\n",
+       "      <td>3.743947</td>\n",
+       "      <td>3.779793</td>\n",
+       "      <td>3.773698</td>\n",
+       "      <td>3.788075</td>\n",
+       "      <td>5.399720</td>\n",
+       "      <td>3.746609</td>\n",
+       "      <td>3.751351</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>267</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>3.755874</td>\n",
+       "      <td>3.745471</td>\n",
+       "      <td>3.737401</td>\n",
+       "      <td>3.738744</td>\n",
+       "      <td>3.769026</td>\n",
+       "      <td>3.769681</td>\n",
+       "      <td>3.772496</td>\n",
+       "      <td>5.769240</td>\n",
+       "      <td>3.741989</td>\n",
+       "      <td>3.745374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>293</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>3.770836</td>\n",
+       "      <td>3.720928</td>\n",
+       "      <td>3.744204</td>\n",
+       "      <td>3.735192</td>\n",
+       "      <td>3.754270</td>\n",
+       "      <td>3.745287</td>\n",
+       "      <td>3.755890</td>\n",
+       "      <td>4.315353</td>\n",
+       "      <td>3.743215</td>\n",
+       "      <td>3.758664</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>225</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>3.750868</td>\n",
+       "      <td>3.707101</td>\n",
+       "      <td>3.706628</td>\n",
+       "      <td>3.710848</td>\n",
+       "      <td>4.302837</td>\n",
+       "      <td>3.743908</td>\n",
+       "      <td>3.716590</td>\n",
+       "      <td>3.394785</td>\n",
+       "      <td>3.737397</td>\n",
+       "      <td>3.733490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>318</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>3.877333</td>\n",
+       "      <td>3.785992</td>\n",
+       "      <td>3.755512</td>\n",
+       "      <td>3.801541</td>\n",
+       "      <td>3.920946</td>\n",
+       "      <td>3.893987</td>\n",
+       "      <td>3.822514</td>\n",
+       "      <td>4.585814</td>\n",
+       "      <td>3.871388</td>\n",
+       "      <td>3.813215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>309</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>3.852976</td>\n",
+       "      <td>3.783950</td>\n",
+       "      <td>3.755535</td>\n",
+       "      <td>3.809775</td>\n",
+       "      <td>3.898005</td>\n",
+       "      <td>3.802692</td>\n",
+       "      <td>3.804490</td>\n",
+       "      <td>4.442185</td>\n",
+       "      <td>3.823960</td>\n",
+       "      <td>3.811463</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>155</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>3.831496</td>\n",
+       "      <td>3.758385</td>\n",
+       "      <td>3.744076</td>\n",
+       "      <td>3.751376</td>\n",
+       "      <td>3.871842</td>\n",
+       "      <td>3.842041</td>\n",
+       "      <td>3.816635</td>\n",
+       "      <td>4.884997</td>\n",
+       "      <td>3.785072</td>\n",
+       "      <td>3.799217</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>243</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>3.871063</td>\n",
+       "      <td>3.786428</td>\n",
+       "      <td>3.756299</td>\n",
+       "      <td>3.774249</td>\n",
+       "      <td>3.976772</td>\n",
+       "      <td>3.927563</td>\n",
+       "      <td>3.852828</td>\n",
+       "      <td>4.748085</td>\n",
+       "      <td>3.799142</td>\n",
+       "      <td>3.836915</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>193</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.758170   \n",
+       "DD3S2_Cell_1                               3.770629   \n",
+       "DD3S2_Cell_2                               3.766498   \n",
+       "DD3S2_Cell_3                               3.755874   \n",
+       "DD3S2_Cell_4                               3.770836   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.750868   \n",
+       "DD3S3_Cell_138968                          3.877333   \n",
+       "DD3S3_Cell_138969                          3.852976   \n",
+       "DD3S3_Cell_138970                          3.831496   \n",
+       "DD3S3_Cell_138971                          3.871063   \n",
+       "\n",
+       "                   CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.726615   \n",
+       "DD3S2_Cell_1                                3.788224   \n",
+       "DD3S2_Cell_2                                3.752550   \n",
+       "DD3S2_Cell_3                                3.745471   \n",
+       "DD3S2_Cell_4                                3.720928   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.707101   \n",
+       "DD3S3_Cell_138968                           3.785992   \n",
+       "DD3S3_Cell_138969                           3.783950   \n",
+       "DD3S3_Cell_138970                           3.758385   \n",
+       "DD3S3_Cell_138971                           3.786428   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          3.737665   \n",
+       "DD3S2_Cell_1                          3.746947   \n",
+       "DD3S2_Cell_2                          3.742886   \n",
+       "DD3S2_Cell_3                          3.737401   \n",
+       "DD3S2_Cell_4                          3.744204   \n",
+       "...                                        ...   \n",
+       "DD3S3_Cell_138967                     3.706628   \n",
+       "DD3S3_Cell_138968                     3.755512   \n",
+       "DD3S3_Cell_138969                     3.755535   \n",
+       "DD3S3_Cell_138970                     3.744076   \n",
+       "DD3S3_Cell_138971                     3.756299   \n",
+       "\n",
+       "                   CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.731945   \n",
+       "DD3S2_Cell_1                               3.740073   \n",
+       "DD3S2_Cell_2                               3.743947   \n",
+       "DD3S2_Cell_3                               3.738744   \n",
+       "DD3S2_Cell_4                               3.735192   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.710848   \n",
+       "DD3S3_Cell_138968                          3.801541   \n",
+       "DD3S3_Cell_138969                          3.809775   \n",
+       "DD3S3_Cell_138970                          3.751376   \n",
+       "DD3S3_Cell_138971                          3.774249   \n",
+       "\n",
+       "                   CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              3.757988   \n",
+       "DD3S2_Cell_1                              3.755463   \n",
+       "DD3S2_Cell_2                              3.779793   \n",
+       "DD3S2_Cell_3                              3.769026   \n",
+       "DD3S2_Cell_4                              3.754270   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         4.302837   \n",
+       "DD3S3_Cell_138968                         3.920946   \n",
+       "DD3S3_Cell_138969                         3.898005   \n",
+       "DD3S3_Cell_138970                         3.871842   \n",
+       "DD3S3_Cell_138971                         3.976772   \n",
+       "\n",
+       "                   CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.757782   \n",
+       "DD3S2_Cell_1                               3.746730   \n",
+       "DD3S2_Cell_2                               3.773698   \n",
+       "DD3S2_Cell_3                               3.769681   \n",
+       "DD3S2_Cell_4                               3.745287   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.743908   \n",
+       "DD3S3_Cell_138968                          3.893987   \n",
+       "DD3S3_Cell_138969                          3.802692   \n",
+       "DD3S3_Cell_138970                          3.842041   \n",
+       "DD3S3_Cell_138971                          3.927563   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.762732   \n",
+       "DD3S2_Cell_1                                3.749495   \n",
+       "DD3S2_Cell_2                                3.788075   \n",
+       "DD3S2_Cell_3                                3.772496   \n",
+       "DD3S2_Cell_4                                3.755890   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.716590   \n",
+       "DD3S3_Cell_138968                           3.822514   \n",
+       "DD3S3_Cell_138969                           3.804490   \n",
+       "DD3S3_Cell_138970                           3.816635   \n",
+       "DD3S3_Cell_138971                           3.852828   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               5.188862   \n",
+       "DD3S2_Cell_1                               4.094320   \n",
+       "DD3S2_Cell_2                               5.399720   \n",
+       "DD3S2_Cell_3                               5.769240   \n",
+       "DD3S2_Cell_4                               4.315353   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          3.394785   \n",
+       "DD3S3_Cell_138968                          4.585814   \n",
+       "DD3S3_Cell_138969                          4.442185   \n",
+       "DD3S3_Cell_138970                          4.884997   \n",
+       "DD3S3_Cell_138971                          4.748085   \n",
+       "\n",
+       "                   CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                3.736770   \n",
+       "DD3S2_Cell_1                                3.747230   \n",
+       "DD3S2_Cell_2                                3.746609   \n",
+       "DD3S2_Cell_3                                3.741989   \n",
+       "DD3S2_Cell_4                                3.743215   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           3.737397   \n",
+       "DD3S3_Cell_138968                           3.871388   \n",
+       "DD3S3_Cell_138969                           3.823960   \n",
+       "DD3S3_Cell_138970                           3.785072   \n",
+       "DD3S3_Cell_138971                           3.799142   \n",
+       "\n",
+       "                   Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                          ...                 \n",
+       "DD3S2_Cell_0                                      3.738262  ...   2033.197510   \n",
+       "DD3S2_Cell_1                                      3.760019  ...   2101.638184   \n",
+       "DD3S2_Cell_2                                      3.751351  ...   2008.148193   \n",
+       "DD3S2_Cell_3                                      3.745374  ...   1992.441162   \n",
+       "DD3S2_Cell_4                                      3.758664  ...   2092.007080   \n",
+       "...                                                    ...  ...           ...   \n",
+       "DD3S3_Cell_138967                                 3.733490  ...  14827.791992   \n",
+       "DD3S3_Cell_138968                                 3.813215  ...  14520.892578   \n",
+       "DD3S3_Cell_138969                                 3.811463  ...  14528.000000   \n",
+       "DD3S3_Cell_138970                                 3.799217  ...  14553.461914   \n",
+       "DD3S3_Cell_138971                                 3.836915  ...  14543.513672   \n",
+       "\n",
+       "                   cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                            \n",
+       "DD3S2_Cell_0               none          0               none  15494.777344   \n",
+       "DD3S2_Cell_1               none          0               none  15495.552734   \n",
+       "DD3S2_Cell_2               none          0               none  15492.370117   \n",
+       "DD3S2_Cell_3               none          0               none  15489.808594   \n",
+       "DD3S2_Cell_4               none          0               none  15488.482422   \n",
+       "...                         ...        ...                ...           ...   \n",
+       "DD3S3_Cell_138967          none         63               none   1648.513916   \n",
+       "DD3S3_Cell_138968          none         63               none   1642.884644   \n",
+       "DD3S3_Cell_138969          none         63               none   1640.038452   \n",
+       "DD3S3_Cell_138970          none         63               none   1629.507690   \n",
+       "DD3S3_Cell_138971          none         63               none   1628.541626   \n",
+       "\n",
+       "                   Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                      \n",
+       "DD3S2_Cell_0                0.792312        307  DD3S2.csv       none   \n",
+       "DD3S2_Cell_1                0.749236        159  DD3S2.csv       none   \n",
+       "DD3S2_Cell_2                0.835324        267  DD3S2.csv       none   \n",
+       "DD3S2_Cell_3                0.671957        293  DD3S2.csv       none   \n",
+       "DD3S2_Cell_4                0.710829        225  DD3S2.csv       none   \n",
+       "...                              ...        ...        ...        ...   \n",
+       "DD3S3_Cell_138967           0.921498        318  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138968           0.740617        309  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138969           0.694157        155  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138970           0.821441        243  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138971           0.816589        193  DD3S3.csv       none   \n",
+       "\n",
+       "                   Nucleus_Size  \n",
+       "ID                               \n",
+       "DD3S2_Cell_0                 81  \n",
+       "DD3S2_Cell_1                 47  \n",
+       "DD3S2_Cell_2                 81  \n",
+       "DD3S2_Cell_3                 68  \n",
+       "DD3S2_Cell_4                141  \n",
+       "...                         ...  \n",
+       "DD3S3_Cell_138967            72  \n",
+       "DD3S3_Cell_138968           130  \n",
+       "DD3S3_Cell_138969            52  \n",
+       "DD3S3_Cell_138970            65  \n",
+       "DD3S3_Cell_138971            72  \n",
+       "\n",
+       "[341938 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_merged = df_markers.merge(df, left_index=True, right_on='ID', how='left')\n",
+    "df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "69ac31a0-1f78-41d7-a8bd-e71e4e4277e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a copy, just in case you need to restart the kernel\n",
+    "df_merged_copy = df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "8ed2fbfe-4967-489d-924b-bf42e485a9bc",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_4</th>\n",
+       "      <td>3.780812</td>\n",
+       "      <td>3.749643</td>\n",
+       "      <td>3.724881</td>\n",
+       "      <td>3.730103</td>\n",
+       "      <td>3.808783</td>\n",
+       "      <td>3.772261</td>\n",
+       "      <td>3.757757</td>\n",
+       "      <td>4.359946</td>\n",
+       "      <td>3.771580</td>\n",
+       "      <td>3.736860</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1603.610596</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>12259.198242</td>\n",
+       "      <td>0.639497</td>\n",
+       "      <td>307</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_300</th>\n",
+       "      <td>3.785247</td>\n",
+       "      <td>3.750925</td>\n",
+       "      <td>3.728579</td>\n",
+       "      <td>3.740359</td>\n",
+       "      <td>3.833438</td>\n",
+       "      <td>3.964515</td>\n",
+       "      <td>3.813220</td>\n",
+       "      <td>5.277452</td>\n",
+       "      <td>3.796030</td>\n",
+       "      <td>3.752645</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1870.953491</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>12142.197266</td>\n",
+       "      <td>0.557669</td>\n",
+       "      <td>271</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1231</th>\n",
+       "      <td>3.774846</td>\n",
+       "      <td>3.748975</td>\n",
+       "      <td>3.722177</td>\n",
+       "      <td>3.731672</td>\n",
+       "      <td>3.836845</td>\n",
+       "      <td>3.771828</td>\n",
+       "      <td>3.732071</td>\n",
+       "      <td>4.035427</td>\n",
+       "      <td>3.764658</td>\n",
+       "      <td>3.737986</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1076.486938</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11927.400391</td>\n",
+       "      <td>0.909491</td>\n",
+       "      <td>215</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1240</th>\n",
+       "      <td>3.781590</td>\n",
+       "      <td>3.724414</td>\n",
+       "      <td>3.722949</td>\n",
+       "      <td>3.737432</td>\n",
+       "      <td>3.824108</td>\n",
+       "      <td>3.846841</td>\n",
+       "      <td>3.755064</td>\n",
+       "      <td>5.029926</td>\n",
+       "      <td>3.768938</td>\n",
+       "      <td>3.740266</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1089.709717</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11927.365234</td>\n",
+       "      <td>0.945592</td>\n",
+       "      <td>198</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_2107</th>\n",
+       "      <td>3.795724</td>\n",
+       "      <td>3.725851</td>\n",
+       "      <td>3.725005</td>\n",
+       "      <td>3.729301</td>\n",
+       "      <td>3.859715</td>\n",
+       "      <td>3.876810</td>\n",
+       "      <td>3.889249</td>\n",
+       "      <td>6.013026</td>\n",
+       "      <td>3.782804</td>\n",
+       "      <td>3.757063</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1375.939697</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11758.577148</td>\n",
+       "      <td>0.664589</td>\n",
+       "      <td>167</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>3.821807</td>\n",
+       "      <td>3.738845</td>\n",
+       "      <td>3.739614</td>\n",
+       "      <td>3.744093</td>\n",
+       "      <td>3.780470</td>\n",
+       "      <td>4.392201</td>\n",
+       "      <td>3.738359</td>\n",
+       "      <td>3.839200</td>\n",
+       "      <td>3.755351</td>\n",
+       "      <td>3.829806</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2663.253418</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>386</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>3.828994</td>\n",
+       "      <td>3.746076</td>\n",
+       "      <td>3.742209</td>\n",
+       "      <td>3.743732</td>\n",
+       "      <td>3.771572</td>\n",
+       "      <td>4.343234</td>\n",
+       "      <td>3.743386</td>\n",
+       "      <td>3.880995</td>\n",
+       "      <td>3.766607</td>\n",
+       "      <td>3.845841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2661.765869</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>270</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>3.805670</td>\n",
+       "      <td>3.740683</td>\n",
+       "      <td>3.738909</td>\n",
+       "      <td>3.741262</td>\n",
+       "      <td>3.764948</td>\n",
+       "      <td>4.553124</td>\n",
+       "      <td>3.739506</td>\n",
+       "      <td>3.838884</td>\n",
+       "      <td>3.749027</td>\n",
+       "      <td>3.802058</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2657.015625</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>202</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>3.794075</td>\n",
+       "      <td>3.740020</td>\n",
+       "      <td>3.739098</td>\n",
+       "      <td>3.740781</td>\n",
+       "      <td>3.763053</td>\n",
+       "      <td>4.331289</td>\n",
+       "      <td>3.738531</td>\n",
+       "      <td>3.841646</td>\n",
+       "      <td>3.749990</td>\n",
+       "      <td>3.817734</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2660.258545</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>182</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>3.841316</td>\n",
+       "      <td>3.740180</td>\n",
+       "      <td>3.740770</td>\n",
+       "      <td>3.744660</td>\n",
+       "      <td>3.783784</td>\n",
+       "      <td>4.744351</td>\n",
+       "      <td>3.745600</td>\n",
+       "      <td>3.855980</td>\n",
+       "      <td>3.758473</td>\n",
+       "      <td>3.850599</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2654.292480</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>295</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>94742 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               3.780812   \n",
+       "TMA_Cell_300                             3.785247   \n",
+       "TMA_Cell_1231                            3.774846   \n",
+       "TMA_Cell_1240                            3.781590   \n",
+       "TMA_Cell_2107                            3.795724   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          3.821807   \n",
+       "TMA_Cell_115756                          3.828994   \n",
+       "TMA_Cell_115757                          3.805670   \n",
+       "TMA_Cell_115758                          3.794075   \n",
+       "TMA_Cell_115760                          3.841316   \n",
+       "\n",
+       "                 CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                                3.749643   \n",
+       "TMA_Cell_300                              3.750925   \n",
+       "TMA_Cell_1231                             3.748975   \n",
+       "TMA_Cell_1240                             3.724414   \n",
+       "TMA_Cell_2107                             3.725851   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           3.738845   \n",
+       "TMA_Cell_115756                           3.746076   \n",
+       "TMA_Cell_115757                           3.740683   \n",
+       "TMA_Cell_115758                           3.740020   \n",
+       "TMA_Cell_115760                           3.740180   \n",
+       "\n",
+       "                 B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                             \n",
+       "TMA_Cell_4                          3.724881   \n",
+       "TMA_Cell_300                        3.728579   \n",
+       "TMA_Cell_1231                       3.722177   \n",
+       "TMA_Cell_1240                       3.722949   \n",
+       "TMA_Cell_2107                       3.725005   \n",
+       "...                                      ...   \n",
+       "TMA_Cell_115755                     3.739614   \n",
+       "TMA_Cell_115756                     3.742209   \n",
+       "TMA_Cell_115757                     3.738909   \n",
+       "TMA_Cell_115758                     3.739098   \n",
+       "TMA_Cell_115760                     3.740770   \n",
+       "\n",
+       "                 CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               3.730103   \n",
+       "TMA_Cell_300                             3.740359   \n",
+       "TMA_Cell_1231                            3.731672   \n",
+       "TMA_Cell_1240                            3.737432   \n",
+       "TMA_Cell_2107                            3.729301   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          3.744093   \n",
+       "TMA_Cell_115756                          3.743732   \n",
+       "TMA_Cell_115757                          3.741262   \n",
+       "TMA_Cell_115758                          3.740781   \n",
+       "TMA_Cell_115760                          3.744660   \n",
+       "\n",
+       "                 CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                              3.808783   \n",
+       "TMA_Cell_300                            3.833438   \n",
+       "TMA_Cell_1231                           3.836845   \n",
+       "TMA_Cell_1240                           3.824108   \n",
+       "TMA_Cell_2107                           3.859715   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         3.780470   \n",
+       "TMA_Cell_115756                         3.771572   \n",
+       "TMA_Cell_115757                         3.764948   \n",
+       "TMA_Cell_115758                         3.763053   \n",
+       "TMA_Cell_115760                         3.783784   \n",
+       "\n",
+       "                 CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               3.772261   \n",
+       "TMA_Cell_300                             3.964515   \n",
+       "TMA_Cell_1231                            3.771828   \n",
+       "TMA_Cell_1240                            3.846841   \n",
+       "TMA_Cell_2107                            3.876810   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          4.392201   \n",
+       "TMA_Cell_115756                          4.343234   \n",
+       "TMA_Cell_115757                          4.553124   \n",
+       "TMA_Cell_115758                          4.331289   \n",
+       "TMA_Cell_115760                          4.744351   \n",
+       "\n",
+       "                 PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                                3.757757   \n",
+       "TMA_Cell_300                              3.813220   \n",
+       "TMA_Cell_1231                             3.732071   \n",
+       "TMA_Cell_1240                             3.755064   \n",
+       "TMA_Cell_2107                             3.889249   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           3.738359   \n",
+       "TMA_Cell_115756                           3.743386   \n",
+       "TMA_Cell_115757                           3.739506   \n",
+       "TMA_Cell_115758                           3.738531   \n",
+       "TMA_Cell_115760                           3.745600   \n",
+       "\n",
+       "                 aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               4.359946   \n",
+       "TMA_Cell_300                             5.277452   \n",
+       "TMA_Cell_1231                            4.035427   \n",
+       "TMA_Cell_1240                            5.029926   \n",
+       "TMA_Cell_2107                            6.013026   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          3.839200   \n",
+       "TMA_Cell_115756                          3.880995   \n",
+       "TMA_Cell_115757                          3.838884   \n",
+       "TMA_Cell_115758                          3.841646   \n",
+       "TMA_Cell_115760                          3.855980   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                                3.771580   \n",
+       "TMA_Cell_300                              3.796030   \n",
+       "TMA_Cell_1231                             3.764658   \n",
+       "TMA_Cell_1240                             3.768938   \n",
+       "TMA_Cell_2107                             3.782804   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           3.755351   \n",
+       "TMA_Cell_115756                           3.766607   \n",
+       "TMA_Cell_115757                           3.749027   \n",
+       "TMA_Cell_115758                           3.749990   \n",
+       "TMA_Cell_115760                           3.758473   \n",
+       "\n",
+       "                 Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                        ...                 \n",
+       "TMA_Cell_4                                      3.736860  ...   1603.610596   \n",
+       "TMA_Cell_300                                    3.752645  ...   1870.953491   \n",
+       "TMA_Cell_1231                                   3.737986  ...   1076.486938   \n",
+       "TMA_Cell_1240                                   3.740266  ...   1089.709717   \n",
+       "TMA_Cell_2107                                   3.757063  ...   1375.939697   \n",
+       "...                                                  ...  ...           ...   \n",
+       "TMA_Cell_115755                                 3.829806  ...  15564.458008   \n",
+       "TMA_Cell_115756                                 3.845841  ...  15629.680664   \n",
+       "TMA_Cell_115757                                 3.802058  ...  15518.421875   \n",
+       "TMA_Cell_115758                                 3.817734  ...  15539.275391   \n",
+       "TMA_Cell_115760                                 3.850599  ...  15542.961914   \n",
+       "\n",
+       "                 cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                          \n",
+       "TMA_Cell_4               none          0               none  12259.198242   \n",
+       "TMA_Cell_300             none          0               none  12142.197266   \n",
+       "TMA_Cell_1231            none          0               none  11927.400391   \n",
+       "TMA_Cell_1240            none          0               none  11927.365234   \n",
+       "TMA_Cell_2107            none          0               none  11758.577148   \n",
+       "...                       ...        ...                ...           ...   \n",
+       "TMA_Cell_115755          none         59               none   2663.253418   \n",
+       "TMA_Cell_115756          none         59               none   2661.765869   \n",
+       "TMA_Cell_115757          none         59               none   2657.015625   \n",
+       "TMA_Cell_115758          none         59               none   2660.258545   \n",
+       "TMA_Cell_115760          none         59               none   2654.292480   \n",
+       "\n",
+       "                 Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                    \n",
+       "TMA_Cell_4                0.639497        307    TMA.csv       none   \n",
+       "TMA_Cell_300              0.557669        271    TMA.csv       none   \n",
+       "TMA_Cell_1231             0.909491        215    TMA.csv       none   \n",
+       "TMA_Cell_1240             0.945592        198    TMA.csv       none   \n",
+       "TMA_Cell_2107             0.664589        167    TMA.csv       none   \n",
+       "...                            ...        ...        ...        ...   \n",
+       "TMA_Cell_115755           0.982196        386    TMA.csv       none   \n",
+       "TMA_Cell_115756           0.775977        270    TMA.csv       none   \n",
+       "TMA_Cell_115757           0.688747        202    TMA.csv       none   \n",
+       "TMA_Cell_115758           0.751402        182    TMA.csv       none   \n",
+       "TMA_Cell_115760           0.674126        295    TMA.csv       none   \n",
+       "\n",
+       "                 Nucleus_Size  \n",
+       "ID                             \n",
+       "TMA_Cell_4                131  \n",
+       "TMA_Cell_300               86  \n",
+       "TMA_Cell_1231             115  \n",
+       "TMA_Cell_1240              93  \n",
+       "TMA_Cell_2107             116  \n",
+       "...                       ...  \n",
+       "TMA_Cell_115755           142  \n",
+       "TMA_Cell_115756            47  \n",
+       "TMA_Cell_115757            64  \n",
+       "TMA_Cell_115758            58  \n",
+       "TMA_Cell_115760           106  \n",
+       "\n",
+       "[94742 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filters the rows of the DataFrame df_merged based on the values in the 'Sample_ID' column\n",
+    "# df_subset will contain a subset of rows from df_merged where the 'Sample_ID' matches the values in the list 'keep' ('TMA.csv' in this case)\n",
+    "keep = ['TMA.csv']\n",
+    "df_subset = df_merged.loc[df_merged['Sample_ID'].isin(keep),:].copy()\n",
+    "df_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "b2260afd-7e3a-4c2a-9cfa-01eb16fb9474",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zscore rows finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Z-score normalization\n",
+    "# Z-score the rows (apply() with axis = 1, only perform on intensity data)\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "df_subset.loc[:,~df_subset.columns.isin(not_intensities)] = \\\n",
+    "    df_subset.loc[:,~df_subset.columns.isin(not_intensities)].apply(\n",
+    "        lambda row: (row - row.median())/(row.std(ddof=0)), axis = 1)\n",
+    "df_subset.dropna(how = 'all', inplace = True, axis = 1)\n",
+    "print('zscore rows finished')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "204cff9e-4964-472b-8be9-12f33ed6b74b",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_4</th>\n",
+       "      <td>0.037975</td>\n",
+       "      <td>-0.090232</td>\n",
+       "      <td>-0.192088</td>\n",
+       "      <td>-0.170609</td>\n",
+       "      <td>0.153029</td>\n",
+       "      <td>0.002801</td>\n",
+       "      <td>-0.056857</td>\n",
+       "      <td>2.420162</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.142813</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1603.610596</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>12259.198242</td>\n",
+       "      <td>0.639497</td>\n",
+       "      <td>307</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_300</th>\n",
+       "      <td>-0.006220</td>\n",
+       "      <td>-0.092313</td>\n",
+       "      <td>-0.148365</td>\n",
+       "      <td>-0.118816</td>\n",
+       "      <td>0.114663</td>\n",
+       "      <td>0.443455</td>\n",
+       "      <td>0.063947</td>\n",
+       "      <td>3.736817</td>\n",
+       "      <td>0.020830</td>\n",
+       "      <td>-0.087998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1870.953491</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>12142.197266</td>\n",
+       "      <td>0.557669</td>\n",
+       "      <td>271</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1231</th>\n",
+       "      <td>0.061042</td>\n",
+       "      <td>-0.064835</td>\n",
+       "      <td>-0.195221</td>\n",
+       "      <td>-0.149023</td>\n",
+       "      <td>0.362701</td>\n",
+       "      <td>0.046357</td>\n",
+       "      <td>-0.147079</td>\n",
+       "      <td>1.328911</td>\n",
+       "      <td>0.011473</td>\n",
+       "      <td>-0.118299</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1076.486938</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11927.400391</td>\n",
+       "      <td>0.909491</td>\n",
+       "      <td>215</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1240</th>\n",
+       "      <td>0.043759</td>\n",
+       "      <td>-0.153990</td>\n",
+       "      <td>-0.159056</td>\n",
+       "      <td>-0.108965</td>\n",
+       "      <td>0.190813</td>\n",
+       "      <td>0.269438</td>\n",
+       "      <td>-0.047983</td>\n",
+       "      <td>4.361285</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.099163</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1089.709717</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11927.365234</td>\n",
+       "      <td>0.945592</td>\n",
+       "      <td>198</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_2107</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.144613</td>\n",
+       "      <td>-0.146364</td>\n",
+       "      <td>-0.137474</td>\n",
+       "      <td>0.132441</td>\n",
+       "      <td>0.167821</td>\n",
+       "      <td>0.193565</td>\n",
+       "      <td>4.589084</td>\n",
+       "      <td>-0.026741</td>\n",
+       "      <td>-0.080016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1375.939697</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>11758.577148</td>\n",
+       "      <td>0.664589</td>\n",
+       "      <td>167</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.167315</td>\n",
+       "      <td>-0.161220</td>\n",
+       "      <td>-0.158175</td>\n",
+       "      <td>-0.140436</td>\n",
+       "      <td>0.003619</td>\n",
+       "      <td>2.426113</td>\n",
+       "      <td>-0.163142</td>\n",
+       "      <td>0.236192</td>\n",
+       "      <td>-0.095854</td>\n",
+       "      <td>0.198991</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2663.253418</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>386</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.168391</td>\n",
+       "      <td>-0.231211</td>\n",
+       "      <td>-0.249849</td>\n",
+       "      <td>-0.242510</td>\n",
+       "      <td>-0.108340</td>\n",
+       "      <td>2.646651</td>\n",
+       "      <td>-0.244177</td>\n",
+       "      <td>0.419000</td>\n",
+       "      <td>-0.132270</td>\n",
+       "      <td>0.249579</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2661.765869</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>270</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.140926</td>\n",
+       "      <td>-0.123519</td>\n",
+       "      <td>-0.130738</td>\n",
+       "      <td>-0.121163</td>\n",
+       "      <td>-0.024781</td>\n",
+       "      <td>3.182450</td>\n",
+       "      <td>-0.128310</td>\n",
+       "      <td>0.276081</td>\n",
+       "      <td>-0.089567</td>\n",
+       "      <td>0.126228</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2657.015625</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>202</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>0.072588</td>\n",
+       "      <td>-0.203340</td>\n",
+       "      <td>-0.208043</td>\n",
+       "      <td>-0.199453</td>\n",
+       "      <td>-0.085765</td>\n",
+       "      <td>2.814824</td>\n",
+       "      <td>-0.210942</td>\n",
+       "      <td>0.315417</td>\n",
+       "      <td>-0.152446</td>\n",
+       "      <td>0.193357</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2660.258545</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>182</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.123770</td>\n",
+       "      <td>-0.187289</td>\n",
+       "      <td>-0.185475</td>\n",
+       "      <td>-0.173511</td>\n",
+       "      <td>-0.053176</td>\n",
+       "      <td>2.901200</td>\n",
+       "      <td>-0.170618</td>\n",
+       "      <td>0.168873</td>\n",
+       "      <td>-0.131025</td>\n",
+       "      <td>0.152323</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>59</td>\n",
+       "      <td>none</td>\n",
+       "      <td>2654.292480</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>295</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>94742 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               0.037975   \n",
+       "TMA_Cell_300                            -0.006220   \n",
+       "TMA_Cell_1231                            0.061042   \n",
+       "TMA_Cell_1240                            0.043759   \n",
+       "TMA_Cell_2107                            0.000000   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.167315   \n",
+       "TMA_Cell_115756                          0.168391   \n",
+       "TMA_Cell_115757                          0.140926   \n",
+       "TMA_Cell_115758                          0.072588   \n",
+       "TMA_Cell_115760                          0.123770   \n",
+       "\n",
+       "                 CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                               -0.090232   \n",
+       "TMA_Cell_300                             -0.092313   \n",
+       "TMA_Cell_1231                            -0.064835   \n",
+       "TMA_Cell_1240                            -0.153990   \n",
+       "TMA_Cell_2107                            -0.144613   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.161220   \n",
+       "TMA_Cell_115756                          -0.231211   \n",
+       "TMA_Cell_115757                          -0.123519   \n",
+       "TMA_Cell_115758                          -0.203340   \n",
+       "TMA_Cell_115760                          -0.187289   \n",
+       "\n",
+       "                 B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                             \n",
+       "TMA_Cell_4                         -0.192088   \n",
+       "TMA_Cell_300                       -0.148365   \n",
+       "TMA_Cell_1231                      -0.195221   \n",
+       "TMA_Cell_1240                      -0.159056   \n",
+       "TMA_Cell_2107                      -0.146364   \n",
+       "...                                      ...   \n",
+       "TMA_Cell_115755                    -0.158175   \n",
+       "TMA_Cell_115756                    -0.249849   \n",
+       "TMA_Cell_115757                    -0.130738   \n",
+       "TMA_Cell_115758                    -0.208043   \n",
+       "TMA_Cell_115760                    -0.185475   \n",
+       "\n",
+       "                 CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                              -0.170609   \n",
+       "TMA_Cell_300                            -0.118816   \n",
+       "TMA_Cell_1231                           -0.149023   \n",
+       "TMA_Cell_1240                           -0.108965   \n",
+       "TMA_Cell_2107                           -0.137474   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                         -0.140436   \n",
+       "TMA_Cell_115756                         -0.242510   \n",
+       "TMA_Cell_115757                         -0.121163   \n",
+       "TMA_Cell_115758                         -0.199453   \n",
+       "TMA_Cell_115760                         -0.173511   \n",
+       "\n",
+       "                 CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                              0.153029   \n",
+       "TMA_Cell_300                            0.114663   \n",
+       "TMA_Cell_1231                           0.362701   \n",
+       "TMA_Cell_1240                           0.190813   \n",
+       "TMA_Cell_2107                           0.132441   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.003619   \n",
+       "TMA_Cell_115756                        -0.108340   \n",
+       "TMA_Cell_115757                        -0.024781   \n",
+       "TMA_Cell_115758                        -0.085765   \n",
+       "TMA_Cell_115760                        -0.053176   \n",
+       "\n",
+       "                 CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               0.002801   \n",
+       "TMA_Cell_300                             0.443455   \n",
+       "TMA_Cell_1231                            0.046357   \n",
+       "TMA_Cell_1240                            0.269438   \n",
+       "TMA_Cell_2107                            0.167821   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          2.426113   \n",
+       "TMA_Cell_115756                          2.646651   \n",
+       "TMA_Cell_115757                          3.182450   \n",
+       "TMA_Cell_115758                          2.814824   \n",
+       "TMA_Cell_115760                          2.901200   \n",
+       "\n",
+       "                 PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                               -0.056857   \n",
+       "TMA_Cell_300                              0.063947   \n",
+       "TMA_Cell_1231                            -0.147079   \n",
+       "TMA_Cell_1240                            -0.047983   \n",
+       "TMA_Cell_2107                             0.193565   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.163142   \n",
+       "TMA_Cell_115756                          -0.244177   \n",
+       "TMA_Cell_115757                          -0.128310   \n",
+       "TMA_Cell_115758                          -0.210942   \n",
+       "TMA_Cell_115760                          -0.170618   \n",
+       "\n",
+       "                 aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               2.420162   \n",
+       "TMA_Cell_300                             3.736817   \n",
+       "TMA_Cell_1231                            1.328911   \n",
+       "TMA_Cell_1240                            4.361285   \n",
+       "TMA_Cell_2107                            4.589084   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.236192   \n",
+       "TMA_Cell_115756                          0.419000   \n",
+       "TMA_Cell_115757                          0.276081   \n",
+       "TMA_Cell_115758                          0.315417   \n",
+       "TMA_Cell_115760                          0.168873   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                                0.000000   \n",
+       "TMA_Cell_300                              0.020830   \n",
+       "TMA_Cell_1231                             0.011473   \n",
+       "TMA_Cell_1240                             0.000000   \n",
+       "TMA_Cell_2107                            -0.026741   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.095854   \n",
+       "TMA_Cell_115756                          -0.132270   \n",
+       "TMA_Cell_115757                          -0.089567   \n",
+       "TMA_Cell_115758                          -0.152446   \n",
+       "TMA_Cell_115760                          -0.131025   \n",
+       "\n",
+       "                 Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                        ...                 \n",
+       "TMA_Cell_4                                     -0.142813  ...   1603.610596   \n",
+       "TMA_Cell_300                                   -0.087998  ...   1870.953491   \n",
+       "TMA_Cell_1231                                  -0.118299  ...   1076.486938   \n",
+       "TMA_Cell_1240                                  -0.099163  ...   1089.709717   \n",
+       "TMA_Cell_2107                                  -0.080016  ...   1375.939697   \n",
+       "...                                                  ...  ...           ...   \n",
+       "TMA_Cell_115755                                 0.198991  ...  15564.458008   \n",
+       "TMA_Cell_115756                                 0.249579  ...  15629.680664   \n",
+       "TMA_Cell_115757                                 0.126228  ...  15518.421875   \n",
+       "TMA_Cell_115758                                 0.193357  ...  15539.275391   \n",
+       "TMA_Cell_115760                                 0.152323  ...  15542.961914   \n",
+       "\n",
+       "                 cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                          \n",
+       "TMA_Cell_4               none          0               none  12259.198242   \n",
+       "TMA_Cell_300             none          0               none  12142.197266   \n",
+       "TMA_Cell_1231            none          0               none  11927.400391   \n",
+       "TMA_Cell_1240            none          0               none  11927.365234   \n",
+       "TMA_Cell_2107            none          0               none  11758.577148   \n",
+       "...                       ...        ...                ...           ...   \n",
+       "TMA_Cell_115755          none         59               none   2663.253418   \n",
+       "TMA_Cell_115756          none         59               none   2661.765869   \n",
+       "TMA_Cell_115757          none         59               none   2657.015625   \n",
+       "TMA_Cell_115758          none         59               none   2660.258545   \n",
+       "TMA_Cell_115760          none         59               none   2654.292480   \n",
+       "\n",
+       "                 Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                    \n",
+       "TMA_Cell_4                0.639497        307    TMA.csv       none   \n",
+       "TMA_Cell_300              0.557669        271    TMA.csv       none   \n",
+       "TMA_Cell_1231             0.909491        215    TMA.csv       none   \n",
+       "TMA_Cell_1240             0.945592        198    TMA.csv       none   \n",
+       "TMA_Cell_2107             0.664589        167    TMA.csv       none   \n",
+       "...                            ...        ...        ...        ...   \n",
+       "TMA_Cell_115755           0.982196        386    TMA.csv       none   \n",
+       "TMA_Cell_115756           0.775977        270    TMA.csv       none   \n",
+       "TMA_Cell_115757           0.688747        202    TMA.csv       none   \n",
+       "TMA_Cell_115758           0.751402        182    TMA.csv       none   \n",
+       "TMA_Cell_115760           0.674126        295    TMA.csv       none   \n",
+       "\n",
+       "                 Nucleus_Size  \n",
+       "ID                             \n",
+       "TMA_Cell_4                131  \n",
+       "TMA_Cell_300               86  \n",
+       "TMA_Cell_1231             115  \n",
+       "TMA_Cell_1240              93  \n",
+       "TMA_Cell_2107             116  \n",
+       "...                       ...  \n",
+       "TMA_Cell_115755           142  \n",
+       "TMA_Cell_115756            47  \n",
+       "TMA_Cell_115757            64  \n",
+       "TMA_Cell_115758            58  \n",
+       "TMA_Cell_115760           106  \n",
+       "\n",
+       "[94742 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "58d297e7-a9af-46b2-9912-b5d402102a4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zscore rows finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Z-score the rows (apply() with axis = 1, only perform on intensity data)\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "df_merged.loc[:,~df_merged.columns.isin(not_intensities)] = \\\n",
+    "    df_merged.loc[:,~df_merged.columns.isin(not_intensities)].apply(\n",
+    "        lambda row: (row - row.median())/(row.std(ddof=0)), axis = 1)\n",
+    "df_merged.dropna(how = 'all', inplace = True, axis = 1)\n",
+    "print('zscore rows finished')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d4de8037-34fb-4353-843b-cd5e616a20e0",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>0.024960</td>\n",
+       "      <td>-0.075347</td>\n",
+       "      <td>-0.040222</td>\n",
+       "      <td>-0.058405</td>\n",
+       "      <td>0.024381</td>\n",
+       "      <td>0.023726</td>\n",
+       "      <td>0.039464</td>\n",
+       "      <td>4.572897</td>\n",
+       "      <td>-0.043068</td>\n",
+       "      <td>-0.038325</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>307</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>0.075136</td>\n",
+       "      <td>0.164953</td>\n",
+       "      <td>-0.045760</td>\n",
+       "      <td>-0.080848</td>\n",
+       "      <td>-0.002287</td>\n",
+       "      <td>-0.046869</td>\n",
+       "      <td>-0.032752</td>\n",
+       "      <td>1.727537</td>\n",
+       "      <td>-0.044315</td>\n",
+       "      <td>0.020972</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>159</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>0.022444</td>\n",
+       "      <td>-0.011432</td>\n",
+       "      <td>-0.034903</td>\n",
+       "      <td>-0.032326</td>\n",
+       "      <td>0.054732</td>\n",
+       "      <td>0.039929</td>\n",
+       "      <td>0.074845</td>\n",
+       "      <td>3.988960</td>\n",
+       "      <td>-0.025861</td>\n",
+       "      <td>-0.014343</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>267</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>0.011138</td>\n",
+       "      <td>-0.014071</td>\n",
+       "      <td>-0.033624</td>\n",
+       "      <td>-0.030370</td>\n",
+       "      <td>0.043005</td>\n",
+       "      <td>0.044592</td>\n",
+       "      <td>0.051412</td>\n",
+       "      <td>4.889609</td>\n",
+       "      <td>-0.022507</td>\n",
+       "      <td>-0.014305</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>293</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>0.077567</td>\n",
+       "      <td>-0.181457</td>\n",
+       "      <td>-0.060652</td>\n",
+       "      <td>-0.107426</td>\n",
+       "      <td>-0.008411</td>\n",
+       "      <td>-0.055030</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.903647</td>\n",
+       "      <td>-0.065787</td>\n",
+       "      <td>0.014397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>225</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>0.069663</td>\n",
+       "      <td>-0.105790</td>\n",
+       "      <td>-0.107686</td>\n",
+       "      <td>-0.090768</td>\n",
+       "      <td>2.282360</td>\n",
+       "      <td>0.041760</td>\n",
+       "      <td>-0.067749</td>\n",
+       "      <td>-1.357780</td>\n",
+       "      <td>0.015659</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>318</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>0.166680</td>\n",
+       "      <td>-0.111050</td>\n",
+       "      <td>-0.203723</td>\n",
+       "      <td>-0.063771</td>\n",
+       "      <td>0.299286</td>\n",
+       "      <td>0.217316</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.320848</td>\n",
+       "      <td>0.148603</td>\n",
+       "      <td>-0.028275</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>309</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>0.128945</td>\n",
+       "      <td>-0.081516</td>\n",
+       "      <td>-0.168154</td>\n",
+       "      <td>-0.002777</td>\n",
+       "      <td>0.266238</td>\n",
+       "      <td>-0.024371</td>\n",
+       "      <td>-0.018890</td>\n",
+       "      <td>1.925448</td>\n",
+       "      <td>0.040476</td>\n",
+       "      <td>0.002371</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>155</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>0.065912</td>\n",
+       "      <td>-0.120796</td>\n",
+       "      <td>-0.157338</td>\n",
+       "      <td>-0.138697</td>\n",
+       "      <td>0.168946</td>\n",
+       "      <td>0.092840</td>\n",
+       "      <td>0.027960</td>\n",
+       "      <td>2.756305</td>\n",
+       "      <td>-0.052645</td>\n",
+       "      <td>-0.016522</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>243</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>0.121561</td>\n",
+       "      <td>-0.179724</td>\n",
+       "      <td>-0.286979</td>\n",
+       "      <td>-0.223079</td>\n",
+       "      <td>0.497862</td>\n",
+       "      <td>0.322688</td>\n",
+       "      <td>0.056645</td>\n",
+       "      <td>3.243583</td>\n",
+       "      <td>-0.134464</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>193</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               0.024960   \n",
+       "DD3S2_Cell_1                               0.075136   \n",
+       "DD3S2_Cell_2                               0.022444   \n",
+       "DD3S2_Cell_3                               0.011138   \n",
+       "DD3S2_Cell_4                               0.077567   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          0.069663   \n",
+       "DD3S3_Cell_138968                          0.166680   \n",
+       "DD3S3_Cell_138969                          0.128945   \n",
+       "DD3S3_Cell_138970                          0.065912   \n",
+       "DD3S3_Cell_138971                          0.121561   \n",
+       "\n",
+       "                   CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                               -0.075347   \n",
+       "DD3S2_Cell_1                                0.164953   \n",
+       "DD3S2_Cell_2                               -0.011432   \n",
+       "DD3S2_Cell_3                               -0.014071   \n",
+       "DD3S2_Cell_4                               -0.181457   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                          -0.105790   \n",
+       "DD3S3_Cell_138968                          -0.111050   \n",
+       "DD3S3_Cell_138969                          -0.081516   \n",
+       "DD3S3_Cell_138970                          -0.120796   \n",
+       "DD3S3_Cell_138971                          -0.179724   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                         -0.040222   \n",
+       "DD3S2_Cell_1                         -0.045760   \n",
+       "DD3S2_Cell_2                         -0.034903   \n",
+       "DD3S2_Cell_3                         -0.033624   \n",
+       "DD3S2_Cell_4                         -0.060652   \n",
+       "...                                        ...   \n",
+       "DD3S3_Cell_138967                    -0.107686   \n",
+       "DD3S3_Cell_138968                    -0.203723   \n",
+       "DD3S3_Cell_138969                    -0.168154   \n",
+       "DD3S3_Cell_138970                    -0.157338   \n",
+       "DD3S3_Cell_138971                    -0.286979   \n",
+       "\n",
+       "                   CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.058405   \n",
+       "DD3S2_Cell_1                              -0.080848   \n",
+       "DD3S2_Cell_2                              -0.032326   \n",
+       "DD3S2_Cell_3                              -0.030370   \n",
+       "DD3S2_Cell_4                              -0.107426   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.090768   \n",
+       "DD3S3_Cell_138968                         -0.063771   \n",
+       "DD3S3_Cell_138969                         -0.002777   \n",
+       "DD3S3_Cell_138970                         -0.138697   \n",
+       "DD3S3_Cell_138971                         -0.223079   \n",
+       "\n",
+       "                   CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              0.024381   \n",
+       "DD3S2_Cell_1                             -0.002287   \n",
+       "DD3S2_Cell_2                              0.054732   \n",
+       "DD3S2_Cell_3                              0.043005   \n",
+       "DD3S2_Cell_4                             -0.008411   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         2.282360   \n",
+       "DD3S3_Cell_138968                         0.299286   \n",
+       "DD3S3_Cell_138969                         0.266238   \n",
+       "DD3S3_Cell_138970                         0.168946   \n",
+       "DD3S3_Cell_138971                         0.497862   \n",
+       "\n",
+       "                   CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               0.023726   \n",
+       "DD3S2_Cell_1                              -0.046869   \n",
+       "DD3S2_Cell_2                               0.039929   \n",
+       "DD3S2_Cell_3                               0.044592   \n",
+       "DD3S2_Cell_4                              -0.055030   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          0.041760   \n",
+       "DD3S3_Cell_138968                          0.217316   \n",
+       "DD3S3_Cell_138969                         -0.024371   \n",
+       "DD3S3_Cell_138970                          0.092840   \n",
+       "DD3S3_Cell_138971                          0.322688   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.039464   \n",
+       "DD3S2_Cell_1                               -0.032752   \n",
+       "DD3S2_Cell_2                                0.074845   \n",
+       "DD3S2_Cell_3                                0.051412   \n",
+       "DD3S2_Cell_4                                0.000000   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                          -0.067749   \n",
+       "DD3S3_Cell_138968                           0.000000   \n",
+       "DD3S3_Cell_138969                          -0.018890   \n",
+       "DD3S3_Cell_138970                           0.027960   \n",
+       "DD3S3_Cell_138971                           0.056645   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               4.572897   \n",
+       "DD3S2_Cell_1                               1.727537   \n",
+       "DD3S2_Cell_2                               3.988960   \n",
+       "DD3S2_Cell_3                               4.889609   \n",
+       "DD3S2_Cell_4                               2.903647   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -1.357780   \n",
+       "DD3S3_Cell_138968                          2.320848   \n",
+       "DD3S3_Cell_138969                          1.925448   \n",
+       "DD3S3_Cell_138970                          2.756305   \n",
+       "DD3S3_Cell_138971                          3.243583   \n",
+       "\n",
+       "                   CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                               -0.043068   \n",
+       "DD3S2_Cell_1                               -0.044315   \n",
+       "DD3S2_Cell_2                               -0.025861   \n",
+       "DD3S2_Cell_3                               -0.022507   \n",
+       "DD3S2_Cell_4                               -0.065787   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           0.015659   \n",
+       "DD3S3_Cell_138968                           0.148603   \n",
+       "DD3S3_Cell_138969                           0.040476   \n",
+       "DD3S3_Cell_138970                          -0.052645   \n",
+       "DD3S3_Cell_138971                          -0.134464   \n",
+       "\n",
+       "                   Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                          ...                 \n",
+       "DD3S2_Cell_0                                     -0.038325  ...   2033.197510   \n",
+       "DD3S2_Cell_1                                      0.020972  ...   2101.638184   \n",
+       "DD3S2_Cell_2                                     -0.014343  ...   2008.148193   \n",
+       "DD3S2_Cell_3                                     -0.014305  ...   1992.441162   \n",
+       "DD3S2_Cell_4                                      0.014397  ...   2092.007080   \n",
+       "...                                                    ...  ...           ...   \n",
+       "DD3S3_Cell_138967                                 0.000000  ...  14827.791992   \n",
+       "DD3S3_Cell_138968                                -0.028275  ...  14520.892578   \n",
+       "DD3S3_Cell_138969                                 0.002371  ...  14528.000000   \n",
+       "DD3S3_Cell_138970                                -0.016522  ...  14553.461914   \n",
+       "DD3S3_Cell_138971                                 0.000000  ...  14543.513672   \n",
+       "\n",
+       "                   cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                            \n",
+       "DD3S2_Cell_0               none          0               none  15494.777344   \n",
+       "DD3S2_Cell_1               none          0               none  15495.552734   \n",
+       "DD3S2_Cell_2               none          0               none  15492.370117   \n",
+       "DD3S2_Cell_3               none          0               none  15489.808594   \n",
+       "DD3S2_Cell_4               none          0               none  15488.482422   \n",
+       "...                         ...        ...                ...           ...   \n",
+       "DD3S3_Cell_138967          none         63               none   1648.513916   \n",
+       "DD3S3_Cell_138968          none         63               none   1642.884644   \n",
+       "DD3S3_Cell_138969          none         63               none   1640.038452   \n",
+       "DD3S3_Cell_138970          none         63               none   1629.507690   \n",
+       "DD3S3_Cell_138971          none         63               none   1628.541626   \n",
+       "\n",
+       "                   Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                      \n",
+       "DD3S2_Cell_0                0.792312        307  DD3S2.csv       none   \n",
+       "DD3S2_Cell_1                0.749236        159  DD3S2.csv       none   \n",
+       "DD3S2_Cell_2                0.835324        267  DD3S2.csv       none   \n",
+       "DD3S2_Cell_3                0.671957        293  DD3S2.csv       none   \n",
+       "DD3S2_Cell_4                0.710829        225  DD3S2.csv       none   \n",
+       "...                              ...        ...        ...        ...   \n",
+       "DD3S3_Cell_138967           0.921498        318  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138968           0.740617        309  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138969           0.694157        155  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138970           0.821441        243  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138971           0.816589        193  DD3S3.csv       none   \n",
+       "\n",
+       "                   Nucleus_Size  \n",
+       "ID                               \n",
+       "DD3S2_Cell_0                 81  \n",
+       "DD3S2_Cell_1                 47  \n",
+       "DD3S2_Cell_2                 81  \n",
+       "DD3S2_Cell_3                 68  \n",
+       "DD3S2_Cell_4                141  \n",
+       "...                         ...  \n",
+       "DD3S3_Cell_138967            72  \n",
+       "DD3S3_Cell_138968           130  \n",
+       "DD3S3_Cell_138969            52  \n",
+       "DD3S3_Cell_138970            65  \n",
+       "DD3S3_Cell_138971            72  \n",
+       "\n",
+       "[341938 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "f08ad579-6fb8-4f9d-a852-d155b0a267a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>-0.064917</td>\n",
+       "      <td>0.076870</td>\n",
+       "      <td>0.095181</td>\n",
+       "      <td>0.111720</td>\n",
+       "      <td>0.036713</td>\n",
+       "      <td>-0.140269</td>\n",
+       "      <td>0.060348</td>\n",
+       "      <td>3.032121</td>\n",
+       "      <td>0.074029</td>\n",
+       "      <td>-0.142342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>307</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>-0.014741</td>\n",
+       "      <td>0.317169</td>\n",
+       "      <td>0.089642</td>\n",
+       "      <td>0.089277</td>\n",
+       "      <td>0.010045</td>\n",
+       "      <td>-0.210864</td>\n",
+       "      <td>-0.011868</td>\n",
+       "      <td>0.186760</td>\n",
+       "      <td>0.072781</td>\n",
+       "      <td>-0.083045</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>159</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>-0.067434</td>\n",
+       "      <td>0.140784</td>\n",
+       "      <td>0.100500</td>\n",
+       "      <td>0.137799</td>\n",
+       "      <td>0.067064</td>\n",
+       "      <td>-0.124066</td>\n",
+       "      <td>0.095729</td>\n",
+       "      <td>2.448184</td>\n",
+       "      <td>0.091235</td>\n",
+       "      <td>-0.118360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>267</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>-0.078740</td>\n",
+       "      <td>0.138145</td>\n",
+       "      <td>0.101778</td>\n",
+       "      <td>0.139755</td>\n",
+       "      <td>0.055337</td>\n",
+       "      <td>-0.119403</td>\n",
+       "      <td>0.072296</td>\n",
+       "      <td>3.348832</td>\n",
+       "      <td>0.094589</td>\n",
+       "      <td>-0.118322</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>293</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>-0.012311</td>\n",
+       "      <td>-0.029241</td>\n",
+       "      <td>0.074751</td>\n",
+       "      <td>0.062699</td>\n",
+       "      <td>0.003921</td>\n",
+       "      <td>-0.219026</td>\n",
+       "      <td>0.020884</td>\n",
+       "      <td>1.362871</td>\n",
+       "      <td>0.051309</td>\n",
+       "      <td>-0.089620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>225</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>-0.020215</td>\n",
+       "      <td>0.046427</td>\n",
+       "      <td>0.027716</td>\n",
+       "      <td>0.079357</td>\n",
+       "      <td>2.294692</td>\n",
+       "      <td>-0.122235</td>\n",
+       "      <td>-0.046865</td>\n",
+       "      <td>-2.898557</td>\n",
+       "      <td>0.132755</td>\n",
+       "      <td>-0.104017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>318</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>0.076802</td>\n",
+       "      <td>0.041167</td>\n",
+       "      <td>-0.068320</td>\n",
+       "      <td>0.106354</td>\n",
+       "      <td>0.311618</td>\n",
+       "      <td>0.053321</td>\n",
+       "      <td>0.020884</td>\n",
+       "      <td>0.780072</td>\n",
+       "      <td>0.265699</td>\n",
+       "      <td>-0.132292</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>309</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>0.039068</td>\n",
+       "      <td>0.070701</td>\n",
+       "      <td>-0.032752</td>\n",
+       "      <td>0.167348</td>\n",
+       "      <td>0.278570</td>\n",
+       "      <td>-0.188367</td>\n",
+       "      <td>0.001995</td>\n",
+       "      <td>0.384672</td>\n",
+       "      <td>0.157572</td>\n",
+       "      <td>-0.101646</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>155</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>-0.023966</td>\n",
+       "      <td>0.031420</td>\n",
+       "      <td>-0.021935</td>\n",
+       "      <td>0.031428</td>\n",
+       "      <td>0.181278</td>\n",
+       "      <td>-0.071155</td>\n",
+       "      <td>0.048845</td>\n",
+       "      <td>1.215529</td>\n",
+       "      <td>0.064451</td>\n",
+       "      <td>-0.120539</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>243</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>0.031683</td>\n",
+       "      <td>-0.027507</td>\n",
+       "      <td>-0.151576</td>\n",
+       "      <td>-0.052954</td>\n",
+       "      <td>0.510193</td>\n",
+       "      <td>0.158693</td>\n",
+       "      <td>0.077529</td>\n",
+       "      <td>1.702806</td>\n",
+       "      <td>-0.017368</td>\n",
+       "      <td>-0.104017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>193</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.064917   \n",
+       "DD3S2_Cell_1                              -0.014741   \n",
+       "DD3S2_Cell_2                              -0.067434   \n",
+       "DD3S2_Cell_3                              -0.078740   \n",
+       "DD3S2_Cell_4                              -0.012311   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.020215   \n",
+       "DD3S3_Cell_138968                          0.076802   \n",
+       "DD3S3_Cell_138969                          0.039068   \n",
+       "DD3S3_Cell_138970                         -0.023966   \n",
+       "DD3S3_Cell_138971                          0.031683   \n",
+       "\n",
+       "                   CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.076870   \n",
+       "DD3S2_Cell_1                                0.317169   \n",
+       "DD3S2_Cell_2                                0.140784   \n",
+       "DD3S2_Cell_3                                0.138145   \n",
+       "DD3S2_Cell_4                               -0.029241   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           0.046427   \n",
+       "DD3S3_Cell_138968                           0.041167   \n",
+       "DD3S3_Cell_138969                           0.070701   \n",
+       "DD3S3_Cell_138970                           0.031420   \n",
+       "DD3S3_Cell_138971                          -0.027507   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          0.095181   \n",
+       "DD3S2_Cell_1                          0.089642   \n",
+       "DD3S2_Cell_2                          0.100500   \n",
+       "DD3S2_Cell_3                          0.101778   \n",
+       "DD3S2_Cell_4                          0.074751   \n",
+       "...                                        ...   \n",
+       "DD3S3_Cell_138967                     0.027716   \n",
+       "DD3S3_Cell_138968                    -0.068320   \n",
+       "DD3S3_Cell_138969                    -0.032752   \n",
+       "DD3S3_Cell_138970                    -0.021935   \n",
+       "DD3S3_Cell_138971                    -0.151576   \n",
+       "\n",
+       "                   CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               0.111720   \n",
+       "DD3S2_Cell_1                               0.089277   \n",
+       "DD3S2_Cell_2                               0.137799   \n",
+       "DD3S2_Cell_3                               0.139755   \n",
+       "DD3S2_Cell_4                               0.062699   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          0.079357   \n",
+       "DD3S3_Cell_138968                          0.106354   \n",
+       "DD3S3_Cell_138969                          0.167348   \n",
+       "DD3S3_Cell_138970                          0.031428   \n",
+       "DD3S3_Cell_138971                         -0.052954   \n",
+       "\n",
+       "                   CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              0.036713   \n",
+       "DD3S2_Cell_1                              0.010045   \n",
+       "DD3S2_Cell_2                              0.067064   \n",
+       "DD3S2_Cell_3                              0.055337   \n",
+       "DD3S2_Cell_4                              0.003921   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                         2.294692   \n",
+       "DD3S3_Cell_138968                         0.311618   \n",
+       "DD3S3_Cell_138969                         0.278570   \n",
+       "DD3S3_Cell_138970                         0.181278   \n",
+       "DD3S3_Cell_138971                         0.510193   \n",
+       "\n",
+       "                   CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.140269   \n",
+       "DD3S2_Cell_1                              -0.210864   \n",
+       "DD3S2_Cell_2                              -0.124066   \n",
+       "DD3S2_Cell_3                              -0.119403   \n",
+       "DD3S2_Cell_4                              -0.219026   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.122235   \n",
+       "DD3S3_Cell_138968                          0.053321   \n",
+       "DD3S3_Cell_138969                         -0.188367   \n",
+       "DD3S3_Cell_138970                         -0.071155   \n",
+       "DD3S3_Cell_138971                          0.158693   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.060348   \n",
+       "DD3S2_Cell_1                               -0.011868   \n",
+       "DD3S2_Cell_2                                0.095729   \n",
+       "DD3S2_Cell_3                                0.072296   \n",
+       "DD3S2_Cell_4                                0.020884   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                          -0.046865   \n",
+       "DD3S3_Cell_138968                           0.020884   \n",
+       "DD3S3_Cell_138969                           0.001995   \n",
+       "DD3S3_Cell_138970                           0.048845   \n",
+       "DD3S3_Cell_138971                           0.077529   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               3.032121   \n",
+       "DD3S2_Cell_1                               0.186760   \n",
+       "DD3S2_Cell_2                               2.448184   \n",
+       "DD3S2_Cell_3                               3.348832   \n",
+       "DD3S2_Cell_4                               1.362871   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -2.898557   \n",
+       "DD3S3_Cell_138968                          0.780072   \n",
+       "DD3S3_Cell_138969                          0.384672   \n",
+       "DD3S3_Cell_138970                          1.215529   \n",
+       "DD3S3_Cell_138971                          1.702806   \n",
+       "\n",
+       "                   CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.074029   \n",
+       "DD3S2_Cell_1                                0.072781   \n",
+       "DD3S2_Cell_2                                0.091235   \n",
+       "DD3S2_Cell_3                                0.094589   \n",
+       "DD3S2_Cell_4                                0.051309   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           0.132755   \n",
+       "DD3S3_Cell_138968                           0.265699   \n",
+       "DD3S3_Cell_138969                           0.157572   \n",
+       "DD3S3_Cell_138970                           0.064451   \n",
+       "DD3S3_Cell_138971                          -0.017368   \n",
+       "\n",
+       "                   Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                          ...                 \n",
+       "DD3S2_Cell_0                                     -0.142342  ...   2033.197510   \n",
+       "DD3S2_Cell_1                                     -0.083045  ...   2101.638184   \n",
+       "DD3S2_Cell_2                                     -0.118360  ...   2008.148193   \n",
+       "DD3S2_Cell_3                                     -0.118322  ...   1992.441162   \n",
+       "DD3S2_Cell_4                                     -0.089620  ...   2092.007080   \n",
+       "...                                                    ...  ...           ...   \n",
+       "DD3S3_Cell_138967                                -0.104017  ...  14827.791992   \n",
+       "DD3S3_Cell_138968                                -0.132292  ...  14520.892578   \n",
+       "DD3S3_Cell_138969                                -0.101646  ...  14528.000000   \n",
+       "DD3S3_Cell_138970                                -0.120539  ...  14553.461914   \n",
+       "DD3S3_Cell_138971                                -0.104017  ...  14543.513672   \n",
+       "\n",
+       "                   cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                            \n",
+       "DD3S2_Cell_0               none          0               none  15494.777344   \n",
+       "DD3S2_Cell_1               none          0               none  15495.552734   \n",
+       "DD3S2_Cell_2               none          0               none  15492.370117   \n",
+       "DD3S2_Cell_3               none          0               none  15489.808594   \n",
+       "DD3S2_Cell_4               none          0               none  15488.482422   \n",
+       "...                         ...        ...                ...           ...   \n",
+       "DD3S3_Cell_138967          none         63               none   1648.513916   \n",
+       "DD3S3_Cell_138968          none         63               none   1642.884644   \n",
+       "DD3S3_Cell_138969          none         63               none   1640.038452   \n",
+       "DD3S3_Cell_138970          none         63               none   1629.507690   \n",
+       "DD3S3_Cell_138971          none         63               none   1628.541626   \n",
+       "\n",
+       "                   Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                      \n",
+       "DD3S2_Cell_0                0.792312        307  DD3S2.csv       none   \n",
+       "DD3S2_Cell_1                0.749236        159  DD3S2.csv       none   \n",
+       "DD3S2_Cell_2                0.835324        267  DD3S2.csv       none   \n",
+       "DD3S2_Cell_3                0.671957        293  DD3S2.csv       none   \n",
+       "DD3S2_Cell_4                0.710829        225  DD3S2.csv       none   \n",
+       "...                              ...        ...        ...        ...   \n",
+       "DD3S3_Cell_138967           0.921498        318  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138968           0.740617        309  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138969           0.694157        155  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138970           0.821441        243  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138971           0.816589        193  DD3S3.csv       none   \n",
+       "\n",
+       "                   Nucleus_Size  \n",
+       "ID                               \n",
+       "DD3S2_Cell_0                 81  \n",
+       "DD3S2_Cell_1                 47  \n",
+       "DD3S2_Cell_2                 81  \n",
+       "DD3S2_Cell_3                 68  \n",
+       "DD3S2_Cell_4                141  \n",
+       "...                         ...  \n",
+       "DD3S3_Cell_138967            72  \n",
+       "DD3S3_Cell_138968           130  \n",
+       "DD3S3_Cell_138969            52  \n",
+       "DD3S3_Cell_138970            65  \n",
+       "DD3S3_Cell_138971            72  \n",
+       "\n",
+       "[341938 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ensuring that the selected columns in df have been adjusted or normalized using the median values\n",
+    "df_merged.loc[:,~df_merged.columns.isin(not_intensities)] = \\\n",
+    "    df_merged.loc[:,~df_merged.columns.isin(not_intensities)] - df_subset.loc[:,~df_subset.columns.isin(not_intensities)].median()\n",
+    "df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e98f6c1a-95f4-47bf-a6a6-b4372af40302",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD11c_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>B7H4_Cell_Intensity_Average</th>\n",
+       "      <th>CD68_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD4_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD44_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PDGFR_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>aSMA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Fibronectin_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_0</th>\n",
+       "      <td>-0.215233</td>\n",
+       "      <td>0.590332</td>\n",
+       "      <td>0.921484</td>\n",
+       "      <td>0.836614</td>\n",
+       "      <td>0.245243</td>\n",
+       "      <td>-0.130029</td>\n",
+       "      <td>0.180935</td>\n",
+       "      <td>1.830807</td>\n",
+       "      <td>0.868011</td>\n",
+       "      <td>-0.355564</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2033.197510</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15494.777344</td>\n",
+       "      <td>0.792312</td>\n",
+       "      <td>307</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_1</th>\n",
+       "      <td>-0.048875</td>\n",
+       "      <td>2.435750</td>\n",
+       "      <td>0.867867</td>\n",
+       "      <td>0.668550</td>\n",
+       "      <td>0.067100</td>\n",
+       "      <td>-0.195470</td>\n",
+       "      <td>-0.035581</td>\n",
+       "      <td>0.112767</td>\n",
+       "      <td>0.853380</td>\n",
+       "      <td>-0.207442</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2101.638184</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15495.552734</td>\n",
+       "      <td>0.749236</td>\n",
+       "      <td>159</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_2</th>\n",
+       "      <td>-0.223577</td>\n",
+       "      <td>1.081173</td>\n",
+       "      <td>0.972982</td>\n",
+       "      <td>1.031907</td>\n",
+       "      <td>0.447987</td>\n",
+       "      <td>-0.115009</td>\n",
+       "      <td>0.287015</td>\n",
+       "      <td>1.478224</td>\n",
+       "      <td>1.069760</td>\n",
+       "      <td>-0.295659</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2008.148193</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15492.370117</td>\n",
+       "      <td>0.835324</td>\n",
+       "      <td>267</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_3</th>\n",
+       "      <td>-0.261062</td>\n",
+       "      <td>1.060907</td>\n",
+       "      <td>0.985361</td>\n",
+       "      <td>1.046554</td>\n",
+       "      <td>0.369650</td>\n",
+       "      <td>-0.110687</td>\n",
+       "      <td>0.216760</td>\n",
+       "      <td>2.022039</td>\n",
+       "      <td>1.109085</td>\n",
+       "      <td>-0.295563</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1992.441162</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15489.808594</td>\n",
+       "      <td>0.671957</td>\n",
+       "      <td>293</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2_Cell_4</th>\n",
+       "      <td>-0.040816</td>\n",
+       "      <td>-0.224558</td>\n",
+       "      <td>0.723692</td>\n",
+       "      <td>0.469517</td>\n",
+       "      <td>0.026192</td>\n",
+       "      <td>-0.203036</td>\n",
+       "      <td>0.062615</td>\n",
+       "      <td>0.822907</td>\n",
+       "      <td>0.601613</td>\n",
+       "      <td>-0.223867</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2092.007080</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>15488.482422</td>\n",
+       "      <td>0.710829</td>\n",
+       "      <td>225</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138967</th>\n",
+       "      <td>-0.067022</td>\n",
+       "      <td>0.356541</td>\n",
+       "      <td>0.268335</td>\n",
+       "      <td>0.594266</td>\n",
+       "      <td>15.328433</td>\n",
+       "      <td>-0.113311</td>\n",
+       "      <td>-0.140511</td>\n",
+       "      <td>-1.750161</td>\n",
+       "      <td>1.556596</td>\n",
+       "      <td>-0.259830</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14827.791992</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1648.513916</td>\n",
+       "      <td>0.921498</td>\n",
+       "      <td>318</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138968</th>\n",
+       "      <td>0.254637</td>\n",
+       "      <td>0.316147</td>\n",
+       "      <td>-0.661440</td>\n",
+       "      <td>0.796430</td>\n",
+       "      <td>2.081596</td>\n",
+       "      <td>0.049428</td>\n",
+       "      <td>0.062615</td>\n",
+       "      <td>0.471011</td>\n",
+       "      <td>3.115412</td>\n",
+       "      <td>-0.330460</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14520.892578</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1642.884644</td>\n",
+       "      <td>0.740617</td>\n",
+       "      <td>309</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138969</th>\n",
+       "      <td>0.129529</td>\n",
+       "      <td>0.542958</td>\n",
+       "      <td>-0.317083</td>\n",
+       "      <td>1.253185</td>\n",
+       "      <td>1.860832</td>\n",
+       "      <td>-0.174615</td>\n",
+       "      <td>0.005980</td>\n",
+       "      <td>0.232267</td>\n",
+       "      <td>1.847584</td>\n",
+       "      <td>-0.253907</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14528.000000</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1640.038452</td>\n",
+       "      <td>0.694157</td>\n",
+       "      <td>155</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138970</th>\n",
+       "      <td>-0.079459</td>\n",
+       "      <td>0.241296</td>\n",
+       "      <td>-0.212365</td>\n",
+       "      <td>0.235347</td>\n",
+       "      <td>1.210926</td>\n",
+       "      <td>-0.065961</td>\n",
+       "      <td>0.146446</td>\n",
+       "      <td>0.733941</td>\n",
+       "      <td>0.755714</td>\n",
+       "      <td>-0.301101</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14553.461914</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1629.507690</td>\n",
+       "      <td>0.821441</td>\n",
+       "      <td>243</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3_Cell_138971</th>\n",
+       "      <td>0.105045</td>\n",
+       "      <td>-0.211245</td>\n",
+       "      <td>-1.467472</td>\n",
+       "      <td>-0.396544</td>\n",
+       "      <td>3.408068</td>\n",
+       "      <td>0.147108</td>\n",
+       "      <td>0.232450</td>\n",
+       "      <td>1.028162</td>\n",
+       "      <td>-0.203649</td>\n",
+       "      <td>-0.259830</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14543.513672</td>\n",
+       "      <td>none</td>\n",
+       "      <td>63</td>\n",
+       "      <td>none</td>\n",
+       "      <td>1628.541626</td>\n",
+       "      <td>0.816589</td>\n",
+       "      <td>193</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341938 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.215233   \n",
+       "DD3S2_Cell_1                              -0.048875   \n",
+       "DD3S2_Cell_2                              -0.223577   \n",
+       "DD3S2_Cell_3                              -0.261062   \n",
+       "DD3S2_Cell_4                              -0.040816   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.067022   \n",
+       "DD3S3_Cell_138968                          0.254637   \n",
+       "DD3S3_Cell_138969                          0.129529   \n",
+       "DD3S3_Cell_138970                         -0.079459   \n",
+       "DD3S3_Cell_138971                          0.105045   \n",
+       "\n",
+       "                   CD11c_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.590332   \n",
+       "DD3S2_Cell_1                                2.435750   \n",
+       "DD3S2_Cell_2                                1.081173   \n",
+       "DD3S2_Cell_3                                1.060907   \n",
+       "DD3S2_Cell_4                               -0.224558   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           0.356541   \n",
+       "DD3S3_Cell_138968                           0.316147   \n",
+       "DD3S3_Cell_138969                           0.542958   \n",
+       "DD3S3_Cell_138970                           0.241296   \n",
+       "DD3S3_Cell_138971                          -0.211245   \n",
+       "\n",
+       "                   B7H4_Cell_Intensity_Average  \\\n",
+       "ID                                               \n",
+       "DD3S2_Cell_0                          0.921484   \n",
+       "DD3S2_Cell_1                          0.867867   \n",
+       "DD3S2_Cell_2                          0.972982   \n",
+       "DD3S2_Cell_3                          0.985361   \n",
+       "DD3S2_Cell_4                          0.723692   \n",
+       "...                                        ...   \n",
+       "DD3S3_Cell_138967                     0.268335   \n",
+       "DD3S3_Cell_138968                    -0.661440   \n",
+       "DD3S3_Cell_138969                    -0.317083   \n",
+       "DD3S3_Cell_138970                    -0.212365   \n",
+       "DD3S3_Cell_138971                    -1.467472   \n",
+       "\n",
+       "                   CD68_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               0.836614   \n",
+       "DD3S2_Cell_1                               0.668550   \n",
+       "DD3S2_Cell_2                               1.031907   \n",
+       "DD3S2_Cell_3                               1.046554   \n",
+       "DD3S2_Cell_4                               0.469517   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                          0.594266   \n",
+       "DD3S3_Cell_138968                          0.796430   \n",
+       "DD3S3_Cell_138969                          1.253185   \n",
+       "DD3S3_Cell_138970                          0.235347   \n",
+       "DD3S3_Cell_138971                         -0.396544   \n",
+       "\n",
+       "                   CD4_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S2_Cell_0                              0.245243   \n",
+       "DD3S2_Cell_1                              0.067100   \n",
+       "DD3S2_Cell_2                              0.447987   \n",
+       "DD3S2_Cell_3                              0.369650   \n",
+       "DD3S2_Cell_4                              0.026192   \n",
+       "...                                            ...   \n",
+       "DD3S3_Cell_138967                        15.328433   \n",
+       "DD3S3_Cell_138968                         2.081596   \n",
+       "DD3S3_Cell_138969                         1.860832   \n",
+       "DD3S3_Cell_138970                         1.210926   \n",
+       "DD3S3_Cell_138971                         3.408068   \n",
+       "\n",
+       "                   CD44_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                              -0.130029   \n",
+       "DD3S2_Cell_1                              -0.195470   \n",
+       "DD3S2_Cell_2                              -0.115009   \n",
+       "DD3S2_Cell_3                              -0.110687   \n",
+       "DD3S2_Cell_4                              -0.203036   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -0.113311   \n",
+       "DD3S3_Cell_138968                          0.049428   \n",
+       "DD3S3_Cell_138969                         -0.174615   \n",
+       "DD3S3_Cell_138970                         -0.065961   \n",
+       "DD3S3_Cell_138971                          0.147108   \n",
+       "\n",
+       "                   PDGFR_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.180935   \n",
+       "DD3S2_Cell_1                               -0.035581   \n",
+       "DD3S2_Cell_2                                0.287015   \n",
+       "DD3S2_Cell_3                                0.216760   \n",
+       "DD3S2_Cell_4                                0.062615   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                          -0.140511   \n",
+       "DD3S3_Cell_138968                           0.062615   \n",
+       "DD3S3_Cell_138969                           0.005980   \n",
+       "DD3S3_Cell_138970                           0.146446   \n",
+       "DD3S3_Cell_138971                           0.232450   \n",
+       "\n",
+       "                   aSMA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S2_Cell_0                               1.830807   \n",
+       "DD3S2_Cell_1                               0.112767   \n",
+       "DD3S2_Cell_2                               1.478224   \n",
+       "DD3S2_Cell_3                               2.022039   \n",
+       "DD3S2_Cell_4                               0.822907   \n",
+       "...                                             ...   \n",
+       "DD3S3_Cell_138967                         -1.750161   \n",
+       "DD3S3_Cell_138968                          0.471011   \n",
+       "DD3S3_Cell_138969                          0.232267   \n",
+       "DD3S3_Cell_138970                          0.733941   \n",
+       "DD3S3_Cell_138971                          1.028162   \n",
+       "\n",
+       "                   CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                     \n",
+       "DD3S2_Cell_0                                0.868011   \n",
+       "DD3S2_Cell_1                                0.853380   \n",
+       "DD3S2_Cell_2                                1.069760   \n",
+       "DD3S2_Cell_3                                1.109085   \n",
+       "DD3S2_Cell_4                                0.601613   \n",
+       "...                                              ...   \n",
+       "DD3S3_Cell_138967                           1.556596   \n",
+       "DD3S3_Cell_138968                           3.115412   \n",
+       "DD3S3_Cell_138969                           1.847584   \n",
+       "DD3S3_Cell_138970                           0.755714   \n",
+       "DD3S3_Cell_138971                          -0.203649   \n",
+       "\n",
+       "                   Fibronectin_Cytoplasm_Intensity_Average  ...         Nuc_X  \\\n",
+       "ID                                                          ...                 \n",
+       "DD3S2_Cell_0                                     -0.355564  ...   2033.197510   \n",
+       "DD3S2_Cell_1                                     -0.207442  ...   2101.638184   \n",
+       "DD3S2_Cell_2                                     -0.295659  ...   2008.148193   \n",
+       "DD3S2_Cell_3                                     -0.295563  ...   1992.441162   \n",
+       "DD3S2_Cell_4                                     -0.223867  ...   2092.007080   \n",
+       "...                                                    ...  ...           ...   \n",
+       "DD3S3_Cell_138967                                -0.259830  ...  14827.791992   \n",
+       "DD3S3_Cell_138968                                -0.330460  ...  14520.892578   \n",
+       "DD3S3_Cell_138969                                -0.253907  ...  14528.000000   \n",
+       "DD3S3_Cell_138970                                -0.301101  ...  14553.461914   \n",
+       "DD3S3_Cell_138971                                -0.259830  ...  14543.513672   \n",
+       "\n",
+       "                   cell_subtype  ROI_index  immune_checkpoint     Nuc_Y_Inv  \\\n",
+       "ID                                                                            \n",
+       "DD3S2_Cell_0               none          0               none  15494.777344   \n",
+       "DD3S2_Cell_1               none          0               none  15495.552734   \n",
+       "DD3S2_Cell_2               none          0               none  15492.370117   \n",
+       "DD3S2_Cell_3               none          0               none  15489.808594   \n",
+       "DD3S2_Cell_4               none          0               none  15488.482422   \n",
+       "...                         ...        ...                ...           ...   \n",
+       "DD3S3_Cell_138967          none         63               none   1648.513916   \n",
+       "DD3S3_Cell_138968          none         63               none   1642.884644   \n",
+       "DD3S3_Cell_138969          none         63               none   1640.038452   \n",
+       "DD3S3_Cell_138970          none         63               none   1629.507690   \n",
+       "DD3S3_Cell_138971          none         63               none   1628.541626   \n",
+       "\n",
+       "                   Nucleus_Roundness  Cell_Size  Sample_ID  cell_type  \\\n",
+       "ID                                                                      \n",
+       "DD3S2_Cell_0                0.792312        307  DD3S2.csv       none   \n",
+       "DD3S2_Cell_1                0.749236        159  DD3S2.csv       none   \n",
+       "DD3S2_Cell_2                0.835324        267  DD3S2.csv       none   \n",
+       "DD3S2_Cell_3                0.671957        293  DD3S2.csv       none   \n",
+       "DD3S2_Cell_4                0.710829        225  DD3S2.csv       none   \n",
+       "...                              ...        ...        ...        ...   \n",
+       "DD3S3_Cell_138967           0.921498        318  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138968           0.740617        309  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138969           0.694157        155  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138970           0.821441        243  DD3S3.csv       none   \n",
+       "DD3S3_Cell_138971           0.816589        193  DD3S3.csv       none   \n",
+       "\n",
+       "                   Nucleus_Size  \n",
+       "ID                               \n",
+       "DD3S2_Cell_0                 81  \n",
+       "DD3S2_Cell_1                 47  \n",
+       "DD3S2_Cell_2                 81  \n",
+       "DD3S2_Cell_3                 68  \n",
+       "DD3S2_Cell_4                141  \n",
+       "...                         ...  \n",
+       "DD3S3_Cell_138967            72  \n",
+       "DD3S3_Cell_138968           130  \n",
+       "DD3S3_Cell_138969            52  \n",
+       "DD3S3_Cell_138970            65  \n",
+       "DD3S3_Cell_138971            72  \n",
+       "\n",
+       "[341938 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_merged.loc[:,~df_merged.columns.isin(not_intensities)] = \\\n",
+    "    df_merged.loc[:,~df_merged.columns.isin(not_intensities)] / df_subset.loc[:,~df_subset.columns.isin(not_intensities)].std(ddof=0)\n",
+    "df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "a0f496dc-8108-4db1-b6be-beb6309a2120",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7ecddf5-caea-4a82-95cc-b11e140255e8",
+   "metadata": {},
+   "source": [
+    "## III.7. SAVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "1e9d7877-c96a-42de-8616-2468b9aeeeef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S2_zscore.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/TMA_zscore.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S1_zscore.csv was created!\n",
+      "File /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_zscore/DD3S3_zscore.csv was created!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the data by Sample_ID\n",
+    "# Check for the existence of the output file first\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0]\n",
+    "    filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "    if os.path.exists(filename):\n",
+    "        print(\"File by name \"+filename+\" already exists.\")\n",
+    "    else:\n",
+    "        sample_id_csv = sample_id + '.csv'\n",
+    "        df_save = df_merged.loc[df['Sample_ID'] == sample_id_csv, :]\n",
+    "        #print(df_save)\n",
+    "        filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "        df_save.to_csv(filename, index=True, index_label='ID')  # Set index parameter to True to retain the index column\n",
+    "        print(\"File \" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad41ae17-9881-4d48-a782-f69e48c58a98",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b99ad434-28e1-487d-97c2-f25f1c52cfc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb b/wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f9450d3743921e95934104b01ccbf54d2648f1f0
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/4_markers_tresholds.ipynb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df84f15a0e72c1a84daae210d6334f49150c5d2aa03aae2688f1f839fdb1aeb1
+size 49760040
diff --git a/wetransfer_data-zip_2024-05-17_1431/5_cells_quant_class.ipynb b/wetransfer_data-zip_2024-05-17_1431/5_cells_quant_class.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ca0d0d8bc993b8ad90dfe9f4e97b8a29b7d940ed
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/5_cells_quant_class.ipynb
@@ -0,0 +1,63574 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d8258b09-f8c5-486c-8206-8ba8f475af5d",
+   "metadata": {},
+   "source": [
+    "# V. CELLS QUANTIFICATION AND CLASSIFICATION"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "7ce34589-aa00-4bd7-b047-49389c6b824a",
+   "metadata": {},
+   "source": [
+    "11/01/2024\n",
+    "Modifications by Zoé Gerber\n",
+    "from an original code from Marilyne Labrie"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "3e2a9466-d945-4f72-abdf-d442f5a37015",
+   "metadata": {},
+   "source": [
+    "V.1. PACKAGES IMPORT\n",
+    "V.2. DIRECTORIES\n",
+    "V.3. FILES\n",
+    "    V.3.1. METADATA\n",
+    "    V.3.2. NOT_INTENSITIES\n",
+    "    V.3.3. FULL_TO_SHORT_COLUMN_NAMES\n",
+    "    V.3.4. SHORT_TO_FULL_COLUMN_NAMES\n",
+    "    V.3.5. SAMPLES COLORS\n",
+    "    V.3.6. CHANNELS COLORS\n",
+    "    V.3.7. ROUNDS COLORS\n",
+    "    V.3.8. CELL TYPES COLORS\n",
+    "    V.3.9. CELL SUBTYPES COLORS\n",
+    "    V.3.10. IMMUNE CHECKPOINT COLORS\n",
+    "    V.3.11. DATA\n",
+    "    V.3.12. UNIQUE ROIs\n",
+    "    V.3.13. NACT/NACT\n",
+    "V.4. COUNTS\n",
+    "    V.4.1. CELL TYPES\n",
+    "    V.4.2. CELL SUBTYPES\n",
+    "        V.4.2.1. \n",
+    "        \n",
+    "    V.4.3. NACT\n",
+    "V.5. XY MAPS\n",
+    "V.6. CORRELATION PLOTS\n",
+    "    V.6.1. OPTION 1\n",
+    "    V.6.2. OPTION 2\n",
+    "V.7. PCA\n",
+    "V.8. KMEANS\n",
+    "V.9. T-SNE\n",
+    "V.10. SAVE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f771a071-3f4c-4d05-954d-0c732ea5bf0e",
+   "metadata": {},
+   "source": [
+    "## V.1. PACKAGES IMPORT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3c51f7fc-8851-48d5-b953-7a7e6a62baa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/5g/xq32h5h56rj2qzygbnhjg65h0000gn/T/ipykernel_22409/1860222382.py:13: DeprecationWarning: Please import `pearsonr` from the `scipy.stats` namespace; the `scipy.stats.stats` namespace is deprecated and will be removed in SciPy 2.0.0.\n",
+      "  from scipy.stats.stats import pearsonr\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sb\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mplc\n",
+    "import plotly.graph_objects as go\n",
+    "import warnings\n",
+    "import plotly.express as px\n",
+    "\n",
+    "from scipy.stats.stats import pearsonr\n",
+    "\n",
+    "from my_modules import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ccdde05-5c79-4b30-a0ff-d6347a96bed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Silence FutureWarnings & UserWarnings\n",
+    "warnings.filterwarnings('ignore', category= FutureWarning)\n",
+    "warnings.filterwarnings('ignore', category= UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4234174-1a38-47f6-9a79-4902ef916c1d",
+   "metadata": {},
+   "source": [
+    "## V.2. *DIRECTORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "590d74b3-bfc9-418d-97b5-f1a9d0519e6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set base directory\n",
+    "\n",
+    "##### MAC WORKSTATION #####\n",
+    "#base_dir = r'/Volumes/LaboLabrie/Projets/OC_TMA_Pejovic/Temp/Zoe/CyCIF_pipeline/'\n",
+    "###########################\n",
+    "\n",
+    "##### WINDOWS WORKSTATION #####\n",
+    "#base_dir = r'C:\\Users\\LaboLabrie\\gerz2701\\Cyc-IF_pipeline\\Set_B'\n",
+    "###############################\n",
+    "\n",
+    "##### LOCAL WORKSTATION #####\n",
+    "base_dir = r'/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431'\n",
+    "#############################\n",
+    "\n",
+    "#set_name = 'Set_A'\n",
+    "set_name = 'test'"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "4b322058-fc47-4933-a7ac-e811187e3c91",
+   "metadata": {},
+   "source": [
+    "The project is organized as :\n",
+    "main dir \n",
+    "    code\n",
+    "    proj_data             > all csv files\n",
+    "    proj_metadata         > exposure time csv file, images dir,...\n",
+    "    proj_qc_eda           > csv after the QC/EDA step\n",
+    "    proj_bs               > csv after the BS step\n",
+    "    proj_zscore           > csv after the ZCORES step\n",
+    "    proj_mt               > csv after the MARKERS TRESHOLDS step\n",
+    "    proj_cqc              > csv after the CELLS QUANTICICATION AND CLASSIFICATION step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3629fde0-6ab2-41c2-b7d8-0d881b6d0311",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431 directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_mt directory already exists !\n",
+      "Creation of the /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_cqc directory...\n",
+      "Creation of the /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_cqc/images directory...\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata directory already exists !\n",
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images directory already exists !\n"
+     ]
+    }
+   ],
+   "source": [
+    "project_name = set_name                 # Project name\n",
+    "step_suffix = 'cqc'                     # Curent part (here part V)\n",
+    "previous_step_suffix_long = \"_mt\"       # Previous part (here MT NOTEBOOK)\n",
+    "\n",
+    "# Initial input data directory\n",
+    "input_data_dir = os.path.join(base_dir, project_name + previous_step_suffix_long) \n",
+    "\n",
+    "# BS output directories\n",
+    "output_data_dir = os.path.join(base_dir, project_name + \"_\" + step_suffix)\n",
+    "# BS images subdirectory\n",
+    "output_images_dir = os.path.join(output_data_dir,\"images\")\n",
+    "\n",
+    "# Data and Metadata directories\n",
+    "# Metadata directories\n",
+    "metadata_dir = os.path.join(base_dir, project_name + \"_metadata\")\n",
+    "# images subdirectory\n",
+    "metadata_images_dir = os.path.join(metadata_dir,\"images\")\n",
+    "\n",
+    "# Create directories if they don't already exist\n",
+    "for d in [base_dir, input_data_dir, output_data_dir, output_images_dir, metadata_dir, metadata_images_dir]:\n",
+    "    if not os.path.exists(d):\n",
+    "        print(\"Creation of the\" , d, \"directory...\")\n",
+    "        os.makedirs(d)\n",
+    "    else :\n",
+    "        print(\"The\", d, \"directory already exists !\")\n",
+    "\n",
+    "os.chdir(input_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "adc46cb2-a2c8-4ee6-ab2a-f2a69c907eef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431\n",
+      "input_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_mt\n",
+      "output_data_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_cqc\n",
+      "output_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_cqc/images\n",
+      "metadata_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata\n",
+      "metadata_images_dir : /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/images\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify paths\n",
+    "print('base_dir :', base_dir)\n",
+    "print('input_data_dir :', input_data_dir)\n",
+    "print('output_data_dir :', output_data_dir)\n",
+    "print('output_images_dir :', output_images_dir)\n",
+    "print('metadata_dir :', metadata_dir)\n",
+    "print('metadata_images_dir :', metadata_images_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd22fe75-ff48-464b-8461-0ec543a7ed79",
+   "metadata": {},
+   "source": [
+    "## V.3. FILES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15862ca9-d6c2-4d7f-a90a-5b86990a4fd4",
+   "metadata": {},
+   "source": [
+    "### V.3.1. METADATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0863ca5e-11aa-4b4b-b87f-1a4a6d0f60ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv file was imported for further analysis!\n",
+      "WARNING: 'Marker metadata file' has the following unexpected item(s): \n",
+      "['Exp']\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>Round</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>Exp</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>target_lower</th>\n",
+       "      <th>full_column</th>\n",
+       "      <th>marker</th>\n",
+       "      <th>localisation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cell_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>300</td>\n",
+       "      <td>c2</td>\n",
+       "      <td>af488</td>\n",
+       "      <td>AF488_Nucleus_Intensity_Average</td>\n",
+       "      <td>AF488</td>\n",
+       "      <td>nucleus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cell_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R0</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>c3</td>\n",
+       "      <td>af555</td>\n",
+       "      <td>AF555_Cytoplasm_Intensity_Average</td>\n",
+       "      <td>AF555</td>\n",
+       "      <td>cytoplasm</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Round Target   Exp Channel target_lower                        full_column  \\\n",
+       "0    R0  AF488   300      c2        af488       AF488_Cell_Intensity_Average   \n",
+       "1    R0  AF488   300      c2        af488  AF488_Cytoplasm_Intensity_Average   \n",
+       "2    R0  AF488   300      c2        af488    AF488_Nucleus_Intensity_Average   \n",
+       "3    R0  AF555  1500      c3        af555       AF555_Cell_Intensity_Average   \n",
+       "4    R0  AF555  1500      c3        af555  AF555_Cytoplasm_Intensity_Average   \n",
+       "\n",
+       "  marker localisation  \n",
+       "0  AF488         cell  \n",
+       "1  AF488    cytoplasm  \n",
+       "2  AF488      nucleus  \n",
+       "3  AF555         cell  \n",
+       "4  AF555    cytoplasm  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import all metadata we need from the BS chapter\n",
+    "\n",
+    "filename = \"marker_intensity_metadata.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "\n",
+    "# Open, read in information\n",
+    "metadata = pd.read_csv(filename)\n",
+    "\n",
+    "# Verify size with verify_line_no() function in my_modules.py\n",
+    "#verify_line_no(filename, metadata.shape[0] + 1)\n",
+    "\n",
+    "# Verify headers\n",
+    "exp_cols = ['Round','Target','Channel','target_lower','full_column','marker','localisation']\n",
+    "compare_headers(exp_cols, metadata.columns.values, \"Marker metadata file\")\n",
+    "\n",
+    "metadata = metadata.dropna()\n",
+    "metadata.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "890f832b-ce09-4194-87d3-d71ca672bfbe",
+   "metadata": {},
+   "source": [
+    "### V.3.2. NOT_INTENSITIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f05a229d-756b-409b-9134-ed698e594a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "not_intensities =\n",
+      " ['Cytoplasm_Size', 'Nuc_X', 'Primary_chem(1)_vs_surg(0)', 'cluster', 'immune_checkpoint', 'Sample_ID', 'Nucleus_Roundness', 'Unique_ROI_index', 'Nuc_Y', 'Nuc_X_Inv', 'Cell_ID', 'cell_subtype', 'ID', 'Nuc_Y_Inv', 'Patient', 'replicate_ID', 'cell_type', 'ROI_index', 'Cell_Size', 'Nucleus_Size']\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"not_intensities.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "not_intensities = []\n",
+    "with open(filename, 'r') as fh:\n",
+    "    not_intensities = fh.read().strip().split(\"\\n\")\n",
+    "    # take str, strip whitespace, split on new line character\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, len(not_intensities))\n",
+    "\n",
+    "# Print to console\n",
+    "print(\"not_intensities =\\n\", not_intensities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8c13e7b-4a11-4375-93e5-a7550e986833",
+   "metadata": {},
+   "source": [
+    "### V.3.3. FULL_TO_SHORT_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dbaa0a13-f499-41d5-abdc-a6f7be1f0600",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "full_to_short_names =\n",
+      " {'AF488_Cell_Intensity_Average': 'AF488_Cell', 'AF488_Cytoplasm_Intensity_Average': 'AF488_Cytoplasm', 'AF488_Nucleus_Intensity_Average': 'AF488_Nucleus', 'AF555_Cell_Intensity_Average': 'AF555_Cell', 'AF555_Cytoplasm_Intensity_Average': 'AF555_Cytoplasm', 'AF555_Nucleus_Intensity_Average': 'AF555_Nucleus', 'AF647_Cell_Intensity_Average': 'AF647_Cell', 'AF647_Cytoplasm_Intensity_Average': 'AF647_Cytoplasm', 'AF647_Nucleus_Intensity_Average': 'AF647_Nucleus', 'AF750_Cell_Intensity_Average': 'AF750_Cell', 'AF750_Cytoplasm_Intensity_Average': 'AF750_Cytoplasm', 'AF750_Nucleus_Intensity_Average': 'AF750_Nucleus', 'aSMA_Cell_Intensity_Average': 'aSMA_Cell', 'aSMA_Cytoplasm_Intensity_Average': 'aSMA_Cytoplasm', 'aSMA_Nucleus_Intensity_Average': 'aSMA_Nucleus', 'AXL_Cell_Intensity_Average': 'AXL_Cell', 'AXL_Cytoplasm_Intensity_Average': 'AXL_Cytoplasm', 'AXL_Nucleus_Intensity_Average': 'AXL_Nucleus', 'B7H4_Cell_Intensity_Average': 'B7H4_Cell', 'B7H4_Cytoplasm_Intensity_Average': 'B7H4_Cytoplasm', 'B7H4_Nucleus_Intensity_Average': 'B7H4_Nucleus', 'CA9_Cell_Intensity_Average': 'CA9_Cell', 'CA9_Cytoplasm_Intensity_Average': 'CA9_Cytoplasm', 'CA9_Nucleus_Intensity_Average': 'CA9_Nucleus', 'CD4_Cell_Intensity_Average': 'CD4_Cell', 'CD4_Cytoplasm_Intensity_Average': 'CD4_Cytoplasm', 'CD4_Nucleus_Intensity_Average': 'CD4_Nucleus', 'CD8_Cell_Intensity_Average': 'CD8_Cell', 'CD8_Cytoplasm_Intensity_Average': 'CD8_Cytoplasm', 'CD8_Nucleus_Intensity_Average': 'CD8_Nucleus', 'CD11b_Cell_Intensity_Average': 'CD11b_Cell', 'CD11b_Cytoplasm_Intensity_Average': 'CD11b_Cytoplasm', 'CD11b_Nucleus_Intensity_Average': 'CD11b_Nucleus', 'CD11c_Cell_Intensity_Average': 'CD11c_Cell', 'CD11c_Cytoplasm_Intensity_Average': 'CD11c_Cytoplasm', 'CD11c_Nucleus_Intensity_Average': 'CD11c_Nucleus', 'CD20_Cell_Intensity_Average': 'CD20_Cell', 'CD20_Cytoplasm_Intensity_Average': 'CD20_Cytoplasm', 'CD20_Nucleus_Intensity_Average': 'CD20_Nucleus', 'CD31_Cell_Intensity_Average': 'CD31_Cell', 'CD31_Cytoplasm_Intensity_Average': 'CD31_Cytoplasm', 'CD31_Nucleus_Intensity_Average': 'CD31_Nucleus', 'CD44_Cell_Intensity_Average': 'CD44_Cell', 'CD44_Cytoplasm_Intensity_Average': 'CD44_Cytoplasm', 'CD44_Nucleus_Intensity_Average': 'CD44_Nucleus', 'CD45_Cell_Intensity_Average': 'CD45_Cell', 'CD45_Cytoplasm_Intensity_Average': 'CD45_Cytoplasm', 'CD45_Nucleus_Intensity_Average': 'CD45_Nucleus', 'CD68_Cell_Intensity_Average': 'CD68_Cell', 'CD68_Cytoplasm_Intensity_Average': 'CD68_Cytoplasm', 'CD68_Nucleus_Intensity_Average': 'CD68_Nucleus', 'CD163_Cell_Intensity_Average': 'CD163_Cell', 'CD163_Cytoplasm_Intensity_Average': 'CD163_Cytoplasm', 'CD163_Nucleus_Intensity_Average': 'CD163_Nucleus', 'CKs_Cell_Intensity_Average': 'CKs_Cell', 'CKs_Cytoplasm_Intensity_Average': 'CKs_Cytoplasm', 'CKs_Nucleus_Intensity_Average': 'CKs_Nucleus', 'ColVI_Cell_Intensity_Average': 'ColVI_Cell', 'ColVI_Cytoplasm_Intensity_Average': 'ColVI_Cytoplasm', 'ColVI_Nucleus_Intensity_Average': 'ColVI_Nucleus', 'Desmin_Cell_Intensity_Average': 'Desmin_Cell', 'Desmin_Cytoplasm_Intensity_Average': 'Desmin_Cytoplasm', 'Desmin_Nucleus_Intensity_Average': 'Desmin_Nucleus', 'Ecad_Cell_Intensity_Average': 'Ecad_Cell', 'Ecad_Cytoplasm_Intensity_Average': 'Ecad_Cytoplasm', 'Ecad_Nucleus_Intensity_Average': 'Ecad_Nucleus', 'Fibronectin_Cell_Intensity_Average': 'Fibronectin_Cell', 'Fibronectin_Cytoplasm_Intensity_Average': 'Fibronectin_Cytoplasm', 'Fibronectin_Nucleus_Intensity_Average': 'Fibronectin_Nucleus', 'FOXP3_Cell_Intensity_Average': 'FOXP3_Cell', 'FOXP3_Cytoplasm_Intensity_Average': 'FOXP3_Cytoplasm', 'FOXP3_Nucleus_Intensity_Average': 'FOXP3_Nucleus', 'GATA3_Cell_Intensity_Average': 'GATA3_Cell', 'GATA3_Cytoplasm_Intensity_Average': 'GATA3_Cytoplasm', 'GATA3_Nucleus_Intensity_Average': 'GATA3_Nucleus', 'HLA_Cell_Intensity_Average': 'HLA_Cell', 'HLA_Cytoplasm_Intensity_Average': 'HLA_Cytoplasm', 'HLA_Nucleus_Intensity_Average': 'HLA_Nucleus', 'Ki67_Cell_Intensity_Average': 'Ki67_Cell', 'Ki67_Cytoplasm_Intensity_Average': 'Ki67_Cytoplasm', 'Ki67_Nucleus_Intensity_Average': 'Ki67_Nucleus', 'MMP9_Cell_Intensity_Average': 'MMP9_Cell', 'MMP9_Cytoplasm_Intensity_Average': 'MMP9_Cytoplasm', 'MMP9_Nucleus_Intensity_Average': 'MMP9_Nucleus', 'PD1_Cell_Intensity_Average': 'PD1_Cell', 'PD1_Cytoplasm_Intensity_Average': 'PD1_Cytoplasm', 'PD1_Nucleus_Intensity_Average': 'PD1_Nucleus', 'PDGFR_Cell_Intensity_Average': 'PDGFR_Cell', 'PDGFR_Cytoplasm_Intensity_Average': 'PDGFR_Cytoplasm', 'PDGFR_Nucleus_Intensity_Average': 'PDGFR_Nucleus', 'PDL1_Cell_Intensity_Average': 'PDL1_Cell', 'PDL1_Cytoplasm_Intensity_Average': 'PDL1_Cytoplasm', 'PDL1_Nucleus_Intensity_Average': 'PDL1_Nucleus', 'r5c2_Cell_Intensity_Average': 'r5c2_Cell', 'r5c2_Cytoplasm_Intensity_Average': 'r5c2_Cytoplasm', 'r5c2_Nucleus_Intensity_Average': 'r5c2_Nucleus', 'r7c2_Cell_Intensity_Average': 'r7c2_Cell', 'r7c2_Cytoplasm_Intensity_Average': 'r7c2_Cytoplasm', 'r7c2_Nucleus_Intensity_Average': 'r7c2_Nucleus', 'r8c2_Cell_Intensity_Average': 'r8c2_Cell', 'r8c2_Cytoplasm_Intensity_Average': 'r8c2_Cytoplasm', 'r8c2_Nucleus_Intensity_Average': 'r8c2_Nucleus', 'Sting_Cell_Intensity_Average': 'Sting_Cell', 'Sting_Cytoplasm_Intensity_Average': 'Sting_Cytoplasm', 'Sting_Nucleus_Intensity_Average': 'Sting_Nucleus', 'Vimentin_Cell_Intensity_Average': 'Vimentin_Cell', 'Vimentin_Cytoplasm_Intensity_Average': 'Vimentin_Cytoplasm', 'Vimentin_Nucleus_Intensity_Average': 'Vimentin_Nucleus'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"full_to_short_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "full_to_short_names = df.set_index('full_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# CD45 instead of CD45b\n",
+    "if project_name == 'Slide_A' :\n",
+    "    full_to_short_names['CD45_Cytoplasm_Intensity_Average'] = full_to_short_names.pop('CD45b_Cytoplasm_Intensity_Average')\n",
+    "    full_to_short_names['CD45_Cytoplasm_Intensity_Average'] = 'CD45_Cytoplasm'\n",
+    "\n",
+    "# Print information\n",
+    "print('full_to_short_names =\\n',full_to_short_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a9431d5-a6a0-4fe0-bb58-97008c662a68",
+   "metadata": {},
+   "source": [
+    "### V.3.4. SHORT_TO_FULL_COLUMN_NAMES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "056adc64-1408-4f48-83d8-dec9f2f0e5cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "short_to_full_names =\n",
+      " {'AF488_Cell': 'AF488_Cell_Intensity_Average', 'AF488_Cytoplasm': 'AF488_Cytoplasm_Intensity_Average', 'AF488_Nucleus': 'AF488_Nucleus_Intensity_Average', 'AF555_Cell': 'AF555_Cell_Intensity_Average', 'AF555_Cytoplasm': 'AF555_Cytoplasm_Intensity_Average', 'AF555_Nucleus': 'AF555_Nucleus_Intensity_Average', 'AF647_Cell': 'AF647_Cell_Intensity_Average', 'AF647_Cytoplasm': 'AF647_Cytoplasm_Intensity_Average', 'AF647_Nucleus': 'AF647_Nucleus_Intensity_Average', 'AF750_Cell': 'AF750_Cell_Intensity_Average', 'AF750_Cytoplasm': 'AF750_Cytoplasm_Intensity_Average', 'AF750_Nucleus': 'AF750_Nucleus_Intensity_Average', 'aSMA_Cell': 'aSMA_Cell_Intensity_Average', 'aSMA_Cytoplasm': 'aSMA_Cytoplasm_Intensity_Average', 'aSMA_Nucleus': 'aSMA_Nucleus_Intensity_Average', 'AXL_Cell': 'AXL_Cell_Intensity_Average', 'AXL_Cytoplasm': 'AXL_Cytoplasm_Intensity_Average', 'AXL_Nucleus': 'AXL_Nucleus_Intensity_Average', 'B7H4_Cell': 'B7H4_Cell_Intensity_Average', 'B7H4_Cytoplasm': 'B7H4_Cytoplasm_Intensity_Average', 'B7H4_Nucleus': 'B7H4_Nucleus_Intensity_Average', 'CA9_Cell': 'CA9_Cell_Intensity_Average', 'CA9_Cytoplasm': 'CA9_Cytoplasm_Intensity_Average', 'CA9_Nucleus': 'CA9_Nucleus_Intensity_Average', 'CD4_Cell': 'CD4_Cell_Intensity_Average', 'CD4_Cytoplasm': 'CD4_Cytoplasm_Intensity_Average', 'CD4_Nucleus': 'CD4_Nucleus_Intensity_Average', 'CD8_Cell': 'CD8_Cell_Intensity_Average', 'CD8_Cytoplasm': 'CD8_Cytoplasm_Intensity_Average', 'CD8_Nucleus': 'CD8_Nucleus_Intensity_Average', 'CD11b_Cell': 'CD11b_Cell_Intensity_Average', 'CD11b_Cytoplasm': 'CD11b_Cytoplasm_Intensity_Average', 'CD11b_Nucleus': 'CD11b_Nucleus_Intensity_Average', 'CD11c_Cell': 'CD11c_Cell_Intensity_Average', 'CD11c_Cytoplasm': 'CD11c_Cytoplasm_Intensity_Average', 'CD11c_Nucleus': 'CD11c_Nucleus_Intensity_Average', 'CD20_Cell': 'CD20_Cell_Intensity_Average', 'CD20_Cytoplasm': 'CD20_Cytoplasm_Intensity_Average', 'CD20_Nucleus': 'CD20_Nucleus_Intensity_Average', 'CD31_Cell': 'CD31_Cell_Intensity_Average', 'CD31_Cytoplasm': 'CD31_Cytoplasm_Intensity_Average', 'CD31_Nucleus': 'CD31_Nucleus_Intensity_Average', 'CD44_Cell': 'CD44_Cell_Intensity_Average', 'CD44_Cytoplasm': 'CD44_Cytoplasm_Intensity_Average', 'CD44_Nucleus': 'CD44_Nucleus_Intensity_Average', 'CD45_Cell': 'CD45_Cell_Intensity_Average', 'CD45_Cytoplasm': 'CD45_Cytoplasm_Intensity_Average', 'CD45_Nucleus': 'CD45_Nucleus_Intensity_Average', 'CD68_Cell': 'CD68_Cell_Intensity_Average', 'CD68_Cytoplasm': 'CD68_Cytoplasm_Intensity_Average', 'CD68_Nucleus': 'CD68_Nucleus_Intensity_Average', 'CD163_Cell': 'CD163_Cell_Intensity_Average', 'CD163_Cytoplasm': 'CD163_Cytoplasm_Intensity_Average', 'CD163_Nucleus': 'CD163_Nucleus_Intensity_Average', 'CKs_Cell': 'CKs_Cell_Intensity_Average', 'CKs_Cytoplasm': 'CKs_Cytoplasm_Intensity_Average', 'CKs_Nucleus': 'CKs_Nucleus_Intensity_Average', 'ColVI_Cell': 'ColVI_Cell_Intensity_Average', 'ColVI_Cytoplasm': 'ColVI_Cytoplasm_Intensity_Average', 'ColVI_Nucleus': 'ColVI_Nucleus_Intensity_Average', 'Desmin_Cell': 'Desmin_Cell_Intensity_Average', 'Desmin_Cytoplasm': 'Desmin_Cytoplasm_Intensity_Average', 'Desmin_Nucleus': 'Desmin_Nucleus_Intensity_Average', 'Ecad_Cell': 'Ecad_Cell_Intensity_Average', 'Ecad_Cytoplasm': 'Ecad_Cytoplasm_Intensity_Average', 'Ecad_Nucleus': 'Ecad_Nucleus_Intensity_Average', 'Fibronectin_Cell': 'Fibronectin_Cell_Intensity_Average', 'Fibronectin_Cytoplasm': 'Fibronectin_Cytoplasm_Intensity_Average', 'Fibronectin_Nucleus': 'Fibronectin_Nucleus_Intensity_Average', 'FOXP3_Cell': 'FOXP3_Cell_Intensity_Average', 'FOXP3_Cytoplasm': 'FOXP3_Cytoplasm_Intensity_Average', 'FOXP3_Nucleus': 'FOXP3_Nucleus_Intensity_Average', 'GATA3_Cell': 'GATA3_Cell_Intensity_Average', 'GATA3_Cytoplasm': 'GATA3_Cytoplasm_Intensity_Average', 'GATA3_Nucleus': 'GATA3_Nucleus_Intensity_Average', 'HLA_Cell': 'HLA_Cell_Intensity_Average', 'HLA_Cytoplasm': 'HLA_Cytoplasm_Intensity_Average', 'HLA_Nucleus': 'HLA_Nucleus_Intensity_Average', 'Ki67_Cell': 'Ki67_Cell_Intensity_Average', 'Ki67_Cytoplasm': 'Ki67_Cytoplasm_Intensity_Average', 'Ki67_Nucleus': 'Ki67_Nucleus_Intensity_Average', 'MMP9_Cell': 'MMP9_Cell_Intensity_Average', 'MMP9_Cytoplasm': 'MMP9_Cytoplasm_Intensity_Average', 'MMP9_Nucleus': 'MMP9_Nucleus_Intensity_Average', 'PD1_Cell': 'PD1_Cell_Intensity_Average', 'PD1_Cytoplasm': 'PD1_Cytoplasm_Intensity_Average', 'PD1_Nucleus': 'PD1_Nucleus_Intensity_Average', 'PDGFR_Cell': 'PDGFR_Cell_Intensity_Average', 'PDGFR_Cytoplasm': 'PDGFR_Cytoplasm_Intensity_Average', 'PDGFR_Nucleus': 'PDGFR_Nucleus_Intensity_Average', 'PDL1_Cell': 'PDL1_Cell_Intensity_Average', 'PDL1_Cytoplasm': 'PDL1_Cytoplasm_Intensity_Average', 'PDL1_Nucleus': 'PDL1_Nucleus_Intensity_Average', 'r5c2_Cell': 'r5c2_Cell_Intensity_Average', 'r5c2_Cytoplasm': 'r5c2_Cytoplasm_Intensity_Average', 'r5c2_Nucleus': 'r5c2_Nucleus_Intensity_Average', 'r7c2_Cell': 'r7c2_Cell_Intensity_Average', 'r7c2_Cytoplasm': 'r7c2_Cytoplasm_Intensity_Average', 'r7c2_Nucleus': 'r7c2_Nucleus_Intensity_Average', 'r8c2_Cell': 'r8c2_Cell_Intensity_Average', 'r8c2_Cytoplasm': 'r8c2_Cytoplasm_Intensity_Average', 'r8c2_Nucleus': 'r8c2_Nucleus_Intensity_Average', 'Sting_Cell': 'Sting_Cell_Intensity_Average', 'Sting_Cytoplasm': 'Sting_Cytoplasm_Intensity_Average', 'Sting_Nucleus': 'Sting_Nucleus_Intensity_Average', 'Vimentin_Cell': 'Vimentin_Cell_Intensity_Average', 'Vimentin_Cytoplasm': 'Vimentin_Cytoplasm_Intensity_Average', 'Vimentin_Nucleus': 'Vimentin_Nucleus_Intensity_Average'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"short_to_full_column_names.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "short_to_full_names = df.set_index('short_name').T.to_dict('records')[0]\n",
+    "\n",
+    "# CD45 instead of CD45b\n",
+    "if project_name == 'Slide_A' :\n",
+    "    short_to_full_names['CD45_Cytoplasm'] = short_to_full_names.pop('CD45b_Cytoplasm')\n",
+    "    short_to_full_names['CD45_Cytoplasm'] = 'CD45_Cytoplasm_Intensity_Average'\n",
+    "\n",
+    "# Print information\n",
+    "print('short_to_full_names =\\n',short_to_full_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eda7993-e4e3-4a19-b1b0-24a5aa088a07",
+   "metadata": {},
+   "source": [
+    "### V.3.5. SAMPLES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bf3c90f9-c395-40a8-a2bd-2df946e2a05b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Could not find desired file: /Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe\u001b[39m\u001b[38;5;124m\"\u001b[39m,filename,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile was imported for further analysis!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Open, read in information\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(filename, header \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     12\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhex\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# our tuple of float values for rgb, (r, g, b) was read in \u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# substrings and convert them back into floats\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[1;32m   1736\u001b[0m     f,\n\u001b[1;32m   1737\u001b[0m     mode,\n\u001b[1;32m   1738\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1739\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1740\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1741\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m   1742\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1743\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1744\u001b[0m )\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/Cyc_If/lib/python3.11/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m    859\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m    860\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m    861\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/harshithakolipaka/Downloads/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"sample_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "sample_color_dict = df.set_index('Sample_ID').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('sample_color_dict =\\n',sample_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27f77fc8-d11f-4598-b66b-436f45ea4be3",
+   "metadata": {},
+   "source": [
+    "### V.3.6. CHANNELS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "50f9f41b-8c2a-4b1c-8787-a198c97ec606",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\channel_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "channel_color_dict =\n",
+      " {'c2': (0.00784313725490196, 0.24313725490196078, 1.0), 'c3': (1.0, 0.48627450980392156, 0.0), 'c4': (0.10196078431372549, 0.788235294117647, 0.2196078431372549), 'c5': (0.9098039215686274, 0.0, 0.043137254901960784)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"channel_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "channel_color_dict = df.set_index('Channel').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('channel_color_dict =\\n',channel_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89fccf1f-692e-494e-b6db-208cbccc58d2",
+   "metadata": {},
+   "source": [
+    "### V.3.7. ROUNDS COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b24c3fbf-c8ca-4b62-a1c0-b1b90ed67ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\round_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "round_color_dict =\n",
+      " {'R0': (0.28685356234627135, 0.13009829239513535, 0.23110332132624437), 'R1': (0.36541462435986094, 0.2025447048359916, 0.37693310021636883), 'R2': (0.40867533458903105, 0.2940761173840091, 0.5166711878800253), 'R3': (0.42890613750051265, 0.4082290173220481, 0.6335348887063806), 'R4': (0.4444462906865238, 0.5264664993764805, 0.7056321892616532), 'R5': (0.47707206309601013, 0.6427061780374552, 0.7418477948908153), 'R6': (0.5414454866716836, 0.7466759172596551, 0.7572905778378964), 'R7': (0.6414710091647722, 0.8321551072276492, 0.7746773027952071), 'R8': (0.7684256891219349, 0.8992667116749021, 0.8171383269422353)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"round_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "round_color_dict = df.set_index('Round').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('round_color_dict =\\n',round_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bca04ed4-76ce-47af-b922-769365c5eed9",
+   "metadata": {},
+   "source": [
+    "### V.3.8. CELL TYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9ddb549a-e104-42de-b8ed-a2e64cfe468a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\celltype_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "cell_type_color_dict =\n",
+      " {'CANCER': (0.1333, 0.5451, 0.1333), 'STROMA': (0.4, 0.4, 0.4), 'IMMUNE': (1.0, 1.0, 0.0), 'ENDOTHELIAL': (0.502, 0.0, 0.502)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"celltype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "cell_type_color_dict = df.set_index('cell_type').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('cell_type_color_dict =\\n',cell_type_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "691eef7d-175a-4d95-b0d5-6d07900e7ed3",
+   "metadata": {},
+   "source": [
+    "### V.3.9. CELL SUBTYPES COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b2cc5232-8d4d-47fa-93a6-7b0520dc04e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\cellsubtype_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "cell_subtype_color_dict =\n",
+      " {'DC': (0.6509803921568628, 0.807843137254902, 0.8901960784313725), 'B': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), 'TCD4': (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), 'TCD8': (0.2, 0.6274509803921569, 0.17254901960784313), 'M1': (0.984313725490196, 0.6039215686274509, 0.6), 'M2': (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), 'Treg': (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), 'IMMUNE_OTHER': (1.0, 0.4980392156862745, 0.0), 'CANCER': (0.792156862745098, 0.6980392156862745, 0.8392156862745098), 'αSMA_myCAF': (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), 'STROMA_OTHER': (1.0, 1.0, 0.6), 'ENDOTHELIAL': (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"cellsubtype_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "cell_subtype_color_dict = df.set_index('cell_subtype').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('cell_subtype_color_dict =\\n',cell_subtype_color_dict)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40d4692c-fcf2-4663-b180-e6183bcbbe22",
+   "metadata": {},
+   "source": [
+    "### V.4.10. IMMUNE CHECKPOINT COLORS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2da347b5-fa4d-40b4-b7f6-4f5c7d462f08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\immunecheckpoint_color_data.csv file was imported for further analysis!\n",
+      "Verifying data read from file is the correct length...\n",
+      "\n",
+      "immune_checkpoint_color_dict =\n",
+      " {'B7H4': (0.9677975592919913, 0.44127456009157356, 0.5358103155058701), 'PDL1': (0.3126890019504329, 0.6928754610296064, 0.1923704830330379), 'PD1': (0.23299120924703914, 0.639586552066035, 0.9260706093977744), 'B7H4_PDL1': (0.6402432806212122, 0.56707501056059, 0.36409039926945397), 'B7H4_PD1': (0.6003943842695152, 0.5404305560788043, 0.7309404624518223), 'PDL1_PD1': (0.27284010559873606, 0.6662310065478207, 0.5592205462154062), 'B7H4_PDL1_PD1': (0.5044925901631545, 0.5912455243957383, 0.5514171359788941), 'None': (0.8, 0.8, 0.8)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"immunecheckpoint_color_data.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \"+filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")\n",
+    "    \n",
+    "# Open, read in information\n",
+    "df = pd.read_csv(filename, header = 0)\n",
+    "df = df.drop(columns = ['hex'])\n",
+    "\n",
+    "# our tuple of float values for rgb, (r, g, b) was read in \n",
+    "# as a string '(r, g, b)'. We need to extract the r-, g-, and b-\n",
+    "# substrings and convert them back into floats\n",
+    "df['rgb'] = df.apply(lambda row: rgb_tuple_from_str(row['rgb']), axis = 1)\n",
+    "\n",
+    "# Verify size\n",
+    "print(\"Verifying data read from file is the correct length...\\n\")\n",
+    "#verify_line_no(filename, df.shape[0] + 1)\n",
+    "\n",
+    "# Turn into dictionary\n",
+    "immune_checkpoint_color_dict = df.set_index('immune_checkpoint').T.to_dict('rgb')[0]\n",
+    "\n",
+    "# Print information\n",
+    "print('immune_checkpoint_color_dict =\\n',immune_checkpoint_color_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ba6cad2-ed2d-4fa7-afa5-6122bfdef493",
+   "metadata": {},
+   "source": [
+    "### V.3.11. DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1ae727be-989a-4618-9c21-d741a6e862cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following CSV files were detected:\n",
+      "['DD3S1_mt.csv', 'DD3S2_mt.csv', 'DD3S3_mt.csv', 'DD4S1_mt.csv', 'DD4S2_mt.csv', 'DD4S3_mt.csv', 'DD5S1_mt.csv', 'DD5S2_mt.csv', 'DD5S3_mt.csv', 'TMA_mt.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# DATA\n",
+    "# List files in the directory\n",
+    "# Check if the directory exists\n",
+    "if os.path.exists(input_data_dir):\n",
+    "    # List files in the directory\n",
+    "    ls_samples = [sample for sample in os.listdir(input_data_dir) if sample.endswith(\"_mt.csv\")]\n",
+    "    print(\"The following CSV files were detected:\")\n",
+    "    print([sample for sample in ls_samples])\n",
+    "else:\n",
+    "    print(f\"The directory {input_data_dir} does not exist.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "18a34f42-7d39-4177-b932-8dbb918732b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['PDL1_Cytoplasm_Intensity_Average' 'HLA_Cytoplasm_Intensity_Average'\n",
+      " 'CKs_Cytoplasm_Intensity_Average' 'Ki67_Nucleus_Intensity_Average'\n",
+      " 'CD163_Cytoplasm_Intensity_Average' 'ColVI_Cytoplasm_Intensity_Average'\n",
+      " 'CD20_Cytoplasm_Intensity_Average' 'PD1_Cytoplasm_Intensity_Average'\n",
+      " 'AXL_Cytoplasm_Intensity_Average' 'CD31_Cytoplasm_Intensity_Average'\n",
+      " 'Fibronectin_Cytoplasm_Intensity_Average'\n",
+      " 'CD45_Cytoplasm_Intensity_Average' 'Ecad_Cytoplasm_Intensity_Average'\n",
+      " 'CD8_Cytoplasm_Intensity_Average' 'GATA3_Nucleus_Intensity_Average'\n",
+      " 'Sting_Cytoplasm_Intensity_Average' 'aSMA_Cytoplasm_Intensity_Average'\n",
+      " 'FOXP3_Nucleus_Intensity_Average' 'CD11c_Cytoplasm_Intensity_Average'\n",
+      " 'CD4_Cytoplasm_Intensity_Average' 'Vimentin_Cytoplasm_Intensity_Average'\n",
+      " 'CD44_Cytoplasm_Intensity_Average' 'CD68_Cytoplasm_Intensity_Average'\n",
+      " 'PDGFR_Cytoplasm_Intensity_Average' 'B7H4_Cell_Intensity_Average'\n",
+      " 'MMP9_Cytoplasm_Intensity_Average' 'Desmin_Cytoplasm_Intensity_Average'\n",
+      " 'cell_subtype' 'cell_type' 'Nuc_Y_Inv' 'Nucleus_Roundness' 'Nuc_X'\n",
+      " 'ROI_index' 'Nucleus_Size' 'Sample_ID' 'immune_checkpoint' 'Cell_Size']\n",
+      "DD3S1_mt.csv file is processed !\n",
+      "\n",
+      "DD3S2_mt.csv file is processed !\n",
+      "\n",
+      "DD3S3_mt.csv file is processed !\n",
+      "\n",
+      "DD4S1_mt.csv file is processed !\n",
+      "\n",
+      "DD4S2_mt.csv file is processed !\n",
+      "\n",
+      "DD4S3_mt.csv file is processed !\n",
+      "\n",
+      "DD5S1_mt.csv file is processed !\n",
+      "\n",
+      "DD5S2_mt.csv file is processed !\n",
+      "\n",
+      "DD5S3_mt.csv file is processed !\n",
+      "\n",
+      "TMA_mt.csv file is processed !\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import all the others files\n",
+    "dfs = {}\n",
+    "\n",
+    "# Set variable to hold default header values\n",
+    "# First gather information on expected headers using first file in ls_samples\n",
+    "# Read in the first row of the file corresponding to the first sample (index = 0) in ls_samples\n",
+    "df = pd.read_csv(os.path.join(input_data_dir, ls_samples[0]) , index_col = 0, nrows = 1)\n",
+    "expected_headers = df.columns.values\n",
+    "print(expected_headers)\n",
+    "\n",
+    "###############################\n",
+    "# !! This may take a while !! #\n",
+    "###############################\n",
+    "for sample in ls_samples:\n",
+    "    file_path = os.path.join(input_data_dir,sample)\n",
+    "   \n",
+    "    try:\n",
+    "        # Read the CSV file\n",
+    "        df = pd.read_csv(file_path, index_col=0)\n",
+    "        # Check if the DataFrame is empty, if so, don't continue trying to process df and remove it\n",
+    "        \n",
+    "        if not df.empty:\n",
+    "            # Reorder the columns to match the expected headers list\n",
+    "            df = df.reindex(columns=expected_headers)\n",
+    "            print(sample, \"file is processed !\\n\")\n",
+    "            #print(df) \n",
+    "   \n",
+    "    except pd.errors.EmptyDataError:\n",
+    "        print(f'\\nEmpty data error in {sample} file. Removing from analysis...')\n",
+    "        ls_samples.remove(sample)      \n",
+    "    \n",
+    "    # Add df to dfs \n",
+    "    dfs[sample] = df\n",
+    "\n",
+    "#print(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6b4c0b53-1c7f-43d7-8adc-f3f0750dc7c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              PDL1_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                               \n",
+      "DD3S1_Cell_0                         -0.677863   \n",
+      "DD3S1_Cell_1                         -0.677863   \n",
+      "DD3S1_Cell_2                         -0.677863   \n",
+      "DD3S1_Cell_3                         -0.741282   \n",
+      "DD3S1_Cell_6                         -0.621521   \n",
+      "\n",
+      "              HLA_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S1_Cell_0                        -0.417494   \n",
+      "DD3S1_Cell_1                        -0.516487   \n",
+      "DD3S1_Cell_2                        -0.141921   \n",
+      "DD3S1_Cell_3                        -0.460472   \n",
+      "DD3S1_Cell_6                        -0.247254   \n",
+      "\n",
+      "              CKs_Cytoplasm_Intensity_Average  Ki67_Nucleus_Intensity_Average  \\\n",
+      "ID                                                                              \n",
+      "DD3S1_Cell_0                        -0.912537                       -0.817876   \n",
+      "DD3S1_Cell_1                        -0.838037                       -0.869685   \n",
+      "DD3S1_Cell_2                        -1.016023                       -0.755879   \n",
+      "DD3S1_Cell_3                        -0.491711                       -0.818084   \n",
+      "DD3S1_Cell_6                        -0.867127                       -0.742544   \n",
+      "\n",
+      "              CD163_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                \n",
+      "DD3S1_Cell_0                           0.930099   \n",
+      "DD3S1_Cell_1                           1.114924   \n",
+      "DD3S1_Cell_2                           0.834577   \n",
+      "DD3S1_Cell_3                           0.648200   \n",
+      "DD3S1_Cell_6                           0.810579   \n",
+      "\n",
+      "              ColVI_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                                \n",
+      "DD3S1_Cell_0                           0.232078   \n",
+      "DD3S1_Cell_1                           0.301333   \n",
+      "DD3S1_Cell_2                           0.259216   \n",
+      "DD3S1_Cell_3                           0.107027   \n",
+      "DD3S1_Cell_6                           0.272128   \n",
+      "\n",
+      "              CD20_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                               \n",
+      "DD3S1_Cell_0                         -0.483158   \n",
+      "DD3S1_Cell_1                         -0.344770   \n",
+      "DD3S1_Cell_2                         -0.438292   \n",
+      "DD3S1_Cell_3                         -0.444889   \n",
+      "DD3S1_Cell_6                         -0.507117   \n",
+      "\n",
+      "              PD1_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S1_Cell_0                         1.535604   \n",
+      "DD3S1_Cell_1                         1.668368   \n",
+      "DD3S1_Cell_2                         1.336308   \n",
+      "DD3S1_Cell_3                         1.249805   \n",
+      "DD3S1_Cell_6                         1.251434   \n",
+      "\n",
+      "              AXL_Cytoplasm_Intensity_Average  \\\n",
+      "ID                                              \n",
+      "DD3S1_Cell_0                         0.807339   \n",
+      "DD3S1_Cell_1                         0.875455   \n",
+      "DD3S1_Cell_2                         0.705088   \n",
+      "DD3S1_Cell_3                         0.660707   \n",
+      "DD3S1_Cell_6                         0.947172   \n",
+      "\n",
+      "              CD31_Cytoplasm_Intensity_Average  ...  cell_subtype  \\\n",
+      "ID                                              ...                 \n",
+      "DD3S1_Cell_0                          1.167755  ...    αSMA_myCAF   \n",
+      "DD3S1_Cell_1                          1.643023  ...    αSMA_myCAF   \n",
+      "DD3S1_Cell_2                          1.053636  ...    αSMA_myCAF   \n",
+      "DD3S1_Cell_3                          1.165861  ...    αSMA_myCAF   \n",
+      "DD3S1_Cell_6                          2.545301  ...   ENDOTHELIAL   \n",
+      "\n",
+      "                cell_type     Nuc_Y_Inv  Nucleus_Roundness        Nuc_X  \\\n",
+      "ID                                                                        \n",
+      "DD3S1_Cell_0       STROMA  16632.205078           0.955040  1484.771729   \n",
+      "DD3S1_Cell_1       STROMA  16627.384766           0.966643  1426.250000   \n",
+      "DD3S1_Cell_2       STROMA  16622.238281           0.721534  1531.110474   \n",
+      "DD3S1_Cell_3       STROMA  16623.007812           0.587196  1518.907593   \n",
+      "DD3S1_Cell_6  ENDOTHELIAL  16619.978516           0.935716  1471.914917   \n",
+      "\n",
+      "              ROI_index  Nucleus_Size  Sample_ID  immune_checkpoint  Cell_Size  \n",
+      "ID                                                                              \n",
+      "DD3S1_Cell_0          0           127  DD3S1.csv               None        339  \n",
+      "DD3S1_Cell_1          0           112  DD3S1.csv               None        344  \n",
+      "DD3S1_Cell_2          0           181  DD3S1.csv               None        422  \n",
+      "DD3S1_Cell_3          0           119  DD3S1.csv               None        278  \n",
+      "DD3S1_Cell_6          0            47  DD3S1.csv               None        204  \n",
+      "\n",
+      "[5 rows x 37 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge dfs into one df\n",
+    "df = pd.concat(dfs.values(), ignore_index=False , sort = False)\n",
+    "del dfs\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dfc864d5-5dfd-4b0d-827d-1ac4c7726099",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(704629, 37)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "66c3ad6e-af2d-40c5-9a14-dad5d9c180dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DD3S1_Cell_0', 'DD3S1_Cell_1', 'DD3S1_Cell_2', 'DD3S1_Cell_3',\n",
+       "       'DD3S1_Cell_6', 'DD3S1_Cell_7', 'DD3S1_Cell_8', 'DD3S1_Cell_10',\n",
+       "       'DD3S1_Cell_11', 'DD3S1_Cell_12',\n",
+       "       ...\n",
+       "       'TMA_Cell_115750', 'TMA_Cell_115751', 'TMA_Cell_115752',\n",
+       "       'TMA_Cell_115753', 'TMA_Cell_115754', 'TMA_Cell_115755',\n",
+       "       'TMA_Cell_115756', 'TMA_Cell_115757', 'TMA_Cell_115758',\n",
+       "       'TMA_Cell_115760'],\n",
+       "      dtype='object', name='ID', length=704629)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4c918ffc-d7ac-463e-a6a4-785a72cf1066",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PDL1_Cytoplasm_Intensity_Average',\n",
+       "       'HLA_Cytoplasm_Intensity_Average',\n",
+       "       'CKs_Cytoplasm_Intensity_Average',\n",
+       "       'Ki67_Nucleus_Intensity_Average',\n",
+       "       'CD163_Cytoplasm_Intensity_Average',\n",
+       "       'ColVI_Cytoplasm_Intensity_Average',\n",
+       "       'CD20_Cytoplasm_Intensity_Average',\n",
+       "       'PD1_Cytoplasm_Intensity_Average',\n",
+       "       'AXL_Cytoplasm_Intensity_Average',\n",
+       "       'CD31_Cytoplasm_Intensity_Average',\n",
+       "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+       "       'CD45_Cytoplasm_Intensity_Average',\n",
+       "       'Ecad_Cytoplasm_Intensity_Average',\n",
+       "       'CD8_Cytoplasm_Intensity_Average',\n",
+       "       'GATA3_Nucleus_Intensity_Average',\n",
+       "       'Sting_Cytoplasm_Intensity_Average',\n",
+       "       'aSMA_Cytoplasm_Intensity_Average',\n",
+       "       'FOXP3_Nucleus_Intensity_Average',\n",
+       "       'CD11c_Cytoplasm_Intensity_Average',\n",
+       "       'CD4_Cytoplasm_Intensity_Average',\n",
+       "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+       "       'CD44_Cytoplasm_Intensity_Average',\n",
+       "       'CD68_Cytoplasm_Intensity_Average',\n",
+       "       'PDGFR_Cytoplasm_Intensity_Average', 'B7H4_Cell_Intensity_Average',\n",
+       "       'MMP9_Cytoplasm_Intensity_Average',\n",
+       "       'Desmin_Cytoplasm_Intensity_Average', 'cell_subtype', 'cell_type',\n",
+       "       'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nuc_X', 'ROI_index',\n",
+       "       'Nucleus_Size', 'Sample_ID', 'immune_checkpoint', 'Cell_Size'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e4dc2462-3461-4a46-a131-561ebe444263",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for NaN entries (should not be any unless columns do not align)\n",
+    "# False means no NaN entries \n",
+    "# True means NaN entries \n",
+    "df.isnull().any().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0829945-2326-4919-994d-9813663ba668",
+   "metadata": {},
+   "source": [
+    "### V.3.12. UNIQUE ROIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "80525ae8-cb95-48d8-875e-5c0d2a35f102",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\Set_B_unique_ROIs.csv file was imported for further analysis!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# UNIQUE ROIs\n",
+    "filename = f'{project_name}_unique_ROIs.csv'\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b6bd5d54-85de-4acf-bc75-47b4b44ff1cb",
+   "metadata": {
+    "tags": []
+   },
+   "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>Sample_ID</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>1</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>2</td>\n",
+       "      <td>63</td>\n",
+       "      <td>63a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>3</td>\n",
+       "      <td>59</td>\n",
+       "      <td>59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>4</td>\n",
+       "      <td>60</td>\n",
+       "      <td>60a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>55</td>\n",
+       "      <td>c55</td>\n",
+       "      <td>c55a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>56</td>\n",
+       "      <td>c56</td>\n",
+       "      <td>c56a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>464</th>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>57</td>\n",
+       "      <td>c57</td>\n",
+       "      <td>c57a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>465</th>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>58</td>\n",
+       "      <td>c58</td>\n",
+       "      <td>c58a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>59</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>467 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sample_ID  ROI_index Patient Unique_ROI_index\n",
+       "0    DD3S1.csv          0      61              61a\n",
+       "1    DD3S1.csv          1      62              62a\n",
+       "2    DD3S1.csv          2      63              63a\n",
+       "3    DD3S1.csv          3      59              59a\n",
+       "4    DD3S1.csv          4      60              60a\n",
+       "..         ...        ...     ...              ...\n",
+       "462    TMA.csv         55     c55             c55a\n",
+       "463    TMA.csv         56     c56             c56a\n",
+       "464    TMA.csv         57     c57             c57a\n",
+       "465    TMA.csv         58     c58             c58a\n",
+       "466    TMA.csv         59     c59             c59a\n",
+       "\n",
+       "[467 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Open, read in information\n",
+    "Unique_ROIs = pd.read_csv(filename,delimiter=';')\n",
+    "\n",
+    "# Verify headers\n",
+    "exp_cols = ['Sample_ID', 'ROI_index','Patient','Unique_ROI_index']\n",
+    "compare_headers(exp_cols, Unique_ROIs.columns.values, \"Unique_ROIs file\")\n",
+    "\n",
+    "Unique_ROIs = Unique_ROIs.dropna()\n",
+    "Unique_ROIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e5fff391-c158-4213-8fd4-7807d0158bdf",
+   "metadata": {
+    "tags": []
+   },
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16632.205078</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16627.384766</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16622.238281</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16623.007812</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16619.978516</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2663.253418</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2661.765869</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2657.015625</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2660.258545</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2654.292480</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>704629 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.677863   \n",
+       "DD3S1_Cell_1                            -0.677863   \n",
+       "DD3S1_Cell_2                            -0.677863   \n",
+       "DD3S1_Cell_3                            -0.741282   \n",
+       "DD3S1_Cell_6                            -0.621521   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.417494   \n",
+       "DD3S1_Cell_1                           -0.516487   \n",
+       "DD3S1_Cell_2                           -0.141921   \n",
+       "DD3S1_Cell_3                           -0.460472   \n",
+       "DD3S1_Cell_6                           -0.247254   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.912537   \n",
+       "DD3S1_Cell_1                           -0.838037   \n",
+       "DD3S1_Cell_2                           -1.016023   \n",
+       "DD3S1_Cell_3                           -0.491711   \n",
+       "DD3S1_Cell_6                           -0.867127   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S1_Cell_0                          -0.817876   \n",
+       "DD3S1_Cell_1                          -0.869685   \n",
+       "DD3S1_Cell_2                          -0.755879   \n",
+       "DD3S1_Cell_3                          -0.818084   \n",
+       "DD3S1_Cell_6                          -0.742544   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.930099   \n",
+       "DD3S1_Cell_1                              1.114924   \n",
+       "DD3S1_Cell_2                              0.834577   \n",
+       "DD3S1_Cell_3                              0.648200   \n",
+       "DD3S1_Cell_6                              0.810579   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.232078   \n",
+       "DD3S1_Cell_1                              0.301333   \n",
+       "DD3S1_Cell_2                              0.259216   \n",
+       "DD3S1_Cell_3                              0.107027   \n",
+       "DD3S1_Cell_6                              0.272128   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.483158   \n",
+       "DD3S1_Cell_1                            -0.344770   \n",
+       "DD3S1_Cell_2                            -0.438292   \n",
+       "DD3S1_Cell_3                            -0.444889   \n",
+       "DD3S1_Cell_6                            -0.507117   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            1.535604   \n",
+       "DD3S1_Cell_1                            1.668368   \n",
+       "DD3S1_Cell_2                            1.336308   \n",
+       "DD3S1_Cell_3                            1.249805   \n",
+       "DD3S1_Cell_6                            1.251434   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            0.807339   \n",
+       "DD3S1_Cell_1                            0.875455   \n",
+       "DD3S1_Cell_2                            0.705088   \n",
+       "DD3S1_Cell_3                            0.660707   \n",
+       "DD3S1_Cell_6                            0.947172   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...     Nuc_Y_Inv  \\\n",
+       "ID                                                 ...                 \n",
+       "DD3S1_Cell_0                             1.167755  ...  16632.205078   \n",
+       "DD3S1_Cell_1                             1.643023  ...  16627.384766   \n",
+       "DD3S1_Cell_2                             1.053636  ...  16622.238281   \n",
+       "DD3S1_Cell_3                             1.165861  ...  16623.007812   \n",
+       "DD3S1_Cell_6                             2.545301  ...  16619.978516   \n",
+       "...                                           ...  ...           ...   \n",
+       "TMA_Cell_115755                          0.090397  ...   2663.253418   \n",
+       "TMA_Cell_115756                         -0.617835  ...   2661.765869   \n",
+       "TMA_Cell_115757                          0.403878  ...   2657.015625   \n",
+       "TMA_Cell_115758                         -0.219145  ...   2660.258545   \n",
+       "TMA_Cell_115760                         -0.495501  ...   2654.292480   \n",
+       "\n",
+       "                 Nucleus_Roundness         Nuc_X  ROI_index  Nucleus_Size  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_0              0.955040   1484.771729          0           127   \n",
+       "DD3S1_Cell_1              0.966643   1426.250000          0           112   \n",
+       "DD3S1_Cell_2              0.721534   1531.110474          0           181   \n",
+       "DD3S1_Cell_3              0.587196   1518.907593          0           119   \n",
+       "DD3S1_Cell_6              0.935716   1471.914917          0            47   \n",
+       "...                            ...           ...        ...           ...   \n",
+       "TMA_Cell_115755           0.982196  15564.458008         59           142   \n",
+       "TMA_Cell_115756           0.775977  15629.680664         59            47   \n",
+       "TMA_Cell_115757           0.688747  15518.421875         59            64   \n",
+       "TMA_Cell_115758           0.751402  15539.275391         59            58   \n",
+       "TMA_Cell_115760           0.674126  15542.961914         59           106   \n",
+       "\n",
+       "                 Sample_ID  immune_checkpoint  Cell_Size  Patient  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_0     DD3S1.csv               None        339       61   \n",
+       "DD3S1_Cell_1     DD3S1.csv               None        344       61   \n",
+       "DD3S1_Cell_2     DD3S1.csv               None        422       61   \n",
+       "DD3S1_Cell_3     DD3S1.csv               None        278       61   \n",
+       "DD3S1_Cell_6     DD3S1.csv               None        204       61   \n",
+       "...                    ...                ...        ...      ...   \n",
+       "TMA_Cell_115755    TMA.csv               None        386      c59   \n",
+       "TMA_Cell_115756    TMA.csv               None        270      c59   \n",
+       "TMA_Cell_115757    TMA.csv               None        202      c59   \n",
+       "TMA_Cell_115758    TMA.csv               None        182      c59   \n",
+       "TMA_Cell_115760    TMA.csv               None        295      c59   \n",
+       "\n",
+       "                 Unique_ROI_index  \n",
+       "ID                                 \n",
+       "DD3S1_Cell_0                  61a  \n",
+       "DD3S1_Cell_1                  61a  \n",
+       "DD3S1_Cell_2                  61a  \n",
+       "DD3S1_Cell_3                  61a  \n",
+       "DD3S1_Cell_6                  61a  \n",
+       "...                           ...  \n",
+       "TMA_Cell_115755              c59a  \n",
+       "TMA_Cell_115756              c59a  \n",
+       "TMA_Cell_115757              c59a  \n",
+       "TMA_Cell_115758              c59a  \n",
+       "TMA_Cell_115760              c59a  \n",
+       "\n",
+       "[704629 rows x 39 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Jointure des deux dataframes sur la colonne 'Unique_ROI_index'\n",
+    "df1 = df.merge(Unique_ROIs[['Sample_ID', 'ROI_index', 'Patient', 'Unique_ROI_index']], on=['Sample_ID', 'ROI_index'], how='left')\n",
+    "\n",
+    "# Set the index of df1 to be the same as the index of df\n",
+    "df1.set_index(df.index, inplace=True)\n",
+    "\n",
+    "# Vérification du nouveau dataframe\n",
+    "display(df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "de6d2be6-a650-44ce-9393-5b765cc5854e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No duplicates found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Resetting the index temporarily for checking for duplicates\n",
+    "# ID + Unique_ROI_index should not return any duplicate\n",
+    "df_temp = df1.copy()\n",
+    "df_temp.reset_index(inplace=True)\n",
+    "df_temp.rename(columns={'index': 'old_index'}, inplace=True)\n",
+    "\n",
+    "# Check for duplicates\n",
+    "duplicates = df_temp[df_temp.duplicated(['ID', 'Unique_ROI_index'], keep=False)]\n",
+    "if not duplicates.empty:\n",
+    "    print(\"Duplicates found:\")\n",
+    "    print(duplicates)\n",
+    "else:\n",
+    "    print(\"No duplicates found.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e1548c-1b97-4353-92b7-f002d571a751",
+   "metadata": {},
+   "source": [
+    "### V.3.13. NACT/ACT"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "c4cff1cb-d829-4651-9074-634be1b84fc5",
+   "metadata": {},
+   "source": [
+    "145 patients HGSC > 33 recieved NACT (23%)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "16310c91-3a80-47ed-adac-8b8a55183b46",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The C:\\Users\\zoege\\Documents\\Temp\\Cyc-IF_pipeline\\Set_B\\Set_B_metadata\\TMA_Clinical_Data_187-OC.csv file was imported for further analysis!\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"TMA_Clinical_Data_187-OC.csv\"\n",
+    "filename = os.path.join(metadata_dir, filename)\n",
+    "\n",
+    "# Check file exists\n",
+    "if not os.path.exists(filename):\n",
+    "    print(\"WARNING: Could not find desired file: \" + filename)\n",
+    "else :\n",
+    "    print(\"The\",filename,\"file was imported for further analysis!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "668392ed-10dd-405d-8761-e9f5c1c8268a",
+   "metadata": {
+    "tags": []
+   },
+   "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>Patient</th>\n",
+       "      <th>%_tumor</th>\n",
+       "      <th>%_necrosis</th>\n",
+       "      <th>Age_Diagnosis</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>CA125</th>\n",
+       "      <th>Race</th>\n",
+       "      <th>Other_Cancers</th>\n",
+       "      <th>BRCAStatus</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Histo_Type</th>\n",
+       "      <th>Optimal_Debulking</th>\n",
+       "      <th>Residual_Disease</th>\n",
+       "      <th>Platinum_sensitive</th>\n",
+       "      <th>Days_surgery_to_recurrence</th>\n",
+       "      <th>Avastin</th>\n",
+       "      <th>Disease_Stat</th>\n",
+       "      <th>Days_from_surgery_to_last_contact_or_death</th>\n",
+       "      <th>Recurrence</th>\n",
+       "      <th>Vital_Status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>506.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>2223</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>944.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>596.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1355</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGtubal</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1783</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1426.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CCC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>377.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>977</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>183</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1036</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>184</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>3788.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>282.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>185</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>1330.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>186</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>564.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>squamouscellcarcinoma</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DOC</td>\n",
+       "      <td>1869</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>187</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>922.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>serousprimarilylowgradewfociofhighgrade</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1279</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>187 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Patient  %_tumor  %_necrosis  Age_Diagnosis   BMI   CA125 Race  \\\n",
+       "0         1       80           5           72.0  35.0     NaN    C   \n",
+       "1         2       50           0           36.0  24.0     NaN    C   \n",
+       "2         3       80           5           55.0  45.0   944.0    C   \n",
+       "3         4       80           5           59.0  26.0    50.0    C   \n",
+       "4         5       50           0           70.0  32.0  1426.0    C   \n",
+       "..      ...      ...         ...            ...   ...     ...  ...   \n",
+       "182     183       70           5           72.0  22.0   597.0    C   \n",
+       "183     184       90           0           72.0  26.0  3788.0    C   \n",
+       "184     185       90           0           70.0  29.0  1330.0    C   \n",
+       "185     186       80           0           84.0  17.0   564.0    C   \n",
+       "186     187       80           0           27.0  22.0   922.0    C   \n",
+       "\n",
+       "             Other_Cancers BRCAStatus  Primary_chem(1)_vs_surg(0)  ...  \\\n",
+       "0                   breast          0                           0  ...   \n",
+       "1                   breast          1                           0  ...   \n",
+       "2                        0        NaN                           0  ...   \n",
+       "3                        0          0                           0  ...   \n",
+       "4                        0        NaN                           1  ...   \n",
+       "..                     ...        ...                         ...  ...   \n",
+       "182                 breast          0                           1  ...   \n",
+       "183                 breast        NaN                           1  ...   \n",
+       "184                      0        NaN                           0  ...   \n",
+       "185  squamouscellcarcinoma        NaN                           0  ...   \n",
+       "186                      0          0                           1  ...   \n",
+       "\n",
+       "                                  Histo_Type  Optimal_Debulking  \\\n",
+       "0                                      HGSOC                0.0   \n",
+       "1                                      HGSOC                1.0   \n",
+       "2                                      HGSOC                1.0   \n",
+       "3                                    HGtubal                1.0   \n",
+       "4                                        CCC                NaN   \n",
+       "..                                       ...                ...   \n",
+       "182                                    HGSOC                1.0   \n",
+       "183                                    HGSOC                1.0   \n",
+       "184                                    HGSOC                1.0   \n",
+       "185                                    HGSOC                1.0   \n",
+       "186  serousprimarilylowgradewfociofhighgrade                1.0   \n",
+       "\n",
+       "    Residual_Disease Platinum_sensitive  Days_surgery_to_recurrence  Avastin  \\\n",
+       "0                1.0                1.0                       506.0      1.0   \n",
+       "1                0.0                1.0                       669.0      1.0   \n",
+       "2                0.0                1.0                       596.0      0.0   \n",
+       "3                0.0                NaN                         NaN      NaN   \n",
+       "4                0.0                1.0                       377.0      1.0   \n",
+       "..               ...                ...                         ...      ...   \n",
+       "182              0.0                1.0                         NaN      NaN   \n",
+       "183              0.0                1.0                       282.0      0.0   \n",
+       "184              0.0                NaN                         NaN      NaN   \n",
+       "185              0.0                NaN                         NaN      NaN   \n",
+       "186              1.0                1.0                       540.0      0.0   \n",
+       "\n",
+       "     Disease_Stat  Days_from_surgery_to_last_contact_or_death  Recurrence  \\\n",
+       "0             DOD                                        1776         1.0   \n",
+       "1             DOD                                        2223         1.0   \n",
+       "2             DOD                                        1355         1.0   \n",
+       "3             NaN                                        1783         NaN   \n",
+       "4             DOD                                         977         1.0   \n",
+       "..            ...                                         ...         ...   \n",
+       "182           DOD                                        1036         NaN   \n",
+       "183           NaN                                         468         1.0   \n",
+       "184           DOD                                        1020         NaN   \n",
+       "185           DOC                                        1869         0.0   \n",
+       "186           DOD                                        1279         1.0   \n",
+       "\n",
+       "    Vital_Status  \n",
+       "0            1.0  \n",
+       "1            1.0  \n",
+       "2            1.0  \n",
+       "3            NaN  \n",
+       "4            1.0  \n",
+       "..           ...  \n",
+       "182          1.0  \n",
+       "183          NaN  \n",
+       "184          1.0  \n",
+       "185          1.0  \n",
+       "186          1.0  \n",
+       "\n",
+       "[187 rows x 23 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open, read in information\n",
+    "clinical_data = pd.read_csv(filename,delimiter=',')\n",
+    "\n",
+    "# Renaming columns using the `rename` function\n",
+    "clinical_data = clinical_data.rename(columns={'ID': 'Patient'})\n",
+    "clinical_data['Patient'] = clinical_data['Patient'].astype(str)\n",
+    "df1['Patient'] = df1['Patient'].astype(str)\n",
+    "\n",
+    "# Display the first few rows to check the DataFrame\n",
+    "display(clinical_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ec19d762-81ad-4373-a67b-508775277930",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Patient', '%_tumor', '%_necrosis', 'Age_Diagnosis', 'BMI', 'CA125',\n",
+      "       'Race', 'Other_Cancers', 'BRCAStatus', 'Primary_chem(1)_vs_surg(0)',\n",
+      "       'Diagnosis_to_Start_Chemo', 'Diagnosis_to_surgery', 'Stage',\n",
+      "       'Histo_Type', 'Optimal_Debulking', 'Residual_Disease',\n",
+      "       'Platinum_sensitive', 'Days_surgery_to_recurrence', 'Avastin',\n",
+      "       'Disease_Stat', 'Days_from_surgery_to_last_contact_or_death',\n",
+      "       'Recurrence', 'Vital_Status'],\n",
+      "      dtype='object')\n",
+      "(187, 23)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(clinical_data.columns)\n",
+    "print(clinical_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "54ec5556-4b1e-43bf-ad0e-2f587eae3f9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['PDL1_Cytoplasm_Intensity_Average', 'HLA_Cytoplasm_Intensity_Average',\n",
+      "       'CKs_Cytoplasm_Intensity_Average', 'Ki67_Nucleus_Intensity_Average',\n",
+      "       'CD163_Cytoplasm_Intensity_Average',\n",
+      "       'ColVI_Cytoplasm_Intensity_Average', 'CD20_Cytoplasm_Intensity_Average',\n",
+      "       'PD1_Cytoplasm_Intensity_Average', 'AXL_Cytoplasm_Intensity_Average',\n",
+      "       'CD31_Cytoplasm_Intensity_Average',\n",
+      "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+      "       'CD45_Cytoplasm_Intensity_Average', 'Ecad_Cytoplasm_Intensity_Average',\n",
+      "       'CD8_Cytoplasm_Intensity_Average', 'GATA3_Nucleus_Intensity_Average',\n",
+      "       'Sting_Cytoplasm_Intensity_Average', 'aSMA_Cytoplasm_Intensity_Average',\n",
+      "       'FOXP3_Nucleus_Intensity_Average', 'CD11c_Cytoplasm_Intensity_Average',\n",
+      "       'CD4_Cytoplasm_Intensity_Average',\n",
+      "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+      "       'CD44_Cytoplasm_Intensity_Average', 'CD68_Cytoplasm_Intensity_Average',\n",
+      "       'PDGFR_Cytoplasm_Intensity_Average', 'B7H4_Cell_Intensity_Average',\n",
+      "       'MMP9_Cytoplasm_Intensity_Average',\n",
+      "       'Desmin_Cytoplasm_Intensity_Average', 'cell_subtype', 'cell_type',\n",
+      "       'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nuc_X', 'ROI_index', 'Nucleus_Size',\n",
+      "       'Sample_ID', 'immune_checkpoint', 'Cell_Size', 'Patient',\n",
+      "       'Unique_ROI_index'],\n",
+      "      dtype='object')\n",
+      "(704629, 39)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df1.columns)\n",
+    "print(df1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "41ddb21b-569c-4e65-8b8e-d3b1b1189246",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PDL1_Cytoplasm_Intensity_Average           float64\n",
+      "HLA_Cytoplasm_Intensity_Average            float64\n",
+      "CKs_Cytoplasm_Intensity_Average            float64\n",
+      "Ki67_Nucleus_Intensity_Average             float64\n",
+      "CD163_Cytoplasm_Intensity_Average          float64\n",
+      "ColVI_Cytoplasm_Intensity_Average          float64\n",
+      "CD20_Cytoplasm_Intensity_Average           float64\n",
+      "PD1_Cytoplasm_Intensity_Average            float64\n",
+      "AXL_Cytoplasm_Intensity_Average            float64\n",
+      "CD31_Cytoplasm_Intensity_Average           float64\n",
+      "Fibronectin_Cytoplasm_Intensity_Average    float64\n",
+      "CD45_Cytoplasm_Intensity_Average           float64\n",
+      "Ecad_Cytoplasm_Intensity_Average           float64\n",
+      "CD8_Cytoplasm_Intensity_Average            float64\n",
+      "GATA3_Nucleus_Intensity_Average            float64\n",
+      "Sting_Cytoplasm_Intensity_Average          float64\n",
+      "aSMA_Cytoplasm_Intensity_Average           float64\n",
+      "FOXP3_Nucleus_Intensity_Average            float64\n",
+      "CD11c_Cytoplasm_Intensity_Average          float64\n",
+      "CD4_Cytoplasm_Intensity_Average            float64\n",
+      "Vimentin_Cytoplasm_Intensity_Average       float64\n",
+      "CD44_Cytoplasm_Intensity_Average           float64\n",
+      "CD68_Cytoplasm_Intensity_Average           float64\n",
+      "PDGFR_Cytoplasm_Intensity_Average          float64\n",
+      "B7H4_Cell_Intensity_Average                float64\n",
+      "MMP9_Cytoplasm_Intensity_Average           float64\n",
+      "Desmin_Cytoplasm_Intensity_Average         float64\n",
+      "cell_subtype                                object\n",
+      "cell_type                                   object\n",
+      "Nuc_Y_Inv                                  float64\n",
+      "Nucleus_Roundness                          float64\n",
+      "Nuc_X                                      float64\n",
+      "ROI_index                                    int64\n",
+      "Nucleus_Size                                 int64\n",
+      "Sample_ID                                   object\n",
+      "immune_checkpoint                           object\n",
+      "Cell_Size                                    int64\n",
+      "Patient                                     object\n",
+      "Unique_ROI_index                            object\n",
+      "dtype: object\n",
+      "\n",
+      "Patient                                        object\n",
+      "%_tumor                                         int64\n",
+      "%_necrosis                                      int64\n",
+      "Age_Diagnosis                                 float64\n",
+      "BMI                                           float64\n",
+      "CA125                                         float64\n",
+      "Race                                           object\n",
+      "Other_Cancers                                  object\n",
+      "BRCAStatus                                     object\n",
+      "Primary_chem(1)_vs_surg(0)                      int64\n",
+      "Diagnosis_to_Start_Chemo                      float64\n",
+      "Diagnosis_to_surgery                            int64\n",
+      "Stage                                          object\n",
+      "Histo_Type                                     object\n",
+      "Optimal_Debulking                             float64\n",
+      "Residual_Disease                              float64\n",
+      "Platinum_sensitive                            float64\n",
+      "Days_surgery_to_recurrence                    float64\n",
+      "Avastin                                       float64\n",
+      "Disease_Stat                                   object\n",
+      "Days_from_surgery_to_last_contact_or_death      int64\n",
+      "Recurrence                                    float64\n",
+      "Vital_Status                                  float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display data types of columns in df1 and NACT\n",
+    "print(df1.dtypes)\n",
+    "print('')\n",
+    "print(clinical_data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "39ed5f55-ac13-4702-9686-dc642853e20a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patients uniques dans df1 mais absents dans clinical_data:\n",
+      "{'c37', 'c50', 'c38', 'c6', 'c52', 'c55', 'c13', 'c0', 'c25', 'c51', 'c19', 'c42', 'c57', 'c41', 'c56', 'c40', 'c18', 'c21', 'c48', 'c49', 'c1', 'c26', 'c23', 'c39', 'c15', 'c45', 'c34', 'c58', 'c43', 'c32', 'c8', 'c9', 'c4', 'c35', 'c14', 'c27', 'c7', 'c20', 'c2', 'c12', 'c16', 'c46', 'c3', 'c31', 'c28', 'c53', 'c24', 'c59', 'c5', 'c22', 'c11', 'c54', 'c44', 'c17', 'c30', 'c29', 'c10', 'c47', 'c33', 'c36'}\n",
+      "\n",
+      "Patients uniques dans clinical_data mais absents dans df1:\n",
+      "{'138', '2', '93', '151', '31', '145', '163', '1', '118', '76'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Liste des valeurs uniques dans la colonne 'Patient' de chaque DataFrame\n",
+    "patients_df1 = set(df1['Patient'].unique())\n",
+    "patients_clinical_data = set(clinical_data['Patient'].unique())\n",
+    "\n",
+    "# Patients présents dans df1 mais absents dans clinical_data\n",
+    "patients_unique_to_df1 = patients_df1 - patients_clinical_data\n",
+    "\n",
+    "# Patients présents dans NACT mais absents dans df1\n",
+    "patients_unique_to_clinical_data = patients_clinical_data - patients_df1\n",
+    "\n",
+    "# Affichage des patients uniques dans chaque DataFrame\n",
+    "print(\"Patients uniques dans df1 mais absents dans clinical_data:\")\n",
+    "print(patients_unique_to_df1)\n",
+    "\n",
+    "# no segmented cores?\n",
+    "print(\"\\nPatients uniques dans clinical_data mais absents dans df1:\")\n",
+    "print(patients_unique_to_clinical_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "14078e4d-2be6-4564-9fd0-6f28362a07c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['PDL1_Cytoplasm_Intensity_Average', 'HLA_Cytoplasm_Intensity_Average',\n",
+      "       'CKs_Cytoplasm_Intensity_Average', 'Ki67_Nucleus_Intensity_Average',\n",
+      "       'CD163_Cytoplasm_Intensity_Average',\n",
+      "       'ColVI_Cytoplasm_Intensity_Average', 'CD20_Cytoplasm_Intensity_Average',\n",
+      "       'PD1_Cytoplasm_Intensity_Average', 'AXL_Cytoplasm_Intensity_Average',\n",
+      "       'CD31_Cytoplasm_Intensity_Average',\n",
+      "       'Fibronectin_Cytoplasm_Intensity_Average',\n",
+      "       'CD45_Cytoplasm_Intensity_Average', 'Ecad_Cytoplasm_Intensity_Average',\n",
+      "       'CD8_Cytoplasm_Intensity_Average', 'GATA3_Nucleus_Intensity_Average',\n",
+      "       'Sting_Cytoplasm_Intensity_Average', 'aSMA_Cytoplasm_Intensity_Average',\n",
+      "       'FOXP3_Nucleus_Intensity_Average', 'CD11c_Cytoplasm_Intensity_Average',\n",
+      "       'CD4_Cytoplasm_Intensity_Average',\n",
+      "       'Vimentin_Cytoplasm_Intensity_Average',\n",
+      "       'CD44_Cytoplasm_Intensity_Average', 'CD68_Cytoplasm_Intensity_Average',\n",
+      "       'PDGFR_Cytoplasm_Intensity_Average', 'B7H4_Cell_Intensity_Average',\n",
+      "       'MMP9_Cytoplasm_Intensity_Average',\n",
+      "       'Desmin_Cytoplasm_Intensity_Average', 'cell_subtype', 'cell_type',\n",
+      "       'Nuc_Y_Inv', 'Nucleus_Roundness', 'Nuc_X', 'ROI_index', 'Nucleus_Size',\n",
+      "       'Sample_ID', 'immune_checkpoint', 'Cell_Size', 'Patient',\n",
+      "       'Unique_ROI_index', 'Primary_chem(1)_vs_surg(0)'],\n",
+      "      dtype='object')\n",
+      "(704629, 40)\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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>704629 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.677863   \n",
+       "DD3S1_Cell_1                            -0.677863   \n",
+       "DD3S1_Cell_2                            -0.677863   \n",
+       "DD3S1_Cell_3                            -0.741282   \n",
+       "DD3S1_Cell_6                            -0.621521   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.417494   \n",
+       "DD3S1_Cell_1                           -0.516487   \n",
+       "DD3S1_Cell_2                           -0.141921   \n",
+       "DD3S1_Cell_3                           -0.460472   \n",
+       "DD3S1_Cell_6                           -0.247254   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.912537   \n",
+       "DD3S1_Cell_1                           -0.838037   \n",
+       "DD3S1_Cell_2                           -1.016023   \n",
+       "DD3S1_Cell_3                           -0.491711   \n",
+       "DD3S1_Cell_6                           -0.867127   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S1_Cell_0                          -0.817876   \n",
+       "DD3S1_Cell_1                          -0.869685   \n",
+       "DD3S1_Cell_2                          -0.755879   \n",
+       "DD3S1_Cell_3                          -0.818084   \n",
+       "DD3S1_Cell_6                          -0.742544   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.930099   \n",
+       "DD3S1_Cell_1                              1.114924   \n",
+       "DD3S1_Cell_2                              0.834577   \n",
+       "DD3S1_Cell_3                              0.648200   \n",
+       "DD3S1_Cell_6                              0.810579   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.232078   \n",
+       "DD3S1_Cell_1                              0.301333   \n",
+       "DD3S1_Cell_2                              0.259216   \n",
+       "DD3S1_Cell_3                              0.107027   \n",
+       "DD3S1_Cell_6                              0.272128   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.483158   \n",
+       "DD3S1_Cell_1                            -0.344770   \n",
+       "DD3S1_Cell_2                            -0.438292   \n",
+       "DD3S1_Cell_3                            -0.444889   \n",
+       "DD3S1_Cell_6                            -0.507117   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            1.535604   \n",
+       "DD3S1_Cell_1                            1.668368   \n",
+       "DD3S1_Cell_2                            1.336308   \n",
+       "DD3S1_Cell_3                            1.249805   \n",
+       "DD3S1_Cell_6                            1.251434   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            0.807339   \n",
+       "DD3S1_Cell_1                            0.875455   \n",
+       "DD3S1_Cell_2                            0.705088   \n",
+       "DD3S1_Cell_3                            0.660707   \n",
+       "DD3S1_Cell_6                            0.947172   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                 ...                      \n",
+       "DD3S1_Cell_0                             1.167755  ...           0.955040   \n",
+       "DD3S1_Cell_1                             1.643023  ...           0.966643   \n",
+       "DD3S1_Cell_2                             1.053636  ...           0.721534   \n",
+       "DD3S1_Cell_3                             1.165861  ...           0.587196   \n",
+       "DD3S1_Cell_6                             2.545301  ...           0.935716   \n",
+       "...                                           ...  ...                ...   \n",
+       "TMA_Cell_115755                          0.090397  ...           0.982196   \n",
+       "TMA_Cell_115756                         -0.617835  ...           0.775977   \n",
+       "TMA_Cell_115757                          0.403878  ...           0.688747   \n",
+       "TMA_Cell_115758                         -0.219145  ...           0.751402   \n",
+       "TMA_Cell_115760                         -0.495501  ...           0.674126   \n",
+       "\n",
+       "                        Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_0      1484.771729          0           127  DD3S1.csv   \n",
+       "DD3S1_Cell_1      1426.250000          0           112  DD3S1.csv   \n",
+       "DD3S1_Cell_2      1531.110474          0           181  DD3S1.csv   \n",
+       "DD3S1_Cell_3      1518.907593          0           119  DD3S1.csv   \n",
+       "DD3S1_Cell_6      1471.914917          0            47  DD3S1.csv   \n",
+       "...                       ...        ...           ...        ...   \n",
+       "TMA_Cell_115755  15564.458008         59           142    TMA.csv   \n",
+       "TMA_Cell_115756  15629.680664         59            47    TMA.csv   \n",
+       "TMA_Cell_115757  15518.421875         59            64    TMA.csv   \n",
+       "TMA_Cell_115758  15539.275391         59            58    TMA.csv   \n",
+       "TMA_Cell_115760  15542.961914         59           106    TMA.csv   \n",
+       "\n",
+       "                 immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                         \n",
+       "DD3S1_Cell_0                  None        339       61               61a   \n",
+       "DD3S1_Cell_1                  None        344       61               61a   \n",
+       "DD3S1_Cell_2                  None        422       61               61a   \n",
+       "DD3S1_Cell_3                  None        278       61               61a   \n",
+       "DD3S1_Cell_6                  None        204       61               61a   \n",
+       "...                            ...        ...      ...               ...   \n",
+       "TMA_Cell_115755               None        386      c59              c59a   \n",
+       "TMA_Cell_115756               None        270      c59              c59a   \n",
+       "TMA_Cell_115757               None        202      c59              c59a   \n",
+       "TMA_Cell_115758               None        182      c59              c59a   \n",
+       "TMA_Cell_115760               None        295      c59              c59a   \n",
+       "\n",
+       "                 Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                           \n",
+       "DD3S1_Cell_0                            1.0  \n",
+       "DD3S1_Cell_1                            1.0  \n",
+       "DD3S1_Cell_2                            1.0  \n",
+       "DD3S1_Cell_3                            1.0  \n",
+       "DD3S1_Cell_6                            1.0  \n",
+       "...                                     ...  \n",
+       "TMA_Cell_115755                         NaN  \n",
+       "TMA_Cell_115756                         NaN  \n",
+       "TMA_Cell_115757                         NaN  \n",
+       "TMA_Cell_115758                         NaN  \n",
+       "TMA_Cell_115760                         NaN  \n",
+       "\n",
+       "[704629 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DataFrames fuson based on 'Patient' col\n",
+    "df2 = pd.merge(df1, clinical_data[['Patient', 'Primary_chem(1)_vs_surg(0)']], on='Patient', how='left')\n",
+    "\n",
+    "# Set the index of df1 to be the same as the index of df\n",
+    "df2.set_index(df.index, inplace=True)\n",
+    "\n",
+    "print(df2.columns)\n",
+    "print(df2.shape)\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "57d022c0-3a43-4c3c-a7e9-54d9f98e1b1a",
+   "metadata": {
+    "tags": []
+   },
+   "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>Patient</th>\n",
+       "      <th>%_tumor</th>\n",
+       "      <th>%_necrosis</th>\n",
+       "      <th>Age_Diagnosis</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>CA125</th>\n",
+       "      <th>Race</th>\n",
+       "      <th>Other_Cancers</th>\n",
+       "      <th>BRCAStatus</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Histo_Type</th>\n",
+       "      <th>Optimal_Debulking</th>\n",
+       "      <th>Residual_Disease</th>\n",
+       "      <th>Platinum_sensitive</th>\n",
+       "      <th>Days_surgery_to_recurrence</th>\n",
+       "      <th>Avastin</th>\n",
+       "      <th>Disease_Stat</th>\n",
+       "      <th>Days_from_surgery_to_last_contact_or_death</th>\n",
+       "      <th>Recurrence</th>\n",
+       "      <th>Vital_Status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1426.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CCC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>377.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>977</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>10965.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>516.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>814</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Signetringcell</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>203.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>263</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>460.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NED</td>\n",
+       "      <td>2585</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>359.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>439</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>1907.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1085</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>11785.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>328</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>90</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>4532.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1157.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>AWD</td>\n",
+       "      <td>1864</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8393.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>131</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>renalcell</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2103.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2124</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>61</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>281.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>metastaticadenocarcinoma</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>85</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>hispanic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>572.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>600</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>89</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>855.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>95</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2799.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>96</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>98</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2461.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>343.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>923</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>104</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>351</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>111</td>\n",
+       "      <td>90</td>\n",
+       "      <td>5</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1960.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>533.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1888</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>116</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7453.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>478</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>119</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>16234.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>655.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>374</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>121</td>\n",
+       "      <td>90</td>\n",
+       "      <td>5</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2207.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>melanoma</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>2445</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>137</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2964.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>neurofibromatosisremotebreast</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>575.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>138</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1210.0</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>471</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>140</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1046.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>skincancer</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>206</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>141</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>575.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>613.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1628</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>146</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>10824.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>263.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>352</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>149</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7363.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>papillarythyroid</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NED</td>\n",
+       "      <td>2706</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>153</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>968.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>othermalignantneoplasmsitenotspecified</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>361.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>2336</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>157</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>1971.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>ENDOOV</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1667</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>160</td>\n",
+       "      <td>90</td>\n",
+       "      <td>5</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>328</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>162</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>7482.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>serousofMullerianorigin</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>536.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>2353</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>169</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>245.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1003.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1442</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>171</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>456</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>174</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>8529.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSPC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>387.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>660</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>177</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>919.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>colon</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>450.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1743</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>178</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>basalcellcarcinoma</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>416.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>977</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>179</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>182</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>381.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1552</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>183</td>\n",
+       "      <td>70</td>\n",
+       "      <td>5</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1036</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>184</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>3788.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>breast</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HGSOC</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>282.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>187</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>922.0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>serousprimarilylowgradewfociofhighgrade</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>DOD</td>\n",
+       "      <td>1279</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>40 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Patient  %_tumor  %_necrosis  Age_Diagnosis   BMI    CA125      Race  \\\n",
+       "4         5       50           0           70.0  32.0   1426.0         C   \n",
+       "6         7       80           5           63.0  29.0  10965.0         C   \n",
+       "10       11       90           0           58.0  20.0     72.0         C   \n",
+       "14       15       60           0           77.0  35.0    183.0         C   \n",
+       "17       18       90           0           75.0  25.0     55.0         C   \n",
+       "25       26       80           0           69.0  29.0   1907.0         C   \n",
+       "30       31       70           0           62.0  26.0  11785.0         C   \n",
+       "31       32       90           5           75.0  41.0   4532.0         C   \n",
+       "39       40       70           5           64.0   NaN   8393.0         C   \n",
+       "50       51       90           0           55.0  33.0   1000.0         C   \n",
+       "60       61       60          10           65.0  23.0    281.0         C   \n",
+       "84       85       90           0           61.0  30.0    116.0  hispanic   \n",
+       "88       89       90           0           64.0  28.0    855.0         C   \n",
+       "94       95       80           5           80.0  24.0   2799.0         C   \n",
+       "97       98       70           0           79.0  26.0   2461.0         C   \n",
+       "103     104       90           0           60.0  29.0     63.0         C   \n",
+       "110     111       90           5           50.0  32.0   1960.0         C   \n",
+       "115     116       80           0           76.0  34.0   7453.0         C   \n",
+       "118     119       90           0           57.0  17.0  16234.0         C   \n",
+       "120     121       90           5           64.0  38.0   2207.0         C   \n",
+       "136     137       90           0           54.0  19.0   2964.0         C   \n",
+       "137     138       70           5           70.0  26.0   1210.0     Asian   \n",
+       "139     140       70           5           71.0  21.0   1046.0         C   \n",
+       "140     141       70           5           31.0  31.0    575.0         C   \n",
+       "145     146       70           0           71.0  21.0  10824.0         C   \n",
+       "148     149       70           0           52.0  26.0   7363.0         C   \n",
+       "152     153       80           5           64.0  23.0    968.0         C   \n",
+       "156     157       80           5           56.0  36.0   1971.0         C   \n",
+       "159     160       90           5           59.0  27.0     89.0         C   \n",
+       "161     162       80           0           75.0  20.0   7482.0         C   \n",
+       "168     169       80           5           75.0  35.0    245.0         C   \n",
+       "170     171       80           5           60.0  34.0    500.0         C   \n",
+       "173     174       50           0           54.0  36.0   8529.0         C   \n",
+       "176     177       90           0           60.0  32.0    919.0         C   \n",
+       "177     178       80           0           67.0  27.0    107.0         C   \n",
+       "178     179       80           0           83.0  25.0      NaN         C   \n",
+       "181     182       80           0           81.0  28.0    990.0         C   \n",
+       "182     183       70           5           72.0  22.0    597.0         C   \n",
+       "183     184       90           0           72.0  26.0   3788.0         C   \n",
+       "186     187       80           0           27.0  22.0    922.0         C   \n",
+       "\n",
+       "                              Other_Cancers BRCAStatus  \\\n",
+       "4                                         0        NaN   \n",
+       "6                                         0        NaN   \n",
+       "10                                        0        NaN   \n",
+       "14                                        0        NaN   \n",
+       "17                                        0          0   \n",
+       "25                                        0          0   \n",
+       "30                                        0        NaN   \n",
+       "31                                        0        NaN   \n",
+       "39                                      NaN        NaN   \n",
+       "50                                renalcell        NaN   \n",
+       "60                                        0        NaN   \n",
+       "84                                        0        NaN   \n",
+       "88                                        0          0   \n",
+       "94                                        0        NaN   \n",
+       "97                                        0        NaN   \n",
+       "103                                       0        NaN   \n",
+       "110                                       0          0   \n",
+       "115                                       0        NaN   \n",
+       "118                                       0        NaN   \n",
+       "120                                melanoma        NaN   \n",
+       "136           neurofibromatosisremotebreast          1   \n",
+       "137                                       0        NaN   \n",
+       "139                              skincancer        NaN   \n",
+       "140                                       0          1   \n",
+       "145                                       0        NaN   \n",
+       "148                        papillarythyroid        NaN   \n",
+       "152  othermalignantneoplasmsitenotspecified          2   \n",
+       "156                                       0          2   \n",
+       "159                                  breast          0   \n",
+       "161                                       0          0   \n",
+       "168                                       0        NaN   \n",
+       "170                                       0        NaN   \n",
+       "173                                       0          0   \n",
+       "176                                   colon          0   \n",
+       "177                      basalcellcarcinoma          0   \n",
+       "178                                       0        NaN   \n",
+       "181                                       0        NaN   \n",
+       "182                                  breast          0   \n",
+       "183                                  breast        NaN   \n",
+       "186                                       0          0   \n",
+       "\n",
+       "     Primary_chem(1)_vs_surg(0)  ...                               Histo_Type  \\\n",
+       "4                             1  ...                                      CCC   \n",
+       "6                             1  ...                                    HGSOC   \n",
+       "10                            1  ...                           Signetringcell   \n",
+       "14                            1  ...                                    HGSOC   \n",
+       "17                            1  ...                                    HGSOC   \n",
+       "25                            1  ...                                    HGSOC   \n",
+       "30                            1  ...                                    HGSOC   \n",
+       "31                            1  ...                                    HGSOC   \n",
+       "39                            1  ...                                    HGSOC   \n",
+       "50                            1  ...                                    HGSOC   \n",
+       "60                            1  ...                 metastaticadenocarcinoma   \n",
+       "84                            1  ...                                    HGSOC   \n",
+       "88                            1  ...                                    HGSOC   \n",
+       "94                            1  ...                                    HGSOC   \n",
+       "97                            1  ...                                    HGSOC   \n",
+       "103                           1  ...                                    HGSOC   \n",
+       "110                           1  ...                                    HGSOC   \n",
+       "115                           1  ...                                    HGSOC   \n",
+       "118                           1  ...                                    HGSOC   \n",
+       "120                           1  ...                                    HGSOC   \n",
+       "136                           1  ...                                    HGSOC   \n",
+       "137                           1  ...                                    HGSOC   \n",
+       "139                           1  ...                                    HGSOC   \n",
+       "140                           1  ...                                    HGSOC   \n",
+       "145                           1  ...                                    HGSOC   \n",
+       "148                           1  ...                                    HGSOC   \n",
+       "152                           1  ...                                    HGSOC   \n",
+       "156                           1  ...                                  ENDOOV    \n",
+       "159                           1  ...                                    HGSOC   \n",
+       "161                           1  ...                  serousofMullerianorigin   \n",
+       "168                           1  ...                                    HGSOC   \n",
+       "170                           1  ...                                    HGSOC   \n",
+       "173                           1  ...                                    HGSPC   \n",
+       "176                           1  ...                                    HGSOC   \n",
+       "177                           1  ...                                    HGSOC   \n",
+       "178                           1  ...                                    HGSOC   \n",
+       "181                           1  ...                                    HGSOC   \n",
+       "182                           1  ...                                    HGSOC   \n",
+       "183                           1  ...                                    HGSOC   \n",
+       "186                           1  ...  serousprimarilylowgradewfociofhighgrade   \n",
+       "\n",
+       "     Optimal_Debulking Residual_Disease Platinum_sensitive  \\\n",
+       "4                  NaN              0.0                1.0   \n",
+       "6                  NaN              0.0                0.0   \n",
+       "10                 NaN              0.0                0.0   \n",
+       "14                 1.0              0.0                1.0   \n",
+       "17                 1.0              0.0                1.0   \n",
+       "25                 1.0              0.0                1.0   \n",
+       "30                 1.0              0.0                1.0   \n",
+       "31                 1.0              0.0                1.0   \n",
+       "39                 1.0              0.0                1.0   \n",
+       "50                 1.0              0.0                1.0   \n",
+       "60                 NaN              NaN                1.0   \n",
+       "84                 1.0              0.0                1.0   \n",
+       "88                 1.0              0.0                1.0   \n",
+       "94                 1.0              0.0                NaN   \n",
+       "97                 1.0              0.0                1.0   \n",
+       "103                1.0              0.0                1.0   \n",
+       "110                1.0              0.0                1.0   \n",
+       "115                1.0              0.0                1.0   \n",
+       "118                0.0              1.0                1.0   \n",
+       "120                1.0              0.0                NaN   \n",
+       "136                1.0              0.0                1.0   \n",
+       "137                1.0              0.0                0.0   \n",
+       "139                1.0              0.0                NaN   \n",
+       "140                1.0              0.0                1.0   \n",
+       "145                1.0              0.0                0.0   \n",
+       "148                1.0              0.0                1.0   \n",
+       "152                1.0              0.0                1.0   \n",
+       "156                1.0              0.0                1.0   \n",
+       "159                0.0              1.0                1.0   \n",
+       "161                1.0              0.0                1.0   \n",
+       "168                1.0              0.0                1.0   \n",
+       "170                1.0              0.0                1.0   \n",
+       "173                1.0              0.0                1.0   \n",
+       "176                1.0              0.0                1.0   \n",
+       "177                0.0              1.0                1.0   \n",
+       "178                1.0              0.0                1.0   \n",
+       "181                1.0              0.0                0.0   \n",
+       "182                1.0              0.0                1.0   \n",
+       "183                1.0              0.0                1.0   \n",
+       "186                1.0              1.0                1.0   \n",
+       "\n",
+       "     Days_surgery_to_recurrence  Avastin  Disease_Stat  \\\n",
+       "4                         377.0      1.0           DOD   \n",
+       "6                         516.0      0.0           DOD   \n",
+       "10                        203.0      0.0           DOD   \n",
+       "14                        460.0      0.0           NED   \n",
+       "17                        359.0      1.0           DOD   \n",
+       "25                          NaN      0.0           DOD   \n",
+       "30                          NaN      1.0           DOD   \n",
+       "31                       1157.0      0.0           AWD   \n",
+       "39                          NaN      0.0           NaN   \n",
+       "50                       2103.0      0.0           NaN   \n",
+       "60                          NaN      0.0           NaN   \n",
+       "84                        572.0      0.0           DOD   \n",
+       "88                        540.0      1.0           DOD   \n",
+       "94                          NaN      0.0           NaN   \n",
+       "97                        343.0      0.0           DOD   \n",
+       "103                       335.0      0.0           DOD   \n",
+       "110                       533.0      1.0           DOD   \n",
+       "115                         NaN      0.0           NaN   \n",
+       "118                       655.0      0.0           DOD   \n",
+       "120                         NaN      1.0           DOD   \n",
+       "136                       575.0      1.0           DOD   \n",
+       "137                       268.0      0.0           DOD   \n",
+       "139                         NaN      0.0           NaN   \n",
+       "140                       613.0      1.0           DOD   \n",
+       "145                       263.0      0.0           DOD   \n",
+       "148                         NaN      0.0           NED   \n",
+       "152                       361.0      1.0           DOD   \n",
+       "156                         NaN      0.0           DOD   \n",
+       "159                       160.0      1.0           DOD   \n",
+       "161                       536.0      0.0           DOD   \n",
+       "168                      1003.0      0.0           DOD   \n",
+       "170                       302.0      0.0           DOD   \n",
+       "173                       387.0      0.0           DOD   \n",
+       "176                       450.0      1.0           DOD   \n",
+       "177                       416.0      0.0           DOD   \n",
+       "178                         NaN      NaN           DOD   \n",
+       "181                       381.0      0.0           DOD   \n",
+       "182                         NaN      NaN           DOD   \n",
+       "183                       282.0      0.0           NaN   \n",
+       "186                       540.0      0.0           DOD   \n",
+       "\n",
+       "     Days_from_surgery_to_last_contact_or_death  Recurrence Vital_Status  \n",
+       "4                                           977         1.0          1.0  \n",
+       "6                                           814         1.0          1.0  \n",
+       "10                                          263         1.0          1.0  \n",
+       "14                                         2585         1.0          0.0  \n",
+       "17                                          439         1.0          1.0  \n",
+       "25                                         1085         0.0          1.0  \n",
+       "30                                          328         0.0          1.0  \n",
+       "31                                         1864         1.0          0.0  \n",
+       "39                                          131         0.0          NaN  \n",
+       "50                                         2124         1.0          NaN  \n",
+       "60                                           23         0.0          NaN  \n",
+       "84                                          600         1.0          1.0  \n",
+       "88                                         1820         1.0          1.0  \n",
+       "94                                           96         0.0          NaN  \n",
+       "97                                          923         1.0          1.0  \n",
+       "103                                         351         1.0          1.0  \n",
+       "110                                        1888         1.0          1.0  \n",
+       "115                                         478         0.0          NaN  \n",
+       "118                                         374         1.0          1.0  \n",
+       "120                                        2445         NaN          1.0  \n",
+       "136                                        1330         1.0          1.0  \n",
+       "137                                         471         1.0          1.0  \n",
+       "139                                         206         NaN          NaN  \n",
+       "140                                        1628         1.0          1.0  \n",
+       "145                                         352         1.0          1.0  \n",
+       "148                                        2706         0.0          0.0  \n",
+       "152                                        2336         1.0          1.0  \n",
+       "156                                        1667         0.0          1.0  \n",
+       "159                                         328         1.0          1.0  \n",
+       "161                                        2353         1.0          1.0  \n",
+       "168                                        1442         1.0          1.0  \n",
+       "170                                         456         1.0          1.0  \n",
+       "173                                         660         1.0          1.0  \n",
+       "176                                        1743         1.0          1.0  \n",
+       "177                                         977         1.0          1.0  \n",
+       "178                                        1070         NaN          1.0  \n",
+       "181                                        1552         1.0          1.0  \n",
+       "182                                        1036         NaN          1.0  \n",
+       "183                                         468         1.0          NaN  \n",
+       "186                                        1279         1.0          1.0  \n",
+       "\n",
+       "[40 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#NACT rows\n",
+    "clinical_data[clinical_data['Primary_chem(1)_vs_surg(0)'] == 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "86cbf508-e723-4993-99d0-d41347a97246",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</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",
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+       "      <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>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>704629 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.677863   \n",
+       "DD3S1_Cell_1                            -0.677863   \n",
+       "DD3S1_Cell_2                            -0.677863   \n",
+       "DD3S1_Cell_3                            -0.741282   \n",
+       "DD3S1_Cell_6                            -0.621521   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.417494   \n",
+       "DD3S1_Cell_1                           -0.516487   \n",
+       "DD3S1_Cell_2                           -0.141921   \n",
+       "DD3S1_Cell_3                           -0.460472   \n",
+       "DD3S1_Cell_6                           -0.247254   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.912537   \n",
+       "DD3S1_Cell_1                           -0.838037   \n",
+       "DD3S1_Cell_2                           -1.016023   \n",
+       "DD3S1_Cell_3                           -0.491711   \n",
+       "DD3S1_Cell_6                           -0.867127   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S1_Cell_0                          -0.817876   \n",
+       "DD3S1_Cell_1                          -0.869685   \n",
+       "DD3S1_Cell_2                          -0.755879   \n",
+       "DD3S1_Cell_3                          -0.818084   \n",
+       "DD3S1_Cell_6                          -0.742544   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.930099   \n",
+       "DD3S1_Cell_1                              1.114924   \n",
+       "DD3S1_Cell_2                              0.834577   \n",
+       "DD3S1_Cell_3                              0.648200   \n",
+       "DD3S1_Cell_6                              0.810579   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.232078   \n",
+       "DD3S1_Cell_1                              0.301333   \n",
+       "DD3S1_Cell_2                              0.259216   \n",
+       "DD3S1_Cell_3                              0.107027   \n",
+       "DD3S1_Cell_6                              0.272128   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.483158   \n",
+       "DD3S1_Cell_1                            -0.344770   \n",
+       "DD3S1_Cell_2                            -0.438292   \n",
+       "DD3S1_Cell_3                            -0.444889   \n",
+       "DD3S1_Cell_6                            -0.507117   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            1.535604   \n",
+       "DD3S1_Cell_1                            1.668368   \n",
+       "DD3S1_Cell_2                            1.336308   \n",
+       "DD3S1_Cell_3                            1.249805   \n",
+       "DD3S1_Cell_6                            1.251434   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            0.807339   \n",
+       "DD3S1_Cell_1                            0.875455   \n",
+       "DD3S1_Cell_2                            0.705088   \n",
+       "DD3S1_Cell_3                            0.660707   \n",
+       "DD3S1_Cell_6                            0.947172   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                 ...                      \n",
+       "DD3S1_Cell_0                             1.167755  ...           0.955040   \n",
+       "DD3S1_Cell_1                             1.643023  ...           0.966643   \n",
+       "DD3S1_Cell_2                             1.053636  ...           0.721534   \n",
+       "DD3S1_Cell_3                             1.165861  ...           0.587196   \n",
+       "DD3S1_Cell_6                             2.545301  ...           0.935716   \n",
+       "...                                           ...  ...                ...   \n",
+       "TMA_Cell_115755                          0.090397  ...           0.982196   \n",
+       "TMA_Cell_115756                         -0.617835  ...           0.775977   \n",
+       "TMA_Cell_115757                          0.403878  ...           0.688747   \n",
+       "TMA_Cell_115758                         -0.219145  ...           0.751402   \n",
+       "TMA_Cell_115760                         -0.495501  ...           0.674126   \n",
+       "\n",
+       "                        Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_0      1484.771729          0           127  DD3S1.csv   \n",
+       "DD3S1_Cell_1      1426.250000          0           112  DD3S1.csv   \n",
+       "DD3S1_Cell_2      1531.110474          0           181  DD3S1.csv   \n",
+       "DD3S1_Cell_3      1518.907593          0           119  DD3S1.csv   \n",
+       "DD3S1_Cell_6      1471.914917          0            47  DD3S1.csv   \n",
+       "...                       ...        ...           ...        ...   \n",
+       "TMA_Cell_115755  15564.458008         59           142    TMA.csv   \n",
+       "TMA_Cell_115756  15629.680664         59            47    TMA.csv   \n",
+       "TMA_Cell_115757  15518.421875         59            64    TMA.csv   \n",
+       "TMA_Cell_115758  15539.275391         59            58    TMA.csv   \n",
+       "TMA_Cell_115760  15542.961914         59           106    TMA.csv   \n",
+       "\n",
+       "                 immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                         \n",
+       "DD3S1_Cell_0                  None        339       61               61a   \n",
+       "DD3S1_Cell_1                  None        344       61               61a   \n",
+       "DD3S1_Cell_2                  None        422       61               61a   \n",
+       "DD3S1_Cell_3                  None        278       61               61a   \n",
+       "DD3S1_Cell_6                  None        204       61               61a   \n",
+       "...                            ...        ...      ...               ...   \n",
+       "TMA_Cell_115755               None        386      c59              c59a   \n",
+       "TMA_Cell_115756               None        270      c59              c59a   \n",
+       "TMA_Cell_115757               None        202      c59              c59a   \n",
+       "TMA_Cell_115758               None        182      c59              c59a   \n",
+       "TMA_Cell_115760               None        295      c59              c59a   \n",
+       "\n",
+       "                 Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                           \n",
+       "DD3S1_Cell_0                            1.0  \n",
+       "DD3S1_Cell_1                            1.0  \n",
+       "DD3S1_Cell_2                            1.0  \n",
+       "DD3S1_Cell_3                            1.0  \n",
+       "DD3S1_Cell_6                            1.0  \n",
+       "...                                     ...  \n",
+       "TMA_Cell_115755                     CONTROL  \n",
+       "TMA_Cell_115756                     CONTROL  \n",
+       "TMA_Cell_115757                     CONTROL  \n",
+       "TMA_Cell_115758                     CONTROL  \n",
+       "TMA_Cell_115760                     CONTROL  \n",
+       "\n",
+       "[704629 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Go through 'Primary_chem(1)_vs_surg(0)' col to replace NaN values by 'CONTROL' if 'c' in 'Patient' col\n",
+    "df2.loc[(df2['Primary_chem(1)_vs_surg(0)'].isnull()) & (df2['Patient'].astype(str).str.contains('c|^c')), 'Primary_chem(1)_vs_surg(0)'] = 'CONTROL'\n",
+    "\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a3987559-aa7d-4638-bafb-096adcbd7e36",
+   "metadata": {
+    "tags": []
+   },
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [PDL1_Cytoplasm_Intensity_Average, HLA_Cytoplasm_Intensity_Average, CKs_Cytoplasm_Intensity_Average, Ki67_Nucleus_Intensity_Average, CD163_Cytoplasm_Intensity_Average, ColVI_Cytoplasm_Intensity_Average, CD20_Cytoplasm_Intensity_Average, PD1_Cytoplasm_Intensity_Average, AXL_Cytoplasm_Intensity_Average, CD31_Cytoplasm_Intensity_Average, Fibronectin_Cytoplasm_Intensity_Average, CD45_Cytoplasm_Intensity_Average, Ecad_Cytoplasm_Intensity_Average, CD8_Cytoplasm_Intensity_Average, GATA3_Nucleus_Intensity_Average, Sting_Cytoplasm_Intensity_Average, aSMA_Cytoplasm_Intensity_Average, FOXP3_Nucleus_Intensity_Average, CD11c_Cytoplasm_Intensity_Average, CD4_Cytoplasm_Intensity_Average, Vimentin_Cytoplasm_Intensity_Average, CD44_Cytoplasm_Intensity_Average, CD68_Cytoplasm_Intensity_Average, PDGFR_Cytoplasm_Intensity_Average, B7H4_Cell_Intensity_Average, MMP9_Cytoplasm_Intensity_Average, Desmin_Cytoplasm_Intensity_Average, cell_subtype, cell_type, Nuc_Y_Inv, Nucleus_Roundness, Nuc_X, ROI_index, Nucleus_Size, Sample_ID, immune_checkpoint, Cell_Size, Patient, Unique_ROI_index, Primary_chem(1)_vs_surg(0)]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# supposed empty\n",
+    "df2[df2['Primary_chem(1)_vs_surg(0)'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5d8089d6-1ca5-4890-abae-20947e1b4941",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Création de trois DataFrames en fonction des valeurs dans 'Primary_chem(1)_vs_surg(0)'\n",
+    "df_NACT = df2[df2['Primary_chem(1)_vs_surg(0)'] == 1]\n",
+    "df_ACT = df2[df2['Primary_chem(1)_vs_surg(0)'] == 0]\n",
+    "df_control = df2[df2['Primary_chem(1)_vs_surg(0)'] == 'CONTROL']\n",
+    "\n",
+    "filename_NACT = os.path.join(output_data_dir, 'df_NACT.csv')\n",
+    "filename_ACT = os.path.join(output_data_dir, 'df_ACT.csv')\n",
+    "filename_control = os.path.join(output_data_dir, 'df_control.csv')\n",
+    "\n",
+    "# Enregistrement des DataFrames dans des fichiers CSV dans le répertoire output_data_dir\n",
+    "df_NACT.to_csv(filename_NACT, index=False)\n",
+    "df_ACT.to_csv(filename_ACT, index=False)\n",
+    "df_control.to_csv(filename_control, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d4038385-3463-487b-8e80-c561adf6249a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62826</th>\n",
+       "      <td>-0.446025</td>\n",
+       "      <td>0.480212</td>\n",
+       "      <td>1.195440</td>\n",
+       "      <td>-0.081463</td>\n",
+       "      <td>-0.634343</td>\n",
+       "      <td>-0.883815</td>\n",
+       "      <td>1.050928</td>\n",
+       "      <td>0.164709</td>\n",
+       "      <td>0.103986</td>\n",
+       "      <td>0.171579</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.609764</td>\n",
+       "      <td>13449.426758</td>\n",
+       "      <td>34</td>\n",
+       "      <td>103</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>237</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62828</th>\n",
+       "      <td>-0.414440</td>\n",
+       "      <td>0.416668</td>\n",
+       "      <td>1.542926</td>\n",
+       "      <td>-0.203248</td>\n",
+       "      <td>-0.076555</td>\n",
+       "      <td>-0.783759</td>\n",
+       "      <td>0.423346</td>\n",
+       "      <td>0.638305</td>\n",
+       "      <td>0.346970</td>\n",
+       "      <td>0.453372</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.736844</td>\n",
+       "      <td>13469.353516</td>\n",
+       "      <td>34</td>\n",
+       "      <td>65</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>231</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62829</th>\n",
+       "      <td>-0.720569</td>\n",
+       "      <td>0.072144</td>\n",
+       "      <td>-0.311962</td>\n",
+       "      <td>-0.481442</td>\n",
+       "      <td>1.236053</td>\n",
+       "      <td>-0.468813</td>\n",
+       "      <td>0.252353</td>\n",
+       "      <td>2.129042</td>\n",
+       "      <td>1.111809</td>\n",
+       "      <td>1.340371</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.691315</td>\n",
+       "      <td>13390.447266</td>\n",
+       "      <td>34</td>\n",
+       "      <td>85</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>224</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62830</th>\n",
+       "      <td>-0.442500</td>\n",
+       "      <td>0.368654</td>\n",
+       "      <td>-1.491754</td>\n",
+       "      <td>-0.316270</td>\n",
+       "      <td>0.029502</td>\n",
+       "      <td>-0.736522</td>\n",
+       "      <td>0.390883</td>\n",
+       "      <td>0.861894</td>\n",
+       "      <td>0.461685</td>\n",
+       "      <td>0.586409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.892707</td>\n",
+       "      <td>13430.178711</td>\n",
+       "      <td>34</td>\n",
+       "      <td>56</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>139</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62831</th>\n",
+       "      <td>0.040892</td>\n",
+       "      <td>1.166606</td>\n",
+       "      <td>-1.676753</td>\n",
+       "      <td>0.249586</td>\n",
+       "      <td>-1.486438</td>\n",
+       "      <td>-1.072878</td>\n",
+       "      <td>2.024627</td>\n",
+       "      <td>-0.730182</td>\n",
+       "      <td>-0.355148</td>\n",
+       "      <td>-0.360887</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.676388</td>\n",
+       "      <td>13445.479492</td>\n",
+       "      <td>34</td>\n",
+       "      <td>48</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>183</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>113959 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                             -0.677863   \n",
+       "DD3S1_Cell_1                             -0.677863   \n",
+       "DD3S1_Cell_2                             -0.677863   \n",
+       "DD3S1_Cell_3                             -0.741282   \n",
+       "DD3S1_Cell_6                             -0.621521   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_62826                         -0.446025   \n",
+       "DD5S3_Cell_62828                         -0.414440   \n",
+       "DD5S3_Cell_62829                         -0.720569   \n",
+       "DD5S3_Cell_62830                         -0.442500   \n",
+       "DD5S3_Cell_62831                          0.040892   \n",
+       "\n",
+       "                  HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.417494   \n",
+       "DD3S1_Cell_1                            -0.516487   \n",
+       "DD3S1_Cell_2                            -0.141921   \n",
+       "DD3S1_Cell_3                            -0.460472   \n",
+       "DD3S1_Cell_6                            -0.247254   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.480212   \n",
+       "DD5S3_Cell_62828                         0.416668   \n",
+       "DD5S3_Cell_62829                         0.072144   \n",
+       "DD5S3_Cell_62830                         0.368654   \n",
+       "DD5S3_Cell_62831                         1.166606   \n",
+       "\n",
+       "                  CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.912537   \n",
+       "DD3S1_Cell_1                            -0.838037   \n",
+       "DD3S1_Cell_2                            -1.016023   \n",
+       "DD3S1_Cell_3                            -0.491711   \n",
+       "DD3S1_Cell_6                            -0.867127   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         1.195440   \n",
+       "DD5S3_Cell_62828                         1.542926   \n",
+       "DD5S3_Cell_62829                        -0.311962   \n",
+       "DD5S3_Cell_62830                        -1.491754   \n",
+       "DD5S3_Cell_62831                        -1.676753   \n",
+       "\n",
+       "                  Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.817876   \n",
+       "DD3S1_Cell_1                           -0.869685   \n",
+       "DD3S1_Cell_2                           -0.755879   \n",
+       "DD3S1_Cell_3                           -0.818084   \n",
+       "DD3S1_Cell_6                           -0.742544   \n",
+       "...                                          ...   \n",
+       "DD5S3_Cell_62826                       -0.081463   \n",
+       "DD5S3_Cell_62828                       -0.203248   \n",
+       "DD5S3_Cell_62829                       -0.481442   \n",
+       "DD5S3_Cell_62830                       -0.316270   \n",
+       "DD5S3_Cell_62831                        0.249586   \n",
+       "\n",
+       "                  CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_0                               0.930099   \n",
+       "DD3S1_Cell_1                               1.114924   \n",
+       "DD3S1_Cell_2                               0.834577   \n",
+       "DD3S1_Cell_3                               0.648200   \n",
+       "DD3S1_Cell_6                               0.810579   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_62826                          -0.634343   \n",
+       "DD5S3_Cell_62828                          -0.076555   \n",
+       "DD5S3_Cell_62829                           1.236053   \n",
+       "DD5S3_Cell_62830                           0.029502   \n",
+       "DD5S3_Cell_62831                          -1.486438   \n",
+       "\n",
+       "                  ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_0                               0.232078   \n",
+       "DD3S1_Cell_1                               0.301333   \n",
+       "DD3S1_Cell_2                               0.259216   \n",
+       "DD3S1_Cell_3                               0.107027   \n",
+       "DD3S1_Cell_6                               0.272128   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_62826                          -0.883815   \n",
+       "DD5S3_Cell_62828                          -0.783759   \n",
+       "DD5S3_Cell_62829                          -0.468813   \n",
+       "DD5S3_Cell_62830                          -0.736522   \n",
+       "DD5S3_Cell_62831                          -1.072878   \n",
+       "\n",
+       "                  CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                             -0.483158   \n",
+       "DD3S1_Cell_1                             -0.344770   \n",
+       "DD3S1_Cell_2                             -0.438292   \n",
+       "DD3S1_Cell_3                             -0.444889   \n",
+       "DD3S1_Cell_6                             -0.507117   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_62826                          1.050928   \n",
+       "DD5S3_Cell_62828                          0.423346   \n",
+       "DD5S3_Cell_62829                          0.252353   \n",
+       "DD5S3_Cell_62830                          0.390883   \n",
+       "DD5S3_Cell_62831                          2.024627   \n",
+       "\n",
+       "                  PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                             1.535604   \n",
+       "DD3S1_Cell_1                             1.668368   \n",
+       "DD3S1_Cell_2                             1.336308   \n",
+       "DD3S1_Cell_3                             1.249805   \n",
+       "DD3S1_Cell_6                             1.251434   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.164709   \n",
+       "DD5S3_Cell_62828                         0.638305   \n",
+       "DD5S3_Cell_62829                         2.129042   \n",
+       "DD5S3_Cell_62830                         0.861894   \n",
+       "DD5S3_Cell_62831                        -0.730182   \n",
+       "\n",
+       "                  AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                             0.807339   \n",
+       "DD3S1_Cell_1                             0.875455   \n",
+       "DD3S1_Cell_2                             0.705088   \n",
+       "DD3S1_Cell_3                             0.660707   \n",
+       "DD3S1_Cell_6                             0.947172   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.103986   \n",
+       "DD5S3_Cell_62828                         0.346970   \n",
+       "DD5S3_Cell_62829                         1.111809   \n",
+       "DD5S3_Cell_62830                         0.461685   \n",
+       "DD5S3_Cell_62831                        -0.355148   \n",
+       "\n",
+       "                  CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                  ...                      \n",
+       "DD3S1_Cell_0                              1.167755  ...           0.955040   \n",
+       "DD3S1_Cell_1                              1.643023  ...           0.966643   \n",
+       "DD3S1_Cell_2                              1.053636  ...           0.721534   \n",
+       "DD3S1_Cell_3                              1.165861  ...           0.587196   \n",
+       "DD3S1_Cell_6                              2.545301  ...           0.935716   \n",
+       "...                                            ...  ...                ...   \n",
+       "DD5S3_Cell_62826                          0.171579  ...           0.609764   \n",
+       "DD5S3_Cell_62828                          0.453372  ...           0.736844   \n",
+       "DD5S3_Cell_62829                          1.340371  ...           0.691315   \n",
+       "DD5S3_Cell_62830                          0.586409  ...           0.892707   \n",
+       "DD5S3_Cell_62831                         -0.360887  ...           0.676388   \n",
+       "\n",
+       "                         Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                   \n",
+       "DD3S1_Cell_0       1484.771729          0           127  DD3S1.csv   \n",
+       "DD3S1_Cell_1       1426.250000          0           112  DD3S1.csv   \n",
+       "DD3S1_Cell_2       1531.110474          0           181  DD3S1.csv   \n",
+       "DD3S1_Cell_3       1518.907593          0           119  DD3S1.csv   \n",
+       "DD3S1_Cell_6       1471.914917          0            47  DD3S1.csv   \n",
+       "...                        ...        ...           ...        ...   \n",
+       "DD5S3_Cell_62826  13449.426758         34           103  DD5S3.csv   \n",
+       "DD5S3_Cell_62828  13469.353516         34            65  DD5S3.csv   \n",
+       "DD5S3_Cell_62829  13390.447266         34            85  DD5S3.csv   \n",
+       "DD5S3_Cell_62830  13430.178711         34            56  DD5S3.csv   \n",
+       "DD5S3_Cell_62831  13445.479492         34            48  DD5S3.csv   \n",
+       "\n",
+       "                  immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_0                   None        339       61               61a   \n",
+       "DD3S1_Cell_1                   None        344       61               61a   \n",
+       "DD3S1_Cell_2                   None        422       61               61a   \n",
+       "DD3S1_Cell_3                   None        278       61               61a   \n",
+       "DD3S1_Cell_6                   None        204       61               61a   \n",
+       "...                             ...        ...      ...               ...   \n",
+       "DD5S3_Cell_62826               None        237      187              187c   \n",
+       "DD5S3_Cell_62828               None        231      187              187c   \n",
+       "DD5S3_Cell_62829               None        224      187              187c   \n",
+       "DD5S3_Cell_62830               None        139      187              187c   \n",
+       "DD5S3_Cell_62831               None        183      187              187c   \n",
+       "\n",
+       "                  Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                            \n",
+       "DD3S1_Cell_0                             1.0  \n",
+       "DD3S1_Cell_1                             1.0  \n",
+       "DD3S1_Cell_2                             1.0  \n",
+       "DD3S1_Cell_3                             1.0  \n",
+       "DD3S1_Cell_6                             1.0  \n",
+       "...                                      ...  \n",
+       "DD5S3_Cell_62826                         1.0  \n",
+       "DD5S3_Cell_62828                         1.0  \n",
+       "DD5S3_Cell_62829                         1.0  \n",
+       "DD5S3_Cell_62830                         1.0  \n",
+       "DD5S3_Cell_62831                         1.0  \n",
+       "\n",
+       "[113959 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1af784e7-585e-4973-8012-c00627d5a685",
+   "metadata": {
+    "tags": []
+   },
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1910</th>\n",
+       "      <td>-1.137775</td>\n",
+       "      <td>-0.495306</td>\n",
+       "      <td>-1.178944</td>\n",
+       "      <td>-0.832994</td>\n",
+       "      <td>1.067765</td>\n",
+       "      <td>1.432729</td>\n",
+       "      <td>-0.220835</td>\n",
+       "      <td>1.952302</td>\n",
+       "      <td>1.021130</td>\n",
+       "      <td>1.235209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.873320</td>\n",
+       "      <td>1143.897461</td>\n",
+       "      <td>1</td>\n",
+       "      <td>78</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>265</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1911</th>\n",
+       "      <td>-1.399789</td>\n",
+       "      <td>0.791106</td>\n",
+       "      <td>0.567668</td>\n",
+       "      <td>0.215155</td>\n",
+       "      <td>0.518692</td>\n",
+       "      <td>-0.627981</td>\n",
+       "      <td>-0.000923</td>\n",
+       "      <td>1.375652</td>\n",
+       "      <td>0.725274</td>\n",
+       "      <td>0.945061</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.566933</td>\n",
+       "      <td>1396.333252</td>\n",
+       "      <td>1</td>\n",
+       "      <td>93</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>210</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1922</th>\n",
+       "      <td>-1.186023</td>\n",
+       "      <td>-0.520883</td>\n",
+       "      <td>-1.104365</td>\n",
+       "      <td>-0.894190</td>\n",
+       "      <td>0.988875</td>\n",
+       "      <td>1.417141</td>\n",
+       "      <td>-0.328588</td>\n",
+       "      <td>1.869449</td>\n",
+       "      <td>0.978622</td>\n",
+       "      <td>1.185912</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.886386</td>\n",
+       "      <td>1148.870972</td>\n",
+       "      <td>1</td>\n",
+       "      <td>62</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>196</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1928</th>\n",
+       "      <td>-0.329397</td>\n",
+       "      <td>0.396547</td>\n",
+       "      <td>1.746212</td>\n",
+       "      <td>1.313160</td>\n",
+       "      <td>1.139536</td>\n",
+       "      <td>-0.546020</td>\n",
+       "      <td>1.586974</td>\n",
+       "      <td>1.763598</td>\n",
+       "      <td>0.924314</td>\n",
+       "      <td>1.122929</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.738159</td>\n",
+       "      <td>1097.823486</td>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>159</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1942</th>\n",
+       "      <td>-0.970875</td>\n",
+       "      <td>-0.126315</td>\n",
+       "      <td>1.072148</td>\n",
+       "      <td>0.178276</td>\n",
+       "      <td>1.103388</td>\n",
+       "      <td>-0.498249</td>\n",
+       "      <td>-0.026061</td>\n",
+       "      <td>1.989714</td>\n",
+       "      <td>1.040325</td>\n",
+       "      <td>1.257470</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.890113</td>\n",
+       "      <td>1402.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>114</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>205</td>\n",
+       "      <td>62</td>\n",
+       "      <td>62a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_64060</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>0.887158</td>\n",
+       "      <td>-0.528183</td>\n",
+       "      <td>0.525142</td>\n",
+       "      <td>5.217188</td>\n",
+       "      <td>-1.300569</td>\n",
+       "      <td>-0.775098</td>\n",
+       "      <td>-1.455787</td>\n",
+       "      <td>-0.908090</td>\n",
+       "      <td>-0.862636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.830379</td>\n",
+       "      <td>13503.915039</td>\n",
+       "      <td>35</td>\n",
+       "      <td>106</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>378</td>\n",
+       "      <td>186</td>\n",
+       "      <td>186a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_64061</th>\n",
+       "      <td>0.479994</td>\n",
+       "      <td>0.415456</td>\n",
+       "      <td>0.819658</td>\n",
+       "      <td>0.623611</td>\n",
+       "      <td>2.082444</td>\n",
+       "      <td>-1.053888</td>\n",
+       "      <td>1.671235</td>\n",
+       "      <td>0.310590</td>\n",
+       "      <td>-0.309031</td>\n",
+       "      <td>-0.235059</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.646098</td>\n",
+       "      <td>13254.210938</td>\n",
+       "      <td>35</td>\n",
+       "      <td>90</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>151</td>\n",
+       "      <td>186</td>\n",
+       "      <td>186a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_64062</th>\n",
+       "      <td>0.660736</td>\n",
+       "      <td>0.440965</td>\n",
+       "      <td>1.409010</td>\n",
+       "      <td>0.406828</td>\n",
+       "      <td>2.082444</td>\n",
+       "      <td>-0.849359</td>\n",
+       "      <td>1.622354</td>\n",
+       "      <td>1.128603</td>\n",
+       "      <td>0.187663</td>\n",
+       "      <td>0.290273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.566233</td>\n",
+       "      <td>13260.901367</td>\n",
+       "      <td>35</td>\n",
+       "      <td>61</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>146</td>\n",
+       "      <td>186</td>\n",
+       "      <td>186a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_64063</th>\n",
+       "      <td>0.232880</td>\n",
+       "      <td>0.505008</td>\n",
+       "      <td>0.798055</td>\n",
+       "      <td>1.721604</td>\n",
+       "      <td>0.625211</td>\n",
+       "      <td>-1.022122</td>\n",
+       "      <td>0.833309</td>\n",
+       "      <td>-0.489938</td>\n",
+       "      <td>-0.231888</td>\n",
+       "      <td>-0.208742</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.788226</td>\n",
+       "      <td>13292.144531</td>\n",
+       "      <td>35</td>\n",
+       "      <td>76</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>326</td>\n",
+       "      <td>186</td>\n",
+       "      <td>186a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_64066</th>\n",
+       "      <td>-0.166463</td>\n",
+       "      <td>0.896852</td>\n",
+       "      <td>-1.903397</td>\n",
+       "      <td>3.436984</td>\n",
+       "      <td>1.873290</td>\n",
+       "      <td>-0.673352</td>\n",
+       "      <td>2.613096</td>\n",
+       "      <td>1.160896</td>\n",
+       "      <td>0.615091</td>\n",
+       "      <td>0.764317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.791907</td>\n",
+       "      <td>13420.065430</td>\n",
+       "      <td>35</td>\n",
+       "      <td>46</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>B7H4</td>\n",
+       "      <td>232</td>\n",
+       "      <td>186</td>\n",
+       "      <td>186a</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>495928 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_1910                          -1.137775   \n",
+       "DD3S1_Cell_1911                          -1.399789   \n",
+       "DD3S1_Cell_1922                          -1.186023   \n",
+       "DD3S1_Cell_1928                          -0.329397   \n",
+       "DD3S1_Cell_1942                          -0.970875   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_64060                         -0.677863   \n",
+       "DD5S3_Cell_64061                          0.479994   \n",
+       "DD5S3_Cell_64062                          0.660736   \n",
+       "DD5S3_Cell_64063                          0.232880   \n",
+       "DD5S3_Cell_64066                         -0.166463   \n",
+       "\n",
+       "                  HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_1910                         -0.495306   \n",
+       "DD3S1_Cell_1911                          0.791106   \n",
+       "DD3S1_Cell_1922                         -0.520883   \n",
+       "DD3S1_Cell_1928                          0.396547   \n",
+       "DD3S1_Cell_1942                         -0.126315   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_64060                         0.887158   \n",
+       "DD5S3_Cell_64061                         0.415456   \n",
+       "DD5S3_Cell_64062                         0.440965   \n",
+       "DD5S3_Cell_64063                         0.505008   \n",
+       "DD5S3_Cell_64066                         0.896852   \n",
+       "\n",
+       "                  CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_1910                         -1.178944   \n",
+       "DD3S1_Cell_1911                          0.567668   \n",
+       "DD3S1_Cell_1922                         -1.104365   \n",
+       "DD3S1_Cell_1928                          1.746212   \n",
+       "DD3S1_Cell_1942                          1.072148   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_64060                        -0.528183   \n",
+       "DD5S3_Cell_64061                         0.819658   \n",
+       "DD5S3_Cell_64062                         1.409010   \n",
+       "DD5S3_Cell_64063                         0.798055   \n",
+       "DD5S3_Cell_64066                        -1.903397   \n",
+       "\n",
+       "                  Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_1910                        -0.832994   \n",
+       "DD3S1_Cell_1911                         0.215155   \n",
+       "DD3S1_Cell_1922                        -0.894190   \n",
+       "DD3S1_Cell_1928                         1.313160   \n",
+       "DD3S1_Cell_1942                         0.178276   \n",
+       "...                                          ...   \n",
+       "DD5S3_Cell_64060                        0.525142   \n",
+       "DD5S3_Cell_64061                        0.623611   \n",
+       "DD5S3_Cell_64062                        0.406828   \n",
+       "DD5S3_Cell_64063                        1.721604   \n",
+       "DD5S3_Cell_64066                        3.436984   \n",
+       "\n",
+       "                  CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_1910                            1.067765   \n",
+       "DD3S1_Cell_1911                            0.518692   \n",
+       "DD3S1_Cell_1922                            0.988875   \n",
+       "DD3S1_Cell_1928                            1.139536   \n",
+       "DD3S1_Cell_1942                            1.103388   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_64060                           5.217188   \n",
+       "DD5S3_Cell_64061                           2.082444   \n",
+       "DD5S3_Cell_64062                           2.082444   \n",
+       "DD5S3_Cell_64063                           0.625211   \n",
+       "DD5S3_Cell_64066                           1.873290   \n",
+       "\n",
+       "                  ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_1910                            1.432729   \n",
+       "DD3S1_Cell_1911                           -0.627981   \n",
+       "DD3S1_Cell_1922                            1.417141   \n",
+       "DD3S1_Cell_1928                           -0.546020   \n",
+       "DD3S1_Cell_1942                           -0.498249   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_64060                          -1.300569   \n",
+       "DD5S3_Cell_64061                          -1.053888   \n",
+       "DD5S3_Cell_64062                          -0.849359   \n",
+       "DD5S3_Cell_64063                          -1.022122   \n",
+       "DD5S3_Cell_64066                          -0.673352   \n",
+       "\n",
+       "                  CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_1910                          -0.220835   \n",
+       "DD3S1_Cell_1911                          -0.000923   \n",
+       "DD3S1_Cell_1922                          -0.328588   \n",
+       "DD3S1_Cell_1928                           1.586974   \n",
+       "DD3S1_Cell_1942                          -0.026061   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_64060                         -0.775098   \n",
+       "DD5S3_Cell_64061                          1.671235   \n",
+       "DD5S3_Cell_64062                          1.622354   \n",
+       "DD5S3_Cell_64063                          0.833309   \n",
+       "DD5S3_Cell_64066                          2.613096   \n",
+       "\n",
+       "                  PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_1910                          1.952302   \n",
+       "DD3S1_Cell_1911                          1.375652   \n",
+       "DD3S1_Cell_1922                          1.869449   \n",
+       "DD3S1_Cell_1928                          1.763598   \n",
+       "DD3S1_Cell_1942                          1.989714   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_64060                        -1.455787   \n",
+       "DD5S3_Cell_64061                         0.310590   \n",
+       "DD5S3_Cell_64062                         1.128603   \n",
+       "DD5S3_Cell_64063                        -0.489938   \n",
+       "DD5S3_Cell_64066                         1.160896   \n",
+       "\n",
+       "                  AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_1910                          1.021130   \n",
+       "DD3S1_Cell_1911                          0.725274   \n",
+       "DD3S1_Cell_1922                          0.978622   \n",
+       "DD3S1_Cell_1928                          0.924314   \n",
+       "DD3S1_Cell_1942                          1.040325   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_64060                        -0.908090   \n",
+       "DD5S3_Cell_64061                        -0.309031   \n",
+       "DD5S3_Cell_64062                         0.187663   \n",
+       "DD5S3_Cell_64063                        -0.231888   \n",
+       "DD5S3_Cell_64066                         0.615091   \n",
+       "\n",
+       "                  CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                  ...                      \n",
+       "DD3S1_Cell_1910                           1.235209  ...           0.873320   \n",
+       "DD3S1_Cell_1911                           0.945061  ...           0.566933   \n",
+       "DD3S1_Cell_1922                           1.185912  ...           0.886386   \n",
+       "DD3S1_Cell_1928                           1.122929  ...           0.738159   \n",
+       "DD3S1_Cell_1942                           1.257470  ...           0.890113   \n",
+       "...                                            ...  ...                ...   \n",
+       "DD5S3_Cell_64060                         -0.862636  ...           0.830379   \n",
+       "DD5S3_Cell_64061                         -0.235059  ...           0.646098   \n",
+       "DD5S3_Cell_64062                          0.290273  ...           0.566233   \n",
+       "DD5S3_Cell_64063                         -0.208742  ...           0.788226   \n",
+       "DD5S3_Cell_64066                          0.764317  ...           0.791907   \n",
+       "\n",
+       "                         Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                   \n",
+       "DD3S1_Cell_1910    1143.897461          1            78  DD3S1.csv   \n",
+       "DD3S1_Cell_1911    1396.333252          1            93  DD3S1.csv   \n",
+       "DD3S1_Cell_1922    1148.870972          1            62  DD3S1.csv   \n",
+       "DD3S1_Cell_1928    1097.823486          1            51  DD3S1.csv   \n",
+       "DD3S1_Cell_1942    1402.000000          1           114  DD3S1.csv   \n",
+       "...                        ...        ...           ...        ...   \n",
+       "DD5S3_Cell_64060  13503.915039         35           106  DD5S3.csv   \n",
+       "DD5S3_Cell_64061  13254.210938         35            90  DD5S3.csv   \n",
+       "DD5S3_Cell_64062  13260.901367         35            61  DD5S3.csv   \n",
+       "DD5S3_Cell_64063  13292.144531         35            76  DD5S3.csv   \n",
+       "DD5S3_Cell_64066  13420.065430         35            46  DD5S3.csv   \n",
+       "\n",
+       "                  immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_1910                None        265       62               62a   \n",
+       "DD3S1_Cell_1911                None        210       62               62a   \n",
+       "DD3S1_Cell_1922                None        196       62               62a   \n",
+       "DD3S1_Cell_1928                None        159       62               62a   \n",
+       "DD3S1_Cell_1942                None        205       62               62a   \n",
+       "...                             ...        ...      ...               ...   \n",
+       "DD5S3_Cell_64060               None        378      186              186a   \n",
+       "DD5S3_Cell_64061               None        151      186              186a   \n",
+       "DD5S3_Cell_64062               None        146      186              186a   \n",
+       "DD5S3_Cell_64063               None        326      186              186a   \n",
+       "DD5S3_Cell_64066               B7H4        232      186              186a   \n",
+       "\n",
+       "                  Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                            \n",
+       "DD3S1_Cell_1910                          0.0  \n",
+       "DD3S1_Cell_1911                          0.0  \n",
+       "DD3S1_Cell_1922                          0.0  \n",
+       "DD3S1_Cell_1928                          0.0  \n",
+       "DD3S1_Cell_1942                          0.0  \n",
+       "...                                      ...  \n",
+       "DD5S3_Cell_64060                         0.0  \n",
+       "DD5S3_Cell_64061                         0.0  \n",
+       "DD5S3_Cell_64062                         0.0  \n",
+       "DD5S3_Cell_64063                         0.0  \n",
+       "DD5S3_Cell_64066                         0.0  \n",
+       "\n",
+       "[495928 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "24481cf9-57bd-4879-b4ad-2e9073dc9993",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_4</th>\n",
+       "      <td>-0.367050</td>\n",
+       "      <td>3.386458</td>\n",
+       "      <td>-2.000166</td>\n",
+       "      <td>-0.459972</td>\n",
+       "      <td>2.082444</td>\n",
+       "      <td>-0.331127</td>\n",
+       "      <td>0.074576</td>\n",
+       "      <td>0.367998</td>\n",
+       "      <td>0.208285</td>\n",
+       "      <td>2.426724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.639497</td>\n",
+       "      <td>1603.610596</td>\n",
+       "      <td>0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>307</td>\n",
+       "      <td>c0</td>\n",
+       "      <td>c0a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_300</th>\n",
+       "      <td>-0.738040</td>\n",
+       "      <td>2.894731</td>\n",
+       "      <td>-2.027276</td>\n",
+       "      <td>-0.536334</td>\n",
+       "      <td>2.463664</td>\n",
+       "      <td>0.615413</td>\n",
+       "      <td>0.434008</td>\n",
+       "      <td>0.145870</td>\n",
+       "      <td>0.094320</td>\n",
+       "      <td>0.796244</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.557669</td>\n",
+       "      <td>1870.953491</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>271</td>\n",
+       "      <td>c0</td>\n",
+       "      <td>c0a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1231</th>\n",
+       "      <td>-0.166333</td>\n",
+       "      <td>3.870697</td>\n",
+       "      <td>-1.953719</td>\n",
+       "      <td>-0.289915</td>\n",
+       "      <td>2.276577</td>\n",
+       "      <td>-0.760463</td>\n",
+       "      <td>0.856060</td>\n",
+       "      <td>0.748574</td>\n",
+       "      <td>0.403544</td>\n",
+       "      <td>1.869273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.909491</td>\n",
+       "      <td>1076.486938</td>\n",
+       "      <td>0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>215</td>\n",
+       "      <td>c0</td>\n",
+       "      <td>c0a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_1240</th>\n",
+       "      <td>-0.293766</td>\n",
+       "      <td>2.246166</td>\n",
+       "      <td>-1.945873</td>\n",
+       "      <td>-0.607328</td>\n",
+       "      <td>2.082444</td>\n",
+       "      <td>-0.615345</td>\n",
+       "      <td>0.243531</td>\n",
+       "      <td>0.812863</td>\n",
+       "      <td>0.436529</td>\n",
+       "      <td>2.194035</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.945592</td>\n",
+       "      <td>1089.709717</td>\n",
+       "      <td>0</td>\n",
+       "      <td>93</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>198</td>\n",
+       "      <td>c0</td>\n",
+       "      <td>c0a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_2107</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>2.130819</td>\n",
+       "      <td>-2.035940</td>\n",
+       "      <td>-0.746582</td>\n",
+       "      <td>1.538367</td>\n",
+       "      <td>1.246304</td>\n",
+       "      <td>0.179943</td>\n",
+       "      <td>0.074879</td>\n",
+       "      <td>0.057898</td>\n",
+       "      <td>0.966884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.664589</td>\n",
+       "      <td>1375.939697</td>\n",
+       "      <td>0</td>\n",
+       "      <td>116</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>167</td>\n",
+       "      <td>c0</td>\n",
+       "      <td>c0a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>94742 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                              -0.367050   \n",
+       "TMA_Cell_300                            -0.738040   \n",
+       "TMA_Cell_1231                           -0.166333   \n",
+       "TMA_Cell_1240                           -0.293766   \n",
+       "TMA_Cell_2107                           -0.677863   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                              3.386458   \n",
+       "TMA_Cell_300                            2.894731   \n",
+       "TMA_Cell_1231                           3.870697   \n",
+       "TMA_Cell_1240                           2.246166   \n",
+       "TMA_Cell_2107                           2.130819   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                             -2.000166   \n",
+       "TMA_Cell_300                           -2.027276   \n",
+       "TMA_Cell_1231                          -1.953719   \n",
+       "TMA_Cell_1240                          -1.945873   \n",
+       "TMA_Cell_2107                          -2.035940   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "TMA_Cell_4                            -0.459972   \n",
+       "TMA_Cell_300                          -0.536334   \n",
+       "TMA_Cell_1231                         -0.289915   \n",
+       "TMA_Cell_1240                         -0.607328   \n",
+       "TMA_Cell_2107                         -0.746582   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                                2.082444   \n",
+       "TMA_Cell_300                              2.463664   \n",
+       "TMA_Cell_1231                             2.276577   \n",
+       "TMA_Cell_1240                             2.082444   \n",
+       "TMA_Cell_2107                             1.538367   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "TMA_Cell_4                               -0.331127   \n",
+       "TMA_Cell_300                              0.615413   \n",
+       "TMA_Cell_1231                            -0.760463   \n",
+       "TMA_Cell_1240                            -0.615345   \n",
+       "TMA_Cell_2107                             1.246304   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "TMA_Cell_4                               0.074576   \n",
+       "TMA_Cell_300                             0.434008   \n",
+       "TMA_Cell_1231                            0.856060   \n",
+       "TMA_Cell_1240                            0.243531   \n",
+       "TMA_Cell_2107                            0.179943   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                              0.367998   \n",
+       "TMA_Cell_300                            0.145870   \n",
+       "TMA_Cell_1231                           0.748574   \n",
+       "TMA_Cell_1240                           0.812863   \n",
+       "TMA_Cell_2107                           0.074879   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "TMA_Cell_4                              0.208285   \n",
+       "TMA_Cell_300                            0.094320   \n",
+       "TMA_Cell_1231                           0.403544   \n",
+       "TMA_Cell_1240                           0.436529   \n",
+       "TMA_Cell_2107                           0.057898   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                 ...                      \n",
+       "TMA_Cell_4                               2.426724  ...           0.639497   \n",
+       "TMA_Cell_300                             0.796244  ...           0.557669   \n",
+       "TMA_Cell_1231                            1.869273  ...           0.909491   \n",
+       "TMA_Cell_1240                            2.194035  ...           0.945592   \n",
+       "TMA_Cell_2107                            0.966884  ...           0.664589   \n",
+       "...                                           ...  ...                ...   \n",
+       "TMA_Cell_115755                          0.090397  ...           0.982196   \n",
+       "TMA_Cell_115756                         -0.617835  ...           0.775977   \n",
+       "TMA_Cell_115757                          0.403878  ...           0.688747   \n",
+       "TMA_Cell_115758                         -0.219145  ...           0.751402   \n",
+       "TMA_Cell_115760                         -0.495501  ...           0.674126   \n",
+       "\n",
+       "                        Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                  \n",
+       "TMA_Cell_4        1603.610596          0           131    TMA.csv   \n",
+       "TMA_Cell_300      1870.953491          0            86    TMA.csv   \n",
+       "TMA_Cell_1231     1076.486938          0           115    TMA.csv   \n",
+       "TMA_Cell_1240     1089.709717          0            93    TMA.csv   \n",
+       "TMA_Cell_2107     1375.939697          0           116    TMA.csv   \n",
+       "...                       ...        ...           ...        ...   \n",
+       "TMA_Cell_115755  15564.458008         59           142    TMA.csv   \n",
+       "TMA_Cell_115756  15629.680664         59            47    TMA.csv   \n",
+       "TMA_Cell_115757  15518.421875         59            64    TMA.csv   \n",
+       "TMA_Cell_115758  15539.275391         59            58    TMA.csv   \n",
+       "TMA_Cell_115760  15542.961914         59           106    TMA.csv   \n",
+       "\n",
+       "                 immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                         \n",
+       "TMA_Cell_4                    None        307       c0               c0a   \n",
+       "TMA_Cell_300                  None        271       c0               c0a   \n",
+       "TMA_Cell_1231                 None        215       c0               c0a   \n",
+       "TMA_Cell_1240                 None        198       c0               c0a   \n",
+       "TMA_Cell_2107                 None        167       c0               c0a   \n",
+       "...                            ...        ...      ...               ...   \n",
+       "TMA_Cell_115755               None        386      c59              c59a   \n",
+       "TMA_Cell_115756               None        270      c59              c59a   \n",
+       "TMA_Cell_115757               None        202      c59              c59a   \n",
+       "TMA_Cell_115758               None        182      c59              c59a   \n",
+       "TMA_Cell_115760               None        295      c59              c59a   \n",
+       "\n",
+       "                 Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                           \n",
+       "TMA_Cell_4                          CONTROL  \n",
+       "TMA_Cell_300                        CONTROL  \n",
+       "TMA_Cell_1231                       CONTROL  \n",
+       "TMA_Cell_1240                       CONTROL  \n",
+       "TMA_Cell_2107                       CONTROL  \n",
+       "...                                     ...  \n",
+       "TMA_Cell_115755                     CONTROL  \n",
+       "TMA_Cell_115756                     CONTROL  \n",
+       "TMA_Cell_115757                     CONTROL  \n",
+       "TMA_Cell_115758                     CONTROL  \n",
+       "TMA_Cell_115760                     CONTROL  \n",
+       "\n",
+       "[94742 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_control"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c43a8fb1-7366-4e96-864d-3ca0b699319e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## V.4. COUNTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "2c36d567-801d-4bac-b6b7-bf8759ab6ce3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = df2.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "89ab1bd6-d58c-4c33-9e1a-5a841d5271c9",
+   "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>counts</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>68697</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>70850</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>116265</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>70748</td>\n",
+       "      <td>DD4S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>51745</td>\n",
+       "      <td>DD4S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>70818</td>\n",
+       "      <td>DD4S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>69463</td>\n",
+       "      <td>DD5S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>45403</td>\n",
+       "      <td>DD5S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>45898</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA.csv</th>\n",
+       "      <td>94742</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           counts  Sample_ID\n",
+       "DD3S1.csv   68697  DD3S1.csv\n",
+       "DD3S2.csv   70850  DD3S2.csv\n",
+       "DD3S3.csv  116265  DD3S3.csv\n",
+       "DD4S1.csv   70748  DD4S1.csv\n",
+       "DD4S2.csv   51745  DD4S2.csv\n",
+       "DD4S3.csv   70818  DD4S3.csv\n",
+       "DD5S1.csv   69463  DD5S1.csv\n",
+       "DD5S2.csv   45403  DD5S2.csv\n",
+       "DD5S3.csv   45898  DD5S3.csv\n",
+       "TMA.csv     94742    TMA.csv"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get counts for each Sample_ID, sorted by Sample_ID\n",
+    "sample_counts = pd.DataFrame(df.Sample_ID.value_counts()).sort_index()\n",
+    "sample_counts = sample_counts.rename(columns = {'Sample_ID':'counts'})\n",
+    "sample_counts['Sample_ID'] = sample_counts.index\n",
+    "#counts['color'] = counts.apply(lambda row: color_dict[row['Sample_ID']], axis = 1)\n",
+    "sample_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "963c7e14-a084-4665-8fa9-def1227ec306",
+   "metadata": {},
+   "source": [
+    "### V.4.1. CELL TYPES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "38a3ca09-85f1-4fca-b769-11eb506b03fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           counts  Sample_ID  stroma  cancer  immune  endothelial\n",
+      "DD3S1.csv   68697  DD3S1.csv   10690   48981    7360         1666\n",
+      "DD3S2.csv   70850  DD3S2.csv   12064   51059    5977         1750\n",
+      "DD3S3.csv  116265  DD3S3.csv   23412   77000   13075         2778\n",
+      "DD4S1.csv   70748  DD4S1.csv    7249   54013    7433         2053\n",
+      "DD4S2.csv   51745  DD4S2.csv    6193   39470    4850         1232\n",
+      "DD4S3.csv   70818  DD4S3.csv    8516   50598   10331         1373\n",
+      "DD5S1.csv   69463  DD5S1.csv   17067   43723    5878         2795\n",
+      "DD5S2.csv   45403  DD5S2.csv   11098   30839    2131         1335\n",
+      "DD5S3.csv   45898  DD5S3.csv    9018   30060    5656         1164\n",
+      "TMA.csv     94742    TMA.csv   23121   59978    9356         2287\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count by cell type\n",
+    "stroma_counts = pd.DataFrame({'stroma':\n",
+    "    df.loc[\n",
+    "        df['cell_type'] == 'STROMA',:].Sample_ID.value_counts()}).sort_index()\n",
+    "\n",
+    "immune_counts = pd.DataFrame({'immune':\n",
+    "    df.loc[\n",
+    "        df['cell_type'] == 'IMMUNE',:].Sample_ID.value_counts()}).sort_index()\n",
+    "\n",
+    "cancer_counts = pd.DataFrame({'cancer':\n",
+    "    df.loc[\n",
+    "        df['cell_type'] == 'CANCER',:].Sample_ID.value_counts()}).sort_index()\n",
+    "\n",
+    "endothelial_counts = pd.DataFrame({'endothelial':\n",
+    "    df.loc[\n",
+    "        df['cell_type'] == 'ENDOTHELIAL',:].Sample_ID.value_counts()}).sort_index()\n",
+    "\n",
+    "counts = pd.concat([sample_counts, stroma_counts,cancer_counts,immune_counts,endothelial_counts], \n",
+    "                   axis = 1, sort = False)\n",
+    "\n",
+    "counts = counts.fillna(0)\n",
+    "print(counts)\n",
+    "\n",
+    "filename = os.path.join(output_data_dir , project_name + \"_cell_types_number.csv\")\n",
+    "counts.to_csv(filename, index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b19b0d76-0f17-4ed2-a41d-1e14d08d60ff",
+   "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>counts</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>stroma</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>immune</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>stroma_perc</th>\n",
+       "      <th>immune_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>68697</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>10690</td>\n",
+       "      <td>48981</td>\n",
+       "      <td>7360</td>\n",
+       "      <td>1666</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>71.3</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>70850</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "      <td>12064</td>\n",
+       "      <td>51059</td>\n",
+       "      <td>5977</td>\n",
+       "      <td>1750</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>72.1</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>116265</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "      <td>23412</td>\n",
+       "      <td>77000</td>\n",
+       "      <td>13075</td>\n",
+       "      <td>2778</td>\n",
+       "      <td>20.1</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>66.2</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>70748</td>\n",
+       "      <td>DD4S1.csv</td>\n",
+       "      <td>7249</td>\n",
+       "      <td>54013</td>\n",
+       "      <td>7433</td>\n",
+       "      <td>2053</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>76.3</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>51745</td>\n",
+       "      <td>DD4S2.csv</td>\n",
+       "      <td>6193</td>\n",
+       "      <td>39470</td>\n",
+       "      <td>4850</td>\n",
+       "      <td>1232</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>76.3</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>70818</td>\n",
+       "      <td>DD4S3.csv</td>\n",
+       "      <td>8516</td>\n",
+       "      <td>50598</td>\n",
+       "      <td>10331</td>\n",
+       "      <td>1373</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>14.6</td>\n",
+       "      <td>71.4</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>69463</td>\n",
+       "      <td>DD5S1.csv</td>\n",
+       "      <td>17067</td>\n",
+       "      <td>43723</td>\n",
+       "      <td>5878</td>\n",
+       "      <td>2795</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>62.9</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>45403</td>\n",
+       "      <td>DD5S2.csv</td>\n",
+       "      <td>11098</td>\n",
+       "      <td>30839</td>\n",
+       "      <td>2131</td>\n",
+       "      <td>1335</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>67.9</td>\n",
+       "      <td>2.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>45898</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>9018</td>\n",
+       "      <td>30060</td>\n",
+       "      <td>5656</td>\n",
+       "      <td>1164</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA.csv</th>\n",
+       "      <td>94742</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>23121</td>\n",
+       "      <td>59978</td>\n",
+       "      <td>9356</td>\n",
+       "      <td>2287</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>63.3</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           counts  Sample_ID  stroma  cancer  immune  endothelial  \\\n",
+       "DD3S1.csv   68697  DD3S1.csv   10690   48981    7360         1666   \n",
+       "DD3S2.csv   70850  DD3S2.csv   12064   51059    5977         1750   \n",
+       "DD3S3.csv  116265  DD3S3.csv   23412   77000   13075         2778   \n",
+       "DD4S1.csv   70748  DD4S1.csv    7249   54013    7433         2053   \n",
+       "DD4S2.csv   51745  DD4S2.csv    6193   39470    4850         1232   \n",
+       "DD4S3.csv   70818  DD4S3.csv    8516   50598   10331         1373   \n",
+       "DD5S1.csv   69463  DD5S1.csv   17067   43723    5878         2795   \n",
+       "DD5S2.csv   45403  DD5S2.csv   11098   30839    2131         1335   \n",
+       "DD5S3.csv   45898  DD5S3.csv    9018   30060    5656         1164   \n",
+       "TMA.csv     94742    TMA.csv   23121   59978    9356         2287   \n",
+       "\n",
+       "           stroma_perc  immune_perc  cancer_perc  endothelial_perc  \n",
+       "DD3S1.csv         15.6         10.7         71.3               2.4  \n",
+       "DD3S2.csv         17.0          8.4         72.1               2.5  \n",
+       "DD3S3.csv         20.1         11.2         66.2               2.4  \n",
+       "DD4S1.csv         10.2         10.5         76.3               2.9  \n",
+       "DD4S2.csv         12.0          9.4         76.3               2.4  \n",
+       "DD4S3.csv         12.0         14.6         71.4               1.9  \n",
+       "DD5S1.csv         24.6          8.5         62.9               4.0  \n",
+       "DD5S2.csv         24.4          4.7         67.9               2.9  \n",
+       "DD5S3.csv         19.6         12.3         65.5               2.5  \n",
+       "TMA.csv           24.4          9.9         63.3               2.4  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute %\n",
+    "# get_perc() function in my_modules.py\n",
+    "\n",
+    "counts['stroma_perc'] = counts.apply(lambda row: get_perc(row, 'stroma'), axis = 1)\n",
+    "counts['immune_perc'] = counts.apply(lambda row: get_perc(row, 'immune'), axis = 1)\n",
+    "counts['cancer_perc'] = counts.apply(lambda row: get_perc(row, 'cancer'), axis = 1)\n",
+    "counts['endothelial_perc'] = counts.apply(lambda row: get_perc(row, 'endothelial'), axis = 1)\n",
+    "\n",
+    "counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "facd7887-3b7b-44f9-b65f-185963a415de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell proportions by Sample ID and tissue type'\n",
+    "\n",
+    "fig = go.Figure(data=[\n",
+    "    go.Bar(name='Stroma', x=counts['Sample_ID'], y=counts['stroma_perc'], \n",
+    "           text = counts['stroma_perc'], textposition='auto',\n",
+    "           marker_color = 'rgb' + str(cell_type_color_dict['STROMA'])),\n",
+    "    go.Bar(name='Immune', x=counts['Sample_ID'], y=counts['immune_perc'], \n",
+    "           text = counts['immune_perc'], textposition='auto',\n",
+    "           marker_color = 'rgb' + str(cell_type_color_dict['IMMUNE'])),\n",
+    "    go.Bar(name='Cancer',x=counts['Sample_ID'], y=counts['cancer_perc'], \n",
+    "           text = counts['cancer_perc'], textposition='auto', \n",
+    "           marker_color = 'rgb' + str(cell_type_color_dict['CANCER'])),\n",
+    "    go.Bar(name='Endothelial',x=counts['Sample_ID'], y=counts['endothelial_perc'], \n",
+    "           text = counts['endothelial_perc'], textposition='auto', \n",
+    "           marker_color = 'rgb' + str(cell_type_color_dict['ENDOTHELIAL']))\n",
+    "])\n",
+    "    \n",
+    "fig.update_layout( plot_bgcolor = 'white',barmode ='stack')#title = title,\n",
+    "fig.update_xaxes( linecolor = 'black')#title = \"Sample\",\n",
+    "fig.update_yaxes(title = \"Cell count (%)\", linecolor = 'black')\n",
+    "plot(fig)\n",
+    "#fig.write_image(output_images_dir + \"/\" + title.replace(\" \",\"_\") + \".png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee1c2bd3-633d-4bcb-a40e-064d90ebd713",
+   "metadata": {},
+   "source": [
+    "### V.4.2. CELL SUBTYPES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70721a22-5cc7-464e-81c3-e1766f943fe2",
+   "metadata": {},
+   "source": [
+    "#### V.4.2.1 BY SCENES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "56f79b66-9d56-4678-bb21-e59a8e3c34f5",
+   "metadata": {
+    "tags": []
+   },
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>total_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2991</td>\n",
+       "      <td>2849</td>\n",
+       "      <td>310</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>994</td>\n",
+       "      <td>48981</td>\n",
+       "      <td>3967</td>\n",
+       "      <td>6723</td>\n",
+       "      <td>1666</td>\n",
+       "      <td>68697.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1915</td>\n",
+       "      <td>2350</td>\n",
+       "      <td>94</td>\n",
+       "      <td>25</td>\n",
+       "      <td>38</td>\n",
+       "      <td>1261</td>\n",
+       "      <td>51059</td>\n",
+       "      <td>4789</td>\n",
+       "      <td>7275</td>\n",
+       "      <td>1750</td>\n",
+       "      <td>70850.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>280</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>6857</td>\n",
+       "      <td>3550</td>\n",
+       "      <td>252</td>\n",
+       "      <td>96</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1874</td>\n",
+       "      <td>77000</td>\n",
+       "      <td>7424</td>\n",
+       "      <td>15988</td>\n",
+       "      <td>2778</td>\n",
+       "      <td>116265.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>272</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>2667</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>76</td>\n",
+       "      <td>38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2389</td>\n",
+       "      <td>54013</td>\n",
+       "      <td>3106</td>\n",
+       "      <td>4143</td>\n",
+       "      <td>2053</td>\n",
+       "      <td>70748.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>130</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1938</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>112</td>\n",
+       "      <td>102</td>\n",
+       "      <td>19</td>\n",
+       "      <td>841</td>\n",
+       "      <td>39470</td>\n",
+       "      <td>1664</td>\n",
+       "      <td>4529</td>\n",
+       "      <td>1232</td>\n",
+       "      <td>51745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>1150</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2353</td>\n",
+       "      <td>2940</td>\n",
+       "      <td>155</td>\n",
+       "      <td>328</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3336</td>\n",
+       "      <td>50598</td>\n",
+       "      <td>3686</td>\n",
+       "      <td>4830</td>\n",
+       "      <td>1373</td>\n",
+       "      <td>70818.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>583</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>2840</td>\n",
+       "      <td>536</td>\n",
+       "      <td>504</td>\n",
+       "      <td>28</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>43723</td>\n",
+       "      <td>6976</td>\n",
+       "      <td>10091</td>\n",
+       "      <td>2795</td>\n",
+       "      <td>69463.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>160</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>131</td>\n",
+       "      <td>63</td>\n",
+       "      <td>14</td>\n",
+       "      <td>467</td>\n",
+       "      <td>30839</td>\n",
+       "      <td>3876</td>\n",
+       "      <td>7222</td>\n",
+       "      <td>1335</td>\n",
+       "      <td>45403.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>1467</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>687</td>\n",
+       "      <td>479</td>\n",
+       "      <td>110</td>\n",
+       "      <td>13</td>\n",
+       "      <td>865</td>\n",
+       "      <td>30060</td>\n",
+       "      <td>4831</td>\n",
+       "      <td>4187</td>\n",
+       "      <td>1164</td>\n",
+       "      <td>45898.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA.csv</th>\n",
+       "      <td>132</td>\n",
+       "      <td>2309.0</td>\n",
+       "      <td>5027</td>\n",
+       "      <td>952</td>\n",
+       "      <td>109</td>\n",
+       "      <td>11</td>\n",
+       "      <td>209</td>\n",
+       "      <td>607</td>\n",
+       "      <td>59978</td>\n",
+       "      <td>4875</td>\n",
+       "      <td>18246</td>\n",
+       "      <td>2287</td>\n",
+       "      <td>94742.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             dc       b  tcd4  tcd8   m1   m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv    81     0.0  2991  2849  310  129     6           994   48981   \n",
+       "DD3S2.csv   294     0.0  1915  2350   94   25    38          1261   51059   \n",
+       "DD3S3.csv   280   138.0  6857  3550  252   96    28          1874   77000   \n",
+       "DD4S1.csv   272    29.0  2667  1950   76   38    12          2389   54013   \n",
+       "DD4S2.csv   130    14.0  1938  1694  112  102    19           841   39470   \n",
+       "DD4S3.csv  1150    31.0  2353  2940  155  328    38          3336   50598   \n",
+       "DD5S1.csv   583   139.0  2840   536  504   28    28          1220   43723   \n",
+       "DD5S2.csv   160    16.0  1135   145  131   63    14           467   30839   \n",
+       "DD5S3.csv  1467    35.0  2000   687  479  110    13           865   30060   \n",
+       "TMA.csv     132  2309.0  5027   952  109   11   209           607   59978   \n",
+       "\n",
+       "           αsma_mycaf  stroma_other  endothelial  total_cells  \n",
+       "DD3S1.csv        3967          6723         1666      68697.0  \n",
+       "DD3S2.csv        4789          7275         1750      70850.0  \n",
+       "DD3S3.csv        7424         15988         2778     116265.0  \n",
+       "DD4S1.csv        3106          4143         2053      70748.0  \n",
+       "DD4S2.csv        1664          4529         1232      51745.0  \n",
+       "DD4S3.csv        3686          4830         1373      70818.0  \n",
+       "DD5S1.csv        6976         10091         2795      69463.0  \n",
+       "DD5S2.csv        3876          7222         1335      45403.0  \n",
+       "DD5S3.csv        4831          4187         1164      45898.0  \n",
+       "TMA.csv          4875         18246         2287      94742.0  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count by cell SUBtype\n",
+    "cell_subtypes = ['DC', 'B', 'TCD4', 'TCD8', 'M1', 'M2', 'Treg', \\\n",
+    "                 'IMMUNE_OTHER', 'CANCER', 'αSMA_myCAF', 'STROMA_OTHER', 'ENDOTHELIAL']\n",
+    "# Initialisation d'un dictionnaire pour stocker les counts des sous-types de cellules\n",
+    "subtype_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants\n",
+    "for subtype in cell_subtypes:\n",
+    "    subtype_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df.loc[\n",
+    "            df['cell_subtype'] == subtype, 'Sample_ID'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_subtypes = pd.concat([pd.DataFrame(v) for v in subtype_counts.values()], axis=1, sort=False)\n",
+    "counts_subtypes = counts_subtypes.fillna(0)\n",
+    "\n",
+    "# Ajouter une colonne pour le compte total de cellules par ligne\n",
+    "counts_subtypes['total_cells'] = counts_subtypes.sum(axis=1)\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules dans un fichier CSV\n",
+    "filename_subtypes = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number_by_scenes.csv\")\n",
+    "counts_subtypes.to_csv(filename_subtypes, index=False)\n",
+    "counts_subtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "46678224-e49f-4ce1-baef-f6757b27c6b7",
+   "metadata": {
+    "tags": []
+   },
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2991</td>\n",
+       "      <td>2849</td>\n",
+       "      <td>310</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>994</td>\n",
+       "      <td>48981</td>\n",
+       "      <td>3967</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.147197</td>\n",
+       "      <td>0.451257</td>\n",
+       "      <td>0.187781</td>\n",
+       "      <td>0.008734</td>\n",
+       "      <td>1.446934</td>\n",
+       "      <td>71.300057</td>\n",
+       "      <td>5.774634</td>\n",
+       "      <td>9.786454</td>\n",
+       "      <td>2.425142</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1915</td>\n",
+       "      <td>2350</td>\n",
+       "      <td>94</td>\n",
+       "      <td>25</td>\n",
+       "      <td>38</td>\n",
+       "      <td>1261</td>\n",
+       "      <td>51059</td>\n",
+       "      <td>4789</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.316867</td>\n",
+       "      <td>0.132675</td>\n",
+       "      <td>0.035286</td>\n",
+       "      <td>0.053634</td>\n",
+       "      <td>1.779817</td>\n",
+       "      <td>72.066337</td>\n",
+       "      <td>6.759351</td>\n",
+       "      <td>10.268172</td>\n",
+       "      <td>2.470007</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>280</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>6857</td>\n",
+       "      <td>3550</td>\n",
+       "      <td>252</td>\n",
+       "      <td>96</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1874</td>\n",
+       "      <td>77000</td>\n",
+       "      <td>7424</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.053369</td>\n",
+       "      <td>0.216746</td>\n",
+       "      <td>0.082570</td>\n",
+       "      <td>0.024083</td>\n",
+       "      <td>1.611835</td>\n",
+       "      <td>66.228014</td>\n",
+       "      <td>6.385413</td>\n",
+       "      <td>13.751344</td>\n",
+       "      <td>2.389369</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>272</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>2667</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>76</td>\n",
+       "      <td>38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2389</td>\n",
+       "      <td>54013</td>\n",
+       "      <td>3106</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.756262</td>\n",
+       "      <td>0.107424</td>\n",
+       "      <td>0.053712</td>\n",
+       "      <td>0.016962</td>\n",
+       "      <td>3.376774</td>\n",
+       "      <td>76.345621</td>\n",
+       "      <td>4.390230</td>\n",
+       "      <td>5.855996</td>\n",
+       "      <td>2.901849</td>\n",
+       "      <td>DD4S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>130</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1938</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>112</td>\n",
+       "      <td>102</td>\n",
+       "      <td>19</td>\n",
+       "      <td>841</td>\n",
+       "      <td>39470</td>\n",
+       "      <td>1664</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.273746</td>\n",
+       "      <td>0.216446</td>\n",
+       "      <td>0.197120</td>\n",
+       "      <td>0.036719</td>\n",
+       "      <td>1.625278</td>\n",
+       "      <td>76.277901</td>\n",
+       "      <td>3.215770</td>\n",
+       "      <td>8.752536</td>\n",
+       "      <td>2.380906</td>\n",
+       "      <td>DD4S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>1150</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2353</td>\n",
+       "      <td>2940</td>\n",
+       "      <td>155</td>\n",
+       "      <td>328</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3336</td>\n",
+       "      <td>50598</td>\n",
+       "      <td>3686</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.151487</td>\n",
+       "      <td>0.218871</td>\n",
+       "      <td>0.463159</td>\n",
+       "      <td>0.053659</td>\n",
+       "      <td>4.710667</td>\n",
+       "      <td>71.447937</td>\n",
+       "      <td>5.204891</td>\n",
+       "      <td>6.820300</td>\n",
+       "      <td>1.938773</td>\n",
+       "      <td>DD4S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>583</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>2840</td>\n",
+       "      <td>536</td>\n",
+       "      <td>504</td>\n",
+       "      <td>28</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>43723</td>\n",
+       "      <td>6976</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.771634</td>\n",
+       "      <td>0.725566</td>\n",
+       "      <td>0.040309</td>\n",
+       "      <td>0.040309</td>\n",
+       "      <td>1.756331</td>\n",
+       "      <td>62.944301</td>\n",
+       "      <td>10.042757</td>\n",
+       "      <td>14.527158</td>\n",
+       "      <td>4.023725</td>\n",
+       "      <td>DD5S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>160</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1135</td>\n",
+       "      <td>145</td>\n",
+       "      <td>131</td>\n",
+       "      <td>63</td>\n",
+       "      <td>14</td>\n",
+       "      <td>467</td>\n",
+       "      <td>30839</td>\n",
+       "      <td>3876</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.319362</td>\n",
+       "      <td>0.288527</td>\n",
+       "      <td>0.138757</td>\n",
+       "      <td>0.030835</td>\n",
+       "      <td>1.028566</td>\n",
+       "      <td>67.922824</td>\n",
+       "      <td>8.536881</td>\n",
+       "      <td>15.906438</td>\n",
+       "      <td>2.940334</td>\n",
+       "      <td>DD5S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>1467</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>687</td>\n",
+       "      <td>479</td>\n",
+       "      <td>110</td>\n",
+       "      <td>13</td>\n",
+       "      <td>865</td>\n",
+       "      <td>30060</td>\n",
+       "      <td>4831</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.496797</td>\n",
+       "      <td>1.043618</td>\n",
+       "      <td>0.239662</td>\n",
+       "      <td>0.028324</td>\n",
+       "      <td>1.884614</td>\n",
+       "      <td>65.493050</td>\n",
+       "      <td>10.525513</td>\n",
+       "      <td>9.122402</td>\n",
+       "      <td>2.536058</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA.csv</th>\n",
+       "      <td>132</td>\n",
+       "      <td>2309.0</td>\n",
+       "      <td>5027</td>\n",
+       "      <td>952</td>\n",
+       "      <td>109</td>\n",
+       "      <td>11</td>\n",
+       "      <td>209</td>\n",
+       "      <td>607</td>\n",
+       "      <td>59978</td>\n",
+       "      <td>4875</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.004834</td>\n",
+       "      <td>0.115049</td>\n",
+       "      <td>0.011610</td>\n",
+       "      <td>0.220599</td>\n",
+       "      <td>0.640687</td>\n",
+       "      <td>63.306664</td>\n",
+       "      <td>5.145553</td>\n",
+       "      <td>19.258618</td>\n",
+       "      <td>2.413924</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             dc       b  tcd4  tcd8   m1   m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv    81     0.0  2991  2849  310  129     6           994   48981   \n",
+       "DD3S2.csv   294     0.0  1915  2350   94   25    38          1261   51059   \n",
+       "DD3S3.csv   280   138.0  6857  3550  252   96    28          1874   77000   \n",
+       "DD4S1.csv   272    29.0  2667  1950   76   38    12          2389   54013   \n",
+       "DD4S2.csv   130    14.0  1938  1694  112  102    19           841   39470   \n",
+       "DD4S3.csv  1150    31.0  2353  2940  155  328    38          3336   50598   \n",
+       "DD5S1.csv   583   139.0  2840   536  504   28    28          1220   43723   \n",
+       "DD5S2.csv   160    16.0  1135   145  131   63    14           467   30839   \n",
+       "DD5S3.csv  1467    35.0  2000   687  479  110    13           865   30060   \n",
+       "TMA.csv     132  2309.0  5027   952  109   11   209           607   59978   \n",
+       "\n",
+       "           αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "DD3S1.csv        3967  ...   4.147197  0.451257  0.187781   0.008734   \n",
+       "DD3S2.csv        4789  ...   3.316867  0.132675  0.035286   0.053634   \n",
+       "DD3S3.csv        7424  ...   3.053369  0.216746  0.082570   0.024083   \n",
+       "DD4S1.csv        3106  ...   2.756262  0.107424  0.053712   0.016962   \n",
+       "DD4S2.csv        1664  ...   3.273746  0.216446  0.197120   0.036719   \n",
+       "DD4S3.csv        3686  ...   4.151487  0.218871  0.463159   0.053659   \n",
+       "DD5S1.csv        6976  ...   0.771634  0.725566  0.040309   0.040309   \n",
+       "DD5S2.csv        3876  ...   0.319362  0.288527  0.138757   0.030835   \n",
+       "DD5S3.csv        4831  ...   1.496797  1.043618  0.239662   0.028324   \n",
+       "TMA.csv          4875  ...   1.004834  0.115049  0.011610   0.220599   \n",
+       "\n",
+       "           immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "DD3S1.csv           1.446934    71.300057         5.774634           9.786454   \n",
+       "DD3S2.csv           1.779817    72.066337         6.759351          10.268172   \n",
+       "DD3S3.csv           1.611835    66.228014         6.385413          13.751344   \n",
+       "DD4S1.csv           3.376774    76.345621         4.390230           5.855996   \n",
+       "DD4S2.csv           1.625278    76.277901         3.215770           8.752536   \n",
+       "DD4S3.csv           4.710667    71.447937         5.204891           6.820300   \n",
+       "DD5S1.csv           1.756331    62.944301        10.042757          14.527158   \n",
+       "DD5S2.csv           1.028566    67.922824         8.536881          15.906438   \n",
+       "DD5S3.csv           1.884614    65.493050        10.525513           9.122402   \n",
+       "TMA.csv             0.640687    63.306664         5.145553          19.258618   \n",
+       "\n",
+       "           endothelial_perc  Sample_ID  \n",
+       "DD3S1.csv          2.425142  DD3S1.csv  \n",
+       "DD3S2.csv          2.470007  DD3S2.csv  \n",
+       "DD3S3.csv          2.389369  DD3S3.csv  \n",
+       "DD4S1.csv          2.901849  DD4S1.csv  \n",
+       "DD4S2.csv          2.380906  DD4S2.csv  \n",
+       "DD4S3.csv          1.938773  DD4S3.csv  \n",
+       "DD5S1.csv          4.023725  DD5S1.csv  \n",
+       "DD5S2.csv          2.940334  DD5S2.csv  \n",
+       "DD5S3.csv          2.536058  DD5S3.csv  \n",
+       "TMA.csv            2.413924    TMA.csv  \n",
+       "\n",
+       "[10 rows x 26 columns]"
+      ]
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+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules\n",
+    "counts_perc = counts_subtypes.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules, en excluant la colonne 'total_cells'\n",
+    "for col in counts_subtypes.columns:\n",
+    "    if col != 'total_cells':\n",
+    "        counts_perc[col + '_perc'] = (counts_perc[col] / counts_perc['total_cells']) * 100\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules\n",
+    "\n",
+    "counts_perc['Sample_ID'] = counts_perc.index\n",
+    "counts_perc.columns.values\n",
+    "display(counts_perc)\n"
+   ]
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+            "ticks": "",
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+            "ticks": "",
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+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+           "line": {
+            "color": "#2a3f5f"
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
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+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Cell subtypes proportions by Sample ID and tissue type"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          9.5
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+        "yaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          0,
+          105.26315789473685
+         ],
+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell subtypes proportions by Sample ID and tissue type'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc['Sample_ID'],\n",
+    "            y=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    title=title,\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black')\n",
+    ")\n",
+    "\n",
+    "# Enregistrer l'image\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53d9fea4-07b2-4a2f-9a61-068160198147",
+   "metadata": {},
+   "source": [
+    "#### V.4.2.2 BY PATIENTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "db7b6ff6-ac31-4dc2-91a6-fbeb10c428c8",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "      <td>CONTROL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>704629 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.677863   \n",
+       "DD3S1_Cell_1                            -0.677863   \n",
+       "DD3S1_Cell_2                            -0.677863   \n",
+       "DD3S1_Cell_3                            -0.741282   \n",
+       "DD3S1_Cell_6                            -0.621521   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.417494   \n",
+       "DD3S1_Cell_1                           -0.516487   \n",
+       "DD3S1_Cell_2                           -0.141921   \n",
+       "DD3S1_Cell_3                           -0.460472   \n",
+       "DD3S1_Cell_6                           -0.247254   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.912537   \n",
+       "DD3S1_Cell_1                           -0.838037   \n",
+       "DD3S1_Cell_2                           -1.016023   \n",
+       "DD3S1_Cell_3                           -0.491711   \n",
+       "DD3S1_Cell_6                           -0.867127   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S1_Cell_0                          -0.817876   \n",
+       "DD3S1_Cell_1                          -0.869685   \n",
+       "DD3S1_Cell_2                          -0.755879   \n",
+       "DD3S1_Cell_3                          -0.818084   \n",
+       "DD3S1_Cell_6                          -0.742544   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.930099   \n",
+       "DD3S1_Cell_1                              1.114924   \n",
+       "DD3S1_Cell_2                              0.834577   \n",
+       "DD3S1_Cell_3                              0.648200   \n",
+       "DD3S1_Cell_6                              0.810579   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.232078   \n",
+       "DD3S1_Cell_1                              0.301333   \n",
+       "DD3S1_Cell_2                              0.259216   \n",
+       "DD3S1_Cell_3                              0.107027   \n",
+       "DD3S1_Cell_6                              0.272128   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.483158   \n",
+       "DD3S1_Cell_1                            -0.344770   \n",
+       "DD3S1_Cell_2                            -0.438292   \n",
+       "DD3S1_Cell_3                            -0.444889   \n",
+       "DD3S1_Cell_6                            -0.507117   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            1.535604   \n",
+       "DD3S1_Cell_1                            1.668368   \n",
+       "DD3S1_Cell_2                            1.336308   \n",
+       "DD3S1_Cell_3                            1.249805   \n",
+       "DD3S1_Cell_6                            1.251434   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            0.807339   \n",
+       "DD3S1_Cell_1                            0.875455   \n",
+       "DD3S1_Cell_2                            0.705088   \n",
+       "DD3S1_Cell_3                            0.660707   \n",
+       "DD3S1_Cell_6                            0.947172   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                 ...                      \n",
+       "DD3S1_Cell_0                             1.167755  ...           0.955040   \n",
+       "DD3S1_Cell_1                             1.643023  ...           0.966643   \n",
+       "DD3S1_Cell_2                             1.053636  ...           0.721534   \n",
+       "DD3S1_Cell_3                             1.165861  ...           0.587196   \n",
+       "DD3S1_Cell_6                             2.545301  ...           0.935716   \n",
+       "...                                           ...  ...                ...   \n",
+       "TMA_Cell_115755                          0.090397  ...           0.982196   \n",
+       "TMA_Cell_115756                         -0.617835  ...           0.775977   \n",
+       "TMA_Cell_115757                          0.403878  ...           0.688747   \n",
+       "TMA_Cell_115758                         -0.219145  ...           0.751402   \n",
+       "TMA_Cell_115760                         -0.495501  ...           0.674126   \n",
+       "\n",
+       "                        Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_0      1484.771729          0           127  DD3S1.csv   \n",
+       "DD3S1_Cell_1      1426.250000          0           112  DD3S1.csv   \n",
+       "DD3S1_Cell_2      1531.110474          0           181  DD3S1.csv   \n",
+       "DD3S1_Cell_3      1518.907593          0           119  DD3S1.csv   \n",
+       "DD3S1_Cell_6      1471.914917          0            47  DD3S1.csv   \n",
+       "...                       ...        ...           ...        ...   \n",
+       "TMA_Cell_115755  15564.458008         59           142    TMA.csv   \n",
+       "TMA_Cell_115756  15629.680664         59            47    TMA.csv   \n",
+       "TMA_Cell_115757  15518.421875         59            64    TMA.csv   \n",
+       "TMA_Cell_115758  15539.275391         59            58    TMA.csv   \n",
+       "TMA_Cell_115760  15542.961914         59           106    TMA.csv   \n",
+       "\n",
+       "                 immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                         \n",
+       "DD3S1_Cell_0                  None        339       61               61a   \n",
+       "DD3S1_Cell_1                  None        344       61               61a   \n",
+       "DD3S1_Cell_2                  None        422       61               61a   \n",
+       "DD3S1_Cell_3                  None        278       61               61a   \n",
+       "DD3S1_Cell_6                  None        204       61               61a   \n",
+       "...                            ...        ...      ...               ...   \n",
+       "TMA_Cell_115755               None        386      c59              c59a   \n",
+       "TMA_Cell_115756               None        270      c59              c59a   \n",
+       "TMA_Cell_115757               None        202      c59              c59a   \n",
+       "TMA_Cell_115758               None        182      c59              c59a   \n",
+       "TMA_Cell_115760               None        295      c59              c59a   \n",
+       "\n",
+       "                 Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                           \n",
+       "DD3S1_Cell_0                            1.0  \n",
+       "DD3S1_Cell_1                            1.0  \n",
+       "DD3S1_Cell_2                            1.0  \n",
+       "DD3S1_Cell_3                            1.0  \n",
+       "DD3S1_Cell_6                            1.0  \n",
+       "...                                     ...  \n",
+       "TMA_Cell_115755                     CONTROL  \n",
+       "TMA_Cell_115756                     CONTROL  \n",
+       "TMA_Cell_115757                     CONTROL  \n",
+       "TMA_Cell_115758                     CONTROL  \n",
+       "TMA_Cell_115760                     CONTROL  \n",
+       "\n",
+       "[704629 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d2681077-1a50-441b-8a56-231f9b09a20a",
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>Total_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981.0</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>530.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>6460.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1449.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>1876.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1896.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3564.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3805.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>2157.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>3192.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>424.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1815.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1854.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1959.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2193.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>2755.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>2309.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>310.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>177 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc    b   tcd4   tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0  0.0  900.0   24.0  167.0  100.0   5.0          28.0  3981.0   \n",
+       "100  21.0  0.0   92.0   46.0    0.0    3.0   1.0          30.0  1449.0   \n",
+       "101   2.0  0.0   20.0   15.0    3.0    1.0   0.0          24.0  1896.0   \n",
+       "102   1.0  2.0   45.0   24.0    5.0    1.0   0.0          38.0  3564.0   \n",
+       "103  57.0  0.0   72.0  555.0    7.0    0.0   1.0          78.0  2157.0   \n",
+       "..    ...  ...    ...    ...    ...    ...   ...           ...     ...   \n",
+       "92    0.0  0.0   11.0  990.0    3.0   25.0   1.0         184.0   424.0   \n",
+       "96    0.0  0.0   76.0    2.0    2.0    1.0   0.0           4.0  1854.0   \n",
+       "97    0.0  0.0  174.0   38.0    0.0    0.0   1.0          56.0  2193.0   \n",
+       "60    0.0  0.0    0.0  107.0    9.0   16.0   1.0          57.0  1979.0   \n",
+       "62    0.0  0.0    0.0   23.0    3.0   15.0   0.0           4.0   151.0   \n",
+       "\n",
+       "     αsma_mycaf  stroma_other  endothelial  Total_cells  \n",
+       "10        388.0         530.0        335.0       6460.0  \n",
+       "100        30.0          73.0        131.0       1876.0  \n",
+       "101        81.0         173.0         60.0       2275.0  \n",
+       "102        18.0          91.0         16.0       3805.0  \n",
+       "103        17.0         131.0        117.0       3192.0  \n",
+       "..          ...           ...          ...          ...  \n",
+       "92        140.0          22.0         15.0       1815.0  \n",
+       "96          1.0          16.0          3.0       1959.0  \n",
+       "97         28.0         163.0        102.0       2755.0  \n",
+       "60        107.0           1.0         32.0       2309.0  \n",
+       "62         91.0          20.0          3.0        310.0  \n",
+       "\n",
+       "[177 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compter par numéro de patiente\n",
+    "patient_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants par patient\n",
+    "for subtype in cell_subtypes:\n",
+    "    patient_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df.loc[\n",
+    "            df['cell_subtype'] == subtype, 'Patient'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_patients = pd.concat([pd.DataFrame(v) for v in patient_counts.values()], axis=1, sort=False)\n",
+    "counts_patients = counts_patients.fillna(0)\n",
+    "\n",
+    "# Ajout de la colonne de total de cellules comptées par patientes\n",
+    "counts_patients['Total_cells'] = counts_patients.sum(axis=1)\n",
+    "counts_patients = counts_patients[~counts_patients.index.str.startswith('c')]\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules par patient dans un fichier CSV\n",
+    "filename_patients = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number_by_patient.csv\")\n",
+    "counts_patients.to_csv(filename_patients, index=False)\n",
+    "counts_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "f95053da-c3ae-46c1-93c4-a6b052887bf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Patient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981.0</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371517</td>\n",
+       "      <td>2.585139</td>\n",
+       "      <td>1.547988</td>\n",
+       "      <td>0.077399</td>\n",
+       "      <td>0.433437</td>\n",
+       "      <td>61.625387</td>\n",
+       "      <td>6.006192</td>\n",
+       "      <td>8.204334</td>\n",
+       "      <td>5.185759</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1449.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.452026</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.159915</td>\n",
+       "      <td>0.053305</td>\n",
+       "      <td>1.599147</td>\n",
+       "      <td>77.238806</td>\n",
+       "      <td>1.599147</td>\n",
+       "      <td>3.891258</td>\n",
+       "      <td>6.982942</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1896.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.659341</td>\n",
+       "      <td>0.131868</td>\n",
+       "      <td>0.043956</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.054945</td>\n",
+       "      <td>83.340659</td>\n",
+       "      <td>3.560440</td>\n",
+       "      <td>7.604396</td>\n",
+       "      <td>2.637363</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3564.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630749</td>\n",
+       "      <td>0.131406</td>\n",
+       "      <td>0.026281</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.998686</td>\n",
+       "      <td>93.666229</td>\n",
+       "      <td>0.473062</td>\n",
+       "      <td>2.391590</td>\n",
+       "      <td>0.420499</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>2157.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.387218</td>\n",
+       "      <td>0.219298</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.031328</td>\n",
+       "      <td>2.443609</td>\n",
+       "      <td>67.575188</td>\n",
+       "      <td>0.532581</td>\n",
+       "      <td>4.104010</td>\n",
+       "      <td>3.665414</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>424.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>54.545455</td>\n",
+       "      <td>0.165289</td>\n",
+       "      <td>1.377410</td>\n",
+       "      <td>0.055096</td>\n",
+       "      <td>10.137741</td>\n",
+       "      <td>23.360882</td>\n",
+       "      <td>7.713499</td>\n",
+       "      <td>1.212121</td>\n",
+       "      <td>0.826446</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1854.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.102093</td>\n",
+       "      <td>0.102093</td>\n",
+       "      <td>0.051046</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.204186</td>\n",
+       "      <td>94.640123</td>\n",
+       "      <td>0.051046</td>\n",
+       "      <td>0.816743</td>\n",
+       "      <td>0.153139</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2193.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.379310</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.036298</td>\n",
+       "      <td>2.032668</td>\n",
+       "      <td>79.600726</td>\n",
+       "      <td>1.016334</td>\n",
+       "      <td>5.916515</td>\n",
+       "      <td>3.702359</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.389779</td>\n",
+       "      <td>0.692941</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>2.468601</td>\n",
+       "      <td>85.708099</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>1.385881</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.419355</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>4.838710</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.290323</td>\n",
+       "      <td>48.709677</td>\n",
+       "      <td>29.354839</td>\n",
+       "      <td>6.451613</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>177 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc    b   tcd4   tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0  0.0  900.0   24.0  167.0  100.0   5.0          28.0  3981.0   \n",
+       "100  21.0  0.0   92.0   46.0    0.0    3.0   1.0          30.0  1449.0   \n",
+       "101   2.0  0.0   20.0   15.0    3.0    1.0   0.0          24.0  1896.0   \n",
+       "102   1.0  2.0   45.0   24.0    5.0    1.0   0.0          38.0  3564.0   \n",
+       "103  57.0  0.0   72.0  555.0    7.0    0.0   1.0          78.0  2157.0   \n",
+       "..    ...  ...    ...    ...    ...    ...   ...           ...     ...   \n",
+       "92    0.0  0.0   11.0  990.0    3.0   25.0   1.0         184.0   424.0   \n",
+       "96    0.0  0.0   76.0    2.0    2.0    1.0   0.0           4.0  1854.0   \n",
+       "97    0.0  0.0  174.0   38.0    0.0    0.0   1.0          56.0  2193.0   \n",
+       "60    0.0  0.0    0.0  107.0    9.0   16.0   1.0          57.0  1979.0   \n",
+       "62    0.0  0.0    0.0   23.0    3.0   15.0   0.0           4.0   151.0   \n",
+       "\n",
+       "     αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "10        388.0  ...   0.371517  2.585139  1.547988   0.077399   \n",
+       "100        30.0  ...   2.452026  0.000000  0.159915   0.053305   \n",
+       "101        81.0  ...   0.659341  0.131868  0.043956   0.000000   \n",
+       "102        18.0  ...   0.630749  0.131406  0.026281   0.000000   \n",
+       "103        17.0  ...  17.387218  0.219298  0.000000   0.031328   \n",
+       "..          ...  ...        ...       ...       ...        ...   \n",
+       "92        140.0  ...  54.545455  0.165289  1.377410   0.055096   \n",
+       "96          1.0  ...   0.102093  0.102093  0.051046   0.000000   \n",
+       "97         28.0  ...   1.379310  0.000000  0.000000   0.036298   \n",
+       "60        107.0  ...   4.634041  0.389779  0.692941   0.043309   \n",
+       "62         91.0  ...   7.419355  0.967742  4.838710   0.000000   \n",
+       "\n",
+       "     immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "10            0.433437    61.625387         6.006192           8.204334   \n",
+       "100           1.599147    77.238806         1.599147           3.891258   \n",
+       "101           1.054945    83.340659         3.560440           7.604396   \n",
+       "102           0.998686    93.666229         0.473062           2.391590   \n",
+       "103           2.443609    67.575188         0.532581           4.104010   \n",
+       "..                 ...          ...              ...                ...   \n",
+       "92           10.137741    23.360882         7.713499           1.212121   \n",
+       "96            0.204186    94.640123         0.051046           0.816743   \n",
+       "97            2.032668    79.600726         1.016334           5.916515   \n",
+       "60            2.468601    85.708099         4.634041           0.043309   \n",
+       "62            1.290323    48.709677        29.354839           6.451613   \n",
+       "\n",
+       "     endothelial_perc  Patient  \n",
+       "10           5.185759       10  \n",
+       "100          6.982942      100  \n",
+       "101          2.637363      101  \n",
+       "102          0.420499      102  \n",
+       "103          3.665414      103  \n",
+       "..                ...      ...  \n",
+       "92           0.826446       92  \n",
+       "96           0.153139       96  \n",
+       "97           3.702359       97  \n",
+       "60           1.385881       60  \n",
+       "62           0.967742       62  \n",
+       "\n",
+       "[177 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules par patient\n",
+    "counts_perc_patients = counts_patients.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules, en excluant la colonne 'total_cells'\n",
+    "for col in counts_perc_patients.columns:\n",
+    "    if col != 'Total_cells':\n",
+    "        counts_perc_patients[col + '_perc'] = (counts_perc_patients[col] / counts_perc_patients['Total_cells']) * 100\n",
+    "\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules par patient\n",
+    "counts_perc_patients['Patient'] = counts_perc_patients.index\n",
+    "counts_perc_patients.columns.values\n",
+    "counts_perc_patients = counts_perc_patients[~counts_perc_patients.index.str.startswith('c')]\n",
+    "counts_perc_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d0c8f835-c414-4247-a701-5ed00ffcdef8",
+   "metadata": {},
+   "outputs": [
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+            "ticks": "",
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+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+          },
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+           "line": {
+            "color": "#2a3f5f"
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Cell subtypes proportions by Patient and tissue type"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          176.5
+         ],
+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          0,
+          105.26315789473688
+         ],
+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell subtypes proportions by Patient and tissue type'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc_patients['Patient'],\n",
+    "            y=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    title=title,\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black'))\n",
+    "\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45841125-32c5-4996-bc6f-3440dc74bcc2",
+   "metadata": {},
+   "source": [
+    "#### V.4.2.3 BY SCENES AND PATIENTS"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "5eb16728-1a84-4624-b2d0-d954667d77cf",
+   "metadata": {},
+   "source": [
+    "!!!!!  DD3 !!!!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "6acfff96-5194-4b24-8ac1-d7ce34f2354c",
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>Total_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>530</td>\n",
+       "      <td>335</td>\n",
+       "      <td>6460.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>556</td>\n",
+       "      <td>409.0</td>\n",
+       "      <td>5103</td>\n",
+       "      <td>87</td>\n",
+       "      <td>6194.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>7142</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>315</td>\n",
+       "      <td>57</td>\n",
+       "      <td>7867.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>747.0</td>\n",
+       "      <td>232</td>\n",
+       "      <td>86</td>\n",
+       "      <td>2958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>983.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>2322</td>\n",
+       "      <td>1543.0</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>228</td>\n",
+       "      <td>6518.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>4317</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>640</td>\n",
+       "      <td>94</td>\n",
+       "      <td>5293.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>416.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>2447</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>163</td>\n",
+       "      <td>58</td>\n",
+       "      <td>3390.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>3370</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>126</td>\n",
+       "      <td>62</td>\n",
+       "      <td>4786.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>472</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4522</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1705</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6429.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>983</td>\n",
+       "      <td>718.0</td>\n",
+       "      <td>905</td>\n",
+       "      <td>56</td>\n",
+       "      <td>2803.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>986.0</td>\n",
+       "      <td>148.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1404</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>302</td>\n",
+       "      <td>5335.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1137.0</td>\n",
+       "      <td>628.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>6327</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>614</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8954.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>263</td>\n",
+       "      <td>312</td>\n",
+       "      <td>2299.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>435.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>1426</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2271.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>861.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>189</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>1205.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>107.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1299.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>2805</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>233</td>\n",
+       "      <td>50</td>\n",
+       "      <td>5059.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>73</td>\n",
+       "      <td>4634.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>4806</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>87</td>\n",
+       "      <td>50</td>\n",
+       "      <td>5135.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>241.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>3889</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>145</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4415.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1730.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1457</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>423</td>\n",
+       "      <td>85</td>\n",
+       "      <td>3769.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>254.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>746.0</td>\n",
+       "      <td>481.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>209.0</td>\n",
+       "      <td>2968</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>291</td>\n",
+       "      <td>164</td>\n",
+       "      <td>5011.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>983</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>362</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1501.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3623</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>321</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4067.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>277.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>4357</td>\n",
+       "      <td>446.0</td>\n",
+       "      <td>716</td>\n",
+       "      <td>214</td>\n",
+       "      <td>6126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2281</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>153</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2546.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>393</td>\n",
+       "      <td>1845.0</td>\n",
+       "      <td>124</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2654.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>3155</td>\n",
+       "      <td>876.0</td>\n",
+       "      <td>692</td>\n",
+       "      <td>440</td>\n",
+       "      <td>5458.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>3656</td>\n",
+       "      <td>668.0</td>\n",
+       "      <td>2124</td>\n",
+       "      <td>139</td>\n",
+       "      <td>6923.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>3079</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>901</td>\n",
+       "      <td>52</td>\n",
+       "      <td>4503.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1133.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>4714</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>400</td>\n",
+       "      <td>128</td>\n",
+       "      <td>6817.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>5765</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>194</td>\n",
+       "      <td>62</td>\n",
+       "      <td>6231.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>134</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>95</td>\n",
+       "      <td>147</td>\n",
+       "      <td>562.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>3275</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>619</td>\n",
+       "      <td>84</td>\n",
+       "      <td>4426.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4629</td>\n",
+       "      <td>389.0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>72</td>\n",
+       "      <td>5431.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>771</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>531</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1573.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4514</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>1294</td>\n",
+       "      <td>224</td>\n",
+       "      <td>7023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>8371</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>192</td>\n",
+       "      <td>237</td>\n",
+       "      <td>9659.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>1844</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>219</td>\n",
+       "      <td>43</td>\n",
+       "      <td>2231.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2448</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>307</td>\n",
+       "      <td>151</td>\n",
+       "      <td>3232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1037</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>899</td>\n",
+       "      <td>66</td>\n",
+       "      <td>2042.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>7448</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>658</td>\n",
+       "      <td>383</td>\n",
+       "      <td>8978.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2128.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>5667</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>803</td>\n",
+       "      <td>116</td>\n",
+       "      <td>7034.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>220.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4129</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>538</td>\n",
+       "      <td>193</td>\n",
+       "      <td>6546.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>234.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7353</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8315.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>463</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>8</td>\n",
+       "      <td>637.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2274</td>\n",
+       "      <td>1562.0</td>\n",
+       "      <td>311</td>\n",
+       "      <td>100</td>\n",
+       "      <td>4282.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2740</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>189</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3221.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1252</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1775.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>131</td>\n",
+       "      <td>1869.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>19</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1459</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>282</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1968.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3482</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>53</td>\n",
+       "      <td>3839.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>5450</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>81</td>\n",
+       "      <td>6013.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>958</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>178</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1869.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4733</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>223</td>\n",
+       "      <td>6921.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2309.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>310.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc     b    tcd4    tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0   0.0   900.0    24.0  167.0  100.0   5.0          28.0    3981   \n",
+       "11    1.0   0.0     7.0     5.0    0.0    0.0   0.0          26.0     556   \n",
+       "14    1.0   0.0   190.0     6.0    2.0    1.0   0.0          53.0    7142   \n",
+       "15    4.0   0.0    32.0     7.0    7.0    1.0   0.0          19.0    1823   \n",
+       "19   38.0   0.0   983.0   258.0    3.0    0.0   4.0          64.0    2322   \n",
+       "20    1.0   0.0    26.0    23.0    0.0    0.0   0.0         152.0    4317   \n",
+       "22    1.0  84.0     5.0   416.0   70.0    1.0   0.0          57.0    2447   \n",
+       "23   67.0   1.0   239.0   750.0    0.0    3.0   1.0         155.0    3370   \n",
+       "26    1.0   0.0   159.0    17.0    0.0    0.0   1.0          15.0    2950   \n",
+       "27    5.0   0.0    45.0     0.0    3.0    2.0   0.0          10.0    4522   \n",
+       "28    1.0   0.0    26.0    23.0    1.0    0.0   0.0          90.0     983   \n",
+       "30    7.0   0.0   986.0   148.0    3.0    0.0   5.0          74.0    1404   \n",
+       "32   30.0  16.0  1137.0   628.0   52.0   15.0   0.0         122.0    6327   \n",
+       "33   14.0   0.0    64.0    33.0    0.0    1.0   1.0          26.0    1187   \n",
+       "34   19.0   0.0    34.0   435.0    0.0    2.0   0.0         219.0    1426   \n",
+       "35   13.0   0.0     2.0   861.0   26.0   13.0   1.0          71.0     189   \n",
+       "36  107.0   0.0    68.0  1299.0    1.0    0.0   0.0         403.0    2805   \n",
+       "37    2.0   0.0     1.0    60.0    0.0    0.0   0.0          83.0    4356   \n",
+       "38    4.0   1.0    20.0    21.0    4.0    4.0   2.0          41.0    4806   \n",
+       "39   11.0   0.0   241.0    35.0    0.0    0.0   0.0          45.0    3889   \n",
+       "4    31.0   2.0  1730.0     1.0    1.0    4.0   1.0          23.0    1457   \n",
+       "41    2.0   0.0   153.0     6.0    0.0    0.0   2.0           2.0      71   \n",
+       "42   50.0   0.0   746.0   481.0   50.0   12.0  32.0         209.0    2968   \n",
+       "43    9.0   0.0    44.0    10.0    0.0    0.0   1.0           7.0     983   \n",
+       "44   12.0   0.0    48.0    14.0    0.0    0.0   0.0           4.0    3623   \n",
+       "45    9.0   0.0   277.0    24.0    0.0    3.0   0.0          80.0    4357   \n",
+       "46    9.0   0.0    37.0    22.0    3.0    0.0   0.0           9.0    2281   \n",
+       "5     1.0   1.0    66.0    40.0   78.0    6.0   0.0          55.0     393   \n",
+       "50    2.0   0.0   130.0    91.0    5.0    0.0   1.0          66.0    3155   \n",
+       "52   37.0   0.0    91.0     7.0   25.0    1.0   0.0         175.0    3656   \n",
+       "53  144.0   0.0    18.0    65.0    0.0    0.0   0.0          75.0    3079   \n",
+       "54    3.0   0.0     2.0  1133.0    3.0    2.0   0.0         340.0    4714   \n",
+       "57    6.0   0.0    69.0    62.0    0.0    0.0   0.0          43.0    5765   \n",
+       "58    1.0   0.0    17.0    39.0    0.0    0.0   1.0           6.0     134   \n",
+       "6     2.0   0.0   235.0     5.0    1.0    5.0   0.0          71.0    3275   \n",
+       "63    5.0   0.0   117.0    99.0    3.0    1.0   0.0          58.0    4629   \n",
+       "7     1.0   0.0   184.0    31.0    0.0    0.0   0.0          11.0     771   \n",
+       "8     2.0   0.0   704.0    18.0    0.0    0.0   4.0          28.0    4514   \n",
+       "18    0.0  27.0   328.0   395.0    1.0    0.0   2.0          83.0    8371   \n",
+       "25    0.0   1.0     5.0    53.0    0.0    2.0   0.0          42.0    1844   \n",
+       "29    0.0   2.0   105.0    27.0  101.0    3.0   1.0          59.0    2448   \n",
+       "3     0.0   3.0     6.0     7.0    0.0    0.0   0.0          17.0    1037   \n",
+       "12    0.0   0.0   318.0     6.0    2.0    0.0   2.0         153.0    7448   \n",
+       "13    0.0   0.0    39.0    28.0    0.0    1.0   0.0          21.0    1864   \n",
+       "16    0.0   0.0    45.0    41.0    0.0    3.0   0.0         119.0    5667   \n",
+       "17    0.0   0.0   220.0    35.0    2.0    0.0   1.0          28.0    4129   \n",
+       "21    0.0   0.0   234.0    91.0    0.0    0.0   0.0         177.0    7353   \n",
+       "24    0.0   0.0     3.0    45.0    1.0    0.0   0.0          27.0     463   \n",
+       "40    0.0   0.0    11.0     6.0    1.0    0.0   0.0          17.0    2274   \n",
+       "47    0.0   0.0    14.0    10.0    1.0    0.0   1.0          12.0    2740   \n",
+       "48    0.0   0.0    22.0    51.0    0.0    0.0   0.0          14.0    1252   \n",
+       "49    0.0   0.0     5.0   206.0    0.0    0.0   0.0          85.0    1300   \n",
+       "51    0.0   0.0     4.0     1.0    1.0    0.0   1.0           0.0      11   \n",
+       "55    0.0   0.0     2.0   114.0    3.0    7.0   0.0          58.0    1459   \n",
+       "56    0.0   0.0     1.0    12.0    2.0    0.0   0.0          20.0    3482   \n",
+       "59    0.0   0.0    24.0    68.0   17.0    6.0   0.0          48.0    5450   \n",
+       "61    0.0   0.0    79.0   144.0    4.0   19.0   0.0          37.0     958   \n",
+       "9     0.0   0.0   465.0    82.0    0.0    1.0   1.0          36.0    4733   \n",
+       "60    0.0   0.0     0.0   107.0    9.0   16.0   1.0          57.0    1979   \n",
+       "62    0.0   0.0     0.0    23.0    3.0   15.0   0.0           4.0     151   \n",
+       "\n",
+       "    αsma_mycaf  stroma_other  endothelial  Total_cells  \n",
+       "10       388.0           530          335       6460.0  \n",
+       "11       409.0          5103           87       6194.0  \n",
+       "14       100.0           315           57       7867.0  \n",
+       "15       747.0           232           86       2958.0  \n",
+       "19      1543.0          1075          228       6518.0  \n",
+       "20        40.0           640           94       5293.0  \n",
+       "22        88.0           163           58       3390.0  \n",
+       "23        12.0           126           62       4786.0  \n",
+       "26        49.0           472           46       3710.0  \n",
+       "27        14.0          1705          123       6429.0  \n",
+       "28       718.0           905           56       2803.0  \n",
+       "30      1049.0          1357          302       5335.0  \n",
+       "32         5.0           614            8       8954.0  \n",
+       "33       398.0           263          312       2299.0  \n",
+       "34        85.0            45            6       2271.0  \n",
+       "35         2.0             2           25       1205.0  \n",
+       "36        93.0           233           50       5059.0  \n",
+       "37        36.0            23           73       4634.0  \n",
+       "38        95.0            87           50       5135.0  \n",
+       "39        22.0           145           27       4415.0  \n",
+       "4         11.0           423           85       3769.0  \n",
+       "41         4.0             8            6        254.0  \n",
+       "42         8.0           291          164       5011.0  \n",
+       "43        15.0           362           70       1501.0  \n",
+       "44        15.0           321           30       4067.0  \n",
+       "45       446.0           716          214       6126.0  \n",
+       "46        21.0           153           11       2546.0  \n",
+       "5       1845.0           124           45       2654.0  \n",
+       "50       876.0           692          440       5458.0  \n",
+       "52       668.0          2124          139       6923.0  \n",
+       "53       169.0           901           52       4503.0  \n",
+       "54        92.0           400          128       6817.0  \n",
+       "57        30.0           194           62       6231.0  \n",
+       "58       122.0            95          147        562.0  \n",
+       "6        129.0           619           84       4426.0  \n",
+       "63       389.0            58           72       5431.0  \n",
+       "7         24.0           531           20       1573.0  \n",
+       "8        235.0          1294          224       7023.0  \n",
+       "18        23.0           192          237       9659.0  \n",
+       "25        22.0           219           43       2231.0  \n",
+       "29        28.0           307          151       3232.0  \n",
+       "3          7.0           899           66       2042.0  \n",
+       "12         8.0           658          383       8978.0  \n",
+       "13         0.0           159           16       2128.0  \n",
+       "16       240.0           803          116       7034.0  \n",
+       "17      1400.0           538          193       6546.0  \n",
+       "21       108.0           270           82       8315.0  \n",
+       "24         9.0            81            8        637.0  \n",
+       "40      1562.0           311          100       4282.0  \n",
+       "47       235.0           189           19       3221.0  \n",
+       "48       310.0            81           45       1775.0  \n",
+       "49       113.0            29          131       1869.0  \n",
+       "51         1.0             6           19         44.0  \n",
+       "55         3.0           282           40       1968.0  \n",
+       "56       199.0            70           53       3839.0  \n",
+       "59       280.0            39           81       6013.0  \n",
+       "61       375.0           178           75       1869.0  \n",
+       "9         67.0          1313          223       6921.0  \n",
+       "60       107.0             1           32       2309.0  \n",
+       "62        91.0            20            3        310.0  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sélectionner les valeurs de 'Sample_ID' spécifiées\n",
+    "allowed_sample_ids = ['DD3S1.csv', 'DD3S2.csv', 'DD3S3.csv']\n",
+    "\n",
+    "df_filtered = df[df['Sample_ID'].isin(allowed_sample_ids)]\n",
+    "df_filtered\n",
+    "\n",
+    "# Compter par numéro de patiente\n",
+    "patient_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants par patient\n",
+    "for subtype in cell_subtypes:\n",
+    "    patient_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df_filtered.loc[\n",
+    "            df_filtered['cell_subtype'] == subtype, 'Patient'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_patients = pd.concat([pd.DataFrame(v) for v in patient_counts.values()], axis=1, sort=False)\n",
+    "counts_patients = counts_patients.fillna(0)\n",
+    "\n",
+    "# Ajout de la colonne de total de cellules comptées par patientes\n",
+    "counts_patients['Total_cells'] = counts_patients.sum(axis=1)\n",
+    "\n",
+    "\n",
+    "counts_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7d05c34a-6c74-45dc-863d-e7eeba61a679",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Patient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371517</td>\n",
+       "      <td>2.585139</td>\n",
+       "      <td>1.547988</td>\n",
+       "      <td>0.077399</td>\n",
+       "      <td>0.433437</td>\n",
+       "      <td>61.625387</td>\n",
+       "      <td>6.006192</td>\n",
+       "      <td>8.204334</td>\n",
+       "      <td>5.185759</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>556</td>\n",
+       "      <td>409.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.080723</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.419761</td>\n",
+       "      <td>8.976429</td>\n",
+       "      <td>6.603164</td>\n",
+       "      <td>82.386180</td>\n",
+       "      <td>1.404585</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>7142</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.076268</td>\n",
+       "      <td>0.025423</td>\n",
+       "      <td>0.012711</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.673700</td>\n",
+       "      <td>90.784289</td>\n",
+       "      <td>1.271133</td>\n",
+       "      <td>4.004068</td>\n",
+       "      <td>0.724546</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>747.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.236646</td>\n",
+       "      <td>0.236646</td>\n",
+       "      <td>0.033807</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.642326</td>\n",
+       "      <td>61.629479</td>\n",
+       "      <td>25.253550</td>\n",
+       "      <td>7.843137</td>\n",
+       "      <td>2.907370</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>983.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>2322</td>\n",
+       "      <td>1543.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.958269</td>\n",
+       "      <td>0.046026</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.061369</td>\n",
+       "      <td>0.981896</td>\n",
+       "      <td>35.624425</td>\n",
+       "      <td>23.672906</td>\n",
+       "      <td>16.492789</td>\n",
+       "      <td>3.498006</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>4317</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.434536</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.871717</td>\n",
+       "      <td>81.560552</td>\n",
+       "      <td>0.755715</td>\n",
+       "      <td>12.091442</td>\n",
+       "      <td>1.775930</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>416.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>2447</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.271386</td>\n",
+       "      <td>2.064897</td>\n",
+       "      <td>0.029499</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.681416</td>\n",
+       "      <td>72.182891</td>\n",
+       "      <td>2.595870</td>\n",
+       "      <td>4.808260</td>\n",
+       "      <td>1.710914</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>3370</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15.670706</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.062683</td>\n",
+       "      <td>0.020894</td>\n",
+       "      <td>3.238613</td>\n",
+       "      <td>70.413707</td>\n",
+       "      <td>0.250731</td>\n",
+       "      <td>2.632679</td>\n",
+       "      <td>1.295445</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.458221</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.026954</td>\n",
+       "      <td>0.404313</td>\n",
+       "      <td>79.514825</td>\n",
+       "      <td>1.320755</td>\n",
+       "      <td>12.722372</td>\n",
+       "      <td>1.239892</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4522</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.046664</td>\n",
+       "      <td>0.031109</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.155545</td>\n",
+       "      <td>70.337533</td>\n",
+       "      <td>0.217763</td>\n",
+       "      <td>26.520454</td>\n",
+       "      <td>1.913206</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>983</td>\n",
+       "      <td>718.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.820549</td>\n",
+       "      <td>0.035676</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.210846</td>\n",
+       "      <td>35.069568</td>\n",
+       "      <td>25.615412</td>\n",
+       "      <td>32.286836</td>\n",
+       "      <td>1.997859</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>986.0</td>\n",
+       "      <td>148.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1404</td>\n",
+       "      <td>1049.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.774133</td>\n",
+       "      <td>0.056232</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093721</td>\n",
+       "      <td>1.387067</td>\n",
+       "      <td>26.316776</td>\n",
+       "      <td>19.662605</td>\n",
+       "      <td>25.435801</td>\n",
+       "      <td>5.660731</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1137.0</td>\n",
+       "      <td>628.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>6327</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.013625</td>\n",
+       "      <td>0.580746</td>\n",
+       "      <td>0.167523</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.362520</td>\n",
+       "      <td>70.661157</td>\n",
+       "      <td>0.055841</td>\n",
+       "      <td>6.857270</td>\n",
+       "      <td>0.089346</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.435407</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.043497</td>\n",
+       "      <td>0.043497</td>\n",
+       "      <td>1.130926</td>\n",
+       "      <td>51.631144</td>\n",
+       "      <td>17.311875</td>\n",
+       "      <td>11.439756</td>\n",
+       "      <td>13.571118</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>435.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>1426</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.154557</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.088067</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.643329</td>\n",
+       "      <td>62.791722</td>\n",
+       "      <td>3.742845</td>\n",
+       "      <td>1.981506</td>\n",
+       "      <td>0.264201</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>861.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>189</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>71.452282</td>\n",
+       "      <td>2.157676</td>\n",
+       "      <td>1.078838</td>\n",
+       "      <td>0.082988</td>\n",
+       "      <td>5.892116</td>\n",
+       "      <td>15.684647</td>\n",
+       "      <td>0.165975</td>\n",
+       "      <td>0.165975</td>\n",
+       "      <td>2.074689</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>107.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1299.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>2805</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.677011</td>\n",
+       "      <td>0.019767</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.966001</td>\n",
+       "      <td>55.445740</td>\n",
+       "      <td>1.838308</td>\n",
+       "      <td>4.605653</td>\n",
+       "      <td>0.988338</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.294778</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.791109</td>\n",
+       "      <td>94.000863</td>\n",
+       "      <td>0.776867</td>\n",
+       "      <td>0.496331</td>\n",
+       "      <td>1.575313</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>4806</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.408958</td>\n",
+       "      <td>0.077897</td>\n",
+       "      <td>0.077897</td>\n",
+       "      <td>0.038948</td>\n",
+       "      <td>0.798442</td>\n",
+       "      <td>93.592989</td>\n",
+       "      <td>1.850049</td>\n",
+       "      <td>1.694255</td>\n",
+       "      <td>0.973710</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>241.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>3889</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.792752</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.019253</td>\n",
+       "      <td>88.086070</td>\n",
+       "      <td>0.498301</td>\n",
+       "      <td>3.284258</td>\n",
+       "      <td>0.611552</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1730.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1457</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.026532</td>\n",
+       "      <td>0.026532</td>\n",
+       "      <td>0.106129</td>\n",
+       "      <td>0.026532</td>\n",
+       "      <td>0.610241</td>\n",
+       "      <td>38.657469</td>\n",
+       "      <td>0.291855</td>\n",
+       "      <td>11.223136</td>\n",
+       "      <td>2.255240</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.362205</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.787402</td>\n",
+       "      <td>0.787402</td>\n",
+       "      <td>27.952756</td>\n",
+       "      <td>1.574803</td>\n",
+       "      <td>3.149606</td>\n",
+       "      <td>2.362205</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>746.0</td>\n",
+       "      <td>481.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>209.0</td>\n",
+       "      <td>2968</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.598882</td>\n",
+       "      <td>0.997805</td>\n",
+       "      <td>0.239473</td>\n",
+       "      <td>0.638595</td>\n",
+       "      <td>4.170824</td>\n",
+       "      <td>59.229695</td>\n",
+       "      <td>0.159649</td>\n",
+       "      <td>5.807224</td>\n",
+       "      <td>3.272800</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>983</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.666223</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.066622</td>\n",
+       "      <td>0.466356</td>\n",
+       "      <td>65.489674</td>\n",
+       "      <td>0.999334</td>\n",
+       "      <td>24.117255</td>\n",
+       "      <td>4.663558</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3623</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.344234</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.098353</td>\n",
+       "      <td>89.082862</td>\n",
+       "      <td>0.368822</td>\n",
+       "      <td>7.892796</td>\n",
+       "      <td>0.737644</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>277.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>4357</td>\n",
+       "      <td>446.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.391773</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.048972</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.305909</td>\n",
+       "      <td>71.123082</td>\n",
+       "      <td>7.280444</td>\n",
+       "      <td>11.687888</td>\n",
+       "      <td>3.493307</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2281</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.864101</td>\n",
+       "      <td>0.117832</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.353496</td>\n",
+       "      <td>89.591516</td>\n",
+       "      <td>0.824823</td>\n",
+       "      <td>6.009427</td>\n",
+       "      <td>0.432050</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>393</td>\n",
+       "      <td>1845.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.507159</td>\n",
+       "      <td>2.938960</td>\n",
+       "      <td>0.226074</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.072344</td>\n",
+       "      <td>14.807837</td>\n",
+       "      <td>69.517709</td>\n",
+       "      <td>4.672193</td>\n",
+       "      <td>1.695554</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>3155</td>\n",
+       "      <td>876.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.667277</td>\n",
+       "      <td>0.091609</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.018322</td>\n",
+       "      <td>1.209234</td>\n",
+       "      <td>57.805057</td>\n",
+       "      <td>16.049835</td>\n",
+       "      <td>12.678637</td>\n",
+       "      <td>8.061561</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>3656</td>\n",
+       "      <td>668.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.101112</td>\n",
+       "      <td>0.361115</td>\n",
+       "      <td>0.014445</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.527806</td>\n",
+       "      <td>52.809476</td>\n",
+       "      <td>9.648996</td>\n",
+       "      <td>30.680341</td>\n",
+       "      <td>2.007800</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>144.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>3079</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.443482</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.665556</td>\n",
+       "      <td>68.376638</td>\n",
+       "      <td>3.753054</td>\n",
+       "      <td>20.008883</td>\n",
+       "      <td>1.154786</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1133.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>4714</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.620214</td>\n",
+       "      <td>0.044008</td>\n",
+       "      <td>0.029338</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.987531</td>\n",
+       "      <td>69.150653</td>\n",
+       "      <td>1.349567</td>\n",
+       "      <td>5.867684</td>\n",
+       "      <td>1.877659</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>5765</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.995025</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.690098</td>\n",
+       "      <td>92.521265</td>\n",
+       "      <td>0.481464</td>\n",
+       "      <td>3.113465</td>\n",
+       "      <td>0.995025</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>134</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.939502</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.177936</td>\n",
+       "      <td>1.067616</td>\n",
+       "      <td>23.843416</td>\n",
+       "      <td>21.708185</td>\n",
+       "      <td>16.903915</td>\n",
+       "      <td>26.156584</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>3275</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.112969</td>\n",
+       "      <td>0.022594</td>\n",
+       "      <td>0.112969</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.604157</td>\n",
+       "      <td>73.994577</td>\n",
+       "      <td>2.914596</td>\n",
+       "      <td>13.985540</td>\n",
+       "      <td>1.897876</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4629</td>\n",
+       "      <td>389.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.822869</td>\n",
+       "      <td>0.055238</td>\n",
+       "      <td>0.018413</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.067943</td>\n",
+       "      <td>85.232922</td>\n",
+       "      <td>7.162585</td>\n",
+       "      <td>1.067943</td>\n",
+       "      <td>1.325723</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>771</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.970757</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.699301</td>\n",
+       "      <td>49.014622</td>\n",
+       "      <td>1.525747</td>\n",
+       "      <td>33.757152</td>\n",
+       "      <td>1.271456</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4514</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.256301</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.056956</td>\n",
+       "      <td>0.398690</td>\n",
+       "      <td>64.274527</td>\n",
+       "      <td>3.346148</td>\n",
+       "      <td>18.425174</td>\n",
+       "      <td>3.189520</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>8371</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.089450</td>\n",
+       "      <td>0.010353</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.020706</td>\n",
+       "      <td>0.859302</td>\n",
+       "      <td>86.665286</td>\n",
+       "      <td>0.238120</td>\n",
+       "      <td>1.987783</td>\n",
+       "      <td>2.453670</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>1844</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.375616</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.089646</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.882564</td>\n",
+       "      <td>82.653519</td>\n",
+       "      <td>0.986105</td>\n",
+       "      <td>9.816226</td>\n",
+       "      <td>1.927387</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2448</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.835396</td>\n",
+       "      <td>3.125000</td>\n",
+       "      <td>0.092822</td>\n",
+       "      <td>0.030941</td>\n",
+       "      <td>1.825495</td>\n",
+       "      <td>75.742574</td>\n",
+       "      <td>0.866337</td>\n",
+       "      <td>9.498762</td>\n",
+       "      <td>4.672030</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1037</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.342801</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.832517</td>\n",
+       "      <td>50.783546</td>\n",
+       "      <td>0.342801</td>\n",
+       "      <td>44.025465</td>\n",
+       "      <td>3.232125</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>7448</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.066830</td>\n",
+       "      <td>0.022277</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.022277</td>\n",
+       "      <td>1.704166</td>\n",
+       "      <td>82.958343</td>\n",
+       "      <td>0.089107</td>\n",
+       "      <td>7.329027</td>\n",
+       "      <td>4.265984</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.315789</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.046992</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.986842</td>\n",
+       "      <td>87.593985</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.471805</td>\n",
+       "      <td>0.751880</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>5667</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.582883</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.042650</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.691783</td>\n",
+       "      <td>80.565823</td>\n",
+       "      <td>3.411999</td>\n",
+       "      <td>11.415980</td>\n",
+       "      <td>1.649133</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>220.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4129</td>\n",
+       "      <td>1400.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.534678</td>\n",
+       "      <td>0.030553</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.015277</td>\n",
+       "      <td>0.427742</td>\n",
+       "      <td>63.076688</td>\n",
+       "      <td>21.387107</td>\n",
+       "      <td>8.218760</td>\n",
+       "      <td>2.948365</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>234.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7353</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.094408</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.128683</td>\n",
+       "      <td>88.430547</td>\n",
+       "      <td>1.298857</td>\n",
+       "      <td>3.247144</td>\n",
+       "      <td>0.986170</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>463</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.064364</td>\n",
+       "      <td>0.156986</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.238619</td>\n",
+       "      <td>72.684458</td>\n",
+       "      <td>1.412873</td>\n",
+       "      <td>12.715856</td>\n",
+       "      <td>1.255887</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2274</td>\n",
+       "      <td>1562.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.140121</td>\n",
+       "      <td>0.023354</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.397011</td>\n",
+       "      <td>53.106025</td>\n",
+       "      <td>36.478281</td>\n",
+       "      <td>7.262961</td>\n",
+       "      <td>2.335357</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2740</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.310463</td>\n",
+       "      <td>0.031046</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.031046</td>\n",
+       "      <td>0.372555</td>\n",
+       "      <td>85.066749</td>\n",
+       "      <td>7.295871</td>\n",
+       "      <td>5.867743</td>\n",
+       "      <td>0.589879</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1252</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.873239</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.788732</td>\n",
+       "      <td>70.535211</td>\n",
+       "      <td>17.464789</td>\n",
+       "      <td>4.563380</td>\n",
+       "      <td>2.535211</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>206.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.021937</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.547887</td>\n",
+       "      <td>69.555912</td>\n",
+       "      <td>6.046014</td>\n",
+       "      <td>1.551632</td>\n",
+       "      <td>7.009096</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>13.636364</td>\n",
+       "      <td>43.181818</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1459</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.792683</td>\n",
+       "      <td>0.152439</td>\n",
+       "      <td>0.355691</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.947154</td>\n",
+       "      <td>74.136179</td>\n",
+       "      <td>0.152439</td>\n",
+       "      <td>14.329268</td>\n",
+       "      <td>2.032520</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3482</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.312581</td>\n",
+       "      <td>0.052097</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.520969</td>\n",
+       "      <td>90.700703</td>\n",
+       "      <td>5.183642</td>\n",
+       "      <td>1.823392</td>\n",
+       "      <td>1.380568</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>5450</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.130883</td>\n",
+       "      <td>0.282721</td>\n",
+       "      <td>0.099784</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.798270</td>\n",
+       "      <td>90.636953</td>\n",
+       "      <td>4.656577</td>\n",
+       "      <td>0.648595</td>\n",
+       "      <td>1.347081</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>958</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.704655</td>\n",
+       "      <td>0.214018</td>\n",
+       "      <td>1.016586</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.979668</td>\n",
+       "      <td>51.257357</td>\n",
+       "      <td>20.064205</td>\n",
+       "      <td>9.523810</td>\n",
+       "      <td>4.012841</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4733</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.184800</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.014449</td>\n",
+       "      <td>0.014449</td>\n",
+       "      <td>0.520156</td>\n",
+       "      <td>68.386071</td>\n",
+       "      <td>0.968068</td>\n",
+       "      <td>18.971247</td>\n",
+       "      <td>3.222078</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.389779</td>\n",
+       "      <td>0.692941</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>2.468601</td>\n",
+       "      <td>85.708099</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>1.385881</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.419355</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>4.838710</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.290323</td>\n",
+       "      <td>48.709677</td>\n",
+       "      <td>29.354839</td>\n",
+       "      <td>6.451613</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>60 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc     b    tcd4    tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0   0.0   900.0    24.0  167.0  100.0   5.0          28.0    3981   \n",
+       "11    1.0   0.0     7.0     5.0    0.0    0.0   0.0          26.0     556   \n",
+       "14    1.0   0.0   190.0     6.0    2.0    1.0   0.0          53.0    7142   \n",
+       "15    4.0   0.0    32.0     7.0    7.0    1.0   0.0          19.0    1823   \n",
+       "19   38.0   0.0   983.0   258.0    3.0    0.0   4.0          64.0    2322   \n",
+       "20    1.0   0.0    26.0    23.0    0.0    0.0   0.0         152.0    4317   \n",
+       "22    1.0  84.0     5.0   416.0   70.0    1.0   0.0          57.0    2447   \n",
+       "23   67.0   1.0   239.0   750.0    0.0    3.0   1.0         155.0    3370   \n",
+       "26    1.0   0.0   159.0    17.0    0.0    0.0   1.0          15.0    2950   \n",
+       "27    5.0   0.0    45.0     0.0    3.0    2.0   0.0          10.0    4522   \n",
+       "28    1.0   0.0    26.0    23.0    1.0    0.0   0.0          90.0     983   \n",
+       "30    7.0   0.0   986.0   148.0    3.0    0.0   5.0          74.0    1404   \n",
+       "32   30.0  16.0  1137.0   628.0   52.0   15.0   0.0         122.0    6327   \n",
+       "33   14.0   0.0    64.0    33.0    0.0    1.0   1.0          26.0    1187   \n",
+       "34   19.0   0.0    34.0   435.0    0.0    2.0   0.0         219.0    1426   \n",
+       "35   13.0   0.0     2.0   861.0   26.0   13.0   1.0          71.0     189   \n",
+       "36  107.0   0.0    68.0  1299.0    1.0    0.0   0.0         403.0    2805   \n",
+       "37    2.0   0.0     1.0    60.0    0.0    0.0   0.0          83.0    4356   \n",
+       "38    4.0   1.0    20.0    21.0    4.0    4.0   2.0          41.0    4806   \n",
+       "39   11.0   0.0   241.0    35.0    0.0    0.0   0.0          45.0    3889   \n",
+       "4    31.0   2.0  1730.0     1.0    1.0    4.0   1.0          23.0    1457   \n",
+       "41    2.0   0.0   153.0     6.0    0.0    0.0   2.0           2.0      71   \n",
+       "42   50.0   0.0   746.0   481.0   50.0   12.0  32.0         209.0    2968   \n",
+       "43    9.0   0.0    44.0    10.0    0.0    0.0   1.0           7.0     983   \n",
+       "44   12.0   0.0    48.0    14.0    0.0    0.0   0.0           4.0    3623   \n",
+       "45    9.0   0.0   277.0    24.0    0.0    3.0   0.0          80.0    4357   \n",
+       "46    9.0   0.0    37.0    22.0    3.0    0.0   0.0           9.0    2281   \n",
+       "5     1.0   1.0    66.0    40.0   78.0    6.0   0.0          55.0     393   \n",
+       "50    2.0   0.0   130.0    91.0    5.0    0.0   1.0          66.0    3155   \n",
+       "52   37.0   0.0    91.0     7.0   25.0    1.0   0.0         175.0    3656   \n",
+       "53  144.0   0.0    18.0    65.0    0.0    0.0   0.0          75.0    3079   \n",
+       "54    3.0   0.0     2.0  1133.0    3.0    2.0   0.0         340.0    4714   \n",
+       "57    6.0   0.0    69.0    62.0    0.0    0.0   0.0          43.0    5765   \n",
+       "58    1.0   0.0    17.0    39.0    0.0    0.0   1.0           6.0     134   \n",
+       "6     2.0   0.0   235.0     5.0    1.0    5.0   0.0          71.0    3275   \n",
+       "63    5.0   0.0   117.0    99.0    3.0    1.0   0.0          58.0    4629   \n",
+       "7     1.0   0.0   184.0    31.0    0.0    0.0   0.0          11.0     771   \n",
+       "8     2.0   0.0   704.0    18.0    0.0    0.0   4.0          28.0    4514   \n",
+       "18    0.0  27.0   328.0   395.0    1.0    0.0   2.0          83.0    8371   \n",
+       "25    0.0   1.0     5.0    53.0    0.0    2.0   0.0          42.0    1844   \n",
+       "29    0.0   2.0   105.0    27.0  101.0    3.0   1.0          59.0    2448   \n",
+       "3     0.0   3.0     6.0     7.0    0.0    0.0   0.0          17.0    1037   \n",
+       "12    0.0   0.0   318.0     6.0    2.0    0.0   2.0         153.0    7448   \n",
+       "13    0.0   0.0    39.0    28.0    0.0    1.0   0.0          21.0    1864   \n",
+       "16    0.0   0.0    45.0    41.0    0.0    3.0   0.0         119.0    5667   \n",
+       "17    0.0   0.0   220.0    35.0    2.0    0.0   1.0          28.0    4129   \n",
+       "21    0.0   0.0   234.0    91.0    0.0    0.0   0.0         177.0    7353   \n",
+       "24    0.0   0.0     3.0    45.0    1.0    0.0   0.0          27.0     463   \n",
+       "40    0.0   0.0    11.0     6.0    1.0    0.0   0.0          17.0    2274   \n",
+       "47    0.0   0.0    14.0    10.0    1.0    0.0   1.0          12.0    2740   \n",
+       "48    0.0   0.0    22.0    51.0    0.0    0.0   0.0          14.0    1252   \n",
+       "49    0.0   0.0     5.0   206.0    0.0    0.0   0.0          85.0    1300   \n",
+       "51    0.0   0.0     4.0     1.0    1.0    0.0   1.0           0.0      11   \n",
+       "55    0.0   0.0     2.0   114.0    3.0    7.0   0.0          58.0    1459   \n",
+       "56    0.0   0.0     1.0    12.0    2.0    0.0   0.0          20.0    3482   \n",
+       "59    0.0   0.0    24.0    68.0   17.0    6.0   0.0          48.0    5450   \n",
+       "61    0.0   0.0    79.0   144.0    4.0   19.0   0.0          37.0     958   \n",
+       "9     0.0   0.0   465.0    82.0    0.0    1.0   1.0          36.0    4733   \n",
+       "60    0.0   0.0     0.0   107.0    9.0   16.0   1.0          57.0    1979   \n",
+       "62    0.0   0.0     0.0    23.0    3.0   15.0   0.0           4.0     151   \n",
+       "\n",
+       "    αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "10       388.0  ...   0.371517  2.585139  1.547988   0.077399   \n",
+       "11       409.0  ...   0.080723  0.000000  0.000000   0.000000   \n",
+       "14       100.0  ...   0.076268  0.025423  0.012711   0.000000   \n",
+       "15       747.0  ...   0.236646  0.236646  0.033807   0.000000   \n",
+       "19      1543.0  ...   3.958269  0.046026  0.000000   0.061369   \n",
+       "20        40.0  ...   0.434536  0.000000  0.000000   0.000000   \n",
+       "22        88.0  ...  12.271386  2.064897  0.029499   0.000000   \n",
+       "23        12.0  ...  15.670706  0.000000  0.062683   0.020894   \n",
+       "26        49.0  ...   0.458221  0.000000  0.000000   0.026954   \n",
+       "27        14.0  ...   0.000000  0.046664  0.031109   0.000000   \n",
+       "28       718.0  ...   0.820549  0.035676  0.000000   0.000000   \n",
+       "30      1049.0  ...   2.774133  0.056232  0.000000   0.093721   \n",
+       "32         5.0  ...   7.013625  0.580746  0.167523   0.000000   \n",
+       "33       398.0  ...   1.435407  0.000000  0.043497   0.043497   \n",
+       "34        85.0  ...  19.154557  0.000000  0.088067   0.000000   \n",
+       "35         2.0  ...  71.452282  2.157676  1.078838   0.082988   \n",
+       "36        93.0  ...  25.677011  0.019767  0.000000   0.000000   \n",
+       "37        36.0  ...   1.294778  0.000000  0.000000   0.000000   \n",
+       "38        95.0  ...   0.408958  0.077897  0.077897   0.038948   \n",
+       "39        22.0  ...   0.792752  0.000000  0.000000   0.000000   \n",
+       "4         11.0  ...   0.026532  0.026532  0.106129   0.026532   \n",
+       "41         4.0  ...   2.362205  0.000000  0.000000   0.787402   \n",
+       "42         8.0  ...   9.598882  0.997805  0.239473   0.638595   \n",
+       "43        15.0  ...   0.666223  0.000000  0.000000   0.066622   \n",
+       "44        15.0  ...   0.344234  0.000000  0.000000   0.000000   \n",
+       "45       446.0  ...   0.391773  0.000000  0.048972   0.000000   \n",
+       "46        21.0  ...   0.864101  0.117832  0.000000   0.000000   \n",
+       "5       1845.0  ...   1.507159  2.938960  0.226074   0.000000   \n",
+       "50       876.0  ...   1.667277  0.091609  0.000000   0.018322   \n",
+       "52       668.0  ...   0.101112  0.361115  0.014445   0.000000   \n",
+       "53       169.0  ...   1.443482  0.000000  0.000000   0.000000   \n",
+       "54        92.0  ...  16.620214  0.044008  0.029338   0.000000   \n",
+       "57        30.0  ...   0.995025  0.000000  0.000000   0.000000   \n",
+       "58       122.0  ...   6.939502  0.000000  0.000000   0.177936   \n",
+       "6        129.0  ...   0.112969  0.022594  0.112969   0.000000   \n",
+       "63       389.0  ...   1.822869  0.055238  0.018413   0.000000   \n",
+       "7         24.0  ...   1.970757  0.000000  0.000000   0.000000   \n",
+       "8        235.0  ...   0.256301  0.000000  0.000000   0.056956   \n",
+       "18        23.0  ...   4.089450  0.010353  0.000000   0.020706   \n",
+       "25        22.0  ...   2.375616  0.000000  0.089646   0.000000   \n",
+       "29        28.0  ...   0.835396  3.125000  0.092822   0.030941   \n",
+       "3          7.0  ...   0.342801  0.000000  0.000000   0.000000   \n",
+       "12         8.0  ...   0.066830  0.022277  0.000000   0.022277   \n",
+       "13         0.0  ...   1.315789  0.000000  0.046992   0.000000   \n",
+       "16       240.0  ...   0.582883  0.000000  0.042650   0.000000   \n",
+       "17      1400.0  ...   0.534678  0.030553  0.000000   0.015277   \n",
+       "21       108.0  ...   1.094408  0.000000  0.000000   0.000000   \n",
+       "24         9.0  ...   7.064364  0.156986  0.000000   0.000000   \n",
+       "40      1562.0  ...   0.140121  0.023354  0.000000   0.000000   \n",
+       "47       235.0  ...   0.310463  0.031046  0.000000   0.031046   \n",
+       "48       310.0  ...   2.873239  0.000000  0.000000   0.000000   \n",
+       "49       113.0  ...  11.021937  0.000000  0.000000   0.000000   \n",
+       "51         1.0  ...   2.272727  2.272727  0.000000   2.272727   \n",
+       "55         3.0  ...   5.792683  0.152439  0.355691   0.000000   \n",
+       "56       199.0  ...   0.312581  0.052097  0.000000   0.000000   \n",
+       "59       280.0  ...   1.130883  0.282721  0.099784   0.000000   \n",
+       "61       375.0  ...   7.704655  0.214018  1.016586   0.000000   \n",
+       "9         67.0  ...   1.184800  0.000000  0.014449   0.014449   \n",
+       "60       107.0  ...   4.634041  0.389779  0.692941   0.043309   \n",
+       "62        91.0  ...   7.419355  0.967742  4.838710   0.000000   \n",
+       "\n",
+       "    immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "10           0.433437    61.625387         6.006192           8.204334   \n",
+       "11           0.419761     8.976429         6.603164          82.386180   \n",
+       "14           0.673700    90.784289         1.271133           4.004068   \n",
+       "15           0.642326    61.629479        25.253550           7.843137   \n",
+       "19           0.981896    35.624425        23.672906          16.492789   \n",
+       "20           2.871717    81.560552         0.755715          12.091442   \n",
+       "22           1.681416    72.182891         2.595870           4.808260   \n",
+       "23           3.238613    70.413707         0.250731           2.632679   \n",
+       "26           0.404313    79.514825         1.320755          12.722372   \n",
+       "27           0.155545    70.337533         0.217763          26.520454   \n",
+       "28           3.210846    35.069568        25.615412          32.286836   \n",
+       "30           1.387067    26.316776        19.662605          25.435801   \n",
+       "32           1.362520    70.661157         0.055841           6.857270   \n",
+       "33           1.130926    51.631144        17.311875          11.439756   \n",
+       "34           9.643329    62.791722         3.742845           1.981506   \n",
+       "35           5.892116    15.684647         0.165975           0.165975   \n",
+       "36           7.966001    55.445740         1.838308           4.605653   \n",
+       "37           1.791109    94.000863         0.776867           0.496331   \n",
+       "38           0.798442    93.592989         1.850049           1.694255   \n",
+       "39           1.019253    88.086070         0.498301           3.284258   \n",
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+       "5            2.072344    14.807837        69.517709           4.672193   \n",
+       "50           1.209234    57.805057        16.049835          12.678637   \n",
+       "52           2.527806    52.809476         9.648996          30.680341   \n",
+       "53           1.665556    68.376638         3.753054          20.008883   \n",
+       "54           4.987531    69.150653         1.349567           5.867684   \n",
+       "57           0.690098    92.521265         0.481464           3.113465   \n",
+       "58           1.067616    23.843416        21.708185          16.903915   \n",
+       "6            1.604157    73.994577         2.914596          13.985540   \n",
+       "63           1.067943    85.232922         7.162585           1.067943   \n",
+       "7            0.699301    49.014622         1.525747          33.757152   \n",
+       "8            0.398690    64.274527         3.346148          18.425174   \n",
+       "18           0.859302    86.665286         0.238120           1.987783   \n",
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+       "3            0.832517    50.783546         0.342801          44.025465   \n",
+       "12           1.704166    82.958343         0.089107           7.329027   \n",
+       "13           0.986842    87.593985         0.000000           7.471805   \n",
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+       "21           2.128683    88.430547         1.298857           3.247144   \n",
+       "24           4.238619    72.684458         1.412873          12.715856   \n",
+       "40           0.397011    53.106025        36.478281           7.262961   \n",
+       "47           0.372555    85.066749         7.295871           5.867743   \n",
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+       "49           4.547887    69.555912         6.046014           1.551632   \n",
+       "51           0.000000    25.000000         2.272727          13.636364   \n",
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+       "56           0.520969    90.700703         5.183642           1.823392   \n",
+       "59           0.798270    90.636953         4.656577           0.648595   \n",
+       "61           1.979668    51.257357        20.064205           9.523810   \n",
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+       "60           2.468601    85.708099         4.634041           0.043309   \n",
+       "62           1.290323    48.709677        29.354839           6.451613   \n",
+       "\n",
+       "    endothelial_perc  Patient  \n",
+       "10          5.185759       10  \n",
+       "11          1.404585       11  \n",
+       "14          0.724546       14  \n",
+       "15          2.907370       15  \n",
+       "19          3.498006       19  \n",
+       "20          1.775930       20  \n",
+       "22          1.710914       22  \n",
+       "23          1.295445       23  \n",
+       "26          1.239892       26  \n",
+       "27          1.913206       27  \n",
+       "28          1.997859       28  \n",
+       "30          5.660731       30  \n",
+       "32          0.089346       32  \n",
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+       "35          2.074689       35  \n",
+       "36          0.988338       36  \n",
+       "37          1.575313       37  \n",
+       "38          0.973710       38  \n",
+       "39          0.611552       39  \n",
+       "4           2.255240        4  \n",
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+       "54          1.877659       54  \n",
+       "57          0.995025       57  \n",
+       "58         26.156584       58  \n",
+       "6           1.897876        6  \n",
+       "63          1.325723       63  \n",
+       "7           1.271456        7  \n",
+       "8           3.189520        8  \n",
+       "18          2.453670       18  \n",
+       "25          1.927387       25  \n",
+       "29          4.672030       29  \n",
+       "3           3.232125        3  \n",
+       "12          4.265984       12  \n",
+       "13          0.751880       13  \n",
+       "16          1.649133       16  \n",
+       "17          2.948365       17  \n",
+       "21          0.986170       21  \n",
+       "24          1.255887       24  \n",
+       "40          2.335357       40  \n",
+       "47          0.589879       47  \n",
+       "48          2.535211       48  \n",
+       "49          7.009096       49  \n",
+       "51         43.181818       51  \n",
+       "55          2.032520       55  \n",
+       "56          1.380568       56  \n",
+       "59          1.347081       59  \n",
+       "61          4.012841       61  \n",
+       "9           3.222078        9  \n",
+       "60          1.385881       60  \n",
+       "62          0.967742       62  \n",
+       "\n",
+       "[60 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filtrer counts_patients pour ne conserver que les lignes avec 'Sample_ID' égal à 'DD3S1.csv', 'DD3S2.csv' ou 'DD3S3.csv'\n",
+    "counts_patients_filtered = counts_patients[counts_patients.index.isin(df_filtered['Patient'])]\n",
+    "\n",
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules par patient\n",
+    "counts_perc_patients = counts_patients_filtered.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules, en excluant la colonne 'total_cells'\n",
+    "for col in counts_perc_patients.columns:\n",
+    "    if col != 'Total_cells':\n",
+    "        counts_perc_patients[col + '_perc'] = (counts_perc_patients[col] / counts_perc_patients['Total_cells']) * 100\n",
+    "\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules par patient\n",
+    "counts_perc_patients['Patient'] = counts_perc_patients.index\n",
+    "counts_perc_patients.columns.values\n",
+    "counts_perc_patients"
+   ]
+  },
+  {
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+   "execution_count": 56,
+   "id": "1f30ab7d-fa1e-4914-a575-da752330c1ea",
+   "metadata": {},
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+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Cell subtypes proportions by Patient and tissue type DD3"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          59.5
+         ],
+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          0,
+          105.26315789473685
+         ],
+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell subtypes proportions by Patient and tissue type DD3'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc_patients['Patient'],\n",
+    "            y=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    title=title,\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black'))\n",
+    "\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff541bd3-236a-4418-90a2-99d44e6ef5a0",
+   "metadata": {},
+   "source": [
+    "#### V.4.3.4 BY TREATMENT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "eaaf663e-446c-4ee8-bb50-89ed23d9359e",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62826</th>\n",
+       "      <td>-0.446025</td>\n",
+       "      <td>0.480212</td>\n",
+       "      <td>1.195440</td>\n",
+       "      <td>-0.081463</td>\n",
+       "      <td>-0.634343</td>\n",
+       "      <td>-0.883815</td>\n",
+       "      <td>1.050928</td>\n",
+       "      <td>0.164709</td>\n",
+       "      <td>0.103986</td>\n",
+       "      <td>0.171579</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.609764</td>\n",
+       "      <td>13449.426758</td>\n",
+       "      <td>34</td>\n",
+       "      <td>103</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>237</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62828</th>\n",
+       "      <td>-0.414440</td>\n",
+       "      <td>0.416668</td>\n",
+       "      <td>1.542926</td>\n",
+       "      <td>-0.203248</td>\n",
+       "      <td>-0.076555</td>\n",
+       "      <td>-0.783759</td>\n",
+       "      <td>0.423346</td>\n",
+       "      <td>0.638305</td>\n",
+       "      <td>0.346970</td>\n",
+       "      <td>0.453372</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.736844</td>\n",
+       "      <td>13469.353516</td>\n",
+       "      <td>34</td>\n",
+       "      <td>65</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>231</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62829</th>\n",
+       "      <td>-0.720569</td>\n",
+       "      <td>0.072144</td>\n",
+       "      <td>-0.311962</td>\n",
+       "      <td>-0.481442</td>\n",
+       "      <td>1.236053</td>\n",
+       "      <td>-0.468813</td>\n",
+       "      <td>0.252353</td>\n",
+       "      <td>2.129042</td>\n",
+       "      <td>1.111809</td>\n",
+       "      <td>1.340371</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.691315</td>\n",
+       "      <td>13390.447266</td>\n",
+       "      <td>34</td>\n",
+       "      <td>85</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>224</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62830</th>\n",
+       "      <td>-0.442500</td>\n",
+       "      <td>0.368654</td>\n",
+       "      <td>-1.491754</td>\n",
+       "      <td>-0.316270</td>\n",
+       "      <td>0.029502</td>\n",
+       "      <td>-0.736522</td>\n",
+       "      <td>0.390883</td>\n",
+       "      <td>0.861894</td>\n",
+       "      <td>0.461685</td>\n",
+       "      <td>0.586409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.892707</td>\n",
+       "      <td>13430.178711</td>\n",
+       "      <td>34</td>\n",
+       "      <td>56</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>139</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3_Cell_62831</th>\n",
+       "      <td>0.040892</td>\n",
+       "      <td>1.166606</td>\n",
+       "      <td>-1.676753</td>\n",
+       "      <td>0.249586</td>\n",
+       "      <td>-1.486438</td>\n",
+       "      <td>-1.072878</td>\n",
+       "      <td>2.024627</td>\n",
+       "      <td>-0.730182</td>\n",
+       "      <td>-0.355148</td>\n",
+       "      <td>-0.360887</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.676388</td>\n",
+       "      <td>13445.479492</td>\n",
+       "      <td>34</td>\n",
+       "      <td>48</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>183</td>\n",
+       "      <td>187</td>\n",
+       "      <td>187c</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>113959 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                             -0.677863   \n",
+       "DD3S1_Cell_1                             -0.677863   \n",
+       "DD3S1_Cell_2                             -0.677863   \n",
+       "DD3S1_Cell_3                             -0.741282   \n",
+       "DD3S1_Cell_6                             -0.621521   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_62826                         -0.446025   \n",
+       "DD5S3_Cell_62828                         -0.414440   \n",
+       "DD5S3_Cell_62829                         -0.720569   \n",
+       "DD5S3_Cell_62830                         -0.442500   \n",
+       "DD5S3_Cell_62831                          0.040892   \n",
+       "\n",
+       "                  HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.417494   \n",
+       "DD3S1_Cell_1                            -0.516487   \n",
+       "DD3S1_Cell_2                            -0.141921   \n",
+       "DD3S1_Cell_3                            -0.460472   \n",
+       "DD3S1_Cell_6                            -0.247254   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.480212   \n",
+       "DD5S3_Cell_62828                         0.416668   \n",
+       "DD5S3_Cell_62829                         0.072144   \n",
+       "DD5S3_Cell_62830                         0.368654   \n",
+       "DD5S3_Cell_62831                         1.166606   \n",
+       "\n",
+       "                  CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.912537   \n",
+       "DD3S1_Cell_1                            -0.838037   \n",
+       "DD3S1_Cell_2                            -1.016023   \n",
+       "DD3S1_Cell_3                            -0.491711   \n",
+       "DD3S1_Cell_6                            -0.867127   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         1.195440   \n",
+       "DD5S3_Cell_62828                         1.542926   \n",
+       "DD5S3_Cell_62829                        -0.311962   \n",
+       "DD5S3_Cell_62830                        -1.491754   \n",
+       "DD5S3_Cell_62831                        -1.676753   \n",
+       "\n",
+       "                  Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.817876   \n",
+       "DD3S1_Cell_1                           -0.869685   \n",
+       "DD3S1_Cell_2                           -0.755879   \n",
+       "DD3S1_Cell_3                           -0.818084   \n",
+       "DD3S1_Cell_6                           -0.742544   \n",
+       "...                                          ...   \n",
+       "DD5S3_Cell_62826                       -0.081463   \n",
+       "DD5S3_Cell_62828                       -0.203248   \n",
+       "DD5S3_Cell_62829                       -0.481442   \n",
+       "DD5S3_Cell_62830                       -0.316270   \n",
+       "DD5S3_Cell_62831                        0.249586   \n",
+       "\n",
+       "                  CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_0                               0.930099   \n",
+       "DD3S1_Cell_1                               1.114924   \n",
+       "DD3S1_Cell_2                               0.834577   \n",
+       "DD3S1_Cell_3                               0.648200   \n",
+       "DD3S1_Cell_6                               0.810579   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_62826                          -0.634343   \n",
+       "DD5S3_Cell_62828                          -0.076555   \n",
+       "DD5S3_Cell_62829                           1.236053   \n",
+       "DD5S3_Cell_62830                           0.029502   \n",
+       "DD5S3_Cell_62831                          -1.486438   \n",
+       "\n",
+       "                  ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_0                               0.232078   \n",
+       "DD3S1_Cell_1                               0.301333   \n",
+       "DD3S1_Cell_2                               0.259216   \n",
+       "DD3S1_Cell_3                               0.107027   \n",
+       "DD3S1_Cell_6                               0.272128   \n",
+       "...                                             ...   \n",
+       "DD5S3_Cell_62826                          -0.883815   \n",
+       "DD5S3_Cell_62828                          -0.783759   \n",
+       "DD5S3_Cell_62829                          -0.468813   \n",
+       "DD5S3_Cell_62830                          -0.736522   \n",
+       "DD5S3_Cell_62831                          -1.072878   \n",
+       "\n",
+       "                  CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                             -0.483158   \n",
+       "DD3S1_Cell_1                             -0.344770   \n",
+       "DD3S1_Cell_2                             -0.438292   \n",
+       "DD3S1_Cell_3                             -0.444889   \n",
+       "DD3S1_Cell_6                             -0.507117   \n",
+       "...                                            ...   \n",
+       "DD5S3_Cell_62826                          1.050928   \n",
+       "DD5S3_Cell_62828                          0.423346   \n",
+       "DD5S3_Cell_62829                          0.252353   \n",
+       "DD5S3_Cell_62830                          0.390883   \n",
+       "DD5S3_Cell_62831                          2.024627   \n",
+       "\n",
+       "                  PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                             1.535604   \n",
+       "DD3S1_Cell_1                             1.668368   \n",
+       "DD3S1_Cell_2                             1.336308   \n",
+       "DD3S1_Cell_3                             1.249805   \n",
+       "DD3S1_Cell_6                             1.251434   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.164709   \n",
+       "DD5S3_Cell_62828                         0.638305   \n",
+       "DD5S3_Cell_62829                         2.129042   \n",
+       "DD5S3_Cell_62830                         0.861894   \n",
+       "DD5S3_Cell_62831                        -0.730182   \n",
+       "\n",
+       "                  AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                             0.807339   \n",
+       "DD3S1_Cell_1                             0.875455   \n",
+       "DD3S1_Cell_2                             0.705088   \n",
+       "DD3S1_Cell_3                             0.660707   \n",
+       "DD3S1_Cell_6                             0.947172   \n",
+       "...                                           ...   \n",
+       "DD5S3_Cell_62826                         0.103986   \n",
+       "DD5S3_Cell_62828                         0.346970   \n",
+       "DD5S3_Cell_62829                         1.111809   \n",
+       "DD5S3_Cell_62830                         0.461685   \n",
+       "DD5S3_Cell_62831                        -0.355148   \n",
+       "\n",
+       "                  CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                  ...                      \n",
+       "DD3S1_Cell_0                              1.167755  ...           0.955040   \n",
+       "DD3S1_Cell_1                              1.643023  ...           0.966643   \n",
+       "DD3S1_Cell_2                              1.053636  ...           0.721534   \n",
+       "DD3S1_Cell_3                              1.165861  ...           0.587196   \n",
+       "DD3S1_Cell_6                              2.545301  ...           0.935716   \n",
+       "...                                            ...  ...                ...   \n",
+       "DD5S3_Cell_62826                          0.171579  ...           0.609764   \n",
+       "DD5S3_Cell_62828                          0.453372  ...           0.736844   \n",
+       "DD5S3_Cell_62829                          1.340371  ...           0.691315   \n",
+       "DD5S3_Cell_62830                          0.586409  ...           0.892707   \n",
+       "DD5S3_Cell_62831                         -0.360887  ...           0.676388   \n",
+       "\n",
+       "                         Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                   \n",
+       "DD3S1_Cell_0       1484.771729          0           127  DD3S1.csv   \n",
+       "DD3S1_Cell_1       1426.250000          0           112  DD3S1.csv   \n",
+       "DD3S1_Cell_2       1531.110474          0           181  DD3S1.csv   \n",
+       "DD3S1_Cell_3       1518.907593          0           119  DD3S1.csv   \n",
+       "DD3S1_Cell_6       1471.914917          0            47  DD3S1.csv   \n",
+       "...                        ...        ...           ...        ...   \n",
+       "DD5S3_Cell_62826  13449.426758         34           103  DD5S3.csv   \n",
+       "DD5S3_Cell_62828  13469.353516         34            65  DD5S3.csv   \n",
+       "DD5S3_Cell_62829  13390.447266         34            85  DD5S3.csv   \n",
+       "DD5S3_Cell_62830  13430.178711         34            56  DD5S3.csv   \n",
+       "DD5S3_Cell_62831  13445.479492         34            48  DD5S3.csv   \n",
+       "\n",
+       "                  immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_0                   None        339       61               61a   \n",
+       "DD3S1_Cell_1                   None        344       61               61a   \n",
+       "DD3S1_Cell_2                   None        422       61               61a   \n",
+       "DD3S1_Cell_3                   None        278       61               61a   \n",
+       "DD3S1_Cell_6                   None        204       61               61a   \n",
+       "...                             ...        ...      ...               ...   \n",
+       "DD5S3_Cell_62826               None        237      187              187c   \n",
+       "DD5S3_Cell_62828               None        231      187              187c   \n",
+       "DD5S3_Cell_62829               None        224      187              187c   \n",
+       "DD5S3_Cell_62830               None        139      187              187c   \n",
+       "DD5S3_Cell_62831               None        183      187              187c   \n",
+       "\n",
+       "                  Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                            \n",
+       "DD3S1_Cell_0                             1.0  \n",
+       "DD3S1_Cell_1                             1.0  \n",
+       "DD3S1_Cell_2                             1.0  \n",
+       "DD3S1_Cell_3                             1.0  \n",
+       "DD3S1_Cell_6                             1.0  \n",
+       "...                                      ...  \n",
+       "DD5S3_Cell_62826                         1.0  \n",
+       "DD5S3_Cell_62828                         1.0  \n",
+       "DD5S3_Cell_62829                         1.0  \n",
+       "DD5S3_Cell_62830                         1.0  \n",
+       "DD5S3_Cell_62831                         1.0  \n",
+       "\n",
+       "[113959 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "4fad07ba-a1f7-4ea7-a9d2-54b18a2082ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['61' '32' '26' '15' '18' '11' '5' '51' '40' '7' '95' '121' '85' '111'\n",
+      " '116' '98' '119' '104' '89' '140' '137' '153' '149' '146' '162' '160'\n",
+      " '157' '171' '169' '183' '182' '179' '177' '174' '178' '187' '184' '141']\n",
+      "['62' '63' '59' '60' '33' '35' '36' '37' '38' '30' '25' '27' '29' '20'\n",
+      " '21' '22' '23' '24' '14' '16' '17' '19' '12' '8' '9' '10' '4' '52' '53'\n",
+      " '54' '55' '56' '57' '50' '42' '43' '44' '45' '47' '39' '41' '49' '46'\n",
+      " '48' '58' '28' '13' '6' '34' '3' '122' '125' '124' '94' '101' '86' '84'\n",
+      " '83' '91' '88' '87' '75' '74' '82' '81' '80' '79' '67' '66' '65' '64'\n",
+      " '73' '71' '69' '68' '123' '115' '114' '113' '120' '117' '126' '105' '103'\n",
+      " '102' '110' '109' '106' '166' '112' '108' '107' '97' '96' '100' '78' '77'\n",
+      " '70' '72' '99' '92' '90' '136' '135' '134' '133' '132' '131' '130' '129'\n",
+      " '144' '143' '155' '154' '150' '148' '147' '164' '161' '159' '128' '127'\n",
+      " '142' '139' '152' '158' '156' '172' '170' '168' '167' '165' '181' '180'\n",
+      " '176' '173' '175' '185' '186']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extraire les valeurs uniques de la colonne 'Patient'\n",
+    "num_NACT_patients = df_NACT['Patient'].unique()\n",
+    "num_ACT_patients = df_ACT['Patient'].unique()\n",
+    "\n",
+    "# Numéros des patientes qui ont reçu le traitement\n",
+    "print(num_NACT_patients)\n",
+    "print(num_ACT_patients)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55464196-3344-4586-8ab1-1284941a070a",
+   "metadata": {},
+   "source": [
+    "##### V.4.3.4.1 NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "387289e7-0272-4a45-ab3d-ea076210cbb2",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>Total_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>556</td>\n",
+       "      <td>409</td>\n",
+       "      <td>5103</td>\n",
+       "      <td>87</td>\n",
+       "      <td>6194.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>899</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1026.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>4</td>\n",
+       "      <td>56</td>\n",
+       "      <td>3</td>\n",
+       "      <td>198.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>4289</td>\n",
+       "      <td>16</td>\n",
+       "      <td>104</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>704</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>3652</td>\n",
+       "      <td>183</td>\n",
+       "      <td>236</td>\n",
+       "      <td>587</td>\n",
+       "      <td>5789.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>409</td>\n",
+       "      <td>202</td>\n",
+       "      <td>101</td>\n",
+       "      <td>2091.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>402.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>385</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>66</td>\n",
+       "      <td>293</td>\n",
+       "      <td>51</td>\n",
+       "      <td>1592.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>158</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>565</td>\n",
+       "      <td>154</td>\n",
+       "      <td>73</td>\n",
+       "      <td>1120.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>916</td>\n",
+       "      <td>89</td>\n",
+       "      <td>331</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1578.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>265.0</td>\n",
+       "      <td>735</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>425</td>\n",
+       "      <td>111</td>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>747</td>\n",
+       "      <td>232</td>\n",
+       "      <td>86</td>\n",
+       "      <td>2958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>567</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>283.0</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>415</td>\n",
+       "      <td>823</td>\n",
+       "      <td>348</td>\n",
+       "      <td>4121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>2463</td>\n",
+       "      <td>132</td>\n",
+       "      <td>45</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2969.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2321</td>\n",
+       "      <td>609</td>\n",
+       "      <td>998</td>\n",
+       "      <td>313</td>\n",
+       "      <td>4360.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>202</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1564</td>\n",
+       "      <td>222</td>\n",
+       "      <td>594</td>\n",
+       "      <td>221</td>\n",
+       "      <td>2911.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1203</td>\n",
+       "      <td>224</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1820.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>259</td>\n",
+       "      <td>768</td>\n",
+       "      <td>275</td>\n",
+       "      <td>457</td>\n",
+       "      <td>1920.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>102</td>\n",
+       "      <td>37</td>\n",
+       "      <td>859.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>557</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1046</td>\n",
+       "      <td>173</td>\n",
+       "      <td>1856.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1708</td>\n",
+       "      <td>5</td>\n",
+       "      <td>40</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1878.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2599</td>\n",
+       "      <td>716</td>\n",
+       "      <td>52</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3490.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>49</td>\n",
+       "      <td>472</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1137</td>\n",
+       "      <td>628.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>6327</td>\n",
+       "      <td>5</td>\n",
+       "      <td>614</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8954.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>393</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>124</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2654.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>771</td>\n",
+       "      <td>24</td>\n",
+       "      <td>531</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1573.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>216</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>4273</td>\n",
+       "      <td>38</td>\n",
+       "      <td>247</td>\n",
+       "      <td>72</td>\n",
+       "      <td>5266.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>253</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>194.0</td>\n",
+       "      <td>2490</td>\n",
+       "      <td>6</td>\n",
+       "      <td>487</td>\n",
+       "      <td>134</td>\n",
+       "      <td>3634.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>69</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>1851</td>\n",
+       "      <td>83</td>\n",
+       "      <td>535</td>\n",
+       "      <td>143</td>\n",
+       "      <td>3484.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>790</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>2156</td>\n",
+       "      <td>25</td>\n",
+       "      <td>322</td>\n",
+       "      <td>94</td>\n",
+       "      <td>3710.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>169</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>289</td>\n",
+       "      <td>180</td>\n",
+       "      <td>147</td>\n",
+       "      <td>2491.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>328</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>8371</td>\n",
+       "      <td>23</td>\n",
+       "      <td>192</td>\n",
+       "      <td>237</td>\n",
+       "      <td>9659.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1121</td>\n",
+       "      <td>33</td>\n",
+       "      <td>188</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1396.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>523</td>\n",
+       "      <td>118</td>\n",
+       "      <td>704</td>\n",
+       "      <td>143</td>\n",
+       "      <td>1704.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1613</td>\n",
+       "      <td>220</td>\n",
+       "      <td>81</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1955.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>312</td>\n",
+       "      <td>272</td>\n",
+       "      <td>318</td>\n",
+       "      <td>11</td>\n",
+       "      <td>932.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2274</td>\n",
+       "      <td>1562</td>\n",
+       "      <td>311</td>\n",
+       "      <td>100</td>\n",
+       "      <td>4282.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>19</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>958</td>\n",
+       "      <td>375</td>\n",
+       "      <td>178</td>\n",
+       "      <td>75</td>\n",
+       "      <td>1869.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        dc     b  tcd4   tcd8     m1    m2  treg  immune_other  cancer  \\\n",
+       "11     1.0   0.0     7    5.0    0.0   0.0   0.0          26.0     556   \n",
+       "111   27.0   1.0     2   30.0   10.0   3.0   1.0          39.0     899   \n",
+       "116    3.0   0.0     4    2.0    0.0   0.0   0.0           1.0     125   \n",
+       "119    1.0   0.0    60    9.0    3.0   0.0   0.0          17.0    4289   \n",
+       "121   15.0   2.0   704   54.0    4.0   0.0   2.0         350.0    3652   \n",
+       "137    2.0   0.0    21   22.0    1.0   1.0   2.0          28.0    1302   \n",
+       "140  402.0   0.0   385    6.0  307.0  38.0   3.0           6.0      35   \n",
+       "141   42.0   0.0   158   21.0   10.0   1.0   0.0          25.0      71   \n",
+       "146    6.0   0.0    80    3.0    1.0   2.0   0.0          52.0     916   \n",
+       "149   20.0   0.0   270   25.0  275.0   3.0   0.0         265.0     735   \n",
+       "15     4.0   0.0    32    7.0    7.0   1.0   0.0          19.0    1823   \n",
+       "153    2.0   1.0   567   24.0   61.0  14.0   3.0         283.0    1580   \n",
+       "157   12.0   1.0    16  173.0    2.0   7.0   0.0          89.0    2463   \n",
+       "162    6.0   1.0    76    3.0    1.0   0.0   1.0          31.0    2321   \n",
+       "169   68.0   0.0   202   21.0    1.0   0.0   1.0          17.0    1564   \n",
+       "174    1.0   0.0    24    4.0    1.0   0.0   0.0          46.0     295   \n",
+       "177    1.0   0.0   131    0.0    1.0   0.0   0.0          28.0     259   \n",
+       "182    5.0   1.0    15    4.0    0.0   3.0   0.0          80.0     610   \n",
+       "183    6.0   0.0    46    3.0    0.0   1.0   4.0           8.0     557   \n",
+       "184   39.0   0.0    46    1.0   13.0  11.0   1.0           1.0    1708   \n",
+       "187    4.0   0.0    27   21.0    2.0   8.0   2.0          45.0    2599   \n",
+       "26     1.0   0.0   159   17.0    0.0   0.0   1.0          15.0    2950   \n",
+       "32    30.0  16.0  1137  628.0   52.0  15.0   0.0         122.0    6327   \n",
+       "5      1.0   1.0    66   40.0   78.0   6.0   0.0          55.0     393   \n",
+       "7      1.0   0.0   184   31.0    0.0   0.0   0.0          11.0     771   \n",
+       "85    32.0   0.0   216   22.0   41.0  16.0   0.0         309.0    4273   \n",
+       "89    18.0   0.0   253   17.0   22.0   8.0   5.0         194.0    2490   \n",
+       "95    37.0   1.0    69  166.0    5.0  44.0   1.0         549.0    1851   \n",
+       "98    25.0   0.0   790   72.0   73.0  70.0   2.0          81.0    2156   \n",
+       "104    0.0   1.0   169   30.0    1.0   2.0   3.0          69.0    1600   \n",
+       "18     0.0  27.0   328  395.0    1.0   0.0   2.0          83.0    8371   \n",
+       "160    0.0   0.0     5    2.0    0.0   0.0   0.0          30.0    1121   \n",
+       "171    0.0   0.0    95   28.0   25.0   1.0   0.0          67.0     523   \n",
+       "178    0.0   0.0     1    0.0    0.0   0.0   0.0           3.0    1613   \n",
+       "179    0.0   0.0    13    1.0    0.0   0.0   2.0           3.0     312   \n",
+       "40     0.0   0.0    11    6.0    1.0   0.0   0.0          17.0    2274   \n",
+       "51     0.0   0.0     4    1.0    1.0   0.0   1.0           0.0      11   \n",
+       "61     0.0   0.0    79  144.0    4.0  19.0   0.0          37.0     958   \n",
+       "\n",
+       "     αsma_mycaf  stroma_other  endothelial  Total_cells  \n",
+       "11          409          5103           87       6194.0  \n",
+       "111           1             3           10       1026.0  \n",
+       "116           4            56            3        198.0  \n",
+       "119          16           104           34       4533.0  \n",
+       "121         183           236          587       5789.0  \n",
+       "137         409           202          101       2091.0  \n",
+       "140          66           293           51       1592.0  \n",
+       "141         565           154           73       1120.0  \n",
+       "146          89           331           98       1578.0  \n",
+       "149        1250           425          111       3379.0  \n",
+       "15          747           232           86       2958.0  \n",
+       "153         415           823          348       4121.0  \n",
+       "157         132            45           29       2969.0  \n",
+       "162         609           998          313       4360.0  \n",
+       "169         222           594          221       2911.0  \n",
+       "174        1203           224           22       1820.0  \n",
+       "177         768           275          457       1920.0  \n",
+       "182           2           102           37        859.0  \n",
+       "183          12          1046          173       1856.0  \n",
+       "184           5            40           13       1878.0  \n",
+       "187         716            52           14       3490.0  \n",
+       "26           49           472           46       3710.0  \n",
+       "32            5           614            8       8954.0  \n",
+       "5          1845           124           45       2654.0  \n",
+       "7            24           531           20       1573.0  \n",
+       "85           38           247           72       5266.0  \n",
+       "89            6           487          134       3634.0  \n",
+       "95           83           535          143       3484.0  \n",
+       "98           25           322           94       3710.0  \n",
+       "104         289           180          147       2491.0  \n",
+       "18           23           192          237       9659.0  \n",
+       "160          33           188           17       1396.0  \n",
+       "171         118           704          143       1704.0  \n",
+       "178         220            81           37       1955.0  \n",
+       "179         272           318           11        932.0  \n",
+       "40         1562           311          100       4282.0  \n",
+       "51            1             6           19         44.0  \n",
+       "61          375           178           75       1869.0  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compter par numéro de patiente pour les patientes NACT uniquement\n",
+    "patient_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants par patient\n",
+    "for subtype in cell_subtypes:\n",
+    "    patient_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df.loc[\n",
+    "            (df['cell_subtype'] == subtype) & (df['Patient'].isin(num_NACT_patients)), 'Patient'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_patients = pd.concat([pd.DataFrame(v) for v in patient_counts.values()], axis=1, sort=False)\n",
+    "counts_patients = counts_patients.fillna(0)\n",
+    "\n",
+    "# Ajout de la colonne de total de cellules comptées par patientes\n",
+    "counts_patients['Total_cells'] = counts_patients.sum(axis=1)\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules par patient dans un fichier CSV\n",
+    "filename_patients = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number_by_patient_NACT.csv\")\n",
+    "counts_patients.to_csv(filename_patients, index=False)\n",
+    "counts_patients\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1745b771-5dbd-4ee7-8f82-d1e308a96eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<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",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Patient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>556</td>\n",
+       "      <td>409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.080723</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.419761</td>\n",
+       "      <td>8.976429</td>\n",
+       "      <td>6.603164</td>\n",
+       "      <td>82.386180</td>\n",
+       "      <td>1.404585</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>899</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.923977</td>\n",
+       "      <td>0.974659</td>\n",
+       "      <td>0.292398</td>\n",
+       "      <td>0.097466</td>\n",
+       "      <td>3.801170</td>\n",
+       "      <td>87.621832</td>\n",
+       "      <td>0.097466</td>\n",
+       "      <td>0.292398</td>\n",
+       "      <td>0.974659</td>\n",
+       "      <td>111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.010101</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.505051</td>\n",
+       "      <td>63.131313</td>\n",
+       "      <td>2.020202</td>\n",
+       "      <td>28.282828</td>\n",
+       "      <td>1.515152</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>4289</td>\n",
+       "      <td>16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.198544</td>\n",
+       "      <td>0.066181</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375028</td>\n",
+       "      <td>94.617251</td>\n",
+       "      <td>0.352967</td>\n",
+       "      <td>2.294286</td>\n",
+       "      <td>0.750055</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>704</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>3652</td>\n",
+       "      <td>183</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.932804</td>\n",
+       "      <td>0.069097</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.034548</td>\n",
+       "      <td>6.045949</td>\n",
+       "      <td>63.085162</td>\n",
+       "      <td>3.161168</td>\n",
+       "      <td>4.076697</td>\n",
+       "      <td>10.139921</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.052128</td>\n",
+       "      <td>0.047824</td>\n",
+       "      <td>0.047824</td>\n",
+       "      <td>0.095648</td>\n",
+       "      <td>1.339072</td>\n",
+       "      <td>62.266858</td>\n",
+       "      <td>19.560019</td>\n",
+       "      <td>9.660450</td>\n",
+       "      <td>4.830225</td>\n",
+       "      <td>137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>402.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>385</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>66</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.376884</td>\n",
+       "      <td>19.283920</td>\n",
+       "      <td>2.386935</td>\n",
+       "      <td>0.188442</td>\n",
+       "      <td>0.376884</td>\n",
+       "      <td>2.198492</td>\n",
+       "      <td>4.145729</td>\n",
+       "      <td>18.404523</td>\n",
+       "      <td>3.203518</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>158</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>565</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.875000</td>\n",
+       "      <td>0.892857</td>\n",
+       "      <td>0.089286</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.232143</td>\n",
+       "      <td>6.339286</td>\n",
+       "      <td>50.446429</td>\n",
+       "      <td>13.750000</td>\n",
+       "      <td>6.517857</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>916</td>\n",
+       "      <td>89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.190114</td>\n",
+       "      <td>0.063371</td>\n",
+       "      <td>0.126743</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.295311</td>\n",
+       "      <td>58.048162</td>\n",
+       "      <td>5.640051</td>\n",
+       "      <td>20.975919</td>\n",
+       "      <td>6.210393</td>\n",
+       "      <td>146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>265.0</td>\n",
+       "      <td>735</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.739864</td>\n",
+       "      <td>8.138503</td>\n",
+       "      <td>0.088784</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.842557</td>\n",
+       "      <td>21.751998</td>\n",
+       "      <td>36.993193</td>\n",
+       "      <td>12.577686</td>\n",
+       "      <td>3.284996</td>\n",
+       "      <td>149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>747</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.236646</td>\n",
+       "      <td>0.236646</td>\n",
+       "      <td>0.033807</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.642326</td>\n",
+       "      <td>61.629479</td>\n",
+       "      <td>25.253550</td>\n",
+       "      <td>7.843137</td>\n",
+       "      <td>2.907370</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>567</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>283.0</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>415</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.582383</td>\n",
+       "      <td>1.480223</td>\n",
+       "      <td>0.339723</td>\n",
+       "      <td>0.072798</td>\n",
+       "      <td>6.867265</td>\n",
+       "      <td>38.340209</td>\n",
+       "      <td>10.070371</td>\n",
+       "      <td>19.970881</td>\n",
+       "      <td>8.444552</td>\n",
+       "      <td>153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>2463</td>\n",
+       "      <td>132</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.826878</td>\n",
+       "      <td>0.067363</td>\n",
+       "      <td>0.235770</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.997642</td>\n",
+       "      <td>82.957225</td>\n",
+       "      <td>4.445941</td>\n",
+       "      <td>1.515662</td>\n",
+       "      <td>0.976760</td>\n",
+       "      <td>157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2321</td>\n",
+       "      <td>609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.068807</td>\n",
+       "      <td>0.022936</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.022936</td>\n",
+       "      <td>0.711009</td>\n",
+       "      <td>53.233945</td>\n",
+       "      <td>13.967890</td>\n",
+       "      <td>22.889908</td>\n",
+       "      <td>7.178899</td>\n",
+       "      <td>162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>68.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>202</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1564</td>\n",
+       "      <td>222</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.721402</td>\n",
+       "      <td>0.034352</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.034352</td>\n",
+       "      <td>0.583992</td>\n",
+       "      <td>53.727241</td>\n",
+       "      <td>7.626245</td>\n",
+       "      <td>20.405359</td>\n",
+       "      <td>7.591893</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.219780</td>\n",
+       "      <td>0.054945</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.527473</td>\n",
+       "      <td>16.208791</td>\n",
+       "      <td>66.098901</td>\n",
+       "      <td>12.307692</td>\n",
+       "      <td>1.208791</td>\n",
+       "      <td>174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>259</td>\n",
+       "      <td>768</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.052083</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.458333</td>\n",
+       "      <td>13.489583</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>14.322917</td>\n",
+       "      <td>23.802083</td>\n",
+       "      <td>177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.465658</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.349243</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.313155</td>\n",
+       "      <td>71.012806</td>\n",
+       "      <td>0.232829</td>\n",
+       "      <td>11.874272</td>\n",
+       "      <td>4.307334</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>557</td>\n",
+       "      <td>12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.161638</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.053879</td>\n",
+       "      <td>0.215517</td>\n",
+       "      <td>0.431034</td>\n",
+       "      <td>30.010776</td>\n",
+       "      <td>0.646552</td>\n",
+       "      <td>56.357759</td>\n",
+       "      <td>9.321121</td>\n",
+       "      <td>183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1708</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.053248</td>\n",
+       "      <td>0.692226</td>\n",
+       "      <td>0.585729</td>\n",
+       "      <td>0.053248</td>\n",
+       "      <td>0.053248</td>\n",
+       "      <td>90.947817</td>\n",
+       "      <td>0.266241</td>\n",
+       "      <td>2.129925</td>\n",
+       "      <td>0.692226</td>\n",
+       "      <td>184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2599</td>\n",
+       "      <td>716</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.601719</td>\n",
+       "      <td>0.057307</td>\n",
+       "      <td>0.229226</td>\n",
+       "      <td>0.057307</td>\n",
+       "      <td>1.289398</td>\n",
+       "      <td>74.469914</td>\n",
+       "      <td>20.515759</td>\n",
+       "      <td>1.489971</td>\n",
+       "      <td>0.401146</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>49</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.458221</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.026954</td>\n",
+       "      <td>0.404313</td>\n",
+       "      <td>79.514825</td>\n",
+       "      <td>1.320755</td>\n",
+       "      <td>12.722372</td>\n",
+       "      <td>1.239892</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1137</td>\n",
+       "      <td>628.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>6327</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.013625</td>\n",
+       "      <td>0.580746</td>\n",
+       "      <td>0.167523</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.362520</td>\n",
+       "      <td>70.661157</td>\n",
+       "      <td>0.055841</td>\n",
+       "      <td>6.857270</td>\n",
+       "      <td>0.089346</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>393</td>\n",
+       "      <td>1845</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.507159</td>\n",
+       "      <td>2.938960</td>\n",
+       "      <td>0.226074</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.072344</td>\n",
+       "      <td>14.807837</td>\n",
+       "      <td>69.517709</td>\n",
+       "      <td>4.672193</td>\n",
+       "      <td>1.695554</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>184</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>771</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.970757</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.699301</td>\n",
+       "      <td>49.014622</td>\n",
+       "      <td>1.525747</td>\n",
+       "      <td>33.757152</td>\n",
+       "      <td>1.271456</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>216</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>4273</td>\n",
+       "      <td>38</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.417774</td>\n",
+       "      <td>0.778580</td>\n",
+       "      <td>0.303836</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.867831</td>\n",
+       "      <td>81.143183</td>\n",
+       "      <td>0.721610</td>\n",
+       "      <td>4.690467</td>\n",
+       "      <td>1.367262</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>253</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>194.0</td>\n",
+       "      <td>2490</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.467804</td>\n",
+       "      <td>0.605394</td>\n",
+       "      <td>0.220143</td>\n",
+       "      <td>0.137589</td>\n",
+       "      <td>5.338470</td>\n",
+       "      <td>68.519538</td>\n",
+       "      <td>0.165107</td>\n",
+       "      <td>13.401211</td>\n",
+       "      <td>3.687397</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>69</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>1851</td>\n",
+       "      <td>83</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.764638</td>\n",
+       "      <td>0.143513</td>\n",
+       "      <td>1.262916</td>\n",
+       "      <td>0.028703</td>\n",
+       "      <td>15.757750</td>\n",
+       "      <td>53.128588</td>\n",
+       "      <td>2.382319</td>\n",
+       "      <td>15.355913</td>\n",
+       "      <td>4.104478</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>790</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>2156</td>\n",
+       "      <td>25</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.940701</td>\n",
+       "      <td>1.967655</td>\n",
+       "      <td>1.886792</td>\n",
+       "      <td>0.053908</td>\n",
+       "      <td>2.183288</td>\n",
+       "      <td>58.113208</td>\n",
+       "      <td>0.673854</td>\n",
+       "      <td>8.679245</td>\n",
+       "      <td>2.533693</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>169</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>289</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.204336</td>\n",
+       "      <td>0.040145</td>\n",
+       "      <td>0.080289</td>\n",
+       "      <td>0.120434</td>\n",
+       "      <td>2.769972</td>\n",
+       "      <td>64.231232</td>\n",
+       "      <td>11.601766</td>\n",
+       "      <td>7.226014</td>\n",
+       "      <td>5.901244</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>328</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>8371</td>\n",
+       "      <td>23</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.089450</td>\n",
+       "      <td>0.010353</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.020706</td>\n",
+       "      <td>0.859302</td>\n",
+       "      <td>86.665286</td>\n",
+       "      <td>0.238120</td>\n",
+       "      <td>1.987783</td>\n",
+       "      <td>2.453670</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1121</td>\n",
+       "      <td>33</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.143266</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.148997</td>\n",
+       "      <td>80.300860</td>\n",
+       "      <td>2.363897</td>\n",
+       "      <td>13.467049</td>\n",
+       "      <td>1.217765</td>\n",
+       "      <td>160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>523</td>\n",
+       "      <td>118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.643192</td>\n",
+       "      <td>1.467136</td>\n",
+       "      <td>0.058685</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.931925</td>\n",
+       "      <td>30.692488</td>\n",
+       "      <td>6.924883</td>\n",
+       "      <td>41.314554</td>\n",
+       "      <td>8.392019</td>\n",
+       "      <td>171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1613</td>\n",
+       "      <td>220</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.153453</td>\n",
+       "      <td>82.506394</td>\n",
+       "      <td>11.253197</td>\n",
+       "      <td>4.143223</td>\n",
+       "      <td>1.892583</td>\n",
+       "      <td>178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>312</td>\n",
+       "      <td>272</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.107296</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.214592</td>\n",
+       "      <td>0.321888</td>\n",
+       "      <td>33.476395</td>\n",
+       "      <td>29.184549</td>\n",
+       "      <td>34.120172</td>\n",
+       "      <td>1.180258</td>\n",
+       "      <td>179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2274</td>\n",
+       "      <td>1562</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.140121</td>\n",
+       "      <td>0.023354</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.397011</td>\n",
+       "      <td>53.106025</td>\n",
+       "      <td>36.478281</td>\n",
+       "      <td>7.262961</td>\n",
+       "      <td>2.335357</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>2.272727</td>\n",
+       "      <td>13.636364</td>\n",
+       "      <td>43.181818</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>958</td>\n",
+       "      <td>375</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.704655</td>\n",
+       "      <td>0.214018</td>\n",
+       "      <td>1.016586</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.979668</td>\n",
+       "      <td>51.257357</td>\n",
+       "      <td>20.064205</td>\n",
+       "      <td>9.523810</td>\n",
+       "      <td>4.012841</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>38 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        dc     b  tcd4   tcd8     m1    m2  treg  immune_other  cancer  \\\n",
+       "11     1.0   0.0     7    5.0    0.0   0.0   0.0          26.0     556   \n",
+       "111   27.0   1.0     2   30.0   10.0   3.0   1.0          39.0     899   \n",
+       "116    3.0   0.0     4    2.0    0.0   0.0   0.0           1.0     125   \n",
+       "119    1.0   0.0    60    9.0    3.0   0.0   0.0          17.0    4289   \n",
+       "121   15.0   2.0   704   54.0    4.0   0.0   2.0         350.0    3652   \n",
+       "137    2.0   0.0    21   22.0    1.0   1.0   2.0          28.0    1302   \n",
+       "140  402.0   0.0   385    6.0  307.0  38.0   3.0           6.0      35   \n",
+       "141   42.0   0.0   158   21.0   10.0   1.0   0.0          25.0      71   \n",
+       "146    6.0   0.0    80    3.0    1.0   2.0   0.0          52.0     916   \n",
+       "149   20.0   0.0   270   25.0  275.0   3.0   0.0         265.0     735   \n",
+       "15     4.0   0.0    32    7.0    7.0   1.0   0.0          19.0    1823   \n",
+       "153    2.0   1.0   567   24.0   61.0  14.0   3.0         283.0    1580   \n",
+       "157   12.0   1.0    16  173.0    2.0   7.0   0.0          89.0    2463   \n",
+       "162    6.0   1.0    76    3.0    1.0   0.0   1.0          31.0    2321   \n",
+       "169   68.0   0.0   202   21.0    1.0   0.0   1.0          17.0    1564   \n",
+       "174    1.0   0.0    24    4.0    1.0   0.0   0.0          46.0     295   \n",
+       "177    1.0   0.0   131    0.0    1.0   0.0   0.0          28.0     259   \n",
+       "182    5.0   1.0    15    4.0    0.0   3.0   0.0          80.0     610   \n",
+       "183    6.0   0.0    46    3.0    0.0   1.0   4.0           8.0     557   \n",
+       "184   39.0   0.0    46    1.0   13.0  11.0   1.0           1.0    1708   \n",
+       "187    4.0   0.0    27   21.0    2.0   8.0   2.0          45.0    2599   \n",
+       "26     1.0   0.0   159   17.0    0.0   0.0   1.0          15.0    2950   \n",
+       "32    30.0  16.0  1137  628.0   52.0  15.0   0.0         122.0    6327   \n",
+       "5      1.0   1.0    66   40.0   78.0   6.0   0.0          55.0     393   \n",
+       "7      1.0   0.0   184   31.0    0.0   0.0   0.0          11.0     771   \n",
+       "85    32.0   0.0   216   22.0   41.0  16.0   0.0         309.0    4273   \n",
+       "89    18.0   0.0   253   17.0   22.0   8.0   5.0         194.0    2490   \n",
+       "95    37.0   1.0    69  166.0    5.0  44.0   1.0         549.0    1851   \n",
+       "98    25.0   0.0   790   72.0   73.0  70.0   2.0          81.0    2156   \n",
+       "104    0.0   1.0   169   30.0    1.0   2.0   3.0          69.0    1600   \n",
+       "18     0.0  27.0   328  395.0    1.0   0.0   2.0          83.0    8371   \n",
+       "160    0.0   0.0     5    2.0    0.0   0.0   0.0          30.0    1121   \n",
+       "171    0.0   0.0    95   28.0   25.0   1.0   0.0          67.0     523   \n",
+       "178    0.0   0.0     1    0.0    0.0   0.0   0.0           3.0    1613   \n",
+       "179    0.0   0.0    13    1.0    0.0   0.0   2.0           3.0     312   \n",
+       "40     0.0   0.0    11    6.0    1.0   0.0   0.0          17.0    2274   \n",
+       "51     0.0   0.0     4    1.0    1.0   0.0   1.0           0.0      11   \n",
+       "61     0.0   0.0    79  144.0    4.0  19.0   0.0          37.0     958   \n",
+       "\n",
+       "     αsma_mycaf  ...  tcd8_perc    m1_perc   m2_perc  treg_perc  \\\n",
+       "11          409  ...   0.080723   0.000000  0.000000   0.000000   \n",
+       "111           1  ...   2.923977   0.974659  0.292398   0.097466   \n",
+       "116           4  ...   1.010101   0.000000  0.000000   0.000000   \n",
+       "119          16  ...   0.198544   0.066181  0.000000   0.000000   \n",
+       "121         183  ...   0.932804   0.069097  0.000000   0.034548   \n",
+       "137         409  ...   1.052128   0.047824  0.047824   0.095648   \n",
+       "140          66  ...   0.376884  19.283920  2.386935   0.188442   \n",
+       "141         565  ...   1.875000   0.892857  0.089286   0.000000   \n",
+       "146          89  ...   0.190114   0.063371  0.126743   0.000000   \n",
+       "149        1250  ...   0.739864   8.138503  0.088784   0.000000   \n",
+       "15          747  ...   0.236646   0.236646  0.033807   0.000000   \n",
+       "153         415  ...   0.582383   1.480223  0.339723   0.072798   \n",
+       "157         132  ...   5.826878   0.067363  0.235770   0.000000   \n",
+       "162         609  ...   0.068807   0.022936  0.000000   0.022936   \n",
+       "169         222  ...   0.721402   0.034352  0.000000   0.034352   \n",
+       "174        1203  ...   0.219780   0.054945  0.000000   0.000000   \n",
+       "177         768  ...   0.000000   0.052083  0.000000   0.000000   \n",
+       "182           2  ...   0.465658   0.000000  0.349243   0.000000   \n",
+       "183          12  ...   0.161638   0.000000  0.053879   0.215517   \n",
+       "184           5  ...   0.053248   0.692226  0.585729   0.053248   \n",
+       "187         716  ...   0.601719   0.057307  0.229226   0.057307   \n",
+       "26           49  ...   0.458221   0.000000  0.000000   0.026954   \n",
+       "32            5  ...   7.013625   0.580746  0.167523   0.000000   \n",
+       "5          1845  ...   1.507159   2.938960  0.226074   0.000000   \n",
+       "7            24  ...   1.970757   0.000000  0.000000   0.000000   \n",
+       "85           38  ...   0.417774   0.778580  0.303836   0.000000   \n",
+       "89            6  ...   0.467804   0.605394  0.220143   0.137589   \n",
+       "95           83  ...   4.764638   0.143513  1.262916   0.028703   \n",
+       "98           25  ...   1.940701   1.967655  1.886792   0.053908   \n",
+       "104         289  ...   1.204336   0.040145  0.080289   0.120434   \n",
+       "18           23  ...   4.089450   0.010353  0.000000   0.020706   \n",
+       "160          33  ...   0.143266   0.000000  0.000000   0.000000   \n",
+       "171         118  ...   1.643192   1.467136  0.058685   0.000000   \n",
+       "178         220  ...   0.000000   0.000000  0.000000   0.000000   \n",
+       "179         272  ...   0.107296   0.000000  0.000000   0.214592   \n",
+       "40         1562  ...   0.140121   0.023354  0.000000   0.000000   \n",
+       "51            1  ...   2.272727   2.272727  0.000000   2.272727   \n",
+       "61          375  ...   7.704655   0.214018  1.016586   0.000000   \n",
+       "\n",
+       "     immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "11            0.419761     8.976429         6.603164          82.386180   \n",
+       "111           3.801170    87.621832         0.097466           0.292398   \n",
+       "116           0.505051    63.131313         2.020202          28.282828   \n",
+       "119           0.375028    94.617251         0.352967           2.294286   \n",
+       "121           6.045949    63.085162         3.161168           4.076697   \n",
+       "137           1.339072    62.266858        19.560019           9.660450   \n",
+       "140           0.376884     2.198492         4.145729          18.404523   \n",
+       "141           2.232143     6.339286        50.446429          13.750000   \n",
+       "146           3.295311    58.048162         5.640051          20.975919   \n",
+       "149           7.842557    21.751998        36.993193          12.577686   \n",
+       "15            0.642326    61.629479        25.253550           7.843137   \n",
+       "153           6.867265    38.340209        10.070371          19.970881   \n",
+       "157           2.997642    82.957225         4.445941           1.515662   \n",
+       "162           0.711009    53.233945        13.967890          22.889908   \n",
+       "169           0.583992    53.727241         7.626245          20.405359   \n",
+       "174           2.527473    16.208791        66.098901          12.307692   \n",
+       "177           1.458333    13.489583        40.000000          14.322917   \n",
+       "182           9.313155    71.012806         0.232829          11.874272   \n",
+       "183           0.431034    30.010776         0.646552          56.357759   \n",
+       "184           0.053248    90.947817         0.266241           2.129925   \n",
+       "187           1.289398    74.469914        20.515759           1.489971   \n",
+       "26            0.404313    79.514825         1.320755          12.722372   \n",
+       "32            1.362520    70.661157         0.055841           6.857270   \n",
+       "5             2.072344    14.807837        69.517709           4.672193   \n",
+       "7             0.699301    49.014622         1.525747          33.757152   \n",
+       "85            5.867831    81.143183         0.721610           4.690467   \n",
+       "89            5.338470    68.519538         0.165107          13.401211   \n",
+       "95           15.757750    53.128588         2.382319          15.355913   \n",
+       "98            2.183288    58.113208         0.673854           8.679245   \n",
+       "104           2.769972    64.231232        11.601766           7.226014   \n",
+       "18            0.859302    86.665286         0.238120           1.987783   \n",
+       "160           2.148997    80.300860         2.363897          13.467049   \n",
+       "171           3.931925    30.692488         6.924883          41.314554   \n",
+       "178           0.153453    82.506394        11.253197           4.143223   \n",
+       "179           0.321888    33.476395        29.184549          34.120172   \n",
+       "40            0.397011    53.106025        36.478281           7.262961   \n",
+       "51            0.000000    25.000000         2.272727          13.636364   \n",
+       "61            1.979668    51.257357        20.064205           9.523810   \n",
+       "\n",
+       "     endothelial_perc  Patient  \n",
+       "11           1.404585       11  \n",
+       "111          0.974659      111  \n",
+       "116          1.515152      116  \n",
+       "119          0.750055      119  \n",
+       "121         10.139921      121  \n",
+       "137          4.830225      137  \n",
+       "140          3.203518      140  \n",
+       "141          6.517857      141  \n",
+       "146          6.210393      146  \n",
+       "149          3.284996      149  \n",
+       "15           2.907370       15  \n",
+       "153          8.444552      153  \n",
+       "157          0.976760      157  \n",
+       "162          7.178899      162  \n",
+       "169          7.591893      169  \n",
+       "174          1.208791      174  \n",
+       "177         23.802083      177  \n",
+       "182          4.307334      182  \n",
+       "183          9.321121      183  \n",
+       "184          0.692226      184  \n",
+       "187          0.401146      187  \n",
+       "26           1.239892       26  \n",
+       "32           0.089346       32  \n",
+       "5            1.695554        5  \n",
+       "7            1.271456        7  \n",
+       "85           1.367262       85  \n",
+       "89           3.687397       89  \n",
+       "95           4.104478       95  \n",
+       "98           2.533693       98  \n",
+       "104          5.901244      104  \n",
+       "18           2.453670       18  \n",
+       "160          1.217765      160  \n",
+       "171          8.392019      171  \n",
+       "178          1.892583      178  \n",
+       "179          1.180258      179  \n",
+       "40           2.335357       40  \n",
+       "51          43.181818       51  \n",
+       "61           4.012841       61  \n",
+       "\n",
+       "[38 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules par patient\n",
+    "counts_perc_patients = counts_patients.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules, en excluant la colonne 'total_cells'\n",
+    "for col in counts_perc_patients.columns:\n",
+    "    if col != 'Total_cells':\n",
+    "        counts_perc_patients[col + '_perc'] = (counts_perc_patients[col] / counts_perc_patients['Total_cells']) * 100\n",
+    "\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules par patient\n",
+    "counts_perc_patients['Patient'] = counts_perc_patients.index\n",
+    "counts_perc_patients.columns.values\n",
+    "counts_perc_patients\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "76a3f907-6618-4032-8e47-8cd428531604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts_perc_patients_NACT = counts_perc_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "49f6c28f-c371-4817-9e70-1b2c1411f061",
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+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
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+            "showbackground": true,
+            "ticks": "",
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
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+           "zaxis": {
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Cell subtypes proportions by Patient and tissue type - NACT group"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          37.5
+         ],
+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          0,
+          105.26315789473688
+         ],
+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell subtypes proportions by Patient and tissue type - NACT group'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc_patients['Patient'],\n",
+    "            y=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    title=title,\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black'))\n",
+    "\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "969a6ead-d94f-4e6f-80c4-48bc8cb716d6",
+   "metadata": {},
+   "source": [
+    "##### V.4.3.4.1 ACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e704c689-1307-43a8-98a2-4bf301f4d451",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "      <th>Total_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>530</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>6460.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1449</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>1876.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1896</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>173</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3564</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3805.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>2157</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>3192.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>424</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1815.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1854</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1959.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2193</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>163</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>2755.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>2309.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>310.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>139 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc    b   tcd4   tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0  0.0  900.0   24.0  167.0  100.0   5.0          28.0    3981   \n",
+       "100  21.0  0.0   92.0   46.0    0.0    3.0   1.0          30.0    1449   \n",
+       "101   2.0  0.0   20.0   15.0    3.0    1.0   0.0          24.0    1896   \n",
+       "102   1.0  2.0   45.0   24.0    5.0    1.0   0.0          38.0    3564   \n",
+       "103  57.0  0.0   72.0  555.0    7.0    0.0   1.0          78.0    2157   \n",
+       "..    ...  ...    ...    ...    ...    ...   ...           ...     ...   \n",
+       "92    0.0  0.0   11.0  990.0    3.0   25.0   1.0         184.0     424   \n",
+       "96    0.0  0.0   76.0    2.0    2.0    1.0   0.0           4.0    1854   \n",
+       "97    0.0  0.0  174.0   38.0    0.0    0.0   1.0          56.0    2193   \n",
+       "60    0.0  0.0    0.0  107.0    9.0   16.0   1.0          57.0    1979   \n",
+       "62    0.0  0.0    0.0   23.0    3.0   15.0   0.0           4.0     151   \n",
+       "\n",
+       "     αsma_mycaf  stroma_other  endothelial  Total_cells  \n",
+       "10        388.0           530        335.0       6460.0  \n",
+       "100        30.0            73        131.0       1876.0  \n",
+       "101        81.0           173         60.0       2275.0  \n",
+       "102        18.0            91         16.0       3805.0  \n",
+       "103        17.0           131        117.0       3192.0  \n",
+       "..          ...           ...          ...          ...  \n",
+       "92        140.0            22         15.0       1815.0  \n",
+       "96          1.0            16          3.0       1959.0  \n",
+       "97         28.0           163        102.0       2755.0  \n",
+       "60        107.0             1         32.0       2309.0  \n",
+       "62         91.0            20          3.0        310.0  \n",
+       "\n",
+       "[139 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compter par numéro de patiente pour les patientes ACT uniquement\n",
+    "patient_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants par patient\n",
+    "for subtype in cell_subtypes:\n",
+    "    patient_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df.loc[\n",
+    "            (df['cell_subtype'] == subtype) & (df['Patient'].isin(num_ACT_patients)), 'Patient'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_patients = pd.concat([pd.DataFrame(v) for v in patient_counts.values()], axis=1, sort=False)\n",
+    "counts_patients = counts_patients.fillna(0)\n",
+    "\n",
+    "# Ajout de la colonne de total de cellules comptées par patientes\n",
+    "counts_patients['Total_cells'] = counts_patients.sum(axis=1)\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules par patient dans un fichier CSV\n",
+    "filename_patients = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number_by_patient_ACT.csv\")\n",
+    "counts_patients.to_csv(filename_patients, index=False)\n",
+    "counts_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c15c1ebe-0fa9-45a3-b753-e7370a75e10d",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Patient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3981</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.371517</td>\n",
+       "      <td>2.585139</td>\n",
+       "      <td>1.547988</td>\n",
+       "      <td>0.077399</td>\n",
+       "      <td>0.433437</td>\n",
+       "      <td>61.625387</td>\n",
+       "      <td>6.006192</td>\n",
+       "      <td>8.204334</td>\n",
+       "      <td>5.185759</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1449</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.452026</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.159915</td>\n",
+       "      <td>0.053305</td>\n",
+       "      <td>1.599147</td>\n",
+       "      <td>77.238806</td>\n",
+       "      <td>1.599147</td>\n",
+       "      <td>3.891258</td>\n",
+       "      <td>6.982942</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1896</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.659341</td>\n",
+       "      <td>0.131868</td>\n",
+       "      <td>0.043956</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.054945</td>\n",
+       "      <td>83.340659</td>\n",
+       "      <td>3.560440</td>\n",
+       "      <td>7.604396</td>\n",
+       "      <td>2.637363</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>3564</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630749</td>\n",
+       "      <td>0.131406</td>\n",
+       "      <td>0.026281</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.998686</td>\n",
+       "      <td>93.666229</td>\n",
+       "      <td>0.473062</td>\n",
+       "      <td>2.391590</td>\n",
+       "      <td>0.420499</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>2157</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.387218</td>\n",
+       "      <td>0.219298</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.031328</td>\n",
+       "      <td>2.443609</td>\n",
+       "      <td>67.575188</td>\n",
+       "      <td>0.532581</td>\n",
+       "      <td>4.104010</td>\n",
+       "      <td>3.665414</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>424</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>54.545455</td>\n",
+       "      <td>0.165289</td>\n",
+       "      <td>1.377410</td>\n",
+       "      <td>0.055096</td>\n",
+       "      <td>10.137741</td>\n",
+       "      <td>23.360882</td>\n",
+       "      <td>7.713499</td>\n",
+       "      <td>1.212121</td>\n",
+       "      <td>0.826446</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1854</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.102093</td>\n",
+       "      <td>0.102093</td>\n",
+       "      <td>0.051046</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.204186</td>\n",
+       "      <td>94.640123</td>\n",
+       "      <td>0.051046</td>\n",
+       "      <td>0.816743</td>\n",
+       "      <td>0.153139</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>2193</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.379310</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.036298</td>\n",
+       "      <td>2.032668</td>\n",
+       "      <td>79.600726</td>\n",
+       "      <td>1.016334</td>\n",
+       "      <td>5.916515</td>\n",
+       "      <td>3.702359</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.389779</td>\n",
+       "      <td>0.692941</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>2.468601</td>\n",
+       "      <td>85.708099</td>\n",
+       "      <td>4.634041</td>\n",
+       "      <td>0.043309</td>\n",
+       "      <td>1.385881</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>151</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.419355</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>4.838710</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.290323</td>\n",
+       "      <td>48.709677</td>\n",
+       "      <td>29.354839</td>\n",
+       "      <td>6.451613</td>\n",
+       "      <td>0.967742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>139 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dc    b   tcd4   tcd8     m1     m2  treg  immune_other  cancer  \\\n",
+       "10    2.0  0.0  900.0   24.0  167.0  100.0   5.0          28.0    3981   \n",
+       "100  21.0  0.0   92.0   46.0    0.0    3.0   1.0          30.0    1449   \n",
+       "101   2.0  0.0   20.0   15.0    3.0    1.0   0.0          24.0    1896   \n",
+       "102   1.0  2.0   45.0   24.0    5.0    1.0   0.0          38.0    3564   \n",
+       "103  57.0  0.0   72.0  555.0    7.0    0.0   1.0          78.0    2157   \n",
+       "..    ...  ...    ...    ...    ...    ...   ...           ...     ...   \n",
+       "92    0.0  0.0   11.0  990.0    3.0   25.0   1.0         184.0     424   \n",
+       "96    0.0  0.0   76.0    2.0    2.0    1.0   0.0           4.0    1854   \n",
+       "97    0.0  0.0  174.0   38.0    0.0    0.0   1.0          56.0    2193   \n",
+       "60    0.0  0.0    0.0  107.0    9.0   16.0   1.0          57.0    1979   \n",
+       "62    0.0  0.0    0.0   23.0    3.0   15.0   0.0           4.0     151   \n",
+       "\n",
+       "     αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "10        388.0  ...   0.371517  2.585139  1.547988   0.077399   \n",
+       "100        30.0  ...   2.452026  0.000000  0.159915   0.053305   \n",
+       "101        81.0  ...   0.659341  0.131868  0.043956   0.000000   \n",
+       "102        18.0  ...   0.630749  0.131406  0.026281   0.000000   \n",
+       "103        17.0  ...  17.387218  0.219298  0.000000   0.031328   \n",
+       "..          ...  ...        ...       ...       ...        ...   \n",
+       "92        140.0  ...  54.545455  0.165289  1.377410   0.055096   \n",
+       "96          1.0  ...   0.102093  0.102093  0.051046   0.000000   \n",
+       "97         28.0  ...   1.379310  0.000000  0.000000   0.036298   \n",
+       "60        107.0  ...   4.634041  0.389779  0.692941   0.043309   \n",
+       "62         91.0  ...   7.419355  0.967742  4.838710   0.000000   \n",
+       "\n",
+       "     immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "10            0.433437    61.625387         6.006192           8.204334   \n",
+       "100           1.599147    77.238806         1.599147           3.891258   \n",
+       "101           1.054945    83.340659         3.560440           7.604396   \n",
+       "102           0.998686    93.666229         0.473062           2.391590   \n",
+       "103           2.443609    67.575188         0.532581           4.104010   \n",
+       "..                 ...          ...              ...                ...   \n",
+       "92           10.137741    23.360882         7.713499           1.212121   \n",
+       "96            0.204186    94.640123         0.051046           0.816743   \n",
+       "97            2.032668    79.600726         1.016334           5.916515   \n",
+       "60            2.468601    85.708099         4.634041           0.043309   \n",
+       "62            1.290323    48.709677        29.354839           6.451613   \n",
+       "\n",
+       "     endothelial_perc  Patient  \n",
+       "10           5.185759       10  \n",
+       "100          6.982942      100  \n",
+       "101          2.637363      101  \n",
+       "102          0.420499      102  \n",
+       "103          3.665414      103  \n",
+       "..                ...      ...  \n",
+       "92           0.826446       92  \n",
+       "96           0.153139       96  \n",
+       "97           3.702359       97  \n",
+       "60           1.385881       60  \n",
+       "62           0.967742       62  \n",
+       "\n",
+       "[139 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules par patient\n",
+    "counts_perc_patients = counts_patients.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules, en excluant la colonne 'total_cells'\n",
+    "for col in counts_perc_patients.columns:\n",
+    "    if col != 'Total_cells':\n",
+    "        counts_perc_patients[col + '_perc'] = (counts_perc_patients[col] / counts_perc_patients['Total_cells']) * 100\n",
+    "\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules par patient\n",
+    "counts_perc_patients['Patient'] = counts_perc_patients.index\n",
+    "counts_perc_patients.columns.values\n",
+    "counts_perc_patients\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "1ab4d7e0-5666-4692-b256-16606b279700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts_perc_patients_ACT = counts_perc_patients"
+   ]
+  },
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+   "execution_count": 66,
+   "id": "4b3e2548-257e-40dc-8c9f-78f3daf28404",
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
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+        },
+        "title": {
+         "text": "Cell subtypes proportions by Patient and tissue type - ACT group"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          138.5
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+         "autorange": true,
+         "linecolor": "black",
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+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'Cell subtypes proportions by Patient and tissue type - ACT group'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc_patients['Patient'],\n",
+    "            y=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc_patients[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    title=title,\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black'))\n",
+    "\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7599e22c-8a7b-4c97-915d-203fd83a899a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58d1fe75-c053-4b11-97f9-1e707e79b7c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "736dd86d-e585-487f-8a67-9c61de47ca32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistique de test de Mann-Whitney : 2845.0\n",
+      "P-value associée : 0.44739619874878456\n",
+      "Il n'y a pas de différence statistiquement significative entre les groupes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import mannwhitneyu\n",
+    "\n",
+    "\n",
+    "# Choisissez la colonne sur laquelle vous voulez effectuer la comparaison\n",
+    "column_to_compare = 'm2'\n",
+    "\n",
+    "# Effectuez le test de Mann-Whitney\n",
+    "statistic, p_value = mannwhitneyu(counts_perc_patients_NACT[column_to_compare], counts_perc_patients_ACT[column_to_compare])\n",
+    "\n",
+    "# Affichez les résultats\n",
+    "print(\"Statistique de test de Mann-Whitney :\", statistic)\n",
+    "print(\"P-value associée :\", p_value)\n",
+    "\n",
+    "# Interprétez les résultats\n",
+    "alpha = 0.05\n",
+    "if p_value < alpha:\n",
+    "    print(\"La différence entre les groupes est statistiquement significative.\")\n",
+    "else:\n",
+    "    print(\"Il n'y a pas de différence statistiquement significative entre les groupes.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "fed49f2c-d2ea-450a-a7a1-c47acc8f5ebf",
+   "metadata": {},
+   "outputs": [
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+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+            "linecolor": "white",
+            "ticks": ""
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+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+           "x": 0.05
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+          "xaxis": {
+           "automargin": true,
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+         "text": "Comparaison de la distribution de 'm2' entre les groupes NACT et ACT"
+        },
+        "xaxis": {
+         "autorange": true,
+         "range": [
+          -0.5,
+          1.5
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+         "autorange": true,
+         "range": [
+          -12.07180287434527,
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+         "title": {
+          "text": "Valeurs de 'm2'"
+         },
+         "type": "linear"
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+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "# Données à comparer\n",
+    "data1 = counts_perc_patients_NACT[column_to_compare]\n",
+    "data2 = counts_perc_patients_ACT[column_to_compare]\n",
+    "\n",
+    "# Créer un graphique boxplot\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Ajouter les données de chaque groupe au graphique\n",
+    "fig.add_trace(go.Violin(y=data1, name='NACT', box_visible=True, meanline_visible=True))\n",
+    "fig.add_trace(go.Violin(y=data2, name='ACT', box_visible=True, meanline_visible=True))\n",
+    "\n",
+    "# Mettre en forme le titre et les axes\n",
+    "fig.update_layout(\n",
+    "    title=\"Comparaison de la distribution de '{}' entre les groupes NACT et ACT\".format(column_to_compare),\n",
+    "    xaxis_title=\"Groupe\",\n",
+    "    yaxis_title=\"Valeurs de '{}'\".format(column_to_compare)\n",
+    ")\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2c60153-a937-472b-b427-fb27ebca41c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c2baa6b-beff-4c73-a09e-c44b1fc7a75d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "ad91909f-ece5-4aef-aac2-3b21a056b531",
+   "metadata": {},
+   "source": [
+    "Regarder les proportion d'immune checkpoint par patientes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "4499f228-c73e-47f3-a086-30ed17518e42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patient  immune_checkpoint\n",
+      "104      None                 2461\n",
+      "         PDL1                   20\n",
+      "         PD1                    10\n",
+      "11       None                 6138\n",
+      "         PDL1                   50\n",
+      "                              ... \n",
+      "95       B7H4_PDL1               1\n",
+      "98       None                 2901\n",
+      "         B7H4                  487\n",
+      "         PDL1                  321\n",
+      "         PD1                     1\n",
+      "Name: immune_checkpoint, Length: 155, dtype: int64\n",
+      "Patient  immune_checkpoint\n",
+      "10       None                 6427\n",
+      "         B7H4                   23\n",
+      "         PD1                     5\n",
+      "         PDL1                    4\n",
+      "         B7H4_PD1                1\n",
+      "                              ... \n",
+      "97       B7H4                    2\n",
+      "99       None                 1064\n",
+      "         B7H4                  182\n",
+      "         PD1                    13\n",
+      "         PDL1                    6\n",
+      "Name: immune_checkpoint, Length: 583, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compter le nombre d'occurrences des checkpoints immunitaires par patiente\n",
+    "occurrences_checkpoint_NACT = df_NACT.groupby('Patient')['immune_checkpoint'].value_counts()\n",
+    "occurrences_checkpoint_ACT = df_ACT.groupby('Patient')['immune_checkpoint'].value_counts()\n",
+    "\n",
+    "# Afficher le résultat\n",
+    "print(occurrences_checkpoint_NACT)\n",
+    "print(occurrences_checkpoint_ACT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "e0956b08-eb10-4f0b-94b7-1993184a6051",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistique de test de chi-deux : 609887.0\n",
+      "P-valeur associée : 0.0\n",
+      "Il y a une différence statistiquement significative entre les distributions des checkpoints immunitaires dans les deux groupes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import chi2_contingency\n",
+    "\n",
+    "# Créer un tableau de contingence à partir des occurrences de checkpoints immunitaires dans chaque tableau\n",
+    "contingency_table = pd.concat([occurrences_checkpoint_NACT, occurrences_checkpoint_ACT], axis=1, keys=['NACT', 'ACT'], sort=False).fillna(0)\n",
+    "\n",
+    "# Effectuer le test de chi-deux\n",
+    "chi2_stat, p_val, _, _ = chi2_contingency(contingency_table)\n",
+    "\n",
+    "# Afficher les résultats\n",
+    "print(\"Statistique de test de chi-deux :\", chi2_stat)\n",
+    "print(\"P-valeur associée :\", p_val)\n",
+    "\n",
+    "# Interpréter les résultats\n",
+    "alpha = 0.05\n",
+    "if p_val < alpha:\n",
+    "    print(\"Il y a une différence statistiquement significative entre les distributions des checkpoints immunitaires dans les deux groupes.\")\n",
+    "else:\n",
+    "    print(\"Il n'y a pas de différence statistiquement significative entre les distributions des checkpoints immunitaires dans les deux groupes.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "337ec728-0e6a-40ae-b9b8-2decc402a95c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>NACT</th>\n",
+       "      <th>ACT</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th>immune_checkpoint</th>\n",
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+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PDL1</th>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PD1</th>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
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+       "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
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+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PDL1</th>\n",
+       "      <td>1.315789</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
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+       "      <td>0.014388</td>\n",
+       "    </tr>\n",
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+       "      <th rowspan=\"4\" valign=\"top\">99</th>\n",
+       "      <th>None</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.654676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B7H4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.309353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PD1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.093525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PDL1</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.043165</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>738 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 NACT       ACT\n",
+       "Patient immune_checkpoint                      \n",
+       "104     None                64.763158  0.000000\n",
+       "        PDL1                 0.526316  0.000000\n",
+       "        PD1                  0.263158  0.000000\n",
+       "11      None               161.526316  0.000000\n",
+       "        PDL1                 1.315789  0.000000\n",
+       "...                               ...       ...\n",
+       "97      B7H4                 0.000000  0.014388\n",
+       "99      None                 0.000000  7.654676\n",
+       "        B7H4                 0.000000  1.309353\n",
+       "        PD1                  0.000000  0.093525\n",
+       "        PDL1                 0.000000  0.043165\n",
+       "\n",
+       "[738 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "# Concaténer les occurrences des checkpoints immunitaires dans les deux tableaux\n",
+    "all_occurrences = pd.concat([occurrences_checkpoint_NACT, occurrences_checkpoint_ACT], axis=1, keys=['NACT', 'ACT'], sort=False).fillna(0)\n",
+    "\n",
+    "# Calculer le nombre total de patientes dans chaque groupe\n",
+    "total_patients_NACT = len(df_NACT['Patient'].unique())\n",
+    "total_patients_ACT = len(df_ACT['Patient'].unique())\n",
+    "\n",
+    "# Diviser le nombre d'occurrences de chaque checkpoint immunitaire par le nombre total de patientes dans le groupe correspondant\n",
+    "all_occurrences_normalized = all_occurrences.copy()\n",
+    "all_occurrences_normalized['NACT'] /= total_patients_NACT\n",
+    "all_occurrences_normalized['ACT'] /= total_patients_ACT\n",
+    "\n",
+    "all_occurrences_normalized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "6ec09056-4b7f-4aa5-8a2c-529f99b921a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['62', '63', '59', '60', '33', '35', '36', '37', '38', '30', '25',\n",
+       "       '27', '29', '20', '21', '22', '23', '24', '14', '16', '17', '19',\n",
+       "       '12', '8', '9', '10', '4', '52', '53', '54', '55', '56', '57',\n",
+       "       '50', '42', '43', '44', '45', '47', '39', '41', '49', '46', '48',\n",
+       "       '58', '28', '13', '6', '34', '3', '122', '125', '124', '94', '101',\n",
+       "       '86', '84', '83', '91', '88', '87', '75', '74', '82', '81', '80',\n",
+       "       '79', '67', '66', '65', '64', '73', '71', '69', '68', '123', '115',\n",
+       "       '114', '113', '120', '117', '126', '105', '103', '102', '110',\n",
+       "       '109', '106', '166', '112', '108', '107', '97', '96', '100', '78',\n",
+       "       '77', '70', '72', '99', '92', '90', '136', '135', '134', '133',\n",
+       "       '132', '131', '130', '129', '144', '143', '155', '154', '150',\n",
+       "       '148', '147', '164', '161', '159', '128', '127', '142', '139',\n",
+       "       '152', '158', '156', '172', '170', '168', '167', '165', '181',\n",
+       "       '180', '176', '173', '175', '185', '186'], dtype=object)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ACT['Patient'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "ec75dc89-a6fa-46ad-9bdc-ba218985da88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "name": "NACT",
+         "type": "bar",
+         "x": [
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+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
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+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
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+           },
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+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Comparaison des proportions de checkpoints immunitaires entre les groupes NACT et ACT"
+        },
+        "xaxis": {
+         "autorange": true,
+         "range": [
+          -0.5,
+          7.5
+         ],
+         "tickangle": -45,
+         "title": {
+          "text": "Checkpoint Immunitaire"
+         },
+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "range": [
+          0,
+          3360.8178720181763
+         ],
+         "title": {
+          "text": "Proportion d'Occurrences"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "# Calculer le nombre total de patientes dans chaque groupe\n",
+    "total_patients_NACT = len(df_NACT['Patient'].unique())\n",
+    "total_patients_ACT = len(df_ACT['Patient'].unique())\n",
+    "\n",
+    "# Diviser le nombre d'occurrences de chaque checkpoint immunitaire par le nombre total de patientes dans le groupe correspondant\n",
+    "all_occurrences_normalized = all_occurrences.copy()\n",
+    "all_occurrences_normalized['NACT'] /= total_patients_NACT\n",
+    "all_occurrences_normalized['ACT'] /= total_patients_ACT\n",
+    "\n",
+    "# Créer un objet Figure de Plotly\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Ajouter les barres pour les proportions normalisées de chaque checkpoint immunitaire dans les deux groupes\n",
+    "for col in all_occurrences_normalized.columns:\n",
+    "    fig.add_trace(go.Bar(x=all_occurrences_normalized.index.get_level_values('immune_checkpoint'), y=all_occurrences_normalized[col], name=col))\n",
+    "\n",
+    "# Mettre en forme le titre et les axes\n",
+    "fig.update_layout(\n",
+    "    title=\"Comparaison des proportions de checkpoints immunitaires entre les groupes NACT et ACT\",\n",
+    "    xaxis_title=\"Checkpoint Immunitaire\",\n",
+    "    yaxis_title=\"Proportion d'Occurrences\",\n",
+    "    xaxis_tickangle=-45,\n",
+    "    legend_title=\"Groupe\"\n",
+    ")\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12a2c3e6-c90f-4a78-b19e-607fbb3219ab",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "935d8f96-9104-4778-b218-07291b128729",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a841c9f8-3cfb-4a34-a99e-e14808dbde71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93837651-60b5-4749-b759-7210fb35c925",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc0bcd77-4132-4c6b-b179-033f06d6b71b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef825078-cabe-4d97-b0f4-f0e64ba1f91d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c4ad36-55cc-4402-8553-aaad40f129e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a796f99-a904-4a3c-870f-d7ff95593de5",
+   "metadata": {},
+   "source": [
+    "### V.4.3. IMMUNE CHECKPOINT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d172bdd-3ddd-4881-9d96-be3813e61572",
+   "metadata": {},
+   "source": [
+    "#### V.4.3.1. NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "34ba6aef-4cee-4a56-9982-867439fae8bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>count</th>\n",
+       "      <th>percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4</td>\n",
+       "      <td>10827</td>\n",
+       "      <td>16.317273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4_PD1</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.210993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4_PDL1</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.028635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>None</td>\n",
+       "      <td>52778</td>\n",
+       "      <td>79.541242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>PD1</td>\n",
+       "      <td>1553</td>\n",
+       "      <td>2.340512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>B7H4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.101634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>B7H4_PD1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.007818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>None</td>\n",
+       "      <td>12746</td>\n",
+       "      <td>99.648190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>PD1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.117270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.125088</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>64 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cell_type cell_subtype immune_checkpoint  count  percentage\n",
+       "0     CANCER       CANCER              B7H4  10827   16.317273\n",
+       "1     CANCER       CANCER          B7H4_PD1    140    0.210993\n",
+       "2     CANCER       CANCER         B7H4_PDL1     19    0.028635\n",
+       "3     CANCER       CANCER              None  52778   79.541242\n",
+       "4     CANCER       CANCER               PD1   1553    2.340512\n",
+       "..       ...          ...               ...    ...         ...\n",
+       "59    STROMA   αSMA_myCAF              B7H4     13    0.101634\n",
+       "60    STROMA   αSMA_myCAF          B7H4_PD1      1    0.007818\n",
+       "61    STROMA   αSMA_myCAF              None  12746   99.648190\n",
+       "62    STROMA   αSMA_myCAF               PD1     15    0.117270\n",
+       "63    STROMA   αSMA_myCAF              PDL1     16    0.125088\n",
+       "\n",
+       "[64 rows x 5 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count of each immune_checkpoint type by cell_subtype\n",
+    "counts_NACT = df_NACT.groupby(['cell_type', 'cell_subtype', 'immune_checkpoint']).size().reset_index(name='count')\n",
+    "\n",
+    "# % for each cell_subtype\n",
+    "counts_NACT['percentage'] = counts_NACT.groupby('cell_subtype')['count'].apply(lambda x: (x / x.sum()) * 100)\n",
+    "\n",
+    "display(counts_NACT)"
+   ]
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+          "xaxis": {
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+        "title": {
+         "text": "Percentage of Each Immune Checkpoint Type by Cell Subtype in NACT"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "autorange": true,
+         "domain": [
+          0,
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+          "text": "Percentage"
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+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = px.bar(counts_NACT, x='immune_checkpoint', y='percentage', color='cell_type', \n",
+    "             title='Percentage of Each Immune Checkpoint Type by Cell Subtype in NACT',\n",
+    "             labels={'immune_checkpoint': 'Immune Checkpoint', 'percentage': 'Percentage', 'cell_subtype': 'Cell Subtype'})\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0910a1d1-84bc-4df2-a490-b3c43e747939",
+   "metadata": {},
+   "source": [
+    "#### V.4.3.2. ACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "07a58e22-600b-4f32-a3b1-ad82ba09988a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   cell_type  cell_subtype immune_checkpoint   count  percentage\n",
+      "0     CANCER        CANCER              B7H4   32646    9.083725\n",
+      "1     CANCER        CANCER          B7H4_PD1     425    0.118256\n",
+      "2     CANCER        CANCER         B7H4_PDL1      84    0.023373\n",
+      "3     CANCER        CANCER              None  314226   87.433151\n",
+      "4     CANCER        CANCER               PD1    7532    2.095773\n",
+      "..       ...           ...               ...     ...         ...\n",
+      "66    STROMA  STROMA_OTHER              PDL1     160    0.332226\n",
+      "67    STROMA    αSMA_myCAF              B7H4      51    0.185266\n",
+      "68    STROMA    αSMA_myCAF              None   27200   98.808486\n",
+      "69    STROMA    αSMA_myCAF               PD1     256    0.929962\n",
+      "70    STROMA    αSMA_myCAF              PDL1      21    0.076286\n",
+      "\n",
+      "[71 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count of each immune_checkpoint type by cell_subtype\n",
+    "counts_ACT = df_ACT.groupby(['cell_type', 'cell_subtype', 'immune_checkpoint']).size().reset_index(name='count')\n",
+    "\n",
+    "# % for each cell_subtype\n",
+    "counts_ACT['percentage'] = counts_ACT.groupby('cell_subtype')['count'].apply(lambda x: (x / x.sum()) * 100)\n",
+    "\n",
+    "print(counts_ACT)"
+   ]
+  },
+  {
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+   "execution_count": 77,
+   "id": "c26c0ee1-0b90-48f4-9959-9d5ae9d5467a",
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+    "# Créer le graphique à barres avec Plotly Express\n",
+    "fig = px.bar(counts_ACT, x='immune_checkpoint', y='percentage', color='cell_type', \n",
+    "             title='Percentage of Each Immune Checkpoint Type by Cell Subtype in ACT',\n",
+    "             labels={'immune_checkpoint': 'Immune Checkpoint', 'percentage': 'Percentage', 'cell_subtype': 'Cell Subtype'})\n",
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+    "# Concaténer les deux DataFrames\n",
+    "combined_counts = pd.concat([counts_NACT, counts_ACT])\n",
+    "\n",
+    "# Ajouter une colonne pour distinguer les deux ensembles de données\n",
+    "combined_counts['dataset'] = ['NACT'] * len(counts_NACT) + ['ACT'] * len(counts_ACT)\n",
+    "\n",
+    "# Créer le graphique à barres avec Plotly Express\n",
+    "fig = px.bar(combined_counts, x='immune_checkpoint', y='percentage', color='cell_subtype', \n",
+    "             facet_col='dataset', facet_col_wrap=1,\n",
+    "             title='Percentage of Each Immune Checkpoint Type by Cell Subtype',\n",
+    "             labels={'immune_checkpoint': 'Immune Checkpoint', 'percentage': 'Percentage', 'cell_subtype': 'Cell Subtype', 'dataset': 'Dataset'})\n",
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+        },
+        "title": {
+         "text": "Percentage of Each Immune Checkpoint Type by Cell Subtype"
+        },
+        "xaxis": {
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+         "domain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Concaténer les deux DataFrames\n",
+    "combined_counts = pd.concat([counts_NACT, counts_ACT])\n",
+    "\n",
+    "# Ajouter une colonne pour distinguer les deux ensembles de données\n",
+    "combined_counts['dataset'] = ['NACT'] * len(counts_NACT) + ['ACT'] * len(counts_ACT)\n",
+    "\n",
+    "# Créer le graphique à barres groupées avec Plotly Express\n",
+    "fig = px.bar(combined_counts, x='immune_checkpoint', y='percentage', color='cell_subtype', \n",
+    "             facet_col='dataset', facet_col_wrap=1,\n",
+    "             barmode='group',\n",
+    "             title='Percentage of Each Immune Checkpoint Type by Cell Subtype',\n",
+    "             labels={'immune_checkpoint': 'Immune Checkpoint', 'percentage': 'Percentage', 'cell_subtype': 'Cell Subtype', 'dataset': 'Dataset'})\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "ba1d34e1-ad9d-4d49-a1dd-b31ba1e1348a",
+   "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>cell_type</th>\n",
+       "      <th>cell_subtype</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>count</th>\n",
+       "      <th>percentage</th>\n",
+       "      <th>dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4</td>\n",
+       "      <td>10827</td>\n",
+       "      <td>16.317273</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4_PD1</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.210993</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>B7H4_PDL1</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.028635</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>None</td>\n",
+       "      <td>52778</td>\n",
+       "      <td>79.541242</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>CANCER</td>\n",
+       "      <td>PD1</td>\n",
+       "      <td>1553</td>\n",
+       "      <td>2.340512</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>STROMA_OTHER</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>160</td>\n",
+       "      <td>0.332226</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>B7H4</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0.185266</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>None</td>\n",
+       "      <td>27200</td>\n",
+       "      <td>98.808486</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>PD1</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.929962</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>STROMA</td>\n",
+       "      <td>αSMA_myCAF</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.076286</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>135 rows × 6 columns</p>\n",
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+      ],
+      "text/plain": [
+       "   cell_type  cell_subtype immune_checkpoint  count  percentage dataset\n",
+       "0     CANCER        CANCER              B7H4  10827   16.317273    NACT\n",
+       "1     CANCER        CANCER          B7H4_PD1    140    0.210993    NACT\n",
+       "2     CANCER        CANCER         B7H4_PDL1     19    0.028635    NACT\n",
+       "3     CANCER        CANCER              None  52778   79.541242    NACT\n",
+       "4     CANCER        CANCER               PD1   1553    2.340512    NACT\n",
+       "..       ...           ...               ...    ...         ...     ...\n",
+       "66    STROMA  STROMA_OTHER              PDL1    160    0.332226     ACT\n",
+       "67    STROMA    αSMA_myCAF              B7H4     51    0.185266     ACT\n",
+       "68    STROMA    αSMA_myCAF              None  27200   98.808486     ACT\n",
+       "69    STROMA    αSMA_myCAF               PD1    256    0.929962     ACT\n",
+       "70    STROMA    αSMA_myCAF              PDL1     21    0.076286     ACT\n",
+       "\n",
+       "[135 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fb00374-bd37-4d47-a465-c0ff2b3a308f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "289bc49c-6d36-47f1-82d2-28f12df146ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28838faa-aac4-4f8b-a123-a8006c28c45d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c82387a-1035-4039-9c71-f7871f219961",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10453d0f-75fd-487b-a696-7f57bebed76a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e497d2e-05dd-4d40-94e0-d3b50906f90e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a60caf94-300d-4d5f-8111-d2bcd6437fe9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8d0c3dc-d5db-452d-af17-ed5fe40c1d61",
+   "metadata": {},
+   "source": [
+    "### V.4.4. NACT VS ACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "7688f4a2-419f-44a9-b7f3-b22544a32e00",
+   "metadata": {
+    "tags": []
+   },
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>551</td>\n",
+       "      <td>539</td>\n",
+       "      <td>73</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8186</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>2380</td>\n",
+       "      <td>283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>728</td>\n",
+       "      <td>69</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>258</td>\n",
+       "      <td>13963</td>\n",
+       "      <td>2334</td>\n",
+       "      <td>5066</td>\n",
+       "      <td>321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1087</td>\n",
+       "      <td>211</td>\n",
+       "      <td>26</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>531</td>\n",
+       "      <td>8862</td>\n",
+       "      <td>274</td>\n",
+       "      <td>843</td>\n",
+       "      <td>778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>807</td>\n",
+       "      <td>47</td>\n",
+       "      <td>78</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>161</td>\n",
+       "      <td>6999</td>\n",
+       "      <td>334</td>\n",
+       "      <td>619</td>\n",
+       "      <td>268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>373</td>\n",
+       "      <td>144</td>\n",
+       "      <td>55</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>917</td>\n",
+       "      <td>5474</td>\n",
+       "      <td>37</td>\n",
+       "      <td>708</td>\n",
+       "      <td>178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>861</td>\n",
+       "      <td>69</td>\n",
+       "      <td>342</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>548</td>\n",
+       "      <td>6529</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>2269</td>\n",
+       "      <td>758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>71.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>413</td>\n",
+       "      <td>63</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>247</td>\n",
+       "      <td>5963</td>\n",
+       "      <td>2673</td>\n",
+       "      <td>3198</td>\n",
+       "      <td>930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>490.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>904</td>\n",
+       "      <td>230</td>\n",
+       "      <td>332</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>312</td>\n",
+       "      <td>8092</td>\n",
+       "      <td>2657</td>\n",
+       "      <td>1428</td>\n",
+       "      <td>581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2285</td>\n",
+       "      <td>1563</td>\n",
+       "      <td>317</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              dc     b  tcd4  tcd8   m1    m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv   11.0   0.0   551   539   73  22.0     1           110    8186   \n",
+       "DD3S3.csv   27.0  44.0  1441   728   69  19.0     2           258   13963   \n",
+       "DD4S1.csv   48.0   3.0  1087   211   26   5.0     5           531    8862   \n",
+       "DD4S2.csv   28.0   1.0   807    47   78  74.0     4           161    6999   \n",
+       "DD4S3.csv   82.0   1.0   373   144   55  64.0     5           917    5474   \n",
+       "DD5S1.csv   55.0   1.0   861    69  342  17.0     7           548    6529   \n",
+       "DD5S2.csv   71.0   0.0   413    63   27   4.0     5           247    5963   \n",
+       "DD5S3.csv  490.0   3.0   904   230  332  69.0     7           312    8092   \n",
+       "DD3S2.csv    0.0   0.0    15     7    2   0.0     1            17    2285   \n",
+       "\n",
+       "           αsma_mycaf  stroma_other  endothelial  \n",
+       "DD3S1.csv        1143          2380          283  \n",
+       "DD3S3.csv        2334          5066          321  \n",
+       "DD4S1.csv         274           843          778  \n",
+       "DD4S2.csv         334           619          268  \n",
+       "DD4S3.csv          37           708          178  \n",
+       "DD5S1.csv        1776          2269          758  \n",
+       "DD5S2.csv        2673          3198          930  \n",
+       "DD5S3.csv        2657          1428          581  \n",
+       "DD3S2.csv        1563           317          119  "
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#######\n",
+    "####### Count by cell SUBtype !! IN NACT !!\n",
+    "#######\n",
+    "cell_subtypes = ['DC', 'B', 'TCD4', 'TCD8', 'M1', 'M2', 'Treg', \\\n",
+    "                 'IMMUNE_OTHER', 'CANCER', 'αSMA_myCAF', 'STROMA_OTHER', 'ENDOTHELIAL']\n",
+    "# Initialisation d'un dictionnaire pour stocker les counts des sous-types de cellules\n",
+    "subtype_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants\n",
+    "for subtype in cell_subtypes:\n",
+    "    subtype_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df_NACT.loc[\n",
+    "            df_NACT['cell_subtype'] == subtype, 'Sample_ID'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_subtypes = pd.concat([pd.DataFrame(v) for v in subtype_counts.values()], axis=1, sort=False)\n",
+    "counts_subtypes = counts_subtypes.fillna(0)\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules dans un fichier CSV\n",
+    "filename_subtypes = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number.csv\")\n",
+    "counts_subtypes.to_csv(filename_subtypes, index=False)\n",
+    "counts_subtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "bbab2efd-bb51-4475-9c3f-75639b9260ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>551</td>\n",
+       "      <td>539</td>\n",
+       "      <td>73</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8186</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.051649</td>\n",
+       "      <td>0.548572</td>\n",
+       "      <td>0.165316</td>\n",
+       "      <td>0.007514</td>\n",
+       "      <td>0.826571</td>\n",
+       "      <td>61.508057</td>\n",
+       "      <td>8.548777</td>\n",
+       "      <td>17.789229</td>\n",
+       "      <td>2.112465</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>728</td>\n",
+       "      <td>69</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>258</td>\n",
+       "      <td>13963</td>\n",
+       "      <td>2334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.998571</td>\n",
+       "      <td>0.284170</td>\n",
+       "      <td>0.078249</td>\n",
+       "      <td>0.008237</td>\n",
+       "      <td>1.062533</td>\n",
+       "      <td>57.501935</td>\n",
+       "      <td>9.589089</td>\n",
+       "      <td>20.805140</td>\n",
+       "      <td>1.317163</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1087</td>\n",
+       "      <td>211</td>\n",
+       "      <td>26</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>531</td>\n",
+       "      <td>8862</td>\n",
+       "      <td>274</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.663778</td>\n",
+       "      <td>0.204988</td>\n",
+       "      <td>0.039420</td>\n",
+       "      <td>0.039420</td>\n",
+       "      <td>4.186401</td>\n",
+       "      <td>69.844899</td>\n",
+       "      <td>2.147679</td>\n",
+       "      <td>6.606528</td>\n",
+       "      <td>6.093973</td>\n",
+       "      <td>DD4S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>807</td>\n",
+       "      <td>47</td>\n",
+       "      <td>78</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>161</td>\n",
+       "      <td>6999</td>\n",
+       "      <td>334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.498469</td>\n",
+       "      <td>0.827202</td>\n",
+       "      <td>0.784713</td>\n",
+       "      <td>0.042413</td>\n",
+       "      <td>1.707131</td>\n",
+       "      <td>74.199057</td>\n",
+       "      <td>3.513225</td>\n",
+       "      <td>6.508632</td>\n",
+       "      <td>2.816027</td>\n",
+       "      <td>DD4S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>373</td>\n",
+       "      <td>144</td>\n",
+       "      <td>55</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>917</td>\n",
+       "      <td>5474</td>\n",
+       "      <td>37</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.790227</td>\n",
+       "      <td>0.683615</td>\n",
+       "      <td>0.795412</td>\n",
+       "      <td>0.062135</td>\n",
+       "      <td>11.395546</td>\n",
+       "      <td>67.929123</td>\n",
+       "      <td>0.455310</td>\n",
+       "      <td>8.711933</td>\n",
+       "      <td>2.187943</td>\n",
+       "      <td>DD4S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>861</td>\n",
+       "      <td>69</td>\n",
+       "      <td>342</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>548</td>\n",
+       "      <td>6529</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.521190</td>\n",
+       "      <td>2.583189</td>\n",
+       "      <td>0.128379</td>\n",
+       "      <td>0.052861</td>\n",
+       "      <td>4.138280</td>\n",
+       "      <td>49.289035</td>\n",
+       "      <td>13.357759</td>\n",
+       "      <td>17.048613</td>\n",
+       "      <td>5.688107</td>\n",
+       "      <td>DD5S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>71.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>413</td>\n",
+       "      <td>63</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>247</td>\n",
+       "      <td>5963</td>\n",
+       "      <td>2673</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.463318</td>\n",
+       "      <td>0.198558</td>\n",
+       "      <td>0.029416</td>\n",
+       "      <td>0.036769</td>\n",
+       "      <td>1.816405</td>\n",
+       "      <td>43.845251</td>\n",
+       "      <td>19.591101</td>\n",
+       "      <td>23.405353</td>\n",
+       "      <td>6.794795</td>\n",
+       "      <td>DD5S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>490.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>904</td>\n",
+       "      <td>230</td>\n",
+       "      <td>332</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>312</td>\n",
+       "      <td>8092</td>\n",
+       "      <td>2657</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.521743</td>\n",
+       "      <td>2.196382</td>\n",
+       "      <td>0.456411</td>\n",
+       "      <td>0.046301</td>\n",
+       "      <td>2.063701</td>\n",
+       "      <td>53.516636</td>\n",
+       "      <td>17.510159</td>\n",
+       "      <td>9.399957</td>\n",
+       "      <td>3.822127</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2285</td>\n",
+       "      <td>1563</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.161799</td>\n",
+       "      <td>0.046227</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.023113</td>\n",
+       "      <td>0.392920</td>\n",
+       "      <td>52.808306</td>\n",
+       "      <td>35.686731</td>\n",
+       "      <td>7.179310</td>\n",
+       "      <td>2.690697</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              dc     b  tcd4  tcd8   m1    m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv   11.0   0.0   551   539   73  22.0     1           110    8186   \n",
+       "DD3S3.csv   27.0  44.0  1441   728   69  19.0     2           258   13963   \n",
+       "DD4S1.csv   48.0   3.0  1087   211   26   5.0     5           531    8862   \n",
+       "DD4S2.csv   28.0   1.0   807    47   78  74.0     4           161    6999   \n",
+       "DD4S3.csv   82.0   1.0   373   144   55  64.0     5           917    5474   \n",
+       "DD5S1.csv   55.0   1.0   861    69  342  17.0     7           548    6529   \n",
+       "DD5S2.csv   71.0   0.0   413    63   27   4.0     5           247    5963   \n",
+       "DD5S3.csv  490.0   3.0   904   230  332  69.0     7           312    8092   \n",
+       "DD3S2.csv    0.0   0.0    15     7    2   0.0     1            17    2285   \n",
+       "\n",
+       "           αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "DD3S1.csv        1143  ...   4.051649  0.548572  0.165316   0.007514   \n",
+       "DD3S3.csv        2334  ...   2.998571  0.284170  0.078249   0.008237   \n",
+       "DD4S1.csv         274  ...   1.663778  0.204988  0.039420   0.039420   \n",
+       "DD4S2.csv         334  ...   0.498469  0.827202  0.784713   0.042413   \n",
+       "DD4S3.csv          37  ...   1.790227  0.683615  0.795412   0.062135   \n",
+       "DD5S1.csv        1776  ...   0.521190  2.583189  0.128379   0.052861   \n",
+       "DD5S2.csv        2673  ...   0.463318  0.198558  0.029416   0.036769   \n",
+       "DD5S3.csv        2657  ...   1.521743  2.196382  0.456411   0.046301   \n",
+       "DD3S2.csv        1563  ...   0.161799  0.046227  0.000000   0.023113   \n",
+       "\n",
+       "           immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "DD3S1.csv           0.826571    61.508057         8.548777          17.789229   \n",
+       "DD3S3.csv           1.062533    57.501935         9.589089          20.805140   \n",
+       "DD4S1.csv           4.186401    69.844899         2.147679           6.606528   \n",
+       "DD4S2.csv           1.707131    74.199057         3.513225           6.508632   \n",
+       "DD4S3.csv          11.395546    67.929123         0.455310           8.711933   \n",
+       "DD5S1.csv           4.138280    49.289035        13.357759          17.048613   \n",
+       "DD5S2.csv           1.816405    43.845251        19.591101          23.405353   \n",
+       "DD5S3.csv           2.063701    53.516636        17.510159           9.399957   \n",
+       "DD3S2.csv           0.392920    52.808306        35.686731           7.179310   \n",
+       "\n",
+       "           endothelial_perc  Sample_ID  \n",
+       "DD3S1.csv          2.112465  DD3S1.csv  \n",
+       "DD3S3.csv          1.317163  DD3S3.csv  \n",
+       "DD4S1.csv          6.093973  DD4S1.csv  \n",
+       "DD4S2.csv          2.816027  DD4S2.csv  \n",
+       "DD4S3.csv          2.187943  DD4S3.csv  \n",
+       "DD5S1.csv          5.688107  DD5S1.csv  \n",
+       "DD5S2.csv          6.794795  DD5S2.csv  \n",
+       "DD5S3.csv          3.822127  DD5S3.csv  \n",
+       "DD3S2.csv          2.690697  DD3S2.csv  \n",
+       "\n",
+       "[9 rows x 25 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules\n",
+    "counts_perc = counts_subtypes.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules\n",
+    "for col in counts_perc.columns:\n",
+    "    counts_perc[col + '_perc'] = (counts_perc[col] / counts_perc.sum(axis=1)) * 100\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules\n",
+    "\n",
+    "counts_perc['Sample_ID'] = counts_perc.index\n",
+    "counts_perc.columns.values\n",
+    "display(counts_perc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "dfd8e431-e4ae-414b-ab2c-a8045567977f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
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+            "ticks": "",
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+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
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+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "NACT_patients_Cell subtypes proportions by Sample ID and tissue type"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
+          8.5
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+         "type": "category"
+        },
+        "yaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          0,
+          105.1309633674881
+         ],
+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'NACT_patients_Cell subtypes proportions by Sample ID and tissue type'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc['Sample_ID'],\n",
+    "            y=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    title=title,\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black')\n",
+    ")\n",
+    "\n",
+    "# Enregistrer l'image\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "ea307a4d-0bfa-41e3-b3e3-feee3254e12d",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>stroma_other</th>\n",
+       "      <th>endothelial</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>237</td>\n",
+       "      <td>107</td>\n",
+       "      <td>5</td>\n",
+       "      <td>884</td>\n",
+       "      <td>40795</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>4343</td>\n",
+       "      <td>1383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1900</td>\n",
+       "      <td>2343</td>\n",
+       "      <td>92</td>\n",
+       "      <td>25</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1244</td>\n",
+       "      <td>48774</td>\n",
+       "      <td>3226</td>\n",
+       "      <td>6958</td>\n",
+       "      <td>1631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>253</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>5416</td>\n",
+       "      <td>2822</td>\n",
+       "      <td>183</td>\n",
+       "      <td>77</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>63037</td>\n",
+       "      <td>5090</td>\n",
+       "      <td>10922</td>\n",
+       "      <td>2457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>224</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>1739</td>\n",
+       "      <td>50</td>\n",
+       "      <td>33</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1858</td>\n",
+       "      <td>45151</td>\n",
+       "      <td>2832</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>1275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>102</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>1647</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28</td>\n",
+       "      <td>15</td>\n",
+       "      <td>680</td>\n",
+       "      <td>32471</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>3910</td>\n",
+       "      <td>964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>1068</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>2796</td>\n",
+       "      <td>100</td>\n",
+       "      <td>264</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2419</td>\n",
+       "      <td>45124</td>\n",
+       "      <td>3649</td>\n",
+       "      <td>4122</td>\n",
+       "      <td>1195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>528</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>467</td>\n",
+       "      <td>162</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>672</td>\n",
+       "      <td>37194</td>\n",
+       "      <td>5200</td>\n",
+       "      <td>7822</td>\n",
+       "      <td>2037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>89</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>722</td>\n",
+       "      <td>82</td>\n",
+       "      <td>104</td>\n",
+       "      <td>59</td>\n",
+       "      <td>9</td>\n",
+       "      <td>220</td>\n",
+       "      <td>24876</td>\n",
+       "      <td>1203</td>\n",
+       "      <td>4024</td>\n",
+       "      <td>405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>977</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1096</td>\n",
+       "      <td>457</td>\n",
+       "      <td>147</td>\n",
+       "      <td>41</td>\n",
+       "      <td>6</td>\n",
+       "      <td>553</td>\n",
+       "      <td>21968</td>\n",
+       "      <td>2174</td>\n",
+       "      <td>2759</td>\n",
+       "      <td>583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             dc      b  tcd4  tcd8   m1   m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv    70    0.0  2440  2310  237  107     5           884   40795   \n",
+       "DD3S2.csv   294    0.0  1900  2343   92   25    37          1244   48774   \n",
+       "DD3S3.csv   253   94.0  5416  2822  183   77    26          1616   63037   \n",
+       "DD4S1.csv   224   26.0  1580  1739   50   33     7          1858   45151   \n",
+       "DD4S2.csv   102   13.0  1131  1647   34   28    15           680   32471   \n",
+       "DD4S3.csv  1068   30.0  1980  2796  100  264    33          2419   45124   \n",
+       "DD5S1.csv   528  138.0  1979   467  162   11    21           672   37194   \n",
+       "DD5S2.csv    89   16.0   722    82  104   59     9           220   24876   \n",
+       "DD5S3.csv   977   32.0  1096   457  147   41     6           553   21968   \n",
+       "\n",
+       "           αsma_mycaf  stroma_other  endothelial  \n",
+       "DD3S1.csv        2824          4343         1383  \n",
+       "DD3S2.csv        3226          6958         1631  \n",
+       "DD3S3.csv        5090         10922         2457  \n",
+       "DD4S1.csv        2832          3300         1275  \n",
+       "DD4S2.csv        1330          3910          964  \n",
+       "DD4S3.csv        3649          4122         1195  \n",
+       "DD5S1.csv        5200          7822         2037  \n",
+       "DD5S2.csv        1203          4024          405  \n",
+       "DD5S3.csv        2174          2759          583  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#######\n",
+    "####### Count by cell SUBtype !! IN ACT !!\n",
+    "#######\n",
+    "cell_subtypes = ['DC', 'B', 'TCD4', 'TCD8', 'M1', 'M2', 'Treg', \\\n",
+    "                 'IMMUNE_OTHER', 'CANCER', 'αSMA_myCAF', 'STROMA_OTHER', 'ENDOTHELIAL']\n",
+    "# Initialisation d'un dictionnaire pour stocker les counts des sous-types de cellules\n",
+    "subtype_counts = {}\n",
+    "\n",
+    "# Boucle sur les sous-types de cellules pour compter les échantillons correspondants\n",
+    "for subtype in cell_subtypes:\n",
+    "    subtype_counts[subtype.lower()] = pd.DataFrame({subtype.lower():\n",
+    "        df_ACT.loc[\n",
+    "            df_ACT['cell_subtype'] == subtype, 'Sample_ID'].value_counts()\n",
+    "    }).sort_index()\n",
+    "\n",
+    "# Concaténation des counts des sous-types de cellules en un seul DataFrame\n",
+    "counts_subtypes = pd.concat([pd.DataFrame(v) for v in subtype_counts.values()], axis=1, sort=False)\n",
+    "counts_subtypes = counts_subtypes.fillna(0)\n",
+    "\n",
+    "# Enregistrement des counts des sous-types de cellules dans un fichier CSV\n",
+    "filename_subtypes = os.path.join(output_data_dir, project_name + \"_cell_subtypes_number.csv\")\n",
+    "counts_subtypes.to_csv(filename_subtypes, index=False)\n",
+    "counts_subtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "41dcf9da-7ae4-49e7-9062-1c762dbc9b9e",
+   "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>dc</th>\n",
+       "      <th>b</th>\n",
+       "      <th>tcd4</th>\n",
+       "      <th>tcd8</th>\n",
+       "      <th>m1</th>\n",
+       "      <th>m2</th>\n",
+       "      <th>treg</th>\n",
+       "      <th>immune_other</th>\n",
+       "      <th>cancer</th>\n",
+       "      <th>αsma_mycaf</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tcd8_perc</th>\n",
+       "      <th>m1_perc</th>\n",
+       "      <th>m2_perc</th>\n",
+       "      <th>treg_perc</th>\n",
+       "      <th>immune_other_perc</th>\n",
+       "      <th>cancer_perc</th>\n",
+       "      <th>αsma_mycaf_perc</th>\n",
+       "      <th>stroma_other_perc</th>\n",
+       "      <th>endothelial_perc</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1.csv</th>\n",
+       "      <td>70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>237</td>\n",
+       "      <td>107</td>\n",
+       "      <td>5</td>\n",
+       "      <td>884</td>\n",
+       "      <td>40795</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.169485</td>\n",
+       "      <td>0.427746</td>\n",
+       "      <td>0.193116</td>\n",
+       "      <td>0.009024</td>\n",
+       "      <td>1.595457</td>\n",
+       "      <td>73.625322</td>\n",
+       "      <td>5.089889</td>\n",
+       "      <td>7.826968</td>\n",
+       "      <td>2.492095</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S2.csv</th>\n",
+       "      <td>294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1900</td>\n",
+       "      <td>2343</td>\n",
+       "      <td>92</td>\n",
+       "      <td>25</td>\n",
+       "      <td>37</td>\n",
+       "      <td>1244</td>\n",
+       "      <td>48774</td>\n",
+       "      <td>3226</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.521863</td>\n",
+       "      <td>0.138282</td>\n",
+       "      <td>0.037576</td>\n",
+       "      <td>0.055613</td>\n",
+       "      <td>1.869804</td>\n",
+       "      <td>73.308069</td>\n",
+       "      <td>4.843391</td>\n",
+       "      <td>10.445711</td>\n",
+       "      <td>2.448158</td>\n",
+       "      <td>DD3S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S3.csv</th>\n",
+       "      <td>253</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>5416</td>\n",
+       "      <td>2822</td>\n",
+       "      <td>183</td>\n",
+       "      <td>77</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>63037</td>\n",
+       "      <td>5090</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.067416</td>\n",
+       "      <td>0.198908</td>\n",
+       "      <td>0.083693</td>\n",
+       "      <td>0.028260</td>\n",
+       "      <td>1.756471</td>\n",
+       "      <td>68.515202</td>\n",
+       "      <td>5.528227</td>\n",
+       "      <td>11.861625</td>\n",
+       "      <td>2.668033</td>\n",
+       "      <td>DD3S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S1.csv</th>\n",
+       "      <td>224</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>1739</td>\n",
+       "      <td>50</td>\n",
+       "      <td>33</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1858</td>\n",
+       "      <td>45151</td>\n",
+       "      <td>2832</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.994241</td>\n",
+       "      <td>0.086086</td>\n",
+       "      <td>0.056817</td>\n",
+       "      <td>0.012052</td>\n",
+       "      <td>3.198964</td>\n",
+       "      <td>77.733303</td>\n",
+       "      <td>4.869139</td>\n",
+       "      <td>5.673310</td>\n",
+       "      <td>2.191747</td>\n",
+       "      <td>DD4S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S2.csv</th>\n",
+       "      <td>102</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>1647</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28</td>\n",
+       "      <td>15</td>\n",
+       "      <td>680</td>\n",
+       "      <td>32471</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.891047</td>\n",
+       "      <td>0.080318</td>\n",
+       "      <td>0.066144</td>\n",
+       "      <td>0.035434</td>\n",
+       "      <td>1.606349</td>\n",
+       "      <td>76.702624</td>\n",
+       "      <td>3.136029</td>\n",
+       "      <td>9.218771</td>\n",
+       "      <td>2.272369</td>\n",
+       "      <td>DD4S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD4S3.csv</th>\n",
+       "      <td>1068</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>2796</td>\n",
+       "      <td>100</td>\n",
+       "      <td>264</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2419</td>\n",
+       "      <td>45124</td>\n",
+       "      <td>3649</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.453300</td>\n",
+       "      <td>0.159263</td>\n",
+       "      <td>0.420452</td>\n",
+       "      <td>0.052556</td>\n",
+       "      <td>3.852525</td>\n",
+       "      <td>71.860551</td>\n",
+       "      <td>5.804437</td>\n",
+       "      <td>6.556229</td>\n",
+       "      <td>1.900504</td>\n",
+       "      <td>DD4S3.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S1.csv</th>\n",
+       "      <td>528</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>467</td>\n",
+       "      <td>162</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>672</td>\n",
+       "      <td>37194</td>\n",
+       "      <td>5200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.830433</td>\n",
+       "      <td>0.288069</td>\n",
+       "      <td>0.019560</td>\n",
+       "      <td>0.037342</td>\n",
+       "      <td>1.194945</td>\n",
+       "      <td>66.136689</td>\n",
+       "      <td>9.235544</td>\n",
+       "      <td>13.890111</td>\n",
+       "      <td>3.616361</td>\n",
+       "      <td>DD5S1.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S2.csv</th>\n",
+       "      <td>89</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>722</td>\n",
+       "      <td>82</td>\n",
+       "      <td>104</td>\n",
+       "      <td>59</td>\n",
+       "      <td>9</td>\n",
+       "      <td>220</td>\n",
+       "      <td>24876</td>\n",
+       "      <td>1203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.257768</td>\n",
+       "      <td>0.326922</td>\n",
+       "      <td>0.185464</td>\n",
+       "      <td>0.028291</td>\n",
+       "      <td>0.691554</td>\n",
+       "      <td>78.194228</td>\n",
+       "      <td>3.772191</td>\n",
+       "      <td>12.616375</td>\n",
+       "      <td>1.269287</td>\n",
+       "      <td>DD5S2.csv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD5S3.csv</th>\n",
+       "      <td>977</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1096</td>\n",
+       "      <td>457</td>\n",
+       "      <td>147</td>\n",
+       "      <td>41</td>\n",
+       "      <td>6</td>\n",
+       "      <td>553</td>\n",
+       "      <td>21968</td>\n",
+       "      <td>2174</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.483774</td>\n",
+       "      <td>0.477252</td>\n",
+       "      <td>0.133109</td>\n",
+       "      <td>0.019479</td>\n",
+       "      <td>1.795341</td>\n",
+       "      <td>71.316005</td>\n",
+       "      <td>7.041282</td>\n",
+       "      <td>8.933977</td>\n",
+       "      <td>1.887279</td>\n",
+       "      <td>DD5S3.csv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             dc      b  tcd4  tcd8   m1   m2  treg  immune_other  cancer  \\\n",
+       "DD3S1.csv    70    0.0  2440  2310  237  107     5           884   40795   \n",
+       "DD3S2.csv   294    0.0  1900  2343   92   25    37          1244   48774   \n",
+       "DD3S3.csv   253   94.0  5416  2822  183   77    26          1616   63037   \n",
+       "DD4S1.csv   224   26.0  1580  1739   50   33     7          1858   45151   \n",
+       "DD4S2.csv   102   13.0  1131  1647   34   28    15           680   32471   \n",
+       "DD4S3.csv  1068   30.0  1980  2796  100  264    33          2419   45124   \n",
+       "DD5S1.csv   528  138.0  1979   467  162   11    21           672   37194   \n",
+       "DD5S2.csv    89   16.0   722    82  104   59     9           220   24876   \n",
+       "DD5S3.csv   977   32.0  1096   457  147   41     6           553   21968   \n",
+       "\n",
+       "           αsma_mycaf  ...  tcd8_perc   m1_perc   m2_perc  treg_perc  \\\n",
+       "DD3S1.csv        2824  ...   4.169485  0.427746  0.193116   0.009024   \n",
+       "DD3S2.csv        3226  ...   3.521863  0.138282  0.037576   0.055613   \n",
+       "DD3S3.csv        5090  ...   3.067416  0.198908  0.083693   0.028260   \n",
+       "DD4S1.csv        2832  ...   2.994241  0.086086  0.056817   0.012052   \n",
+       "DD4S2.csv        1330  ...   3.891047  0.080318  0.066144   0.035434   \n",
+       "DD4S3.csv        3649  ...   4.453300  0.159263  0.420452   0.052556   \n",
+       "DD5S1.csv        5200  ...   0.830433  0.288069  0.019560   0.037342   \n",
+       "DD5S2.csv        1203  ...   0.257768  0.326922  0.185464   0.028291   \n",
+       "DD5S3.csv        2174  ...   1.483774  0.477252  0.133109   0.019479   \n",
+       "\n",
+       "           immune_other_perc  cancer_perc  αsma_mycaf_perc  stroma_other_perc  \\\n",
+       "DD3S1.csv           1.595457    73.625322         5.089889           7.826968   \n",
+       "DD3S2.csv           1.869804    73.308069         4.843391          10.445711   \n",
+       "DD3S3.csv           1.756471    68.515202         5.528227          11.861625   \n",
+       "DD4S1.csv           3.198964    77.733303         4.869139           5.673310   \n",
+       "DD4S2.csv           1.606349    76.702624         3.136029           9.218771   \n",
+       "DD4S3.csv           3.852525    71.860551         5.804437           6.556229   \n",
+       "DD5S1.csv           1.194945    66.136689         9.235544          13.890111   \n",
+       "DD5S2.csv           0.691554    78.194228         3.772191          12.616375   \n",
+       "DD5S3.csv           1.795341    71.316005         7.041282           8.933977   \n",
+       "\n",
+       "           endothelial_perc  Sample_ID  \n",
+       "DD3S1.csv          2.492095  DD3S1.csv  \n",
+       "DD3S2.csv          2.448158  DD3S2.csv  \n",
+       "DD3S3.csv          2.668033  DD3S3.csv  \n",
+       "DD4S1.csv          2.191747  DD4S1.csv  \n",
+       "DD4S2.csv          2.272369  DD4S2.csv  \n",
+       "DD4S3.csv          1.900504  DD4S3.csv  \n",
+       "DD5S1.csv          3.616361  DD5S1.csv  \n",
+       "DD5S2.csv          1.269287  DD5S2.csv  \n",
+       "DD5S3.csv          1.887279  DD5S3.csv  \n",
+       "\n",
+       "[9 rows x 25 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ajout des colonnes de pourcentages pour chaque sous-type de cellules\n",
+    "counts_perc = counts_subtypes.copy()\n",
+    "\n",
+    "# Calcul des pourcentages pour chaque sous-type de cellules\n",
+    "for col in counts_perc.columns:\n",
+    "    counts_perc[col + '_perc'] = (counts_perc[col] / counts_perc.sum(axis=1)) * 100\n",
+    "\n",
+    "# Affichage des pourcentages des sous-types de cellules\n",
+    "\n",
+    "counts_perc['Sample_ID'] = counts_perc.index\n",
+    "counts_perc.columns.values\n",
+    "display(counts_perc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "7fe02123-a51b-41d3-a874-1dfdefe193a2",
+   "metadata": {},
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+            "linecolor": "white",
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+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           "title": {
+            "standoff": 15
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
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+        },
+        "title": {
+         "text": "ACT_patients_Cell subtypes proportions by Sample ID and tissue type"
+        },
+        "xaxis": {
+         "autorange": true,
+         "linecolor": "black",
+         "range": [
+          -0.5,
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+         "autorange": true,
+         "linecolor": "black",
+         "range": [
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+         "title": {
+          "text": "Cell count (%)"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure()\n",
+    "title = 'ACT_patients_Cell subtypes proportions by Sample ID and tissue type'\n",
+    "\n",
+    "for cell_subtype in cell_subtypes:\n",
+    "    fig.add_trace(\n",
+    "        go.Bar(\n",
+    "            name=cell_subtype,\n",
+    "            x=counts_perc['Sample_ID'],\n",
+    "            y=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            text=counts_perc[f'{cell_subtype.lower()}_perc'],\n",
+    "            textposition='auto',\n",
+    "            marker_color='rgb' + str(cell_subtype_color_dict[cell_subtype])))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    barmode='stack',\n",
+    "    xaxis=dict(linecolor='black'),\n",
+    "    title=title,\n",
+    "    yaxis=dict(title='Cell count (%)', linecolor='black')\n",
+    ")\n",
+    "\n",
+    "# Enregistrer l'image\n",
+    "output_filename = title.replace(\" \", \"_\") + \".png\"\n",
+    "#fig.write_image(output_images_dir + \"/\" + output_filename, scale=1000)\n",
+    "\n",
+    "# Afficher le graphique\n",
+    "fig.show()\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc016170-cec5-4981-b074-0cf1622a73ec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "471b7d74-09e9-4db8-b5d8-a9e764363439",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "d149cb8e-6819-40f5-89ec-827ff60ff3d1",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuc_Y_Inv</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_0</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.417494</td>\n",
+       "      <td>-0.912537</td>\n",
+       "      <td>-0.817876</td>\n",
+       "      <td>0.930099</td>\n",
+       "      <td>0.232078</td>\n",
+       "      <td>-0.483158</td>\n",
+       "      <td>1.535604</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>1.167755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16632.205078</td>\n",
+       "      <td>0.955040</td>\n",
+       "      <td>1484.771729</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>339</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_1</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.516487</td>\n",
+       "      <td>-0.838037</td>\n",
+       "      <td>-0.869685</td>\n",
+       "      <td>1.114924</td>\n",
+       "      <td>0.301333</td>\n",
+       "      <td>-0.344770</td>\n",
+       "      <td>1.668368</td>\n",
+       "      <td>0.875455</td>\n",
+       "      <td>1.643023</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16627.384766</td>\n",
+       "      <td>0.966643</td>\n",
+       "      <td>1426.250000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>344</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_2</th>\n",
+       "      <td>-0.677863</td>\n",
+       "      <td>-0.141921</td>\n",
+       "      <td>-1.016023</td>\n",
+       "      <td>-0.755879</td>\n",
+       "      <td>0.834577</td>\n",
+       "      <td>0.259216</td>\n",
+       "      <td>-0.438292</td>\n",
+       "      <td>1.336308</td>\n",
+       "      <td>0.705088</td>\n",
+       "      <td>1.053636</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16622.238281</td>\n",
+       "      <td>0.721534</td>\n",
+       "      <td>1531.110474</td>\n",
+       "      <td>0</td>\n",
+       "      <td>181</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>422</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_3</th>\n",
+       "      <td>-0.741282</td>\n",
+       "      <td>-0.460472</td>\n",
+       "      <td>-0.491711</td>\n",
+       "      <td>-0.818084</td>\n",
+       "      <td>0.648200</td>\n",
+       "      <td>0.107027</td>\n",
+       "      <td>-0.444889</td>\n",
+       "      <td>1.249805</td>\n",
+       "      <td>0.660707</td>\n",
+       "      <td>1.165861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16623.007812</td>\n",
+       "      <td>0.587196</td>\n",
+       "      <td>1518.907593</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_6</th>\n",
+       "      <td>-0.621521</td>\n",
+       "      <td>-0.247254</td>\n",
+       "      <td>-0.867127</td>\n",
+       "      <td>-0.742544</td>\n",
+       "      <td>0.810579</td>\n",
+       "      <td>0.272128</td>\n",
+       "      <td>-0.507117</td>\n",
+       "      <td>1.251434</td>\n",
+       "      <td>0.947172</td>\n",
+       "      <td>2.545301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16619.978516</td>\n",
+       "      <td>0.935716</td>\n",
+       "      <td>1471.914917</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>204</td>\n",
+       "      <td>61</td>\n",
+       "      <td>61a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115755</th>\n",
+       "      <td>0.478275</td>\n",
+       "      <td>0.558670</td>\n",
+       "      <td>-0.962840</td>\n",
+       "      <td>1.732291</td>\n",
+       "      <td>0.507434</td>\n",
+       "      <td>-0.912641</td>\n",
+       "      <td>0.311322</td>\n",
+       "      <td>0.816068</td>\n",
+       "      <td>0.596520</td>\n",
+       "      <td>0.090397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2663.253418</td>\n",
+       "      <td>0.982196</td>\n",
+       "      <td>15564.458008</td>\n",
+       "      <td>59</td>\n",
+       "      <td>142</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>386</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115756</th>\n",
+       "      <td>0.297418</td>\n",
+       "      <td>0.420594</td>\n",
+       "      <td>-0.971632</td>\n",
+       "      <td>1.966955</td>\n",
+       "      <td>0.304365</td>\n",
+       "      <td>-1.164112</td>\n",
+       "      <td>0.866636</td>\n",
+       "      <td>-0.092857</td>\n",
+       "      <td>-0.241830</td>\n",
+       "      <td>-0.617835</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2661.765869</td>\n",
+       "      <td>0.775977</td>\n",
+       "      <td>15629.680664</td>\n",
+       "      <td>59</td>\n",
+       "      <td>47</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>270</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115757</th>\n",
+       "      <td>0.346950</td>\n",
+       "      <td>0.453951</td>\n",
+       "      <td>-0.602893</td>\n",
+       "      <td>1.338956</td>\n",
+       "      <td>0.559435</td>\n",
+       "      <td>-0.801333</td>\n",
+       "      <td>0.447061</td>\n",
+       "      <td>0.988156</td>\n",
+       "      <td>1.567869</td>\n",
+       "      <td>0.403878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2657.015625</td>\n",
+       "      <td>0.688747</td>\n",
+       "      <td>15518.421875</td>\n",
+       "      <td>59</td>\n",
+       "      <td>64</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>202</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115758</th>\n",
+       "      <td>-0.189415</td>\n",
+       "      <td>0.508840</td>\n",
+       "      <td>-0.886041</td>\n",
+       "      <td>0.647980</td>\n",
+       "      <td>-0.227224</td>\n",
+       "      <td>-1.022549</td>\n",
+       "      <td>-0.099256</td>\n",
+       "      <td>0.219755</td>\n",
+       "      <td>0.603715</td>\n",
+       "      <td>-0.219145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2660.258545</td>\n",
+       "      <td>0.751402</td>\n",
+       "      <td>15539.275391</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>182</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TMA_Cell_115760</th>\n",
+       "      <td>0.245777</td>\n",
+       "      <td>0.460219</td>\n",
+       "      <td>-1.003840</td>\n",
+       "      <td>0.191158</td>\n",
+       "      <td>-0.313127</td>\n",
+       "      <td>-1.120675</td>\n",
+       "      <td>0.004962</td>\n",
+       "      <td>-0.316199</td>\n",
+       "      <td>1.727776</td>\n",
+       "      <td>-0.495501</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2654.292480</td>\n",
+       "      <td>0.674126</td>\n",
+       "      <td>15542.961914</td>\n",
+       "      <td>59</td>\n",
+       "      <td>106</td>\n",
+       "      <td>TMA.csv</td>\n",
+       "      <td>None</td>\n",
+       "      <td>295</td>\n",
+       "      <td>c59</td>\n",
+       "      <td>c59a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>704629 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.677863   \n",
+       "DD3S1_Cell_1                            -0.677863   \n",
+       "DD3S1_Cell_2                            -0.677863   \n",
+       "DD3S1_Cell_3                            -0.741282   \n",
+       "DD3S1_Cell_6                            -0.621521   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.478275   \n",
+       "TMA_Cell_115756                          0.297418   \n",
+       "TMA_Cell_115757                          0.346950   \n",
+       "TMA_Cell_115758                         -0.189415   \n",
+       "TMA_Cell_115760                          0.245777   \n",
+       "\n",
+       "                 HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.417494   \n",
+       "DD3S1_Cell_1                           -0.516487   \n",
+       "DD3S1_Cell_2                           -0.141921   \n",
+       "DD3S1_Cell_3                           -0.460472   \n",
+       "DD3S1_Cell_6                           -0.247254   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.558670   \n",
+       "TMA_Cell_115756                         0.420594   \n",
+       "TMA_Cell_115757                         0.453951   \n",
+       "TMA_Cell_115758                         0.508840   \n",
+       "TMA_Cell_115760                         0.460219   \n",
+       "\n",
+       "                 CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                           -0.912537   \n",
+       "DD3S1_Cell_1                           -0.838037   \n",
+       "DD3S1_Cell_2                           -1.016023   \n",
+       "DD3S1_Cell_3                           -0.491711   \n",
+       "DD3S1_Cell_6                           -0.867127   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                        -0.962840   \n",
+       "TMA_Cell_115756                        -0.971632   \n",
+       "TMA_Cell_115757                        -0.602893   \n",
+       "TMA_Cell_115758                        -0.886041   \n",
+       "TMA_Cell_115760                        -1.003840   \n",
+       "\n",
+       "                 Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                \n",
+       "DD3S1_Cell_0                          -0.817876   \n",
+       "DD3S1_Cell_1                          -0.869685   \n",
+       "DD3S1_Cell_2                          -0.755879   \n",
+       "DD3S1_Cell_3                          -0.818084   \n",
+       "DD3S1_Cell_6                          -0.742544   \n",
+       "...                                         ...   \n",
+       "TMA_Cell_115755                        1.732291   \n",
+       "TMA_Cell_115756                        1.966955   \n",
+       "TMA_Cell_115757                        1.338956   \n",
+       "TMA_Cell_115758                        0.647980   \n",
+       "TMA_Cell_115760                        0.191158   \n",
+       "\n",
+       "                 CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.930099   \n",
+       "DD3S1_Cell_1                              1.114924   \n",
+       "DD3S1_Cell_2                              0.834577   \n",
+       "DD3S1_Cell_3                              0.648200   \n",
+       "DD3S1_Cell_6                              0.810579   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                           0.507434   \n",
+       "TMA_Cell_115756                           0.304365   \n",
+       "TMA_Cell_115757                           0.559435   \n",
+       "TMA_Cell_115758                          -0.227224   \n",
+       "TMA_Cell_115760                          -0.313127   \n",
+       "\n",
+       "                 ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_0                              0.232078   \n",
+       "DD3S1_Cell_1                              0.301333   \n",
+       "DD3S1_Cell_2                              0.259216   \n",
+       "DD3S1_Cell_3                              0.107027   \n",
+       "DD3S1_Cell_6                              0.272128   \n",
+       "...                                            ...   \n",
+       "TMA_Cell_115755                          -0.912641   \n",
+       "TMA_Cell_115756                          -1.164112   \n",
+       "TMA_Cell_115757                          -0.801333   \n",
+       "TMA_Cell_115758                          -1.022549   \n",
+       "TMA_Cell_115760                          -1.120675   \n",
+       "\n",
+       "                 CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_0                            -0.483158   \n",
+       "DD3S1_Cell_1                            -0.344770   \n",
+       "DD3S1_Cell_2                            -0.438292   \n",
+       "DD3S1_Cell_3                            -0.444889   \n",
+       "DD3S1_Cell_6                            -0.507117   \n",
+       "...                                           ...   \n",
+       "TMA_Cell_115755                          0.311322   \n",
+       "TMA_Cell_115756                          0.866636   \n",
+       "TMA_Cell_115757                          0.447061   \n",
+       "TMA_Cell_115758                         -0.099256   \n",
+       "TMA_Cell_115760                          0.004962   \n",
+       "\n",
+       "                 PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            1.535604   \n",
+       "DD3S1_Cell_1                            1.668368   \n",
+       "DD3S1_Cell_2                            1.336308   \n",
+       "DD3S1_Cell_3                            1.249805   \n",
+       "DD3S1_Cell_6                            1.251434   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.816068   \n",
+       "TMA_Cell_115756                        -0.092857   \n",
+       "TMA_Cell_115757                         0.988156   \n",
+       "TMA_Cell_115758                         0.219755   \n",
+       "TMA_Cell_115760                        -0.316199   \n",
+       "\n",
+       "                 AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_0                            0.807339   \n",
+       "DD3S1_Cell_1                            0.875455   \n",
+       "DD3S1_Cell_2                            0.705088   \n",
+       "DD3S1_Cell_3                            0.660707   \n",
+       "DD3S1_Cell_6                            0.947172   \n",
+       "...                                          ...   \n",
+       "TMA_Cell_115755                         0.596520   \n",
+       "TMA_Cell_115756                        -0.241830   \n",
+       "TMA_Cell_115757                         1.567869   \n",
+       "TMA_Cell_115758                         0.603715   \n",
+       "TMA_Cell_115760                         1.727776   \n",
+       "\n",
+       "                 CD31_Cytoplasm_Intensity_Average  ...     Nuc_Y_Inv  \\\n",
+       "ID                                                 ...                 \n",
+       "DD3S1_Cell_0                             1.167755  ...  16632.205078   \n",
+       "DD3S1_Cell_1                             1.643023  ...  16627.384766   \n",
+       "DD3S1_Cell_2                             1.053636  ...  16622.238281   \n",
+       "DD3S1_Cell_3                             1.165861  ...  16623.007812   \n",
+       "DD3S1_Cell_6                             2.545301  ...  16619.978516   \n",
+       "...                                           ...  ...           ...   \n",
+       "TMA_Cell_115755                          0.090397  ...   2663.253418   \n",
+       "TMA_Cell_115756                         -0.617835  ...   2661.765869   \n",
+       "TMA_Cell_115757                          0.403878  ...   2657.015625   \n",
+       "TMA_Cell_115758                         -0.219145  ...   2660.258545   \n",
+       "TMA_Cell_115760                         -0.495501  ...   2654.292480   \n",
+       "\n",
+       "                 Nucleus_Roundness         Nuc_X  ROI_index  Nucleus_Size  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_0              0.955040   1484.771729          0           127   \n",
+       "DD3S1_Cell_1              0.966643   1426.250000          0           112   \n",
+       "DD3S1_Cell_2              0.721534   1531.110474          0           181   \n",
+       "DD3S1_Cell_3              0.587196   1518.907593          0           119   \n",
+       "DD3S1_Cell_6              0.935716   1471.914917          0            47   \n",
+       "...                            ...           ...        ...           ...   \n",
+       "TMA_Cell_115755           0.982196  15564.458008         59           142   \n",
+       "TMA_Cell_115756           0.775977  15629.680664         59            47   \n",
+       "TMA_Cell_115757           0.688747  15518.421875         59            64   \n",
+       "TMA_Cell_115758           0.751402  15539.275391         59            58   \n",
+       "TMA_Cell_115760           0.674126  15542.961914         59           106   \n",
+       "\n",
+       "                 Sample_ID  immune_checkpoint  Cell_Size  Patient  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_0     DD3S1.csv               None        339       61   \n",
+       "DD3S1_Cell_1     DD3S1.csv               None        344       61   \n",
+       "DD3S1_Cell_2     DD3S1.csv               None        422       61   \n",
+       "DD3S1_Cell_3     DD3S1.csv               None        278       61   \n",
+       "DD3S1_Cell_6     DD3S1.csv               None        204       61   \n",
+       "...                    ...                ...        ...      ...   \n",
+       "TMA_Cell_115755    TMA.csv               None        386      c59   \n",
+       "TMA_Cell_115756    TMA.csv               None        270      c59   \n",
+       "TMA_Cell_115757    TMA.csv               None        202      c59   \n",
+       "TMA_Cell_115758    TMA.csv               None        182      c59   \n",
+       "TMA_Cell_115760    TMA.csv               None        295      c59   \n",
+       "\n",
+       "                 Unique_ROI_index  \n",
+       "ID                                 \n",
+       "DD3S1_Cell_0                  61a  \n",
+       "DD3S1_Cell_1                  61a  \n",
+       "DD3S1_Cell_2                  61a  \n",
+       "DD3S1_Cell_3                  61a  \n",
+       "DD3S1_Cell_6                  61a  \n",
+       "...                           ...  \n",
+       "TMA_Cell_115755              c59a  \n",
+       "TMA_Cell_115756              c59a  \n",
+       "TMA_Cell_115757              c59a  \n",
+       "TMA_Cell_115758              c59a  \n",
+       "TMA_Cell_115760              c59a  \n",
+       "\n",
+       "[704629 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f7aeca8-71f4-44fc-b869-d93911a13167",
+   "metadata": {},
+   "source": [
+    "## RATIO M2/M1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "89bf3e70-3862-408b-a898-54a7c7da47be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filtrer les lignes correspondant aux cellules M1 et M2 pour les patients NACT\n",
+    "df_M1_NACT = df[(df['cell_subtype'] == 'M1') & df['Patient'].isin(num_NACT_patients)]\n",
+    "df_M2_NACT = df[(df['cell_subtype'] == 'M2') & df['Patient'].isin(num_NACT_patients)]\n",
+    "\n",
+    "# Filtrer les lignes correspondant aux cellules M1 et M2 pour les patients ACT\n",
+    "df_M1_ACT = df[(df['cell_subtype'] == 'M1') & df['Patient'].isin(num_ACT_patients)]\n",
+    "df_M2_ACT = df[(df['cell_subtype'] == 'M2') & df['Patient'].isin(num_ACT_patients)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e474111d-be52-49f6-8543-1d407ec612af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regrouper les données par patient et calculer le total de cellules M1 et M2 pour les patients NACT\n",
+    "result_M1_NACT = df_M1_NACT.groupby('Patient').size().rename('total_M1_cells')\n",
+    "result_M2_NACT = df_M2_NACT.groupby('Patient').size().rename('total_M2_cells')\n",
+    "\n",
+    "# Regrouper les données par patient et calculer le total de cellules M1 et M2 pour les patients ACT\n",
+    "result_M1_ACT = df_M1_ACT.groupby('Patient').size().rename('total_M1_cells')\n",
+    "result_M2_ACT = df_M2_ACT.groupby('Patient').size().rename('total_M2_cells')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "79c6b7fd-d9c8-475b-badd-734ab73733ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Créer le DataFrame résultant avec Patient comme index pour les patients NACT\n",
+    "result_NACT = pd.concat([result_M1_NACT, result_M2_NACT], axis=1, sort=False).fillna(0)\n",
+    "\n",
+    "# Créer le DataFrame résultant avec Patient comme index pour les patients ACT\n",
+    "result_ACT = pd.concat([result_M1_ACT, result_M2_ACT], axis=1, sort=False).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "076cc0b8-e874-4fdb-9481-b2e3655d12e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ajouter une colonne pour le ratio de M2/M1 pour les patients NACT\n",
+    "result_NACT['M2_M1_ratio'] = result_NACT['total_M2_cells'] / result_NACT['total_M1_cells']\n",
+    "result_NACT['Treatment_Type'] = 'NACT'  # Ajouter une colonne pour indiquer le traitement\n",
+    "\n",
+    "# Ajouter une colonne pour le ratio de M2/M1 pour les patients ACT\n",
+    "result_ACT['M2_M1_ratio'] = result_ACT['total_M2_cells'] / result_ACT['total_M1_cells']\n",
+    "result_ACT['Treatment_Type'] = 'ACT'  # Ajouter une colonne pour indiquer le traitement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "d37f1727-7136-49a5-b563-07c7a7281379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regrouper les données par patient et calculer le total de cellules M1 et M2 pour les patients NACT\n",
+    "result_M1_NACT = df_M1_NACT.groupby('Patient').size().rename('total_M1_cells')\n",
+    "result_M2_NACT = df_M2_NACT.groupby('Patient').size().rename('total_M2_cells')\n",
+    "\n",
+    "# Regrouper les données par patient et calculer le total de cellules M1 et M2 pour les patients ACT\n",
+    "result_M1_ACT = df_M1_ACT.groupby('Patient').size().rename('total_M1_cells')\n",
+    "result_M2_ACT = df_M2_ACT.groupby('Patient').size().rename('total_M2_cells')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "2b7dcdd6-e409-4295-ad02-25143ab87ff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_M1_cells</th>\n",
+       "      <th>total_M2_cells</th>\n",
+       "      <th>M2_M1_ratio</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.598802</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>103 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_M1_cells  total_M2_cells  M2_M1_ratio Treatment_Type\n",
+       "Patient                                                            \n",
+       "10                167.0           100.0     0.598802            ACT\n",
+       "101                 3.0             1.0     0.333333            ACT\n",
+       "102                 5.0             1.0     0.200000            ACT\n",
+       "103                 7.0             0.0     0.000000            ACT\n",
+       "105                 3.0             3.0     1.000000            ACT\n",
+       "...                 ...             ...          ...            ...\n",
+       "34                  0.0             2.0          inf            ACT\n",
+       "45                  0.0             3.0          inf            ACT\n",
+       "71                  0.0            16.0          inf            ACT\n",
+       "72                  0.0             1.0          inf            ACT\n",
+       "9                   0.0             1.0          inf            ACT\n",
+       "\n",
+       "[103 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_ACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "09a86c99-e546-4c9a-81ae-5b77ed419fae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_M1_cells</th>\n",
+       "      <th>total_M2_cells</th>\n",
+       "      <th>M2_M1_ratio</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>307.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0.123779</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>275.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.010909</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>61.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.229508</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.040000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.846154</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>52.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.288462</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>78.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>4.750000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>41.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.390244</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>73.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>0.958904</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_M1_cells  total_M2_cells  M2_M1_ratio Treatment_Type\n",
+       "Patient                                                            \n",
+       "104                 1.0             2.0     2.000000           NACT\n",
+       "111                10.0             3.0     0.300000           NACT\n",
+       "119                 3.0             0.0     0.000000           NACT\n",
+       "121                 4.0             0.0     0.000000           NACT\n",
+       "137                 1.0             1.0     1.000000           NACT\n",
+       "140               307.0            38.0     0.123779           NACT\n",
+       "141                10.0             1.0     0.100000           NACT\n",
+       "146                 1.0             2.0     2.000000           NACT\n",
+       "149               275.0             3.0     0.010909           NACT\n",
+       "15                  7.0             1.0     0.142857           NACT\n",
+       "153                61.0            14.0     0.229508           NACT\n",
+       "157                 2.0             7.0     3.500000           NACT\n",
+       "162                 1.0             0.0     0.000000           NACT\n",
+       "169                 1.0             0.0     0.000000           NACT\n",
+       "171                25.0             1.0     0.040000           NACT\n",
+       "174                 1.0             0.0     0.000000           NACT\n",
+       "177                 1.0             0.0     0.000000           NACT\n",
+       "18                  1.0             0.0     0.000000           NACT\n",
+       "184                13.0            11.0     0.846154           NACT\n",
+       "187                 2.0             8.0     4.000000           NACT\n",
+       "32                 52.0            15.0     0.288462           NACT\n",
+       "40                  1.0             0.0     0.000000           NACT\n",
+       "5                  78.0             6.0     0.076923           NACT\n",
+       "51                  1.0             0.0     0.000000           NACT\n",
+       "61                  4.0            19.0     4.750000           NACT\n",
+       "85                 41.0            16.0     0.390244           NACT\n",
+       "89                 22.0             8.0     0.363636           NACT\n",
+       "95                  5.0            44.0     8.800000           NACT\n",
+       "98                 73.0            70.0     0.958904           NACT\n",
+       "182                 0.0             3.0          inf           NACT\n",
+       "183                 0.0             1.0          inf           NACT"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1def865f-3d17-4bf2-ab48-8a87cc564f58",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "f9acacbe-4654-45c4-86cf-a4d840f1f65f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_M1_cells</th>\n",
+       "      <th>total_M2_cells</th>\n",
+       "      <th>M2_M1_ratio</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.598802</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>8.333333</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>73.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>0.958904</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_M1_cells  total_M2_cells  M2_M1_ratio Treatment_Type\n",
+       "Patient                                                            \n",
+       "10                167.0           100.0     0.598802            ACT\n",
+       "100                 0.0             3.0          inf            ACT\n",
+       "101                 3.0             1.0     0.333333            ACT\n",
+       "102                 5.0             1.0     0.200000            ACT\n",
+       "103                 7.0             0.0     0.000000            ACT\n",
+       "...                 ...             ...          ...            ...\n",
+       "92                  3.0            25.0     8.333333            ACT\n",
+       "95                  5.0            44.0     8.800000           NACT\n",
+       "96                  2.0             1.0     0.500000            ACT\n",
+       "98                 73.0            70.0     0.958904           NACT\n",
+       "99                  5.0             5.0     1.000000            ACT\n",
+       "\n",
+       "[134 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Concaténer les résultats pour obtenir le DataFrame final\n",
+    "result_final = pd.concat([result_NACT, result_ACT])\n",
+    "\n",
+    "# Trier le DataFrame par index\n",
+    "result_sorted = result_final.sort_index()\n",
+    "\n",
+    "# Définir l'option pour afficher toutes les lignes du DataFrame\n",
+    "#pd.set_option('display.max_rows', None)\n",
+    "\n",
+    "display(result_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "492238f5-352d-412e-a960-166b0ea6808b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_M1_cells</th>\n",
+       "      <th>total_M2_cells</th>\n",
+       "      <th>M2_M1_ratio</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>167.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.598802</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>8.333333</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>73.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>0.958904</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_M1_cells  total_M2_cells  M2_M1_ratio Treatment_Type\n",
+       "Patient                                                            \n",
+       "10                167.0           100.0     0.598802            ACT\n",
+       "100                 0.0             3.0          inf            ACT\n",
+       "101                 3.0             1.0     0.333333            ACT\n",
+       "102                 5.0             1.0     0.200000            ACT\n",
+       "103                 7.0             0.0     0.000000            ACT\n",
+       "...                 ...             ...          ...            ...\n",
+       "92                  3.0            25.0     8.333333            ACT\n",
+       "95                  5.0            44.0     8.800000           NACT\n",
+       "96                  2.0             1.0     0.500000            ACT\n",
+       "98                 73.0            70.0     0.958904           NACT\n",
+       "99                  5.0             5.0     1.000000            ACT\n",
+       "\n",
+       "[134 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Remplacer les valeurs négatives du ratio M2/M1 par 0\n",
+    "#result_sorted['M2_M1_ratio'] = result_sorted['M2_M1_ratio'].clip(lower=0)\n",
+    "\n",
+    "#result_sorted['M2_M1_ratio'] = result_sorted['M2_M1_ratio'].replace([-float('inf'), float('inf')], 0)\n",
+    "#result_sorted['M2_M1_ratio'] = result_sorted['M2_M1_ratio'].apply(lambda x: 0 if x < 0 else x)\n",
+    "\n",
+    "# Définir l'option pour afficher toutes les lignes du DataFrame\n",
+    "#pd.set_option('display.max_rows', None)\n",
+    "\n",
+    "display(result_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "b873bac3-4490-4fcf-b964-6073acd2b5f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+             0.6666666666666666,
+             "#ed7953"
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+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Violin Plot du Ratio M2/M1 en fonction du Traitement"
+        },
+        "violinmode": "group",
+        "xaxis": {
+         "anchor": "y",
+         "autorange": true,
+         "domain": [
+          0,
+          1
+         ],
+         "range": [
+          -0.5,
+          1.5
+         ],
+         "title": {
+          "text": "Traitement"
+         },
+         "type": "category"
+        },
+        "yaxis": {
+         "anchor": "x",
+         "autorange": true,
+         "domain": [
+          0,
+          1
+         ],
+         "range": [
+          -3.0062104703092807,
+          34.00621047030928
+         ],
+         "title": {
+          "text": "Ratio M2/M1"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Créer le violin plot avec des points individuels\n",
+    "fig = px.violin(result_sorted, x='Treatment_Type', y='M2_M1_ratio', \n",
+    "                box=True, hover_data=result_sorted.columns)\n",
+    "\n",
+    "# Afficher le violin plot\n",
+    "fig.update_layout(title=f'Violin Plot du Ratio M2/M1 en fonction du Traitement',\n",
+    "                  xaxis_title='Traitement', yaxis_title='Ratio M2/M1')\n",
+    "\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)\n",
+    "import plotly.io as pio\n",
+    "#pio.write_image(fig, 'Violinplot_ratio_M2_M1.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06291b29-7ce3-4629-a0d6-c301afeced76",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67d75d80-d22e-40b9-9992-958d1d33b3bc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42cb23d6-bcbc-4069-94ad-fd4c385a5a51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "3b69fd7a-9b2f-4989-ab52-73aadbcb1d3e",
+   "metadata": {},
+   "source": [
+    "Chercher parmis cellules cancer express B7H4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "4bea449b-6a2c-4ae5-a65f-29f5ec0ee85b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filtrer les lignes correspondant aux cellules CANCER B7H4/PDL1 pour les patients NACT\n",
+    "df_B7H4_NACT = df[(df['immune_checkpoint'] == 'B7H4') & df['Patient'].isin(num_NACT_patients) & (df['cell_type'] == 'CANCER')]\n",
+    "df_PDL1_NACT = df[(df['immune_checkpoint'] == 'PDL1') & df['Patient'].isin(num_NACT_patients) & ((df['cell_type'] == 'CANCER') | (df['cell_type'] == 'IMMUNE'))]\n",
+    "\n",
+    "# Filtrer les lignes correspondant aux cellules CANCER B7H4/PDL1 pour les patients ACT\n",
+    "df_B7H4_ACT = df[(df['immune_checkpoint'] == 'B7H4') & df['Patient'].isin(num_ACT_patients) & (df['cell_type'] == 'CANCER')]\n",
+    "df_PDL1_ACT = df[(df['immune_checkpoint'] == 'PDL1') & df['Patient'].isin(num_ACT_patients) & ((df['cell_type'] == 'CANCER') | (df['cell_type'] == 'IMMUNE'))]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "22682aac-310a-419e-b5e5-0ab5e51cf234",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10827, 40)\n",
+      "(1229, 40)\n",
+      "(32646, 40)\n",
+      "(5434, 40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pd.set_option('display.max_rows', None)\n",
+    "print(df_B7H4_NACT.shape)\n",
+    "print(df_PDL1_NACT.shape)\n",
+    "\n",
+    "print(df_B7H4_ACT.shape)\n",
+    "print(df_PDL1_ACT.shape)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "93b1f3f3-d201-43cc-b593-c9b769232501",
+   "metadata": {},
+   "source": [
+    "# Filtrer les lignes correspondant aux cellules CANCER B7H4/PDL1 pour les patients NACT\n",
+    "df_B7H4_NACT = df[(df['immune_checkpoint'] == 'B7H4') & df['Patient'].isin(num_NACT_patients) & (df['cell_type'] == 'CANCER')]\n",
+    "df_PDL1_NACT = df[(df['immune_checkpoint'] == 'PDL1') & df['Patient'].isin(num_NACT_patients) & (df['cell_type'] == 'CANCER')]\n",
+    "\n",
+    "# Filtrer les lignes correspondant aux cellules CANCER B7H4/PDL1 pour les patients ACT\n",
+    "df_B7H4_ACT = df[(df['immune_checkpoint'] == 'B7H4') & df['Patient'].isin(num_ACT_patients) & (df['cell_type'] == 'CANCER')]\n",
+    "df_PDL1_ACT = df[(df['immune_checkpoint'] == 'PDL1') & df['Patient'].isin(num_ACT_patients) & (df['cell_type'] == 'CANCER')]\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "821010a8-b0f4-4f40-9ea1-2a54337b22a3",
+   "metadata": {},
+   "source": [
+    "#pd.set_option('display.max_rows', None)\n",
+    "print(df_B7H4_NACT.shape)\n",
+    "print(df_PDL1_NACT.shape)\n",
+    "\n",
+    "print(df_B7H4_ACT.shape)\n",
+    "print(df_PDL1_ACT.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "2d0a55fe-70ac-4b4c-b5db-65bd9cf1a525",
+   "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>PDL1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>HLA_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CKs_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>Ki67_Nucleus_Intensity_Average</th>\n",
+       "      <th>CD163_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>ColVI_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD20_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>PD1_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>AXL_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>CD31_Cytoplasm_Intensity_Average</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nucleus_Roundness</th>\n",
+       "      <th>Nuc_X</th>\n",
+       "      <th>ROI_index</th>\n",
+       "      <th>Nucleus_Size</th>\n",
+       "      <th>Sample_ID</th>\n",
+       "      <th>immune_checkpoint</th>\n",
+       "      <th>Cell_Size</th>\n",
+       "      <th>Patient</th>\n",
+       "      <th>Unique_ROI_index</th>\n",
+       "      <th>Primary_chem(1)_vs_surg(0)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_21828</th>\n",
+       "      <td>1.609792</td>\n",
+       "      <td>2.521615</td>\n",
+       "      <td>0.223023</td>\n",
+       "      <td>1.084166</td>\n",
+       "      <td>0.338872</td>\n",
+       "      <td>-1.112327</td>\n",
+       "      <td>1.374637</td>\n",
+       "      <td>0.236768</td>\n",
+       "      <td>-0.450948</td>\n",
+       "      <td>-0.132787</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.868000</td>\n",
+       "      <td>3246.450684</td>\n",
+       "      <td>11</td>\n",
+       "      <td>91</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>220</td>\n",
+       "      <td>32</td>\n",
+       "      <td>32a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_21831</th>\n",
+       "      <td>1.590608</td>\n",
+       "      <td>2.493721</td>\n",
+       "      <td>0.231741</td>\n",
+       "      <td>0.251524</td>\n",
+       "      <td>1.185084</td>\n",
+       "      <td>-1.203795</td>\n",
+       "      <td>1.610041</td>\n",
+       "      <td>-0.700345</td>\n",
+       "      <td>-0.673078</td>\n",
+       "      <td>-0.641906</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.580756</td>\n",
+       "      <td>2951.785645</td>\n",
+       "      <td>11</td>\n",
+       "      <td>70</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>153</td>\n",
+       "      <td>32</td>\n",
+       "      <td>32a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_21937</th>\n",
+       "      <td>1.611230</td>\n",
+       "      <td>2.189588</td>\n",
+       "      <td>0.558693</td>\n",
+       "      <td>0.249337</td>\n",
+       "      <td>0.313269</td>\n",
+       "      <td>-1.248514</td>\n",
+       "      <td>1.501920</td>\n",
+       "      <td>-0.669547</td>\n",
+       "      <td>-0.781676</td>\n",
+       "      <td>-0.848153</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.829105</td>\n",
+       "      <td>3137.495117</td>\n",
+       "      <td>11</td>\n",
+       "      <td>101</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>200</td>\n",
+       "      <td>32</td>\n",
+       "      <td>32a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_21939</th>\n",
+       "      <td>1.630944</td>\n",
+       "      <td>2.690321</td>\n",
+       "      <td>-0.185765</td>\n",
+       "      <td>1.258937</td>\n",
+       "      <td>0.552631</td>\n",
+       "      <td>-1.223585</td>\n",
+       "      <td>1.519502</td>\n",
+       "      <td>-0.700822</td>\n",
+       "      <td>-0.721136</td>\n",
+       "      <td>-0.785331</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.925649</td>\n",
+       "      <td>3217.131348</td>\n",
+       "      <td>11</td>\n",
+       "      <td>99</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>183</td>\n",
+       "      <td>32</td>\n",
+       "      <td>32a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DD3S1_Cell_21959</th>\n",
+       "      <td>1.517887</td>\n",
+       "      <td>2.719076</td>\n",
+       "      <td>0.000214</td>\n",
+       "      <td>-0.039537</td>\n",
+       "      <td>0.431631</td>\n",
+       "      <td>-1.257583</td>\n",
+       "      <td>1.471392</td>\n",
+       "      <td>-0.863404</td>\n",
+       "      <td>-0.803700</td>\n",
+       "      <td>-0.732914</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.777269</td>\n",
+       "      <td>2996.028564</td>\n",
+       "      <td>11</td>\n",
+       "      <td>105</td>\n",
+       "      <td>DD3S1.csv</td>\n",
+       "      <td>PDL1</td>\n",
+       "      <td>154</td>\n",
+       "      <td>32</td>\n",
+       "      <td>32a</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PDL1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_21828                          1.609792   \n",
+       "DD3S1_Cell_21831                          1.590608   \n",
+       "DD3S1_Cell_21937                          1.611230   \n",
+       "DD3S1_Cell_21939                          1.630944   \n",
+       "DD3S1_Cell_21959                          1.517887   \n",
+       "\n",
+       "                  HLA_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_21828                         2.521615   \n",
+       "DD3S1_Cell_21831                         2.493721   \n",
+       "DD3S1_Cell_21937                         2.189588   \n",
+       "DD3S1_Cell_21939                         2.690321   \n",
+       "DD3S1_Cell_21959                         2.719076   \n",
+       "\n",
+       "                  CKs_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_21828                         0.223023   \n",
+       "DD3S1_Cell_21831                         0.231741   \n",
+       "DD3S1_Cell_21937                         0.558693   \n",
+       "DD3S1_Cell_21939                        -0.185765   \n",
+       "DD3S1_Cell_21959                         0.000214   \n",
+       "\n",
+       "                  Ki67_Nucleus_Intensity_Average  \\\n",
+       "ID                                                 \n",
+       "DD3S1_Cell_21828                        1.084166   \n",
+       "DD3S1_Cell_21831                        0.251524   \n",
+       "DD3S1_Cell_21937                        0.249337   \n",
+       "DD3S1_Cell_21939                        1.258937   \n",
+       "DD3S1_Cell_21959                       -0.039537   \n",
+       "\n",
+       "                  CD163_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_21828                           0.338872   \n",
+       "DD3S1_Cell_21831                           1.185084   \n",
+       "DD3S1_Cell_21937                           0.313269   \n",
+       "DD3S1_Cell_21939                           0.552631   \n",
+       "DD3S1_Cell_21959                           0.431631   \n",
+       "\n",
+       "                  ColVI_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                    \n",
+       "DD3S1_Cell_21828                          -1.112327   \n",
+       "DD3S1_Cell_21831                          -1.203795   \n",
+       "DD3S1_Cell_21937                          -1.248514   \n",
+       "DD3S1_Cell_21939                          -1.223585   \n",
+       "DD3S1_Cell_21959                          -1.257583   \n",
+       "\n",
+       "                  CD20_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                   \n",
+       "DD3S1_Cell_21828                          1.374637   \n",
+       "DD3S1_Cell_21831                          1.610041   \n",
+       "DD3S1_Cell_21937                          1.501920   \n",
+       "DD3S1_Cell_21939                          1.519502   \n",
+       "DD3S1_Cell_21959                          1.471392   \n",
+       "\n",
+       "                  PD1_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_21828                         0.236768   \n",
+       "DD3S1_Cell_21831                        -0.700345   \n",
+       "DD3S1_Cell_21937                        -0.669547   \n",
+       "DD3S1_Cell_21939                        -0.700822   \n",
+       "DD3S1_Cell_21959                        -0.863404   \n",
+       "\n",
+       "                  AXL_Cytoplasm_Intensity_Average  \\\n",
+       "ID                                                  \n",
+       "DD3S1_Cell_21828                        -0.450948   \n",
+       "DD3S1_Cell_21831                        -0.673078   \n",
+       "DD3S1_Cell_21937                        -0.781676   \n",
+       "DD3S1_Cell_21939                        -0.721136   \n",
+       "DD3S1_Cell_21959                        -0.803700   \n",
+       "\n",
+       "                  CD31_Cytoplasm_Intensity_Average  ...  Nucleus_Roundness  \\\n",
+       "ID                                                  ...                      \n",
+       "DD3S1_Cell_21828                         -0.132787  ...           0.868000   \n",
+       "DD3S1_Cell_21831                         -0.641906  ...           0.580756   \n",
+       "DD3S1_Cell_21937                         -0.848153  ...           0.829105   \n",
+       "DD3S1_Cell_21939                         -0.785331  ...           0.925649   \n",
+       "DD3S1_Cell_21959                         -0.732914  ...           0.777269   \n",
+       "\n",
+       "                        Nuc_X  ROI_index  Nucleus_Size  Sample_ID  \\\n",
+       "ID                                                                  \n",
+       "DD3S1_Cell_21828  3246.450684         11            91  DD3S1.csv   \n",
+       "DD3S1_Cell_21831  2951.785645         11            70  DD3S1.csv   \n",
+       "DD3S1_Cell_21937  3137.495117         11           101  DD3S1.csv   \n",
+       "DD3S1_Cell_21939  3217.131348         11            99  DD3S1.csv   \n",
+       "DD3S1_Cell_21959  2996.028564         11           105  DD3S1.csv   \n",
+       "\n",
+       "                  immune_checkpoint  Cell_Size  Patient  Unique_ROI_index  \\\n",
+       "ID                                                                          \n",
+       "DD3S1_Cell_21828               PDL1        220       32               32a   \n",
+       "DD3S1_Cell_21831               PDL1        153       32               32a   \n",
+       "DD3S1_Cell_21937               PDL1        200       32               32a   \n",
+       "DD3S1_Cell_21939               PDL1        183       32               32a   \n",
+       "DD3S1_Cell_21959               PDL1        154       32               32a   \n",
+       "\n",
+       "                  Primary_chem(1)_vs_surg(0)  \n",
+       "ID                                            \n",
+       "DD3S1_Cell_21828                         1.0  \n",
+       "DD3S1_Cell_21831                         1.0  \n",
+       "DD3S1_Cell_21937                         1.0  \n",
+       "DD3S1_Cell_21939                         1.0  \n",
+       "DD3S1_Cell_21959                         1.0  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_PDL1_NACT.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "8b4bb2f2-baa7-4b68-a04c-d3ad2c26eeb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regrouper les données par patient et calculer le total de cellules B7H4 et PDL1 pour les patients NACT\n",
+    "result_B7H4_NACT = df_B7H4_NACT.groupby('Patient').size().rename('B7H4_cells')\n",
+    "result_PDL1_NACT = df_PDL1_NACT.groupby('Patient').size().rename('PDL1_cells')\n",
+    "\n",
+    "# Regrouper les données par patient et calculer le total de cellules B7H4 et PDL1 pour les patients ACT\n",
+    "result_B7H4_ACT = df_B7H4_ACT.groupby('Patient').size().rename('B7H4_cells')\n",
+    "result_PDL1_ACT = df_PDL1_ACT.groupby('Patient').size().rename('PDL1_cells')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "58019f7b-1ac3-481f-b878-86353d38d833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Patient\n",
+       "104     20\n",
+       "111      1\n",
+       "116      3\n",
+       "119     18\n",
+       "121      4\n",
+       "137      4\n",
+       "140      3\n",
+       "141      2\n",
+       "149      1\n",
+       "15       6\n",
+       "153     30\n",
+       "157      7\n",
+       "160    198\n",
+       "162      5\n",
+       "169     12\n",
+       "171      1\n",
+       "178      6\n",
+       "18     307\n",
+       "182     46\n",
+       "183      2\n",
+       "184      3\n",
+       "187      7\n",
+       "26      19\n",
+       "32     126\n",
+       "5       34\n",
+       "61      18\n",
+       "7        6\n",
+       "85       9\n",
+       "89       5\n",
+       "95      23\n",
+       "98     303\n",
+       "Name: PDL1_cells, dtype: int64"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_PDL1_NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "54c9bc84-f96c-4ea9-a5b1-c7d3240ca507",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Créer le DataFrame résultant avec Patient comme index pour les patients NACT\n",
+    "result_NACT = pd.concat([result_B7H4_NACT, result_PDL1_NACT], axis=1, sort=False).fillna(0)\n",
+    "\n",
+    "# Créer le DataFrame résultant avec Patient comme index pour les patients ACT\n",
+    "result_ACT = pd.concat([result_B7H4_ACT, result_PDL1_ACT], axis=1, sort=False).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "f004e2a0-34c0-43ff-859e-e115e0582f7e",
+   "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>B7H4_cells</th>\n",
+       "      <th>PDL1_cells</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>2151.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>2361.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         B7H4_cells  PDL1_cells\n",
+       "Patient                        \n",
+       "111            35.0         1.0\n",
+       "116            24.0         3.0\n",
+       "119          2151.0        18.0\n",
+       "121          2361.0         4.0\n",
+       "137            25.0         4.0"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_NACT.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "43bc13d1-bb0b-4116-9796-8d4fd8f3d17c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66353"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_NACT[df_NACT['cell_type'] == 'CANCER'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "f4409dc4-7053-4bcf-bbb2-9239090f5cab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  pour les patients NACT\n",
+    "result_NACT['%_B7H4'] = result_NACT['B7H4_cells'] / len(df_NACT[df_NACT['cell_type'] == 'CANCER'])\n",
+    "result_NACT['%_PDL1'] = result_NACT['PDL1_cells'] / len(df_NACT[(df_NACT['cell_type'] == 'CANCER') | (df_NACT['cell_type'] == 'IMMUNE')])\n",
+    "result_NACT['Treatment_Type'] = 'NACT'  # Ajouter une colonne pour indiquer le traitement\n",
+    "\n",
+    "#  pour le ratio de M2/M1 pour les patients ACT\n",
+    "result_ACT['%_B7H4'] = result_ACT['B7H4_cells'] / len(df_ACT[df_ACT['cell_type'] == 'CANCER'])\n",
+    "result_ACT['%_PDL1'] = result_ACT['PDL1_cells'] / len(df_ACT[(df_ACT['cell_type'] == 'CANCER') | (df_ACT['cell_type'] == 'IMMUNE')])\n",
+    "result_ACT['Treatment_Type'] = 'ACT'  # Ajouter une colonne pour indiquer le traitement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "387e6ee3-c71a-4157-a538-79c0260b92f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80124"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_NACT[(df_NACT['cell_type'] == 'CANCER') | (df_NACT['cell_type'] == 'IMMUNE')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "50f2ce62-fa9a-4560-ae82-2e99b5a6e3b8",
+   "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>B7H4_cells</th>\n",
+       "      <th>PDL1_cells</th>\n",
+       "      <th>%_B7H4</th>\n",
+       "      <th>%_PDL1</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</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>111</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000527</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000362</td>\n",
+       "      <td>0.000037</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>2151.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.032418</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>2361.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.035582</td>\n",
+       "      <td>0.000050</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.000377</td>\n",
+       "      <td>0.000050</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>71.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001070</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>281.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.004235</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1008.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.015191</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>0.000374</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>0.000087</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>0.000015</td>\n",
+       "      <td>0.002471</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.000603</td>\n",
+       "      <td>0.000062</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.002110</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000422</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000708</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>124.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001869</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000090</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>285.0</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>0.004295</td>\n",
+       "      <td>0.003832</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.000588</td>\n",
+       "      <td>0.000574</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>152.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.002291</td>\n",
+       "      <td>0.000025</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>1260.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.018989</td>\n",
+       "      <td>0.000037</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.000211</td>\n",
+       "      <td>0.000087</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.000045</td>\n",
+       "      <td>0.000237</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>215.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>0.003240</td>\n",
+       "      <td>0.001573</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>90.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001356</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.000015</td>\n",
+       "      <td>0.000424</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.000226</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>1563.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.023556</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>154.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.002321</td>\n",
+       "      <td>0.000062</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>207.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.003120</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>472.0</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>0.007113</td>\n",
+       "      <td>0.003782</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000250</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000037</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000025</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         B7H4_cells  PDL1_cells    %_B7H4    %_PDL1 Treatment_Type\n",
+       "Patient                                                           \n",
+       "111            35.0         1.0  0.000527  0.000012           NACT\n",
+       "116            24.0         3.0  0.000362  0.000037           NACT\n",
+       "119          2151.0        18.0  0.032418  0.000225           NACT\n",
+       "121          2361.0         4.0  0.035582  0.000050           NACT\n",
+       "137            25.0         4.0  0.000377  0.000050           NACT\n",
+       "146            71.0         0.0  0.001070  0.000000           NACT\n",
+       "149           281.0         1.0  0.004235  0.000012           NACT\n",
+       "15           1008.0         6.0  0.015191  0.000075           NACT\n",
+       "153             9.0        30.0  0.000136  0.000374           NACT\n",
+       "157             2.0         7.0  0.000030  0.000087           NACT\n",
+       "160             1.0       198.0  0.000015  0.002471           NACT\n",
+       "162            40.0         5.0  0.000603  0.000062           NACT\n",
+       "169           140.0        12.0  0.002110  0.000150           NACT\n",
+       "171            28.0         1.0  0.000422  0.000012           NACT\n",
+       "174            47.0         0.0  0.000708  0.000000           NACT\n",
+       "177           124.0         0.0  0.001869  0.000000           NACT\n",
+       "178             2.0         6.0  0.000030  0.000075           NACT\n",
+       "179             6.0         0.0  0.000090  0.000000           NACT\n",
+       "18            285.0       307.0  0.004295  0.003832           NACT\n",
+       "182            39.0        46.0  0.000588  0.000574           NACT\n",
+       "183           152.0         2.0  0.002291  0.000025           NACT\n",
+       "184          1260.0         3.0  0.018989  0.000037           NACT\n",
+       "187            14.0         7.0  0.000211  0.000087           NACT\n",
+       "26              3.0        19.0  0.000045  0.000237           NACT\n",
+       "32            215.0       126.0  0.003240  0.001573           NACT\n",
+       "40             90.0         0.0  0.001356  0.000000           NACT\n",
+       "5               1.0        34.0  0.000015  0.000424           NACT\n",
+       "61              2.0        18.0  0.000030  0.000225           NACT\n",
+       "7              15.0         6.0  0.000226  0.000075           NACT\n",
+       "85           1563.0         9.0  0.023556  0.000112           NACT\n",
+       "89            154.0         5.0  0.002321  0.000062           NACT\n",
+       "95            207.0        23.0  0.003120  0.000287           NACT\n",
+       "98            472.0       303.0  0.007113  0.003782           NACT\n",
+       "104             0.0        20.0  0.000000  0.000250           NACT\n",
+       "140             0.0         3.0  0.000000  0.000037           NACT\n",
+       "141             0.0         2.0  0.000000  0.000025           NACT"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_NACT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "5bf30f0f-cf19-468d-b30a-aa3f1d3794a5",
+   "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>B7H4_cells</th>\n",
+       "      <th>PDL1_cells</th>\n",
+       "      <th>%_B7H4</th>\n",
+       "      <th>%_PDL1</th>\n",
+       "      <th>Treatment_Type</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Patient</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>10</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.000050</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>663.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001845</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>61.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.000170</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>135.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>0.000376</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>0.000033</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>207.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.003120</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>812.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002259</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.000113</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>472.0</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>0.007113</td>\n",
+       "      <td>0.003782</td>\n",
+       "      <td>NACT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>161.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000448</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>ACT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>172 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         B7H4_cells  PDL1_cells    %_B7H4    %_PDL1 Treatment_Type\n",
+       "Patient                                                           \n",
+       "10             18.0         4.0  0.000050  0.000010            ACT\n",
+       "100           663.0         0.0  0.001845  0.000000            ACT\n",
+       "101            61.0        41.0  0.000170  0.000100            ACT\n",
+       "102           135.0        31.0  0.000376  0.000076            ACT\n",
+       "103            12.0       120.0  0.000033  0.000294            ACT\n",
+       "...             ...         ...       ...       ...            ...\n",
+       "95            207.0        23.0  0.003120  0.000287           NACT\n",
+       "96            812.0         0.0  0.002259  0.000000            ACT\n",
+       "97              2.0        46.0  0.000006  0.000113            ACT\n",
+       "98            472.0       303.0  0.007113  0.003782           NACT\n",
+       "99            161.0         2.0  0.000448  0.000005            ACT\n",
+       "\n",
+       "[172 rows x 5 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Concaténer les résultats pour obtenir le DataFrame final\n",
+    "result_final = pd.concat([result_NACT, result_ACT])\n",
+    "\n",
+    "# Trier le DataFrame par index\n",
+    "result_sorted = result_final.sort_index()\n",
+    "\n",
+    "# Définir l'option pour afficher toutes les lignes du DataFrame\n",
+    "#pd.set_option('display.max_rows', None)\n",
+    "\n",
+    "display(result_sorted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2961b7b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "3f238363-d9db-40a6-abb8-f3a12f2b26b8",
+   "metadata": {},
+   "outputs": [
+    {
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+        "title": {
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+        "xaxis": {
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+         "autorange": true,
+         "categoryarray": [
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+         "showline": true,
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+           "size": 14
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+          "text": "% B7H4"
+         },
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[120], line 41\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmulticomp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pairwise_tukeyhsd\n\u001b[0;32m     39\u001b[0m \u001b[38;5;66;03m# Assuming 'result_sorted' is your DataFrame and already imported\u001b[39;00m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# Perform ANOVA\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mols\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43m_B7H4 ~ C(Treatment_Type)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresult_sorted\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     42\u001b[0m anova_table \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39mstats\u001b[38;5;241m.\u001b[39manova_lm(model, typ\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)  \u001b[38;5;66;03m# Type 2 ANOVA DataFrame\u001b[39;00m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28mprint\u001b[39m(anova_table)\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\statsmodels\\base\\model.py:203\u001b[0m, in \u001b[0;36mModel.from_formula\u001b[1;34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m:  \u001b[38;5;66;03m# with patsy it's drop or raise. let's raise.\u001b[39;00m\n\u001b[0;32m    201\u001b[0m     missing \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 203\u001b[0m tmp \u001b[38;5;241m=\u001b[39m \u001b[43mhandle_formula_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    205\u001b[0m ((endog, exog), missing_idx, design_info) \u001b[38;5;241m=\u001b[39m tmp\n\u001b[0;32m    206\u001b[0m max_endog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_formula_max_endog\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\statsmodels\\formula\\formulatools.py:63\u001b[0m, in \u001b[0;36mhandle_formula_data\u001b[1;34m(Y, X, formula, depth, missing)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     62\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_util\u001b[38;5;241m.\u001b[39m_is_using_pandas(Y, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m---> 63\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mdmatrices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdataframe\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     64\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     65\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     66\u001b[0m         result \u001b[38;5;241m=\u001b[39m dmatrices(formula, Y, depth, return_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdataframe\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     67\u001b[0m                            NA_action\u001b[38;5;241m=\u001b[39mna_action)\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\highlevel.py:309\u001b[0m, in \u001b[0;36mdmatrices\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    299\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Construct two design matrices given a formula_like and data.\u001b[39;00m\n\u001b[0;32m    300\u001b[0m \n\u001b[0;32m    301\u001b[0m \u001b[38;5;124;03mThis function is identical to :func:`dmatrix`, except that it requires\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    306\u001b[0m \u001b[38;5;124;03mSee :func:`dmatrix` for details.\u001b[39;00m\n\u001b[0;32m    307\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    308\u001b[0m eval_env \u001b[38;5;241m=\u001b[39m EvalEnvironment\u001b[38;5;241m.\u001b[39mcapture(eval_env, reference\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m--> 309\u001b[0m (lhs, rhs) \u001b[38;5;241m=\u001b[39m \u001b[43m_do_highlevel_design\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    310\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lhs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    312\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel is missing required outcome variables\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\highlevel.py:164\u001b[0m, in \u001b[0;36m_do_highlevel_design\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdata_iter_maker\u001b[39m():\n\u001b[0;32m    163\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m([data])\n\u001b[1;32m--> 164\u001b[0m design_infos \u001b[38;5;241m=\u001b[39m \u001b[43m_try_incr_builders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    165\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m design_infos \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    167\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m build_design_matrices(design_infos, data,\n\u001b[0;32m    168\u001b[0m                                  NA_action\u001b[38;5;241m=\u001b[39mNA_action,\n\u001b[0;32m    169\u001b[0m                                  return_type\u001b[38;5;241m=\u001b[39mreturn_type)\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\highlevel.py:62\u001b[0m, in \u001b[0;36m_try_incr_builders\u001b[1;34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\n\u001b[0;32m     56\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOn Python 2, formula strings must be either \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m objects, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     57\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor else \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124municode\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m objects containing only ascii \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     58\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcharacters. You passed a unicode string with non-ascii \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     59\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcharacters. I\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mm afraid you\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mll have to either switch to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     60\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii-only, or else upgrade to Python 3.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(formula_like, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m---> 62\u001b[0m     formula_like \u001b[38;5;241m=\u001b[39m \u001b[43mModelDesc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_formula\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     63\u001b[0m     \u001b[38;5;66;03m# fallthrough\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(formula_like, ModelDesc):\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:165\u001b[0m, in \u001b[0;36mModelDesc.from_formula\u001b[1;34m(cls, tree_or_string)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    164\u001b[0m     tree \u001b[38;5;241m=\u001b[39m parse_formula(tree_or_string)\n\u001b[1;32m--> 165\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mEvaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequire_evalexpr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    166\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m    167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:400\u001b[0m, in \u001b[0;36mEvaluator.eval\u001b[1;34m(self, tree, require_evalexpr)\u001b[0m\n\u001b[0;32m    396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluators:\n\u001b[0;32m    397\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt know how to evaluate this \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    398\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m operator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (tree\u001b[38;5;241m.\u001b[39mtype,),\n\u001b[0;32m    399\u001b[0m                         tree\u001b[38;5;241m.\u001b[39mtoken)\n\u001b[1;32m--> 400\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_evaluators\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m require_evalexpr \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, IntermediateExpr):\n\u001b[0;32m    402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, ModelDesc):\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:221\u001b[0m, in \u001b[0;36m_eval_any_tilde\u001b[1;34m(evaluator, tree)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_eval_any_tilde\u001b[39m(evaluator, tree):\n\u001b[1;32m--> 221\u001b[0m     exprs \u001b[38;5;241m=\u001b[39m [evaluator\u001b[38;5;241m.\u001b[39meval(arg) \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39margs]    \n\u001b[0;32m    222\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(exprs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    223\u001b[0m         \u001b[38;5;66;03m# Formula was like: \"~ foo\"\u001b[39;00m\n\u001b[0;32m    224\u001b[0m         \u001b[38;5;66;03m# We pretend that instead it was like: \"0 ~ foo\"\u001b[39;00m\n\u001b[0;32m    225\u001b[0m         exprs\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m0\u001b[39m, IntermediateExpr(\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, []))\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:221\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_eval_any_tilde\u001b[39m(evaluator, tree):\n\u001b[1;32m--> 221\u001b[0m     exprs \u001b[38;5;241m=\u001b[39m [\u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m tree\u001b[38;5;241m.\u001b[39margs]    \n\u001b[0;32m    222\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(exprs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    223\u001b[0m         \u001b[38;5;66;03m# Formula was like: \"~ foo\"\u001b[39;00m\n\u001b[0;32m    224\u001b[0m         \u001b[38;5;66;03m# We pretend that instead it was like: \"0 ~ foo\"\u001b[39;00m\n\u001b[0;32m    225\u001b[0m         exprs\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m0\u001b[39m, IntermediateExpr(\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mTrue\u001b[39;00m, []))\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:400\u001b[0m, in \u001b[0;36mEvaluator.eval\u001b[1;34m(self, tree, require_evalexpr)\u001b[0m\n\u001b[0;32m    396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluators:\n\u001b[0;32m    397\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt know how to evaluate this \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    398\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m operator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (tree\u001b[38;5;241m.\u001b[39mtype,),\n\u001b[0;32m    399\u001b[0m                         tree\u001b[38;5;241m.\u001b[39mtoken)\n\u001b[1;32m--> 400\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_evaluators\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m require_evalexpr \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, IntermediateExpr):\n\u001b[0;32m    402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, ModelDesc):\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\desc.py:358\u001b[0m, in \u001b[0;36m_eval_python_expr\u001b[1;34m(evaluator, tree)\u001b[0m\n\u001b[0;32m    357\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_eval_python_expr\u001b[39m(evaluator, tree):\n\u001b[1;32m--> 358\u001b[0m     factor \u001b[38;5;241m=\u001b[39m \u001b[43mEvalFactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextra\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morigin\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    359\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m IntermediateExpr(\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m, [Term([factor])])\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\eval.py:452\u001b[0m, in \u001b[0;36mEvalFactor.__init__\u001b[1;34m(self, code, origin)\u001b[0m\n\u001b[0;32m    430\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A factor class that executes arbitrary Python code and supports\u001b[39;00m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;124;03mstateful transforms.\u001b[39;00m\n\u001b[0;32m    432\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[38;5;124;03m  assert EvalFactor(\"a + b\") != EvalFactor(\"b + a\")\u001b[39;00m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;66;03m# For parsed formulas, the code will already have been normalized by\u001b[39;00m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;66;03m# the parser. But let's normalize anyway, so we can be sure of having\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \u001b[38;5;66;03m# consistent semantics for __eq__ and __hash__.\u001b[39;00m\n\u001b[1;32m--> 452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcode \u001b[38;5;241m=\u001b[39m \u001b[43mnormalize_token_spacing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morigin \u001b[38;5;241m=\u001b[39m origin\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\tokens.py:157\u001b[0m, in \u001b[0;36mnormalize_token_spacing\u001b[1;34m(code)\u001b[0m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnormalize_token_spacing\u001b[39m(code):\n\u001b[0;32m    155\u001b[0m     tokens \u001b[38;5;241m=\u001b[39m [(t[\u001b[38;5;241m0\u001b[39m], t[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    156\u001b[0m               \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tokenize\u001b[38;5;241m.\u001b[39mgenerate_tokens(StringIO(code)\u001b[38;5;241m.\u001b[39mreadline)]\n\u001b[1;32m--> 157\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpretty_untokenize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Logiciels\\Anaconda\\envs\\CycIF-python\\lib\\site-packages\\patsy\\tokens.py:103\u001b[0m, in \u001b[0;36mpretty_untokenize\u001b[1;34m(typed_tokens)\u001b[0m\n\u001b[0;32m    101\u001b[0m brackets \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m    102\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m token_type, token \u001b[38;5;129;01min\u001b[39;00m typed_tokens:\n\u001b[1;32m--> 103\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m token_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (tokenize\u001b[38;5;241m.\u001b[39mINDENT, tokenize\u001b[38;5;241m.\u001b[39mDEDENT,\n\u001b[0;32m    104\u001b[0m                               tokenize\u001b[38;5;241m.\u001b[39mNL)\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m token_type \u001b[38;5;241m==\u001b[39m tokenize\u001b[38;5;241m.\u001b[39mNEWLINE:\n\u001b[0;32m    106\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.box(result_sorted, x='Treatment_Type', y='%_B7H4', \n",
+    "                hover_data=result_sorted.columns,\n",
+    "                color='Treatment_Type',  # This can be set to a single color if preferred\n",
+    "                template='plotly_white',  # Starting with a clean white template\n",
+    "                )\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title_text='Distribution of % B7H4 in Cancer Cells by Treatment',\n",
+    "    title_x=0.5,  # Center the title\n",
+    "    title_font=dict(size=18, family='Arial, sans-serif'),\n",
+    "    xaxis_title='Treatment Type',\n",
+    "    yaxis_title='% B7H4',\n",
+    "    xaxis=dict(tickangle=-45, title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    yaxis=dict(title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    legend_title_text='Treatment',\n",
+    "    legend_title_font=dict(size=12, family='Arial, sans-serif'),\n",
+    ")\n",
+    "\n",
+    "# Adjusting gridlines to mimic R's ggplot2 style\n",
+    "fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='white')\n",
+    "fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)\n",
+    "\n",
+    "import plotly.io as pio\n",
+    "#pio.write_image(fig, 'Violinplot_%B7H4.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "d65d70d0-84f0-49d0-afed-06629bf4fa45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.box(result_sorted, x='Treatment_Type', y='%_PDL1', \n",
+    "                hover_data=result_sorted.columns,\n",
+    "                color='Treatment_Type',  # This can be set to a single color if preferred\n",
+    "                template='plotly_white',  # Starting with a clean white template\n",
+    "                )\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title_text='Distribution of % PDL1 in Cancer Cells by Treatment',\n",
+    "    title_x=0.5,  # Center the title\n",
+    "    title_font=dict(size=18, family='Arial, sans-serif'),\n",
+    "    xaxis_title='Treatment Type',\n",
+    "    yaxis_title='% PDL1',\n",
+    "    xaxis=dict(tickangle=-45, title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    yaxis=dict(title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    legend_title_text='Treatment',\n",
+    "    legend_title_font=dict(size=12, family='Arial, sans-serif'),\n",
+    ")\n",
+    "\n",
+    "# Adjusting gridlines to mimic R's ggplot2 style\n",
+    "fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='white')\n",
+    "fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "\n",
+    "\n",
+    "fig.show()\n",
+    "plot(fig)\n",
+    "\n",
+    "\n",
+    "import plotly.io as pio\n",
+    "#pio.write_image(fig, 'Violinplot_%PDL1.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "b846e26c-ffe2-4996-8c80-b7e80d85aa73",
+   "metadata": {},
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+         "text": "Distribution of % B7H4 and % PDL1 in Cancer Cells by Treatment",
+         "x": 0.5
+        },
+        "width": 950,
+        "xaxis": {
+         "anchor": "y",
+         "autorange": true,
+         "domain": [
+          0,
+          0.495
+         ],
+         "linecolor": "black",
+         "linewidth": 2,
+         "mirror": true,
+         "range": [
+          -0.5,
+          1.5
+         ],
+         "showline": true,
+         "tickangle": -45,
+         "title": {
+          "font": {
+           "family": "Arial, sans-serif",
+           "size": 14
+          },
+          "text": "Treatment Type"
+         },
+         "type": "category"
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "autorange": true,
+         "domain": [
+          0.505,
+          1
+         ],
+         "linecolor": "black",
+         "linewidth": 2,
+         "mirror": true,
+         "range": [
+          -0.5,
+          1.5
+         ],
+         "showline": true,
+         "type": "category"
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "gridcolor": "white",
+         "gridwidth": 1,
+         "linecolor": "black",
+         "linewidth": 2,
+         "mirror": true,
+         "range": [
+          -0.001,
+          0.036
+         ],
+         "showgrid": true,
+         "showline": true,
+         "title": {
+          "font": {
+           "family": "Arial, sans-serif",
+           "size": 14
+          },
+          "text": "Percentage"
+         },
+         "type": "linear"
+        },
+        "yaxis2": {
+         "anchor": "x2",
+         "domain": [
+          0,
+          1
+         ],
+         "gridcolor": "white",
+         "gridwidth": 1,
+         "linecolor": "black",
+         "linewidth": 2,
+         "matches": "y",
+         "mirror": true,
+         "range": [
+          -0.001,
+          0.036
+         ],
+         "showgrid": true,
+         "showline": true,
+         "showticklabels": false,
+         "type": "linear"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# both on the same plot\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "import plotly.express as px\n",
+    "\n",
+    "# Assuming 'result_sorted' is your DataFrame\n",
+    "\n",
+    "# Create subplots with a shared y-axis\n",
+    "fig = make_subplots(rows=1, cols=2,\n",
+    "                    shared_yaxes=True, \n",
+    "                    horizontal_spacing=0.01,\n",
+    "                    subplot_titles=('Distribution of % B7H4', 'Distribution of % PDL1'))\n",
+    "\n",
+    "# Define a color map for the treatment types\n",
+    "color_map = {'ACT': \"#327EBA\", 'NACT': \"#E06663\"}\n",
+    "\n",
+    "# Add first plot for % B7H4\n",
+    "fig1 = px.box(result_sorted, x='Treatment_Type', y='%_B7H4', template='plotly_white',\n",
+    "              color='Treatment_Type', color_discrete_map=color_map)\n",
+    "for trace in fig1.data:\n",
+    "    fig.add_trace(trace, row=1, col=1)\n",
+    "\n",
+    "# Add second plot for % PDL1\n",
+    "fig2 = px.box(result_sorted, x='Treatment_Type', y='%_PDL1', template='plotly_white',\n",
+    "              color='Treatment_Type', color_discrete_map=color_map)\n",
+    "for trace in fig2.data:\n",
+    "    fig.add_trace(trace, row=1, col=2)\n",
+    "\n",
+    "# Update layout to match the style and adjust gridlines\n",
+    "fig.update_layout(\n",
+    "    title_text='Distribution of % B7H4 and % PDL1 in Cancer Cells by Treatment',\n",
+    "    title_x=0.5,\n",
+    "    title_font=dict(size=18, family='Arial, sans-serif'),\n",
+    "    xaxis_title='Treatment Type',\n",
+    "    yaxis_title='Percentage',\n",
+    "    xaxis=dict(tickangle=-45, title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    yaxis=dict(title_font=dict(size=14, family='Arial, sans-serif')),\n",
+    "    legend_title_text='Treatment',\n",
+    "    legend_title_font=dict(size=12, family='Arial, sans-serif'),\n",
+    "    template='plotly_white',\n",
+    "    width=950,  # Increase the width of the figure\n",
+    "    height=700,  # Increase the height of the figure\n",
+    ")\n",
+    "\n",
+    "# Set y-axis range to enhance visibility\n",
+    "fig.update_yaxes(range=[-0.001, 0.036])  # Adjust this range based on your data\n",
+    " \n",
+    "# Adjust grid and lines\n",
+    "fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='white')\n",
+    "fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
+    "\n",
+    "fig.show()\n",
+    "\n",
+    "\n",
+    "plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81ee2131-cf48-466b-8919-63a3da78b30c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a81b0396",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e33eb527",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d82f232-533c-46b5-9f6a-051d9271f51b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6961332e-8b09-41f6-8360-53799f0a348b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03832944-4339-4a44-b3c1-070ce13595ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "857a014e-49d0-4a4c-b582-09e28d44e754",
+   "metadata": {},
+   "source": [
+    "## V.5. XY MAPS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51c2588d-e4ba-4891-80bd-e30dd1cd3ee9",
+   "metadata": {},
+   "source": [
+    "#### V.5.1. CELL SUBTYPES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f175de05-afb0-43e0-b145-d380b7a28643",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create a x, y map and visualize the tissues architecture\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0] + '.csv'\n",
+    "    sample_id2 = sample.split('_')[0]\n",
+    "    location_colors = df.loc[df['Sample_ID'] == sample_id,['Nuc_X','Nuc_Y_Inv','cell_type']]\n",
+    "\n",
+    "    #print('nb c endo',len(location_colors.loc[location_colors['cell_type']=='ENDOTHELIAL']))\n",
+    "    #print('nb c immune',len(location_colors.loc[location_colors['cell_type']=='IMMUNE']))\n",
+    "    #print('nb c cancer',len(location_colors.loc[location_colors['cell_type']=='CANCER']))\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    title = sample_id2 + \" Background Subtracted XY Map cell types\"\n",
+    "\n",
+    "    for celltype in df.loc[df['Sample_ID'] == sample_id,'cell_type'].unique():\n",
+    "        fig.add_scatter(\n",
+    "            mode = 'markers',\n",
+    "            marker=dict(size=3, opacity=0.5, color='rgb' + str(cell_type_color_dict[celltype])),\n",
+    "         x = location_colors.loc[location_colors['cell_type']==celltype,'Nuc_X'],\n",
+    "         y = location_colors.loc[location_colors['cell_type']==celltype,'Nuc_Y_Inv'],\n",
+    "         name = celltype)\n",
+    "\n",
+    "    fig.update_layout(title = title, plot_bgcolor = 'white')\n",
+    "    fig.update_xaxes(title_text = 'Nuc_X', linecolor = 'black')\n",
+    "    fig.update_yaxes(title_text = 'Nuc_Y_Inv', linecolor = 'black')\n",
+    "    \n",
+    "    # Adjust the size of the points\n",
+    "    for trace in fig.data:\n",
+    "        trace.marker.size = 2  \n",
+    "        # Adjust the size of the points\n",
+    "    for trace in fig.data:\n",
+    "        trace.marker.size = 2  \n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        plot_bgcolor='white',\n",
+    "        legend=dict(\n",
+    "            title='Cell Types',  # Titre de la légende\n",
+    "            font=dict(\n",
+    "                family='Arial',\n",
+    "                size=12,\n",
+    "                color='black'\n",
+    "            ),\n",
+    "            bgcolor='white',\n",
+    "            bordercolor='black',\n",
+    "            borderwidth=0.4,\n",
+    "            itemsizing='constant'\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "\n",
+    "    #fig.write_image(output_images_dir + \"/\" + title.replace(\" \", \"_\") + \".png\", width=1200, height=800, scale=4)\n",
+    "    print(sample_id,  \"processed!\")\n",
+    "\n",
+    "    fig.show(renderer='png')  # Display the figure as png\n",
+    "    plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aeab8d32-f133-4853-85e3-b7643458c415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create a  x,y  map and visualize the tissues architecture\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0] + '.csv'\n",
+    "    sample_id2 = sample.split('_')[0]\n",
+    "    location_colors = df.loc[df['Sample_ID'] == sample_id,['Nuc_X','Nuc_Y_Inv','cell_subtype']]\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    title = sample_id2 + \" Background Subtracted XY Map cell subtypes\"\n",
+    "\n",
+    "    for cellsubtype in df.loc[df['Sample_ID'] == sample_id,'cell_subtype'].unique():\n",
+    "        fig.add_scatter(\n",
+    "            mode = 'markers',\n",
+    "            marker = dict(size=3, \n",
+    "                          opacity=0.5, \n",
+    "                          color='rgb' + str(cell_subtype_color_dict[cellsubtype])),\n",
+    "            x = location_colors.loc[location_colors['cell_subtype']==cellsubtype,'Nuc_X'],\n",
+    "            y = location_colors.loc[location_colors['cell_subtype']==cellsubtype,'Nuc_Y_Inv'],\n",
+    "            name = cellsubtype)\n",
+    "\n",
+    "    fig.update_layout(title = title, plot_bgcolor = 'white')\n",
+    "    fig.update_xaxes(title_text = 'Nuc_X', linecolor = 'black')\n",
+    "    fig.update_yaxes(title_text = 'Nuc_Y_Inv', linecolor = 'black')\n",
+    "    \n",
+    "    # Adjust the size of the points\n",
+    "    for trace in fig.data:\n",
+    "        trace.marker.size = 2  \n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        plot_bgcolor='white',\n",
+    "        legend=dict(\n",
+    "            title='Cell Subtypes',  #Legende title\n",
+    "            font=dict(\n",
+    "                family='Arial',\n",
+    "                size=12,\n",
+    "                color='black'\n",
+    "            ),\n",
+    "            bgcolor='white',\n",
+    "            bordercolor='black',\n",
+    "            borderwidth=0.4,\n",
+    "            itemsizing='constant'\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "\n",
+    "    fig.write_image(output_images_dir + \"/\" + title.replace(\" \", \"_\") + \".png\", width=1200, height=800, scale=4)\n",
+    "    print(sample_id,  \"processed!\")\n",
+    "\n",
+    "    plot(fig)\n",
+    "    fig.show(renderer='png')  # Display the figure as png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09d8a845-4bdd-4c86-b17f-84639d88b8fc",
+   "metadata": {},
+   "source": [
+    "#### V.5.1. IMMUNE CHECKPOINTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55cccde4-5288-45d9-aedf-6d49c0eb5187",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "immune_checkpoint_color_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af2bd6ea-df4c-4e66-8a69-f5c491a35384",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !!!! test for B7H4 !!!!\n",
+    "\n",
+    "\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0] + '.csv'\n",
+    "    sample_id2 = sample.split('_')[0]\n",
+    "    location_colors = df.loc[df['Sample_ID'] == sample_id,['Nuc_X','Nuc_Y_Inv','immune_checkpoint']]\n",
+    "\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    title = sample_id2 + \" Background Subtracted XY Map immune checkpoint\"\n",
+    "\n",
+    "    for immunecheckpoint in df.loc[df['Sample_ID'] == sample_id,'immune_checkpoint'].unique():\n",
+    "        fig.add_scatter(\n",
+    "            mode = 'markers',\n",
+    "            marker=dict(size=3, opacity=0.5, color='rgb' + str(immune_checkpoint_color_dict[immunecheckpoint])),\n",
+    "         x = location_colors.loc[location_colors['immune_checkpoint']==immunecheckpoint,'Nuc_X'],\n",
+    "         y = location_colors.loc[location_colors['immune_checkpoint']==immunecheckpoint,'Nuc_Y_Inv'],\n",
+    "         name = immunecheckpoint)\n",
+    "\n",
+    "    fig.update_layout(title = title, plot_bgcolor = 'white')\n",
+    "    fig.update_xaxes(title_text = 'Nuc_X', linecolor = 'black')\n",
+    "    fig.update_yaxes(title_text = 'Nuc_Y_Inv', linecolor = 'black')\n",
+    "    \n",
+    "    # Adjust the size of the points\n",
+    "    for trace in fig.data:\n",
+    "        trace.marker.size = 2  \n",
+    "        # Adjust the size of the points\n",
+    "    for trace in fig.data:\n",
+    "        trace.marker.size = 2  \n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        plot_bgcolor='white',\n",
+    "        legend=dict(\n",
+    "            title='Immune checkpoint',  # Titre de la légende\n",
+    "            font=dict(\n",
+    "                family='Arial',\n",
+    "                size=12,\n",
+    "                color='black'\n",
+    "            ),\n",
+    "            bgcolor='white',\n",
+    "            bordercolor='black',\n",
+    "            borderwidth=0.4,\n",
+    "            itemsizing='constant'\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "\n",
+    "    fig.write_image(output_images_dir + \"/\" + title.replace(\" \", \"_\") + \".png\", width=1200, height=800, scale=4)\n",
+    "    print(sample_id,  \"processed!\")\n",
+    "\n",
+    "    fig.show(renderer='png')  # Display the figure as png\n",
+    "    plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67915e19-5963-4441-a86d-7181d39f01cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0] + '.csv'\n",
+    "    sample_id2 = sample.split('_')[0]\n",
+    "    location_colors = df.loc[df['Sample_ID'] == sample_id, ['Nuc_X', 'Nuc_Y_Inv', 'immune_checkpoint']]\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    title = sample_id2 + \" Background Subtracted XY Map immune checkpoint\"\n",
+    "\n",
+    "    for immunecheckpoint in df.loc[df['Sample_ID'] == sample_id, 'immune_checkpoint'].unique():\n",
+    "        # Vérifier si la combinaison est dans le dictionnaire avant d'ajouter la trace\n",
+    "        if immunecheckpoint in immune_checkpoint_color_dict:\n",
+    "            color = 'rgb' + str(immune_checkpoint_color_dict[immunecheckpoint])\n",
+    "            \n",
+    "            fig.add_scatter(\n",
+    "                mode='markers',\n",
+    "                marker=dict(size=3, opacity=0.5, color=color),\n",
+    "                x=location_colors.loc[location_colors['immune_checkpoint'] == immunecheckpoint, 'Nuc_X'],\n",
+    "                y=location_colors.loc[location_colors['immune_checkpoint'] == immunecheckpoint, 'Nuc_Y_Inv'],\n",
+    "                name=immunecheckpoint\n",
+    "            )\n",
+    "    \n",
+    "    fig.update_layout(\n",
+    "        title=title,\n",
+    "        plot_bgcolor='white',\n",
+    "        xaxis_title='Nuc_X',\n",
+    "        yaxis_title='Nuc_Y_Inv',\n",
+    "        legend_title='Immune checkpoint',\n",
+    "        legend=dict(\n",
+    "            font=dict(\n",
+    "                family='Arial',\n",
+    "                size=12,\n",
+    "                color='black'\n",
+    "            ),\n",
+    "            bgcolor='white',\n",
+    "            bordercolor='black',\n",
+    "            borderwidth=0.4,\n",
+    "            itemsizing='constant'\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    fig.write_image(output_images_dir + \"/\" + title.replace(\" \", \"_\") + \".png\", width=1200, height=800, scale=4)\n",
+    "    print(sample_id,  \"processed!\")\n",
+    "\n",
+    "    fig.show(renderer='png')  # Display the figure as png\n",
+    "    plot(fig)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29ffb8ee-aed1-4f0e-b229-9cd3da3896f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23803970-60db-4ad1-988f-6e3cc5febe45",
+   "metadata": {},
+   "source": [
+    "### V.5.2. CELL SUBTYPE DENSITY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3feb8aa-727a-4356-97a4-18ca52c584ae",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ba082fc-7b05-462d-a7fb-880043b381e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shapely.geometry import MultiPoint\n",
+    "\n",
+    " \n",
+    "\n",
+    "# Convert pixel to mm\n",
+    "\n",
+    "df['x_mm'] = df['Nuc_X'] * 0.650 / 1000\n",
+    "\n",
+    "df['y_mm'] = df['Nuc_Y_Inv'] * 0.650 / 1000\n",
+    "\n",
+    " \n",
+    "\n",
+    "# Group by Sample_ID and ROI_Index and calculate the convex hull for each group\n",
+    "\n",
+    "df_grouped = df.groupby(['Sample_ID', 'ROI_index']).apply(lambda group: MultiPoint(group[['x_mm', 'y_mm']].values).convex_hull)\n",
+    "\n",
+    " \n",
+    "\n",
+    "# Calculate the area of each convex hull\n",
+    "\n",
+    "df_area = df_grouped.apply(lambda hull: hull.area if hull.geom_type == 'Polygon' else 0)\n",
+    "\n",
+    " \n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "\n",
+    "df_area = pd.DataFrame(df_area, columns=['Area_mm2'])\n",
+    "\n",
+    " \n",
+    "\n",
+    "print(df_area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfd53782-4f82-49ac-a9e0-9a6ead74914c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "405c5c7b-4d99-433e-8b62-176298d61e3f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdfe357d-a691-49a5-836b-0b49c4dd8f6f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da02b174-decc-483f-bc5a-9e4828ce96f7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83d006dd-7e2a-4e57-9d21-b76717d7b4f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "d0999992-3a79-4c69-8b8c-e25eb084ccc7",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "24ce3877-172e-47d8-9fbb-4006fd72e04e",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "raw",
+   "id": "463687d9-ce5a-4ab0-9993-2b30402379a0",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "953cd672-52c6-4a72-9ad7-1833e2c07cf4",
+   "metadata": {},
+   "source": [
+    "## V.6. CORRELATION PLOTS"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "483b222a-93ed-456f-9eb8-3f5d03fe4b92",
+   "metadata": {},
+   "source": [
+    "!! Correlation plots are not relevant !!\n",
+    "!! We can keep this code but do not use this as interpretation material !!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4340ae3a-7c78-41c5-9dbf-27cdd35e38a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get Pearson correlations and P values for all marker values\n",
+    "# First, get we need to determine how many columns we will be evaluating. \n",
+    "# And prepare empty Numpy arrays to hold our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08144838-b58e-425d-b154-808ed22a195c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#This is a work in progress section, need to run this on individual cell types instead of all cell types together\n",
+    "\n",
+    "keep_sample_Set_A = [ 'TMA.csv', 'D3S1.csv', 'D3S2.csv','D3S3.csv','D4S1.csv','D4S2.csv','D4S3.csv','D5S1.csv','D5S2.csv','D5S3.csv']\n",
+    "keep_sample_Set_B = [ 'TMA.csv', 'DD3S1.csv','DD3S2.csv','DD3S3.csv','DD4S1.csv','DD4S2.csv','DD4S3.csv','DD5S1.csv','DD5S2.csv','DD5S3.csv']\n",
+    "keep_cell_type = [ 'CANCER', 'STROMA', 'ENDOTHELIAL', 'IMMUNE' ]\n",
+    "\n",
+    "# Check project name and execute corresponding operations\n",
+    "if project_name == 'Set_A':\n",
+    "    keep_sample = keep_sample_Set_A\n",
+    "elif project_name == 'Set_B':\n",
+    "    keep_sample = keep_sample_Set_B\n",
+    "else:\n",
+    "    raise ValueError(\"Unknown project name.\")\n",
+    "\n",
+    "df_keep_sample = df.loc[(df['Sample_ID'].isin(keep_sample))\n",
+    "                      & (df['Sample_ID'].isin(keep_sample)), :].copy()\n",
+    "\n",
+    "# df_keep_sample will change regarding which sample you decided to keep in the kee_sample_proj list\n",
+    "df_keep_sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e82126b0-dcc2-4a2f-800b-be7b9beef32f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# n_corr_cols is the number of colums you will compute the correlations with\n",
+    "# (columns in df_keep_sample that are not in the list not_intensities)\n",
+    "n_corr_cols = len(df_keep_sample.columns[~df_keep_sample.columns.isin(not_intensities)])\n",
+    "print(n_corr_cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c25e4d8-75d9-40a6-b8cd-5585af9c3dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# An empty 2D NumPy array is created with dimensions n_corr_cols by n_corr_cols and assigned to the variable pvalues \n",
+    "# This array is intended to store p-values.\n",
+    "pvalues = np.empty((n_corr_cols, n_corr_cols))\n",
+    "\n",
+    "# Similarly, another empty 2D NumPy array with dimensions n_corr_cols by n_corr_cols is created and assigned to the variable corrvalues. \n",
+    "# This array is intended to store correlation values.\n",
+    "corrvalues = np.empty((n_corr_cols,n_corr_cols))\n",
+    "\n",
+    "#print(pvalues)\n",
+    "#print(corrvalues)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b86a1fb3-74e9-4925-aaaa-690dcf8531cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Columns of the DataFrame for_corr are renamed using the dictionary full_to_short_names\n",
+    "for_corr = df_keep_sample.loc[:,~df_keep_sample.columns.isin(not_intensities)].copy()\n",
+    "for_corr = for_corr.rename(columns = full_to_short_names)\n",
+    "for_corr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbf3be17-3e89-466f-9258-5c990a540ecf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute Pearson correlation coefficient and the corresponding p-value for each pair of columns in dataframe\n",
+    "for i in range(for_corr.shape[1]):\n",
+    "    for j in range(0,for_corr.shape[1]):\n",
+    "        col1 = for_corr[for_corr.columns.values[i]]\n",
+    "        col2 = for_corr[for_corr.columns.values[j]]\n",
+    "        corrvalues[i,j] = pearsonr(col1,col2)[0]\n",
+    "        pvalues[i,j] = pearsonr(col1,col2)[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b2928ab-4f81-44b1-8fea-ca7295febf58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correlation and p-value calculations are being organized and formatted into dataframes\n",
+    "corrvalues = pd.DataFrame(corrvalues).round(3)\n",
+    "corrvalues.columns = for_corr.columns.values\n",
+    "corrvalues.index = for_corr.columns.values\n",
+    "\n",
+    "pvalues = pd.DataFrame(pvalues)\n",
+    "pvalues.columns = for_corr.columns.values\n",
+    "pvalues.index = for_corr.columns.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e595da1a-aa7e-44c0-a203-a052576c2fd7",
+   "metadata": {},
+   "source": [
+    "### V.6.1. OPTION 1"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "75a032cd-2002-4a10-a8cb-e90af570b5ae",
+   "metadata": {},
+   "source": [
+    "Option 1: no correlation value on plot, just put p value and have star is p<=0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be4d1d04-11d5-4e3b-a01e-a7d2ba898a62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualizing and marking statistically significant p-values with an asterisk (*) \n",
+    "# for ease of interpretation\n",
+    "# p-values that are less than or equal to 0.05\n",
+    "\n",
+    "# p_add_star() in my_modules.py\n",
+    "p_w_star = pvalues.copy()\n",
+    "p_w_star = p_w_star.apply(lambda row: p_add_star(row), axis = 1)\n",
+    "p_w_star.columns = for_corr.columns.values\n",
+    "p_w_star.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d98ac87b-f60b-4a80-a04f-6692c4180423",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if there is non-significant values\n",
+    "for index, row in p_w_star.iterrows():\n",
+    "    for col in p_w_star.columns:\n",
+    "        value = p_w_star.loc[index, col]\n",
+    "        if \"*\" not in value:\n",
+    "            print(f\"Value without asterisk found at index: {index}, column: {col}, value: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4024da71-dd6b-4b05-a92c-36e8a6ecc90a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correlation visualization\n",
+    "sb.set()\n",
+    "\n",
+    "x_axis_labels = for_corr.columns.values.tolist()\n",
+    "y_axis_labels = for_corr.columns.values.tolist()\n",
+    "\n",
+    "ax = sb.heatmap(corrvalues, \n",
+    "                annot = p_w_star, \n",
+    "                annot_kws = {\"size\": 1.25},\n",
+    "                fmt = 's',\n",
+    "                xticklabels = x_axis_labels, \n",
+    "                yticklabels = y_axis_labels,\n",
+    "                cbar_kws = {'label':'Pearson correlation'},\n",
+    "                linecolor = 'black', \n",
+    "                linewidth = 0.5,\n",
+    "                cmap = 'coolwarm')\n",
+    "\n",
+    "plt.yticks(rotation=0, size = 5)\n",
+    "ax.xaxis.tick_top() # x axis on top\n",
+    "ax.xaxis.set_label_position('top')\n",
+    "ax.tick_params(length=0)\n",
+    "plt.xticks(rotation=45, size = 5)\n",
+    "plt.setp(ax.xaxis.get_majorticklabels(), ha='left')\n",
+    "\n",
+    "ax.set_title(label = \"Correlations option 1\", fontsize = 20)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "filename = \"correlations_option1.png\"\n",
+    "filename = os.path.join(output_images_dir, filename)\n",
+    "plt.savefig(filename,dpi=500)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf458a5-00a4-4e63-b583-668c23fef0b1",
+   "metadata": {},
+   "source": [
+    "### V.6.2. OPTION 2"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "44434cb5-7ff3-468c-ad6b-5d49ee50debe",
+   "metadata": {},
+   "source": [
+    "Option 2: include correlation and 1-3 stars depending on p-value. \n",
+    "1 star: p-value <= 0.05; \n",
+    "2 stars: p-value <= 0.01; \n",
+    "3 stars: p-value <= 0.001."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7428d69a-00a7-44b1-af3f-ad14a395d448",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If the item is less than or equal to 0.001, it assigns 3 asterisks, \n",
+    "# if less than or equal to 0.01, it assigns 2 asterisks, \n",
+    "# if less than or equal to 0.05, it assigns 1 asterisk, \n",
+    "# and if greater than 0.05, it assigns 0 asterisks. \n",
+    "\n",
+    "# p_to_star() in my_modules.py\n",
+    "p_as_stars = pvalues.copy()\n",
+    "p_as_stars = p_as_stars.apply(lambda row: p_to_star(row), axis = 1)\n",
+    "p_as_stars.columns = for_corr.columns.values\n",
+    "p_as_stars.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42327081-1765-4335-bda6-907ac21455d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_w_star = corrvalues.round(2).astype(str) + p_as_stars\n",
+    "corr_w_star.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa5b1ca4-4bd7-4987-b41d-6f7d6b5eef82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if there is non-significant values\n",
+    "for index, row in corr_w_star.iterrows():\n",
+    "    for col in corr_w_star.columns:\n",
+    "        value = corr_w_star.loc[index, col]\n",
+    "        if \"*\" not in value:\n",
+    "            print(f\"Value without asterisk found at index: {index}, column: {col}, value: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b8134e4-58ff-4a6c-b194-008ce95b6734",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corrvalues.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4837b7b3-bee4-42cf-afd1-013dfd9b7c32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_w_star.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0527383-3b44-4277-88e8-cbcf3bb2bfd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correlation visualization\n",
+    "sb.set()\n",
+    "\n",
+    "x_axis_labels = for_corr.columns.values.tolist()\n",
+    "y_axis_labels = for_corr.columns.values.tolist()\n",
+    "\n",
+    "ax = sb.heatmap(corrvalues, \n",
+    "                annot = corr_w_star, \n",
+    "                annot_kws = {\"size\": 1.25},\n",
+    "                fmt = 's',\n",
+    "                xticklabels = x_axis_labels, \n",
+    "                yticklabels=y_axis_labels,\n",
+    "                cbar_kws = {'label':'Pearson correlation'},\n",
+    "                linecolor = 'black', linewidth = 0.5,\n",
+    "                cmap = 'coolwarm')\n",
+    "\n",
+    "plt.yticks(rotation=0, size = 5)\n",
+    "ax.xaxis.tick_top() # x axis on top\n",
+    "ax.xaxis.set_label_position('top')\n",
+    "ax.tick_params(length=0)\n",
+    "plt.xticks(rotation=45, size = 5)\n",
+    "plt.setp(ax.xaxis.get_majorticklabels(), ha='left')\n",
+    "\n",
+    "ax.set_title(label = \"Correlations option 2\", fontsize = 20)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "filename = \"correlations_option2.png\"\n",
+    "filename = os.path.join(output_images_dir, filename)\n",
+    "plt.savefig(filename,dpi=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccf3ece0-a64b-49b9-b4d4-255ba5744ae0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"zscore_pearson_correlations.csv\"\n",
+    "filename = os.path.join(output_data_dir, filename)\n",
+    "corrvalues.to_csv(filename, index = True)\n",
+    "\n",
+    "filename = \"zscore_pearson_p-values.csv\"\n",
+    "filename = os.path.join(output_data_dir, filename)\n",
+    "pvalues.to_csv(filename, index = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d306e691-e82f-4fd5-a818-e7ac594e2d35",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a0bcaff-b1bd-4f60-9ff5-faafc907134c",
+   "metadata": {},
+   "source": [
+    "## V.7. PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aeda3882-5cca-4fb8-8239-2872eb4184cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def demo_PCA(data, c):\n",
+    "    reduced_data = PCA(n_components=2).fit_transform(data)\n",
+    "    kmeans = KMeans(init=\"k-means++\", n_clusters=c, n_init=4)\n",
+    "    kmeans.fit(reduced_data)\n",
+    "\n",
+    "    # Step size of the mesh. Decrease to increase the quality of the VQ.\n",
+    "    h = 0.02  # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
+    "    y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # Obtain labels for each point in mesh. Use last trained model.\n",
+    "    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.figure(1)\n",
+    "    plt.clf()\n",
+    "    plt.imshow(\n",
+    "        Z,\n",
+    "        interpolation=\"nearest\",\n",
+    "        extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
+    "        cmap=plt.cm.Paired,\n",
+    "        aspect=\"auto\",\n",
+    "        origin=\"lower\",\n",
+    "    )\n",
+    "\n",
+    "    plt.plot(reduced_data[:, 0], reduced_data[:, 1], \"k.\", markersize=2)\n",
+    "    # Plot the centroids as a white X\n",
+    "    centroids = kmeans.cluster_centers_\n",
+    "    plt.scatter(\n",
+    "        centroids[:, 0],\n",
+    "        centroids[:, 1],\n",
+    "        marker=\"x\",\n",
+    "        s=169,\n",
+    "        linewidths=3,\n",
+    "        color=\"w\",\n",
+    "        zorder=10,\n",
+    "    )\n",
+    "    plt.title(\n",
+    "        \"K-means clustering on the dataset (PCA-reduced data)\\n\"\n",
+    "        \"Centroids are marked with white cross\"\n",
+    "    )\n",
+    "    plt.xlim(x_min, x_max)\n",
+    "    plt.ylim(y_min, y_max)\n",
+    "    plt.xticks(())\n",
+    "    plt.yticks(())\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def other_PCA(data, c):\n",
+    "    reduced_data = PCA(n_components=2).fit_transform(data)\n",
+    "    ###kmeans = KMeans(init=\"k-means++\", n_clusters=c, n_init=4)\n",
+    "    ###kmeans.fit(reduced_data)\n",
+    "    \n",
+    "    plt.scatter(reduced_data[:,0], reduced_data[:,1], cmap = 'plasma')\n",
+    "    plt.xlabel('1st Principal Component')\n",
+    "    plt.ylabel('2nd Principal Component')\n",
+    "    plt.title('Scatter Plot of the 2 Principal Component')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0b598d9-7355-4f58-8065-e6e4cf073526",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test=df.loc[:,~df.columns.isin(not_intensities)]\n",
+    "demo_PCA(df_test, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e15b7d17-f6ce-498d-b92f-5c11081798f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_PCA(df_test, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bd0536e-59f9-4d21-916b-93151a94cf5f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fb1db45-9f4a-480e-b51e-3f8722cf8292",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24c08b97-1995-4f15-9587-396c369a6ce1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efaea32f-0cbc-4f11-9618-efc8736a87d1",
+   "metadata": {},
+   "source": [
+    "## V.8. KMEANS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "978dc47e-e957-4922-a731-87bec80112c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define number of clusters - according to set & cell type\n",
+    "\n",
+    "def kmeans_analysis(df, c):\n",
+    "    analyzed_df = df.loc[:,~df.columns.isin(not_intensities)]\n",
+    "    ###kM = KMeans(n_clusters=c).fit_predict(analyzed_df)\n",
+    "    kM = KMeans(n_clusters=c).fit(analyzed_df)\n",
+    "    ###centroids = kmeans.cluster_centers_ #list of clusters. Each cluster is a matrix of means for each parameter (protein) analyzed\n",
+    "    labels = kM.labels_\n",
+    "    clusterCount = np.bincount(labels) #holds information for how many points are in each cluster\n",
+    "    ###plt.scatter(normed_V1, normed_V2, s=10, c=labels, cmap='coolwarm') # instead of c=bn_class\n",
+    "    return None\n",
+    "\n",
+    "def scale_data(df):\n",
+    "    pca = PCA(n_components=2) #We are doing a 2D data visualisation, so we need to select 2 principal components for PCA\n",
+    "    sc = StandardScaler()\n",
+    "    df_scaled = sc.fit_transform(df.loc[:,~df.columns.isin(not_intensities)])\n",
+    "    PCs = pca.fit_transform(df_scaled)\n",
+    "    return PCs, df_scaled\n",
+    "\n",
+    "def plot_kmeans(df, nb_cl):\n",
+    "    pc, scaled_df = scale_data(df)\n",
+    "    ###kmeans = KMeans(n_clusters= nb_cl).fit(scaled_df)\n",
+    "    ###df_predict = kmeans.predict(scaled_df)\n",
+    "    test = KMeans(n_clusters= nb_cl).fit_predict(scaled_df)\n",
+    "    sb.scatterplot(x=pc[:,0],y=pc[:,1],hue=test)\n",
+    "    return None\n",
+    "\n",
+    "def kmeans_plotting(df, c):\n",
+    "    analyzed_df = df.loc[:,~df.columns.isin(not_intensities)]\n",
+    "    #Initialize the class object\n",
+    "    kmeans = KMeans(n_clusters= c)\n",
+    "    #Predict the labels of clusters.\n",
+    "    l = kmeans.fit_predict(analyzed_df) #returns the array of cluster labels each data point belongs to\n",
+    "    plot_results(l)\n",
+    "    return l\n",
+    "\n",
+    "def plot_results(label): #https://www.askpython.com/python/examples/plot-k-means-clusters-python\n",
+    "    #Getting unique labels\n",
+    "    u_labels = np.unique(label)\n",
+    "    #Plotting the results\n",
+    "    for i in u_labels:\n",
+    "        plt.scatter(df.iloc[label == i , 0] , df.iloc[label == i , 1] , label = i)\n",
+    "    plt.title(\"K-means clustering\")\n",
+    "    plt.legend(loc='center left', title = \"Clusters\", bbox_to_anchor=(1, 0.5))\n",
+    "    plt.show()\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a63f40f4-0756-46e5-97e0-0d07fa066bc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define number of clusters - according to set & cell type\n",
+    "\n",
+    "clusterList = kmeans_analysis(df, 20)\n",
+    "plot_kmeans(df, 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caebf01f-b05b-4b39-a6ae-daa55646cff1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "clusterLabels_array = kmeans_plotting(df, 20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "213f638b-5d2d-49fd-af69-34835c7f854b",
+   "metadata": {},
+   "source": [
+    "## pca + kmean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efa95946-e460-4d62-910c-99f43c28cc37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def standardization(df):\n",
+    "    df = df.loc[:,~df.columns.isin(not_intensities)]\n",
+    "    scaler = StandardScaler()\n",
+    "    data_std = scaler.fit_transform(df)\n",
+    "    return data_std\n",
+    "\n",
+    "def determine_features_kept(x, y):\n",
+    "    real_componentValue = np.interp(0.8, y, x)\n",
+    "    rounded_componentValue = round(real_componentValue)\n",
+    "    print(\"Number of features/components kept : {}\".format(rounded_componentValue))\n",
+    "    return rounded_componentValue\n",
+    "\n",
+    "def explained_variance(pCA):\n",
+    "    y = pCA.explained_variance_ratio_\n",
+    "    max_range = len(y) + 1\n",
+    "    x = range(1,max_range)\n",
+    "    plt.figure(figsize = (10,8))\n",
+    "    plt.plot(x, y.cumsum(), marker = 'o', linestyle = '--')\n",
+    "    plt.title(\"Explained Variance by Components\")\n",
+    "    plt.xlabel(\"Number of components\")\n",
+    "    plt.ylabel(\"Cumulative Explained Variance\")\n",
+    "    plt.show()\n",
+    "    nb_features = determine_features_kept(x, y.cumsum())\n",
+    "    return nb_features\n",
+    "\n",
+    "def pca_analysis(nb_feat, df):\n",
+    "    pca = PCA(n_components = nb_feat)\n",
+    "    pca.fit(df)\n",
+    "    scores_pca = pca.transform(df)\n",
+    "    return scores_pca\n",
+    "\n",
+    "def dimensionality_reduction(df): #using PCA\n",
+    "    standard_df = standardization(df)\n",
+    "    pca = PCA()\n",
+    "    pca.fit(standard_df)\n",
+    "    nb_components = explained_variance(pca)\n",
+    "    pca_scores = pca_analysis(nb_components, standard_df)\n",
+    "    return pca_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe2808df-29f3-40d1-94e2-83ae0e95c9ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "scoresPCA = dimensionality_reduction(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c60a038-0950-49e6-adaf-b56be3a7d3b7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def determine_slope(a, b):\n",
+    "    return abs((b[1] - a[1]) / (b[0] - a[0]))\n",
+    "\n",
+    "def record_slopes(X, Y):\n",
+    "    slope_list = []\n",
+    "    if len(X) == len(Y):\n",
+    "        for i in range(len(X)-1):\n",
+    "            A = (X[i], Y[i])\n",
+    "            B = (X[i+1], Y[i+1])\n",
+    "            slope = determine_slope(A, B)\n",
+    "            slope_list.append(slope)\n",
+    "    else:\n",
+    "        print(\"Error! length of X and Y coordinates do not match!\")\n",
+    "        slope_list.append(0)\n",
+    "    return slope_list\n",
+    "\n",
+    "def elbow_coordinates(diff_ls, x_coord):\n",
+    "    max_diff = max(diff_ls) #the slopes with the highest difference correspond to the \"sharpest\" elbow\n",
+    "    index_max = diff_ls.index(max_diff)   \n",
+    "    s_coord = x_coord[index_max+1] #we determine the X position of the elbow\n",
+    "    return s_coord\n",
+    "\n",
+    "def highest_slope_diff(ls_slopes, coord_x):\n",
+    "    ls_diffs = [] #list of slope differences\n",
+    "    for i in range(len(ls_slopes)-1):\n",
+    "        diff = abs(ls_slopes[i] - ls_slopes[i+1]) #compare differences between each slope\n",
+    "        ls_diffs.append(diff)\n",
+    "    elbow = elbow_coordinates(ls_diffs, coord_x) #returns the X coordinates where the elbow is\n",
+    "    return elbow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d61cc628-2bc0-4c78-bdde-1e99fa75b6ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "liste = [46405799.34079681, 38240577.23298195, 35026058.95518527, 32185738.815936424, 30500185.571518555, 28678764.340087377, 27418667.363556623, 26234063.97845333, 25405258.778627113, 24454635.699228957]\n",
+    "x = list(range(1, 11))\n",
+    "\n",
+    "highest_slope_diff(liste, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b83187a4-476e-4d54-b3f3-e152b6ab654e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50092353-e190-42d5-bf76-1f59b9579989",
+   "metadata": {},
+   "source": [
+    "## V.9. T-SNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34e3b33d-f2f6-4791-9c20-e69c69917b9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Implémente notre dataset\n",
+    "## A partir df_tsne, ajoute colonne(s) d'intérêt\n",
+    "cols = list(df.columns)\n",
+    "df['Patient']= df['Patient']\n",
+    "df['cell_type']= df['cell_type']\n",
+    "print('Size of the dataframe: {}'.format(df.shape))\n",
+    "\n",
+    "## Selectionner nb random de valeurs (temp, pr performance)\n",
+    "np.random.seed(50)\n",
+    "rndperm = np.random.permutation(df.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3caff5dd-8998-49ae-b920-990d66d3770e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Remove index values (unneeded & handicapping for T-SNE analysis)\n",
+    "df = df.reset_index(drop=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e98e3e1-7e1c-4e45-809e-314a6298c812",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Keep only the numbers in the patient ID column\n",
+    "df['Patient'] = df['Patient'].map(lambda x: x.lstrip('Pt'))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "161bb688-aada-4219-a8bf-701ca75c5527",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#t-sne\n",
+    "N = 20000\n",
+    "\n",
+    "df_subset = df.loc[rndperm[:N],:].copy() #remove later for full subset? (since it is already a subset)\n",
+    "\n",
+    "data_subset = df_subset[cols].values\n",
+    "\n",
+    "pca = PCA(n_components=3)\n",
+    "data_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f025c11-0ad8-4fe9-97e8-5e909bedbb51",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "pca_result = pca.fit_transform(data_subset)\n",
+    "\n",
+    "df_subset['pca-one'] = pca_result[:,0]\n",
+    "df_subset['pca-two'] = pca_result[:,1] \n",
+    "df_subset['pca-three'] = pca_result[:,2]\n",
+    "\n",
+    "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01654dbe-b1ab-4e59-98bd-ac66b4092d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b64686e7-ed59-4fc5-9047-7b957f2265f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c7ac1e3-0dea-4143-aea0-71247e2272e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09ff9130-a963-4fdd-aa66-353f559292b6",
+   "metadata": {},
+   "source": [
+    "## V.10. SAVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c06a342d-a60e-4688-ace6-e358590f70e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the data by Sample_ID\n",
+    "# Check for the existence of the output file first\n",
+    "for sample in ls_samples:\n",
+    "    sample_id = sample.split('_')[0]\n",
+    "    filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "    if os.path.exists(filename):\n",
+    "        print(\"File by name \"+filename+\" already exists.\")\n",
+    "    else:\n",
+    "        sample_id_csv = sample_id + '.csv'\n",
+    "        df_save = df.loc[df['Sample_ID'] == sample_id_csv, :]\n",
+    "        #print(df_save)\n",
+    "        filename = os.path.join(output_data_dir,  sample_id + \"_\" + step_suffix + \".csv\")\n",
+    "        df_save.to_csv(filename, index=True, index_label='ID')  # Set index parameter to True to retain the index column\n",
+    "        print(\"File \" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2701ab54-d228-46f3-ba6f-7698ceede677",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the dataset as a single file for TSNE/UMAP notebook\n",
+    "filename = \"all_Samples_\" + project_name + \".csv\"\n",
+    "filename = os.path.join(output_data_dir, filename)\n",
+    "\n",
+    "if os.path.exists(filename):\n",
+    "    print(\"File by name \"+filename+\" already exists.\")\n",
+    "else :\n",
+    "    df.to_csv(filename, index = False)\n",
+    "    print(\"File \" + filename + \" was created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b09ecaa6-e836-4380-b1f2-428a02049481",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+version https://git-lfs.github.com/spec/v1
+oid sha256:72623169e7fa754d5892a19670be3a1f4cd2bec5fb4736db5e8de103d70c5fe4
+size 326996556
diff --git a/wetransfer_data-zip_2024-05-17_1431/my_modules.py b/wetransfer_data-zip_2024-05-17_1431/my_modules.py
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index 0000000000000000000000000000000000000000..ced6e8b96a63181aca02de202f5f76b9a1a06239
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/my_modules.py
@@ -0,0 +1,468 @@
+import os
+import numpy as np
+import pandas as pd
+import subprocess
+import os
+import random
+import re
+import pandas as pd
+import numpy as np
+import seaborn as sb
+import matplotlib.pyplot as plt
+import matplotlib.colors as mplc
+import subprocess
+
+
+from scipy import signal
+
+import plotly.figure_factory as ff
+import plotly
+import plotly.graph_objs as go
+from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
+
+
+# This function takes in a dataframe, changes the names
+# of the column in various ways, and returns the dataframe.
+# For best accuracy and generalizability, the code uses
+# regular expressions (regex) to find strings for replacement.
+def apply_header_changes(df):
+    # remove lowercase x at beginning of name
+    df.columns = df.columns.str.replace("^x","")
+    # remove space at beginning of name
+    df.columns = df.columns.str.replace("^ ","")
+    # replace space with underscore
+    df.columns = df.columns.str.replace(" ","_")
+    # fix typos
+    df.columns = df.columns.str.replace("AF_AF","AF")
+    # change "Cell Id" into "ID"
+    df.columns = df.columns.str.replace("Cell Id","ID")
+    # if the ID is the index, change "Cell Id" into "ID"
+    df.index.name = "ID"
+    # 
+    df.columns = df.columns.str.replace("","")
+    return df
+
+def apply_df_changes(df):
+    # Remove "@1" after the ID in the index
+    df.index = df.index.str.replace(r'@1$', '')
+    return df
+
+def compare_headers(expected, actual, name):
+    missing_actual = np.setdiff1d(expected, actual)
+    extra_actual = np.setdiff1d(actual, expected)
+    if len(missing_actual) > 0:
+        #print("WARNING: File '" + name + "' lacks the following expected header(s) after import header reformatting: \n" 
+        #      + str(missing_actual))
+        print("WARNING: File '" + name + "' lacks the following expected item(s): \n" + str(missing_actual))
+    if len(extra_actual) > 0:
+        #print("WARNING: '" + name + "' has the following unexpected header(s) after import header reformatting: \n" 
+        #      + str(extra_actual))
+        print("WARNING: '" + name + "' has the following unexpected item(s): \n" + str(extra_actual))
+    
+    return None
+
+
+def add_metadata_location(row):
+    fc = row['full_column'].lower()
+    if 'cytoplasm' in fc and 'cell' not in fc and 'nucleus' not in fc:
+        return 'cytoplasm'
+    elif 'cell' in fc and 'cytoplasm' not in fc and 'nucleus' not in fc:
+        return 'cell'
+    elif 'nucleus' in fc and 'cell' not in fc and 'cytoplasm' not in fc:
+        return 'nucleus'
+    else:
+        return 'unknown'
+
+    
+def get_perc(row, cell_type):
+    total = row['stroma'] + row['immune'] + row['cancer']+row['endothelial']
+    return round(row[cell_type]/total *100,1)
+
+
+
+# Divide each marker (and its localisation) by the right exposure setting for each group of samples
+def divide_exp_time(col, exp_col, metadata):
+    exp_time = metadata.loc[metadata['full_column'] == col.name, exp_col].values[0]
+    return col/exp_time
+
+
+def do_background_sub(col, df, metadata):
+    #print(col.name)
+    location = metadata.loc[metadata['full_column'] == col.name, 'localisation'].values[0]
+    #print('location = ' + location)
+    channel = metadata.loc[metadata['full_column'] == col.name, 'Channel'].values[0]
+    #print('channel = ' + channel)
+    af_target = metadata.loc[
+        (metadata['Channel']==channel) \
+        & (metadata['localisation']==location) \
+        & (metadata['target_lower'].str.contains(r'^af\d{3}$')),\
+        'full_column'].values[0]
+    return col - df.loc[:,af_target]
+
+
+"""
+This function plots distributions. It takes in a string title (title), a list of
+dataframes from which to plot (dfs), a list of dataframe names for the legend
+(names), a list of the desired colors for the plotted samples (colors),
+a string for the x-axis label (x_label), ```a float binwidth for histrogram (bin_size)```,
+a boolean to show the legend or not (legend),
+and the names of the marker(s) to plot (input_labels). If not specified,
+the function will plot all markers in one plot. input_labels can either be a 
+single string, e.g., 'my_marker', or a list, e.g., ['my_marker1','my_marker2'].
+
+The function will create a distribution plot and save it to png. It requires 
+a list of items not to be considered as markers when evaluating column names
+(not_markers) to be in memory. It also requires a desired output location of
+the files (output_dir) to already be in memory. 
+"""
+
+
+
+def make_distr_plot_per_sample(title, location, dfs, df_names, colors, x_label, legend, xlims = None, markers = ['all'],not_intensities = None):
+    ### GET LIST OF MARKERS TO PLOT ###
+    # Get list of markers to plot if not specified by user, using columns in first df
+    # Writing function(parameter = FILLER) makes that parameter optional when user calls function,
+    # since it is given a default value!
+    if markers == ["all"]:
+        markers = [c for c in dfs[0].columns.values if c not in not_intensities]
+    elif not isinstance(markers, list):
+        markers = [markers]
+    # Make input labels a set to get only unique values, then put back into list
+    markers = list(set(markers))
+    
+    ### GET XLIMS ###
+    if xlims == None:
+        mins = [df.loc[:,markers].min().min() for df in dfs]
+        maxes = [df.loc[:,markers].max().max() for df in dfs]
+        xlims = [min(mins), max(maxes)]
+    if not isinstance(xlims, list):
+        print("Problem - xlmis not list. Exiting method...")
+        return None
+    ### CHECK DATA CAN BE PLOTTED ###
+    # Check for data with only 1 unique value - this will cause error if plotted
+    group_labels = []
+    hist_data = []
+    # Iterate through all dataframes (dfs)
+    for i in range(len(dfs)):
+        # Iterate through all marker labels
+        for f in markers:
+            # If there is only one unique value in the marker data for this dataframe,
+            # you cannot plot a distribution plot. It gives you a linear algebra
+            # singular value matrix error
+            if dfs[i][f].nunique() != 1:
+                # Add df name and marker name to labels list
+                # If we have >1 df, we want to make clear
+                # which legend label is associated with which df
+                if len(df_names) > 1:
+                    group_labels.append(df_names[i]+"_"+f)
+                else:
+                    group_labels.append(f)
+                # add the data to the data list
+                hist_data.append(dfs[i][f])
+    # if no data had >1 unique values, there is nothing to plot
+    if len(group_labels) < 1:
+        print("No markers plotted - all were singular value. Names and markers were " + str(df_names) + ", " + str(markers))
+        return None
+
+    ### TRANSFORM COLOR ITEMS TO CORRECT TYPE ###
+    if isinstance(colors[0], tuple):
+        colors = ['rgb' + str(color) for color in colors]
+    
+    ### PLOT DATA ###
+    # Create plot
+    fig = ff.create_distplot(hist_data, group_labels, bin_size=0.1,
+        #colors=colors, bin_size=bin_size,  show_rug=False)#show_hist=False,
+        colors=colors, show_rug=False)
+    # Adjust title, font, background color, legend...
+    fig.update_layout(title_text=title, font=dict(size=18), 
+        plot_bgcolor = 'white', showlegend = legend)#, legend_x = 3)
+    # Adjust opacity
+    fig.update_traces(opacity=0.6)
+    # Adjust x-axis parameters
+    fig.update_xaxes(title_text = x_label, showline=True, linewidth=2, linecolor='black', 
+        tickfont=dict(size=18), range = xlims) # x lims was here
+    # Adjust y-axis parameters
+    fig.update_yaxes(title_text = "Kernel density estimate",showline=True, linewidth=1, linecolor='black',
+        tickfont=dict(size=18))
+    
+
+    ### SAVE/DISPLAY PLOT ###
+    # Save plot to HTML
+    # plotly.io.write_html(fig, file = output_dir + "/" + title + ".html")
+    # Plot in new tab
+    #plot(fig)   
+    # Save to png
+    filename = os.path.join(location, title.replace(" ","_") + ".png")
+    fig.write_image(filename)
+    return None
+
+
+
+
+
+    # this could be changed to use recursion and make it 'smarter'
+
+def shorten_feature_names(long_names):
+    name_dict = dict(zip(long_names,[n.split('_')[0] for n in long_names]))
+    names_lts, long_names, iteration = shorten_feature_names_helper(name_dict, long_names, 1)
+    # names_lts = names long-to-short
+    # names_stl = names stl
+    names_stl = {}
+    for n in names_lts.items():
+        names_stl[n[1]] = n[0]
+    return names_lts, names_stl
+    
+
+def shorten_feature_names_helper(name_dict, long_names, iteration):
+    #print("\nThis is iteration #"+str(iteration))
+    #print("name_dict is: " + str(name_dict))
+    #print("long_names is: " + str(long_names))
+    ## If the number of unique nicknames == number of long names
+    ## then the work here is done
+    #print('\nCompare lengths: ' + str(len(set(name_dict.values()))) + ", " + str(len(long_names)))
+    #print('set(name_dict.values()): ' + str(set(name_dict.values())))
+    #print('long_names: ' + str(long_names))
+    if len(set(name_dict.values())) == len(long_names): 
+        #print('All done!')
+        return name_dict, long_names, iteration
+    
+    ## otherwise, if the number of unique nicknames is not
+    ## equal to the number of long names (must be shorter than),
+    ## then we need to find more unique names
+    iteration += 1
+    nicknames_set = set()
+    non_unique_nicknames = set()
+    # construct set of current nicknames
+    for long_name in long_names:
+        #print('long_name is ' + long_name + ' and non_unique_nicknames set is ' + str(non_unique_nicknames))
+        short_name = name_dict[long_name]
+        if short_name in nicknames_set:
+            non_unique_nicknames.add(short_name)
+        else:
+            nicknames_set.add(short_name)
+    #print('non_unique_nicknames are: ' + str(non_unique_nicknames))
+    
+    # figure out all long names associated
+    # with the non-unique short names
+    trouble_long_names = set()
+    for long_name in long_names:
+        short_name = name_dict[long_name]
+        if short_name in non_unique_nicknames:
+            trouble_long_names.add(long_name)
+    
+    #print('troublesome long names are: ' + str(trouble_long_names))
+    #print('name_dict: ' + str(name_dict))
+    # operate on all names that are associated with
+    # the non-unique short nicknames
+    for long_name in trouble_long_names:
+        #print('trouble long name is: ' + long_name)
+        #print('old nickname is: ' + name_dict[long_name])
+        name_dict[long_name] = '_'.join(long_name.split('_')[0:iteration])
+        #print('new nickname is: ' + name_dict[long_name])
+    shorten_feature_names_helper(name_dict, long_names, iteration)
+    return name_dict, long_names, iteration
+
+
+def heatmap_function2(title,
+            data,
+              method, metric, cmap,
+              cbar_kws, xticklabels, save_loc,
+              row_cluster, col_cluster, 
+            annotations = {'rows':[],'cols':[]}):
+    
+    sb.set(font_scale= 6.0)
+
+    # Extract row and column mappings
+    row_mappings = []
+    col_mappings = []
+    for ann in annotations['rows']:
+        row_mappings.append(ann['mapping'])
+    for ann in annotations['cols']:
+        col_mappings.append(ann['mapping'])
+    # If empty lists, convert to None so seaborn accepts
+    # as the row_colors or col_colors objects
+    if len(row_mappings) == 0:
+        row_mappings = None
+    if len(col_mappings) == 0:
+        col_mappings = None
+
+def heatmap_function(title,
+            data,
+              method, metric, cmap,
+              cbar_kws, xticklabels, save_loc,
+              row_cluster, col_cluster, 
+            annotations = {'rows':[],'cols':[]}):
+    
+    sb.set(font_scale= 2.0)
+
+    # Extract row and column mappings
+    row_mappings = []
+    col_mappings = []
+    for ann in annotations['rows']:
+        row_mappings.append(ann['mapping'])
+    for ann in annotations['cols']:
+        col_mappings.append(ann['mapping'])
+    # If empty lists, convert to None so seaborn accepts
+    # as the row_colors or col_colors objects
+    if len(row_mappings) == 0:
+        row_mappings = None
+    if len(col_mappings) == 0:
+        col_mappings = None
+
+    # Create clustermap
+    g = sb.clustermap(data = data, 
+                  robust = True,
+                  method = method, metric = metric,
+                  cmap = cmap,
+                  row_cluster = row_cluster, col_cluster = col_cluster,
+                  figsize = (40,30),
+                  row_colors=row_mappings, col_colors=col_mappings,
+                      yticklabels = False,
+                     cbar_kws = cbar_kws,
+                     xticklabels = xticklabels)
+    
+    # To rotate slightly the x labels
+    plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), rotation=45) 
+    
+    # Add title
+    g.fig.suptitle(title, fontsize = 60.0)
+    
+    #And now for the legends:
+    # iterate through 'rows', 'cols'
+    for ann_type in annotations.keys():
+        # iterate through each individual annotation feature
+        for ann in annotations[ann_type]:
+            color_dict = ann['dict']
+            handles = []
+            for item in color_dict.keys():
+                h = g.ax_col_dendrogram.bar(0,0, color = color_dict[item], label = item,
+                                           linewidth = 0)
+                handles.append(h)
+            legend = plt.legend(handles = handles, loc = ann['location'], title = ann['label'],
+                               bbox_to_anchor=ann['bbox_to_anchor'],
+                               bbox_transform=plt.gcf().transFigure)
+            ax = plt.gca().add_artist(legend)
+
+    # Save image
+    filename = os.path.join(save_loc, title.lower().replace(" ","_") + ".png")
+    g.savefig(filename)
+    
+    return None
+
+
+    
+# sources - 
+#https://stackoverflow.com/questions/27988846/how-to-express-classes-on-the-axis-of-a-heatmap-in-seaborn
+# https://matplotlib.org/3.1.1/tutorials/intermediate/legend_guide.html
+
+
+def verify_line_no(filename, lines_read):
+    # Use Linux "wc -l" command to get the number of lines in the unopened file
+    wc = subprocess.check_output(['wc', '-l', filename]).decode("utf-8")
+    # Take that string, turn it into a list, extract the first item,
+    # and make that an int - this is the number of lines in the file
+    wc = int(wc.split()[0])
+    if lines_read != wc:
+        print("WARNING: '" + filename + "' has " + str(wc) + 
+            " lines, but imported dataframe has " 
+              + str(lines_read) + " (including header).")
+    return None
+
+
+def rgb_tuple_from_str(rgb_str):
+    rgb_str = rgb_str.replace("(","").replace(")","").replace(" ","")
+    rgb = list(map(float,rgb_str.split(",")))
+    return tuple(rgb)
+
+def color_dict_to_df(cd, column_name):
+    df = pd.DataFrame.from_dict(cd, orient = 'index')
+    df['rgb'] = df.apply(lambda row: (np.float64(row[0]), np.float64(row[1]), np.float64(row[2])), axis = 1)
+    df = df.drop(columns = [0,1,2])
+    df['hex'] = df.apply(lambda row: mplc.to_hex(row['rgb']), axis = 1)
+    df[column_name] = df.index
+    return df
+
+
+# p-values that are less than or equal to 0.05
+def p_add_star(row):
+    m = [str('{:0.3e}'.format(m)) + "*" 
+         if m <= 0.05 \
+         else str('{:0.3e}'.format(m))
+        for m in row ]
+    return pd.Series(m)
+
+# assigns a specific number of asterisks based on the thresholds 
+def p_to_star(row):
+    output  = []
+    for item in row:
+        if item <= 0.001:
+            stars = 3
+        elif item <= 0.01:
+            stars = 2
+        elif item <= 0.05:
+            stars = 1
+        else:
+            stars = 0
+        value = ''
+        for i in range(stars):
+            value += '*'
+        output.append(value)
+    return pd.Series(output)
+
+
+
+def plot_gaussian_distributions(df):
+    # Initialize thresholds list to store all calculated thresholds
+    all_thresholds = []
+
+    # Iterate over all columns except the first one (assuming the first one is non-numeric or an index)
+    for column in df.columns:  
+        # Extract the marker data
+        marker_data = df[column]
+
+        # Calculating mean and standard deviation for each marker
+        m_mean, m_std = np.mean(marker_data), np.std(marker_data)
+
+        # Generating x values for the Gaussian curve
+        x_vals = np.linspace(marker_data.min(), marker_data.max(), 100)
+
+        # Calculating Gaussian distribution curve
+        gaussian_curve = (1 / (m_std * np.sqrt(2 * np.pi))) * np.exp(-(x_vals - m_mean) ** 2 / (2 * m_std ** 2))
+
+        # Creating figure for Gaussian distribution for each marker
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=x_vals, y=gaussian_curve, mode='lines', name=f'{column} Gaussian Distribution'))
+        fig.update_layout(title=f'Gaussian Distribution for {column} Marker')
+
+        # Calculating thresholds based on each marker's distribution
+        seuil_1sigma = m_mean + m_std
+        seuil_2sigma = m_mean + 2 * m_std
+        seuil_3sigma = m_mean + 3 * m_std
+
+        # Display the figures with thresholds
+        fig.add_shape(type='line', x0=seuil_1sigma, y0=0, x1=seuil_1sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='red', dash='dash'), name=f'Seuil 1σ: {seuil_1sigma:.2f}')
+        fig.add_shape(type='line', x0=seuil_2sigma, y0=0, x1=seuil_2sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='green', dash='dash'), name=f'Seuil 2σ: {seuil_2sigma:.2f}')
+        fig.add_shape(type='line', x0=seuil_3sigma, y0=0, x1=seuil_3sigma, y1=np.max(gaussian_curve),
+                      line=dict(color='blue', dash='dash'), name=f'Seuil 3σ: {seuil_3sigma:.2f}')
+
+        # Add markers and values to the plot
+        fig.add_trace(go.Scatter(x=[seuil_1sigma, seuil_2sigma, seuil_3sigma],
+                                 y=[0, 0, 0],
+                                 mode='markers+text',
+                                 text=[f'{seuil_1sigma:.2f}', f'{seuil_2sigma:.2f}', f'{seuil_3sigma:.2f}'],
+                                 textposition="top center",
+                                 marker=dict(size=10, color=['red', 'green', 'blue']),
+                                 name='Threshold Values'))
+
+        fig.show()
+
+        # Append thresholds for each marker to the list
+        all_thresholds.append((column, seuil_1sigma, seuil_2sigma, seuil_3sigma))  # Include the column name
+
+    # Return thresholds for all markers
+    return all_thresholds
+
+
+
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv b/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv
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--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S1_bs.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e554b52d689c4163abcc6256d41c996caff2eb545a6c6dceef7a4ab66f9541db
+size 133327656
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S2_bs.csv b/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S2_bs.csv
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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e7b08b8eda9e9c78adec651f40aa373646543a1425dcbff5394f5e83459460c
+size 138615691
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S3_bs.csv b/wetransfer_data-zip_2024-05-17_1431/test_bs/DD3S3_bs.csv
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+version https://git-lfs.github.com/spec/v1
+oid sha256:92a152e8e267d5d835d84edb1e93ae8b940b655bcde7a1ffda639ce21d19fcce
+size 225018985
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_bs/TMA_bs.csv b/wetransfer_data-zip_2024-05-17_1431/test_bs/TMA_bs.csv
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 181932794
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_cqc/test_cell_subtypes_number_by_scenes.csv b/wetransfer_data-zip_2024-05-17_1431/test_cqc/test_cell_subtypes_number_by_scenes.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6c866174e116a6d0ef30c289c29027b02b7113a0
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_cqc/test_cell_subtypes_number_by_scenes.csv
@@ -0,0 +1 @@
+dc,b,tcd4,tcd8,m1,m2,treg,immune_other,cancer,αsma_mycaf,stroma_other,endothelial,total_cells
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_data/Ashlar_Exposure_Time.csv b/wetransfer_data-zip_2024-05-17_1431/test_data/Ashlar_Exposure_Time.csv
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--- /dev/null
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+Target,Round,Channel,ExposureTime
+DAPI0,R0,c0,30.0
+AF488,R0,c1,300.0
+AF555,R0,c2,1500.0
+AF647,R0,c3,1500.0
+AF750,R0,c4,1500.0
+DAPI1,R1,c0,20.0
+ColVI,R1,c1,300.0
+CD31,R1,c2,1200.0
+CD4,R1,c3,1500.0
+Ecad,R1,c4,800.0
+DAPI2,R2,c0,15.0
+Desmin,R2,c1,300.0
+B7H4,R2,c2,1500.0
+CD8,R2,c3,1500.0
+CD20,R2,c4,1500.0
+DAPI3,R3,c0,20.0
+aSMA,R3,c1,50.0
+CD68,R3,c2,1500.0
+PD1,R3,c3,1500.0
+CD45,R3,c4,1500.0
+DAPI4,R4,c0,10.0
+Vimentin,R4,c1,150.0
+AXL,R4,c2,1500.0
+PDL1,R4,c3,1500.0
+FOXP3,R4,c4,1500.0
+DAPI5,R5,c0,10.0
+r5c2,R5,c1,20.0
+CA9,R5,c2,1500.0
+CD163,R5,c3,1500.0
+Ki67,R5,c4,1000.0
+DAPI6,R6,c0,10.0
+CKs,R6,c1,200.0
+Fibronectin,R6,c2,1500.0
+CD44,R6,c3,1200.0
+HLA,R6,c4,500.0
+DAPI7,R7,c0,10.0
+r7c2,R7,c1,20.0
+PDGFR,R7,c2,1500.0
+MMP9,R7,c3,1500.0
+GATA3,R7,c4,1500.0
+DAPI8,R8,c0,10.0
+r8c2,R8,c1,25.0
+CD11c,R8,c2,1500.0
+Sting,R8,c3,1000.0
+CD11b,R8,c4,1500.0
+DAPI0,R0,c0,30.0
+AF488,R0,c1,300.0
+AF555,R0,c2,1500.0
+AF647,R0,c3,1500.0
+AF750,R0,c4,1500.0
+DAPI1,R1,c0,20.0
+ColVI,R1,c1,300.0
+CD31,R1,c2,1200.0
+CD4,R1,c3,1500.0
+Ecad,R1,c4,800.0
+DAPI2,R2,c0,15.0
+Desmin,R2,c1,300.0
+B7H4,R2,c2,1500.0
+CD8,R2,c3,1500.0
+CD20,R2,c4,1500.0
+DAPI3,R3,c0,20.0
+aSMA,R3,c1,50.0
+CD68,R3,c2,1500.0
+PD1,R3,c3,1500.0
+CD45,R3,c4,1500.0
+DAPI4,R4,c0,10.0
+Vimentin,R4,c1,150.0
+AXL,R4,c2,1500.0
+PDL1,R4,c3,1500.0
+FOXP3,R4,c4,1500.0
+DAPI5,R5,c0,10.0
+r5c2,R5,c1,20.0
+CA9,R5,c2,1500.0
+CD163,R5,c3,1500.0
+Ki67,R5,c4,1000.0
+DAPI6,R6,c0,10.0
+CKs,R6,c1,200.0
+Fibronectin,R6,c2,1500.0
+CD44,R6,c3,1200.0
+HLA,R6,c4,500.0
+DAPI7,R7,c0,10.0
+r7c2,R7,c1,20.0
+PDGFR,R7,c2,1500.0
+MMP9,R7,c3,1500.0
+GATA3,R7,c4,1500.0
+DAPI8,R8,c0,10.0
+r8c2,R8,c1,25.0
+CD11c,R8,c2,1500.0
+Sting,R8,c3,1000.0
+CD11b,R8,c4,1500.0
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_data/DD3S1.csv b/wetransfer_data-zip_2024-05-17_1431/test_data/DD3S1.csv
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+version https://git-lfs.github.com/spec/v1
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diff --git a/wetransfer_data-zip_2024-05-17_1431/test_data/DD3S2.csv b/wetransfer_data-zip_2024-05-17_1431/test_data/DD3S2.csv
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diff --git a/wetransfer_data-zip_2024-05-17_1431/test_data/TMA.csv b/wetransfer_data-zip_2024-05-17_1431/test_data/TMA.csv
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diff --git a/wetransfer_data-zip_2024-05-17_1431/test_data/stored_variables.json b/wetransfer_data-zip_2024-05-17_1431/test_data/stored_variables.json
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+DD5S3.csv;3;175;175b
+DD5S3.csv;4;183;183c
+DD5S3.csv;5;182;182b
+DD5S3.csv;6;180;180c
+DD5S3.csv;7;187;187a
+DD5S3.csv;8;185;185a
+DD5S3.csv;9;184;184a
+DD5S3.csv;10;187;187b
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+DD5S3.csv;13;129;129c
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+DD5S3.csv;24;153;153c
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+DD5S3.csv;29;162;162c
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+DD5S3.csv;31;159;159c
+DD5S3.csv;32;185;185c
+DD5S3.csv;33;184;184b
+DD5S3.csv;34;187;187c
+DD5S3.csv;35;186;186a
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+TMA.csv;2;c2;c2a
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+TMA.csv;4;c4;c4a
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+TMA.csv;20;c20;c20a
+TMA.csv;21;c21;c21a
+TMA.csv;22;c22;c22a
+TMA.csv;23;c23;c23a
+TMA.csv;24;c24;c24a
+TMA.csv;25;c25;c25a
+TMA.csv;26;c26;c26a
+TMA.csv;27;c27;c27a
+TMA.csv;28;c28;c28a
+TMA.csv;29;c29;c29a
+TMA.csv;30;c30;c30a
+TMA.csv;31;c31;c31a
+TMA.csv;32;c32;c32a
+TMA.csv;33;c33;c33a
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+TMA.csv;36;c36;c36a
+TMA.csv;37;c37;c37a
+TMA.csv;38;c38;c38a
+TMA.csv;39;c39;c39a
+TMA.csv;40;c40;c40a
+TMA.csv;41;c41;c41a
+TMA.csv;42;c42;c42a
+TMA.csv;43;c43;c43a
+TMA.csv;44;c44;c44a
+TMA.csv;45;c45;c45a
+TMA.csv;46;c46;c46a
+TMA.csv;47;c47;c47a
+TMA.csv;48;c48;c48a
+TMA.csv;49;c49;c49a
+TMA.csv;50;c50;c50a
+TMA.csv;51;c51;c51a
+TMA.csv;52;c52;c52a
+TMA.csv;53;c53;c53a
+TMA.csv;54;c54;c54a
+TMA.csv;55;c55;c55a
+TMA.csv;56;c56;c56a
+TMA.csv;57;c57;c57a
+TMA.csv;58;c58;c58a
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diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_1.tif.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_1.tif.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c1ba01be0d64992c49c22139bf677aa721692f75
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_1.tif.csv
@@ -0,0 +1,46 @@
+Channel,Name,Cycle,ChannelIndex,ExposureTime,ExposureTimeUnit,Fluor,AcquisitionMode,IlluminationType,ContrastMethod,ExcitationWavelength,ExcitationWavelengthUnit,EmissionWavelength,EmissionWavelengthUnit,Color
+0,DAPI0,0,0,30.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+1,AF488,0,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+2,AF555,0,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+3,AF647,0,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+4,AF750,0,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+5,DAPI1,1,0,20.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+6,ColVI,1,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+7,CD31,1,2,1200.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+8,CD4,1,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+9,Ecad,1,4,800.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+10,DAPI2,2,0,15.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+11,Desmin,2,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+12,B7H4,2,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+13,CD8,2,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+14,CD20,2,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+15,DAPI3,3,0,20.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+16,aSMA,3,1,50.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+17,CD68,3,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+18,PD1,3,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+19,CD45,3,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+20,DAPI4,4,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+21,Vimentin,4,1,150.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+22,AXL,4,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+23,PDL1,4,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+24,FOXP3,4,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+25,DAPI5,5,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+26,r5c2,5,1,20.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+27,CA9,5,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+28,CD163,5,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+29,Ki67,5,4,1000.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+30,DAPI6,6,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+31,CKs,6,1,200.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+32,Fibronectin,6,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+33,CD44,6,3,1200.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+34,HLA,6,4,500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+35,DAPI7,7,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+36,r7c2,7,1,20.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+37,PDGFR,7,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+38,MMP9,7,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+39,GATA3,7,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+40,DAPI8,8,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+41,r8c2,8,1,25.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+42,CD11c,8,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+43,Sting,8,3,1000.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+44,CD11b,8,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_2.tif.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_2.tif.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c1ba01be0d64992c49c22139bf677aa721692f75
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/Slide_B_DD1s1.one_2.tif.csv
@@ -0,0 +1,46 @@
+Channel,Name,Cycle,ChannelIndex,ExposureTime,ExposureTimeUnit,Fluor,AcquisitionMode,IlluminationType,ContrastMethod,ExcitationWavelength,ExcitationWavelengthUnit,EmissionWavelength,EmissionWavelengthUnit,Color
+0,DAPI0,0,0,30.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+1,AF488,0,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+2,AF555,0,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+3,AF647,0,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+4,AF750,0,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+5,DAPI1,1,0,20.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+6,ColVI,1,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+7,CD31,1,2,1200.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+8,CD4,1,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+9,Ecad,1,4,800.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+10,DAPI2,2,0,15.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+11,Desmin,2,1,300.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+12,B7H4,2,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+13,CD8,2,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+14,CD20,2,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+15,DAPI3,3,0,20.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+16,aSMA,3,1,50.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+17,CD68,3,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+18,PD1,3,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+19,CD45,3,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+20,DAPI4,4,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+21,Vimentin,4,1,150.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+22,AXL,4,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+23,PDL1,4,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+24,FOXP3,4,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+25,DAPI5,5,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+26,r5c2,5,1,20.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+27,CA9,5,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+28,CD163,5,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+29,Ki67,5,4,1000.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+30,DAPI6,6,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+31,CKs,6,1,200.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+32,Fibronectin,6,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+33,CD44,6,3,1200.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+34,HLA,6,4,500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+35,DAPI7,7,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+36,r7c2,7,1,20.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+37,PDGFR,7,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+38,MMP9,7,3,1500.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+39,GATA3,7,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
+40,DAPI8,8,0,10.000000,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.000000,nm,465.000000,nm,#FF0000FF
+41,r8c2,8,1,25.000000,ms,FITC,WideField,Epifluorescence,Fluorescence,495.000000,nm,519.000000,nm,#FF00FF28
+42,CD11c,8,2,1500.000000,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.000000,nm,568.000000,nm,#FFFFFF00
+43,Sting,8,3,1000.000000,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.000000,nm,668.000000,nm,#FFFF0000
+44,CD11b,8,4,1500.000000,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.000000,nm,779.000000,nm,#FF00FFFF
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/TMA_Clinical_Data_187-OC.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/TMA_Clinical_Data_187-OC.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7b813a671684b0bcf57295195c76f1c17718026b
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/TMA_Clinical_Data_187-OC.csv
@@ -0,0 +1,188 @@
+ID;%_tumor;%_necrosis;Age_Diagnosis;BMI;CA125;Race;Other_Cancers;BRCAStatus;NACT_vs_ACT;Diagnosis_to_Start_Chemo;Diagnosis_to_surgery;Stage;Histo_Type;Optimal_Debulking;Residual_Disease;Platinum_sensitive;Days_surgery_to_recurrence;Avastin;Disease_Stat;Days_from_surgery_to_last_contact_or_death;Recurrence;Vital_Status
+1;80;5;72;35;;C;breast;0;ACT;;8;3C;HGSOC;0;1;1;506;1;DOD;1776;Yes;1
+2;50;0;36;24;;C;breast;1;ACT;;0;3C;HGSOC;1;0;1;669;1;DOD;2223;Yes;1
+3;80;5;55;45;944;C;0;;ACT;;0;3C;HGSOC;1;0;1;596;0;DOD;1355;Yes;1
+4;80;5;59;26;50;C;0;0;ACT;;0;3A;HGtubal;1;0;;;;;1783;;
+5;50;0;70;32;1426;C;0;;NACT;19;90;X;CCC;;0;1;377;1;DOD;977;Yes;1
+6;80;5;44;24;788;C;0;;ACT;;0;3C;HGSOC;0;1;;471;0;;1263;Yes;
+7;80;5;63;29;10965;C;0;;NACT;5;69;X;HGSOC;;0;0;516;0;DOD;814;Yes;1
+8;90;5;54;24;2465;C;0;;ACT;;0;3C;HGStubal;0;1;0;144;1;DOD;693;Yes;1
+9;90;5;57;20;3694;C;0;;ACT;;0;3C;HGSOC;0;1;0;428;0;DOD;475;Yes;1
+10;90;5;50;20;316;C;0;1;ACT;;0;3C;serousfallopian;1;0;1;;0;;2162;No;
+11;90;0;58;20;72;C;0;;NACT;35;133;X;Signetringcell;;0;0;203;0;DOD;263;Yes;1
+12;90;0;73;30;1704;C;breast;;ACT;;0;3C;HGSOC;0;1;;;0;DOD;1448;No;1
+13;60;30;62;31;219;C;0;;ACT;;0;3C;HGSOC;1;0;0;162;0;DOD;2286;Yes;1
+14;80;0;52;27;1222;C;breastuterine;;ACT;;0;2B;ENDOOV ;1;0;1;;0;NED;2933;No;0
+15;60;0;77;35;183;C;0;;NACT;10;57;X;HGSOC;1;0;1;460;0;NED;2585;Yes;0
+16;80;0;36;23;333;C;0;0;ACT;;0;1A;ENDOOV ;1;0;;;0;NED;1442;No;0
+17;80;0;66;19;503;C;0;;ACT;;21;3C;HGSOC;1;0;0;;0;DOD;383;;1
+18;90;0;75;25;55;C;0;0;NACT;7;153;X;HGSOC;1;0;1;359;1;DOD;439;Yes;1
+19;80;0;49;35;1100;C;0;;ACT;;0;3C;HGSOC;0;1;1;892;0;AWD;2543;Yes;0
+20;90;0;49;26;25000;C;0;;ACT;;0;1A;CCC;1;0;1;;0;NED;2217;No;0
+21;90;5;55;20;235;C;0;;ACT;;0;1C;ENDOOV ;1;0;1;;0;NED;2087;No;0
+22;80;5;60;19;11;C;squamousskincarcinoma;0;ACT;;0;1A;mucinouscarcinoma;1;0;;;0;NED;1636;No;0
+23;90;5;60;36;38;C;0;;ACT;;0;3B;HGSOC;1;0;1;960;0;DOD;2352;Yes;1
+24;90;5;68;50;28;C;0;;ACT;;0;1C;CCC;1;0;1;;0;NED;1336;;0
+25;80;0;66;25;168;C;0;;ACT;;0;1C;CCC;1;0;1;;0;NED;1092;No;0
+26;80;0;69;29;1907;C;0;0;NACT;9;50;X;HGSOC;1;0;1;;0;DOD;1085;No;1
+27;90;0;51;22;1692;C;0;;ACT;;0;3C;HGSOC;0;1;0;233;1;DOD;465;Yes;1
+28;50;0;70;43;109;C;0;0;ACT;;0;1A;CCC;1;0;;;0;NED;2423;No;0
+29;90;0;76;23;234;C;0;;ACT;;0;3A;CCC;1;0;1;;0;NED;2036;No;0
+30;80;5;46;31;5632;C;0;;ACT;;0;3C;HGSOC;1;0;;789;0;NED;1846;Yes;0
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diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b5e72f27a37c063dd21bb360077318a90cfa4698
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/cellsubtype_color_data.csv
@@ -0,0 +1,13 @@
+rgb,hex,cell_subtype
+"(0.6509803921568628, 0.807843137254902, 0.8901960784313725)",#a6cee3,DC
+"(0.12156862745098039, 0.47058823529411764, 0.7058823529411765)",#1f78b4,B
+"(0.6980392156862745, 0.8745098039215686, 0.5411764705882353)",#b2df8a,TCD4
+"(0.2, 0.6274509803921569, 0.17254901960784313)",#33a02c,TCD8
+"(0.984313725490196, 0.6039215686274509, 0.6)",#fb9a99,M1
+"(0.8901960784313725, 0.10196078431372549, 0.10980392156862745)",#e31a1c,M2
+"(0.9921568627450981, 0.7490196078431373, 0.43529411764705883)",#fdbf6f,Treg
+"(1.0, 0.4980392156862745, 0.0)",#ff7f00,IMMUNE_OTHER
+"(0.792156862745098, 0.6980392156862745, 0.8392156862745098)",#cab2d6,CANCER
+"(0.41568627450980394, 0.23921568627450981, 0.6039215686274509)",#6a3d9a,αSMA_myCAF
+"(1.0, 1.0, 0.6)",#ffff99,STROMA_OTHER
+"(0.6941176470588235, 0.34901960784313724, 0.1568627450980392)",#b15928,ENDOTHELIAL
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0600b2cebb13dca8c5a4d3eab7b9a44a3bce29ff
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/celltype_color_data.csv
@@ -0,0 +1,5 @@
+rgb,hex,cell_type
+"(0.1333, 0.5451, 0.1333)",#228b22,CANCER
+"(0.4, 0.4, 0.4)",#666666,STROMA
+"(1.0, 1.0, 0.0)",#ffff00,IMMUNE
+"(0.502, 0.0, 0.502)",#800080,ENDOTHELIAL
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..835cd11cda39d090244169a22adb022e10752b2e
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/channel_color_data.csv
@@ -0,0 +1,5 @@
+rgb,hex,Channel
+"(0.00784313725490196, 0.24313725490196078, 1.0)",#023eff,c2
+"(1.0, 0.48627450980392156, 0.0)",#ff7c00,c3
+"(0.10196078431372549, 0.788235294117647, 0.2196078431372549)",#1ac938,c4
+"(0.9098039215686274, 0.0, 0.043137254901960784)",#e8000b,c5
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/combined_metadata.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/combined_metadata.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9a3e5b2eb2e24a07ef2ec9d570f47a02425a1264
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/combined_metadata.csv
@@ -0,0 +1,91 @@
+Channel,Name,Cycle,ChannelIndex,ExposureTime,ExposureTimeUnit,Fluor,AcquisitionMode,IlluminationType,ContrastMethod,ExcitationWavelength,ExcitationWavelengthUnit,EmissionWavelength,EmissionWavelengthUnit,Color
+0,DAPI0,0,0,30.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+1,AF488,0,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+2,AF555,0,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+3,AF647,0,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+4,AF750,0,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+5,DAPI1,1,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+6,ColVI,1,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+7,CD31,1,2,1200.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+8,CD4,1,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+9,Ecad,1,4,800.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+10,DAPI2,2,0,15.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+11,Desmin,2,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+12,B7H4,2,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+13,CD8,2,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+14,CD20,2,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+15,DAPI3,3,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+16,aSMA,3,1,50.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+17,CD68,3,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+18,PD1,3,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+19,CD45,3,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+20,DAPI4,4,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+21,Vimentin,4,1,150.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+22,AXL,4,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+23,PDL1,4,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+24,FOXP3,4,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+25,DAPI5,5,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+26,r5c2,5,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+27,CA9,5,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+28,CD163,5,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+29,Ki67,5,4,1000.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+30,DAPI6,6,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+31,CKs,6,1,200.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+32,Fibronectin,6,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+33,CD44,6,3,1200.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+34,HLA,6,4,500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+35,DAPI7,7,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+36,r7c2,7,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+37,PDGFR,7,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+38,MMP9,7,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+39,GATA3,7,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+40,DAPI8,8,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+41,r8c2,8,1,25.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+42,CD11c,8,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+43,Sting,8,3,1000.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+44,CD11b,8,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+0,DAPI0,0,0,30.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+1,AF488,0,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+2,AF555,0,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+3,AF647,0,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+4,AF750,0,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+5,DAPI1,1,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+6,ColVI,1,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+7,CD31,1,2,1200.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+8,CD4,1,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+9,Ecad,1,4,800.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+10,DAPI2,2,0,15.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+11,Desmin,2,1,300.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+12,B7H4,2,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+13,CD8,2,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+14,CD20,2,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+15,DAPI3,3,0,20.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+16,aSMA,3,1,50.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+17,CD68,3,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+18,PD1,3,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+19,CD45,3,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+20,DAPI4,4,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+21,Vimentin,4,1,150.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+22,AXL,4,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+23,PDL1,4,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+24,FOXP3,4,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+25,DAPI5,5,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+26,r5c2,5,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+27,CA9,5,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+28,CD163,5,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+29,Ki67,5,4,1000.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+30,DAPI6,6,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+31,CKs,6,1,200.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+32,Fibronectin,6,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+33,CD44,6,3,1200.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+34,HLA,6,4,500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+35,DAPI7,7,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+36,r7c2,7,1,20.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+37,PDGFR,7,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+38,MMP9,7,3,1500.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+39,GATA3,7,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
+40,DAPI8,8,0,10.0,ms,DAPI,WideField,Epifluorescence,Fluorescence,353.0,nm,465.0,nm,#FF0000FF
+41,r8c2,8,1,25.0,ms,FITC,WideField,Epifluorescence,Fluorescence,495.0,nm,519.0,nm,#FF00FF28
+42,CD11c,8,2,1500.0,ms,Alexa Fluor 555,WideField,Epifluorescence,Fluorescence,553.0,nm,568.0,nm,#FFFFFF00
+43,Sting,8,3,1000.0,ms,Alexa Fluor 647,WideField,Epifluorescence,Fluorescence,653.0,nm,668.0,nm,#FFFF0000
+44,CD11b,8,4,1500.0,ms,Alexa Fluor 750,WideField,Epifluorescence,Fluorescence,752.0,nm,779.0,nm,#FF00FFFF
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1559ab340f6d91a6b42bef36e8c5d74b7bde5e64
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/full_to_short_column_names.csv
@@ -0,0 +1,109 @@
+full_name,short_name
+AF488_Cell_Intensity_Average,AF488_Cell
+AF488_Cytoplasm_Intensity_Average,AF488_Cytoplasm
+AF488_Nucleus_Intensity_Average,AF488_Nucleus
+AF555_Cell_Intensity_Average,AF555_Cell
+AF555_Cytoplasm_Intensity_Average,AF555_Cytoplasm
+AF555_Nucleus_Intensity_Average,AF555_Nucleus
+AF647_Cell_Intensity_Average,AF647_Cell
+AF647_Cytoplasm_Intensity_Average,AF647_Cytoplasm
+AF647_Nucleus_Intensity_Average,AF647_Nucleus
+AF750_Cell_Intensity_Average,AF750_Cell
+AF750_Cytoplasm_Intensity_Average,AF750_Cytoplasm
+AF750_Nucleus_Intensity_Average,AF750_Nucleus
+aSMA_Cell_Intensity_Average,aSMA_Cell
+aSMA_Cytoplasm_Intensity_Average,aSMA_Cytoplasm
+aSMA_Nucleus_Intensity_Average,aSMA_Nucleus
+AXL_Cell_Intensity_Average,AXL_Cell
+AXL_Cytoplasm_Intensity_Average,AXL_Cytoplasm
+AXL_Nucleus_Intensity_Average,AXL_Nucleus
+B7H4_Cell_Intensity_Average,B7H4_Cell
+B7H4_Cytoplasm_Intensity_Average,B7H4_Cytoplasm
+B7H4_Nucleus_Intensity_Average,B7H4_Nucleus
+CA9_Cell_Intensity_Average,CA9_Cell
+CA9_Cytoplasm_Intensity_Average,CA9_Cytoplasm
+CA9_Nucleus_Intensity_Average,CA9_Nucleus
+CD4_Cell_Intensity_Average,CD4_Cell
+CD4_Cytoplasm_Intensity_Average,CD4_Cytoplasm
+CD4_Nucleus_Intensity_Average,CD4_Nucleus
+CD8_Cell_Intensity_Average,CD8_Cell
+CD8_Cytoplasm_Intensity_Average,CD8_Cytoplasm
+CD8_Nucleus_Intensity_Average,CD8_Nucleus
+CD11b_Cell_Intensity_Average,CD11b_Cell
+CD11b_Cytoplasm_Intensity_Average,CD11b_Cytoplasm
+CD11b_Nucleus_Intensity_Average,CD11b_Nucleus
+CD11c_Cell_Intensity_Average,CD11c_Cell
+CD11c_Cytoplasm_Intensity_Average,CD11c_Cytoplasm
+CD11c_Nucleus_Intensity_Average,CD11c_Nucleus
+CD20_Cell_Intensity_Average,CD20_Cell
+CD20_Cytoplasm_Intensity_Average,CD20_Cytoplasm
+CD20_Nucleus_Intensity_Average,CD20_Nucleus
+CD31_Cell_Intensity_Average,CD31_Cell
+CD31_Cytoplasm_Intensity_Average,CD31_Cytoplasm
+CD31_Nucleus_Intensity_Average,CD31_Nucleus
+CD44_Cell_Intensity_Average,CD44_Cell
+CD44_Cytoplasm_Intensity_Average,CD44_Cytoplasm
+CD44_Nucleus_Intensity_Average,CD44_Nucleus
+CD45_Cell_Intensity_Average,CD45_Cell
+CD45_Cytoplasm_Intensity_Average,CD45_Cytoplasm
+CD45_Nucleus_Intensity_Average,CD45_Nucleus
+CD68_Cell_Intensity_Average,CD68_Cell
+CD68_Cytoplasm_Intensity_Average,CD68_Cytoplasm
+CD68_Nucleus_Intensity_Average,CD68_Nucleus
+CD163_Cell_Intensity_Average,CD163_Cell
+CD163_Cytoplasm_Intensity_Average,CD163_Cytoplasm
+CD163_Nucleus_Intensity_Average,CD163_Nucleus
+CKs_Cell_Intensity_Average,CKs_Cell
+CKs_Cytoplasm_Intensity_Average,CKs_Cytoplasm
+CKs_Nucleus_Intensity_Average,CKs_Nucleus
+ColVI_Cell_Intensity_Average,ColVI_Cell
+ColVI_Cytoplasm_Intensity_Average,ColVI_Cytoplasm
+ColVI_Nucleus_Intensity_Average,ColVI_Nucleus
+Desmin_Cell_Intensity_Average,Desmin_Cell
+Desmin_Cytoplasm_Intensity_Average,Desmin_Cytoplasm
+Desmin_Nucleus_Intensity_Average,Desmin_Nucleus
+Ecad_Cell_Intensity_Average,Ecad_Cell
+Ecad_Cytoplasm_Intensity_Average,Ecad_Cytoplasm
+Ecad_Nucleus_Intensity_Average,Ecad_Nucleus
+Fibronectin_Cell_Intensity_Average,Fibronectin_Cell
+Fibronectin_Cytoplasm_Intensity_Average,Fibronectin_Cytoplasm
+Fibronectin_Nucleus_Intensity_Average,Fibronectin_Nucleus
+FOXP3_Cell_Intensity_Average,FOXP3_Cell
+FOXP3_Cytoplasm_Intensity_Average,FOXP3_Cytoplasm
+FOXP3_Nucleus_Intensity_Average,FOXP3_Nucleus
+GATA3_Cell_Intensity_Average,GATA3_Cell
+GATA3_Cytoplasm_Intensity_Average,GATA3_Cytoplasm
+GATA3_Nucleus_Intensity_Average,GATA3_Nucleus
+HLA_Cell_Intensity_Average,HLA_Cell
+HLA_Cytoplasm_Intensity_Average,HLA_Cytoplasm
+HLA_Nucleus_Intensity_Average,HLA_Nucleus
+Ki67_Cell_Intensity_Average,Ki67_Cell
+Ki67_Cytoplasm_Intensity_Average,Ki67_Cytoplasm
+Ki67_Nucleus_Intensity_Average,Ki67_Nucleus
+MMP9_Cell_Intensity_Average,MMP9_Cell
+MMP9_Cytoplasm_Intensity_Average,MMP9_Cytoplasm
+MMP9_Nucleus_Intensity_Average,MMP9_Nucleus
+PD1_Cell_Intensity_Average,PD1_Cell
+PD1_Cytoplasm_Intensity_Average,PD1_Cytoplasm
+PD1_Nucleus_Intensity_Average,PD1_Nucleus
+PDGFR_Cell_Intensity_Average,PDGFR_Cell
+PDGFR_Cytoplasm_Intensity_Average,PDGFR_Cytoplasm
+PDGFR_Nucleus_Intensity_Average,PDGFR_Nucleus
+PDL1_Cell_Intensity_Average,PDL1_Cell
+PDL1_Cytoplasm_Intensity_Average,PDL1_Cytoplasm
+PDL1_Nucleus_Intensity_Average,PDL1_Nucleus
+r5c2_Cell_Intensity_Average,r5c2_Cell
+r5c2_Cytoplasm_Intensity_Average,r5c2_Cytoplasm
+r5c2_Nucleus_Intensity_Average,r5c2_Nucleus
+r7c2_Cell_Intensity_Average,r7c2_Cell
+r7c2_Cytoplasm_Intensity_Average,r7c2_Cytoplasm
+r7c2_Nucleus_Intensity_Average,r7c2_Nucleus
+r8c2_Cell_Intensity_Average,r8c2_Cell
+r8c2_Cytoplasm_Intensity_Average,r8c2_Cytoplasm
+r8c2_Nucleus_Intensity_Average,r8c2_Nucleus
+Sting_Cell_Intensity_Average,Sting_Cell
+Sting_Cytoplasm_Intensity_Average,Sting_Cytoplasm
+Sting_Nucleus_Intensity_Average,Sting_Nucleus
+Vimentin_Cell_Intensity_Average,Vimentin_Cell
+Vimentin_Cytoplasm_Intensity_Average,Vimentin_Cytoplasm
+Vimentin_Nucleus_Intensity_Average,Vimentin_Nucleus
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Cellsubtype_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Cellsubtype_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..d240e07e43c243939f89e11c06a2757f5d38385e
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Cellsubtype_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Celltype_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Celltype_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..931e609afadc0e4785d890426ec79c1689769c2d
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Celltype_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Channel_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Channel_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..ac6c2e7bcdc651e2abf6d8616d5996aafa586a9a
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Channel_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Round_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Round_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..b355b3d49f423f042e46427c63d9760529057abb
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Round_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Sample_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Sample_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..4dc24fa65944c412d470bd0b76fab60bbea89b55
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/Sample_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/immune_checkpoint_legend.png b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/immune_checkpoint_legend.png
new file mode 100644
index 0000000000000000000000000000000000000000..d240e07e43c243939f89e11c06a2757f5d38385e
Binary files /dev/null and b/wetransfer_data-zip_2024-05-17_1431/test_metadata/images/immune_checkpoint_legend.png differ
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4945727898a0cc33a5bf3b906a3131e6f0cdc7ee
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/immunecheckpoint_color_data.csv
@@ -0,0 +1,6 @@
+rgb,hex,immune_checkpoint
+"(0.9677975592919913, 0.44127456009157356, 0.5358103155058701)",#f77189,B7H4
+"(0.3126890019504329, 0.6928754610296064, 0.1923704830330379)",#50b131,PDL1
+"(0.23299120924703914, 0.639586552066035, 0.9260706093977744)",#3ba3ec,PD1
+"(0.6402432806212122, 0.56707501056059, 0.36409039926945397)",#a3915d,B7H4_PDL1
+"(0.5044925901631545, 0.5912455243957383, 0.5514171359788941)",#81978d,None
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv
new file mode 100644
index 0000000000000000000000000000000000000000..80dd04a89820bf39d377899a36a68d433e99a309
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/marker_intensity_metadata.csv
@@ -0,0 +1,109 @@
+Round,Target,Exp,Channel,target_lower,full_column,marker,localisation
+R0,AF488,300,c2,af488,AF488_Cell_Intensity_Average,AF488,cell
+R0,AF488,300,c2,af488,AF488_Cytoplasm_Intensity_Average,AF488,cytoplasm
+R0,AF488,300,c2,af488,AF488_Nucleus_Intensity_Average,AF488,nucleus
+R0,AF555,1500,c3,af555,AF555_Cell_Intensity_Average,AF555,cell
+R0,AF555,1500,c3,af555,AF555_Cytoplasm_Intensity_Average,AF555,cytoplasm
+R0,AF555,1500,c3,af555,AF555_Nucleus_Intensity_Average,AF555,nucleus
+R0,AF647,1500,c4,af647,AF647_Cell_Intensity_Average,AF647,cell
+R0,AF647,1500,c4,af647,AF647_Cytoplasm_Intensity_Average,AF647,cytoplasm
+R0,AF647,1500,c4,af647,AF647_Nucleus_Intensity_Average,AF647,nucleus
+R0,AF750,1500,c5,af750,AF750_Cell_Intensity_Average,AF750,cell
+R0,AF750,1500,c5,af750,AF750_Cytoplasm_Intensity_Average,AF750,cytoplasm
+R0,AF750,1500,c5,af750,AF750_Nucleus_Intensity_Average,AF750,nucleus
+R1,ColVI,300,c2,colvi,ColVI_Cell_Intensity_Average,ColVI,cell
+R1,ColVI,300,c2,colvi,ColVI_Cytoplasm_Intensity_Average,ColVI,cytoplasm
+R1,ColVI,300,c2,colvi,ColVI_Nucleus_Intensity_Average,ColVI,nucleus
+R1,CD31,1200,c3,cd31,CD31_Cell_Intensity_Average,CD31,cell
+R1,CD31,1200,c3,cd31,CD31_Cytoplasm_Intensity_Average,CD31,cytoplasm
+R1,CD31,1200,c3,cd31,CD31_Nucleus_Intensity_Average,CD31,nucleus
+R1,CD4,1500,c4,cd4,CD4_Cell_Intensity_Average,CD4,cell
+R1,CD4,1500,c4,cd4,CD4_Cytoplasm_Intensity_Average,CD4,cytoplasm
+R1,CD4,1500,c4,cd4,CD4_Nucleus_Intensity_Average,CD4,nucleus
+R1,Ecad,800,c5,ecad,Ecad_Cell_Intensity_Average,Ecad,cell
+R1,Ecad,800,c5,ecad,Ecad_Cytoplasm_Intensity_Average,Ecad,cytoplasm
+R1,Ecad,800,c5,ecad,Ecad_Nucleus_Intensity_Average,Ecad,nucleus
+R2,Desmin,300,c2,desmin,Desmin_Cell_Intensity_Average,Desmin,cell
+R2,Desmin,300,c2,desmin,Desmin_Cytoplasm_Intensity_Average,Desmin,cytoplasm
+R2,Desmin,300,c2,desmin,Desmin_Nucleus_Intensity_Average,Desmin,nucleus
+R2,B7H4,1500,c3,b7h4,B7H4_Cell_Intensity_Average,B7H4,cell
+R2,B7H4,1500,c3,b7h4,B7H4_Cytoplasm_Intensity_Average,B7H4,cytoplasm
+R2,B7H4,1500,c3,b7h4,B7H4_Nucleus_Intensity_Average,B7H4,nucleus
+R2,CD8,1500,c4,cd8,CD8_Cell_Intensity_Average,CD8,cell
+R2,CD8,1500,c4,cd8,CD8_Cytoplasm_Intensity_Average,CD8,cytoplasm
+R2,CD8,1500,c4,cd8,CD8_Nucleus_Intensity_Average,CD8,nucleus
+R2,CD20,1500,c5,cd20,CD20_Cell_Intensity_Average,CD20,cell
+R2,CD20,1500,c5,cd20,CD20_Cytoplasm_Intensity_Average,CD20,cytoplasm
+R2,CD20,1500,c5,cd20,CD20_Nucleus_Intensity_Average,CD20,nucleus
+R3,aSMA,50,c2,asma,aSMA_Cell_Intensity_Average,aSMA,cell
+R3,aSMA,50,c2,asma,aSMA_Cytoplasm_Intensity_Average,aSMA,cytoplasm
+R3,aSMA,50,c2,asma,aSMA_Nucleus_Intensity_Average,aSMA,nucleus
+R3,CD68,1500,c3,cd68,CD68_Cell_Intensity_Average,CD68,cell
+R3,CD68,1500,c3,cd68,CD68_Cytoplasm_Intensity_Average,CD68,cytoplasm
+R3,CD68,1500,c3,cd68,CD68_Nucleus_Intensity_Average,CD68,nucleus
+R3,PD1,1500,c4,pd1,PD1_Cell_Intensity_Average,PD1,cell
+R3,PD1,1500,c4,pd1,PD1_Cytoplasm_Intensity_Average,PD1,cytoplasm
+R3,PD1,1500,c4,pd1,PD1_Nucleus_Intensity_Average,PD1,nucleus
+R3,CD45,1500,c5,cd45,CD45_Cell_Intensity_Average,CD45,cell
+R3,CD45,1500,c5,cd45,CD45_Cytoplasm_Intensity_Average,CD45,cytoplasm
+R3,CD45,1500,c5,cd45,CD45_Nucleus_Intensity_Average,CD45,nucleus
+R4,Vimentin,150,c2,vimentin,Vimentin_Cell_Intensity_Average,Vimentin,cell
+R4,Vimentin,150,c2,vimentin,Vimentin_Cytoplasm_Intensity_Average,Vimentin,cytoplasm
+R4,Vimentin,150,c2,vimentin,Vimentin_Nucleus_Intensity_Average,Vimentin,nucleus
+R4,AXL,1500,c3,axl,AXL_Cell_Intensity_Average,AXL,cell
+R4,AXL,1500,c3,axl,AXL_Cytoplasm_Intensity_Average,AXL,cytoplasm
+R4,AXL,1500,c3,axl,AXL_Nucleus_Intensity_Average,AXL,nucleus
+R4,PDL1,1500,c4,pdl1,PDL1_Cell_Intensity_Average,PDL1,cell
+R4,PDL1,1500,c4,pdl1,PDL1_Cytoplasm_Intensity_Average,PDL1,cytoplasm
+R4,PDL1,1500,c4,pdl1,PDL1_Nucleus_Intensity_Average,PDL1,nucleus
+R4,FOXP3,1500,c5,foxp3,FOXP3_Cell_Intensity_Average,FOXP3,cell
+R4,FOXP3,1500,c5,foxp3,FOXP3_Cytoplasm_Intensity_Average,FOXP3,cytoplasm
+R4,FOXP3,1500,c5,foxp3,FOXP3_Nucleus_Intensity_Average,FOXP3,nucleus
+R5,r5c2,20,c2,r5c2,r5c2_Cell_Intensity_Average,r5c2,cell
+R5,r5c2,20,c2,r5c2,r5c2_Cytoplasm_Intensity_Average,r5c2,cytoplasm
+R5,r5c2,20,c2,r5c2,r5c2_Nucleus_Intensity_Average,r5c2,nucleus
+R5,CA9,1500,c3,ca9,CA9_Cell_Intensity_Average,CA9,cell
+R5,CA9,1500,c3,ca9,CA9_Cytoplasm_Intensity_Average,CA9,cytoplasm
+R5,CA9,1500,c3,ca9,CA9_Nucleus_Intensity_Average,CA9,nucleus
+R5,CD163,1500,c4,cd163,CD163_Cell_Intensity_Average,CD163,cell
+R5,CD163,1500,c4,cd163,CD163_Cytoplasm_Intensity_Average,CD163,cytoplasm
+R5,CD163,1500,c4,cd163,CD163_Nucleus_Intensity_Average,CD163,nucleus
+R5,Ki67,1000,c5,ki67,Ki67_Cell_Intensity_Average,Ki67,cell
+R5,Ki67,1000,c5,ki67,Ki67_Cytoplasm_Intensity_Average,Ki67,cytoplasm
+R5,Ki67,1000,c5,ki67,Ki67_Nucleus_Intensity_Average,Ki67,nucleus
+R6,CKs,200,c2,cks,CKs_Cell_Intensity_Average,CKs,cell
+R6,CKs,200,c2,cks,CKs_Cytoplasm_Intensity_Average,CKs,cytoplasm
+R6,CKs,200,c2,cks,CKs_Nucleus_Intensity_Average,CKs,nucleus
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Cell_Intensity_Average,Fibronectin,cell
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Cytoplasm_Intensity_Average,Fibronectin,cytoplasm
+R6,Fibronectin,1500,c3,fibronectin,Fibronectin_Nucleus_Intensity_Average,Fibronectin,nucleus
+R6,CD44,1200,c4,cd44,CD44_Cell_Intensity_Average,CD44,cell
+R6,CD44,1200,c4,cd44,CD44_Cytoplasm_Intensity_Average,CD44,cytoplasm
+R6,CD44,1200,c4,cd44,CD44_Nucleus_Intensity_Average,CD44,nucleus
+R6,HLA,500,c5,hla,HLA_Cell_Intensity_Average,HLA,cell
+R6,HLA,500,c5,hla,HLA_Cytoplasm_Intensity_Average,HLA,cytoplasm
+R6,HLA,500,c5,hla,HLA_Nucleus_Intensity_Average,HLA,nucleus
+R7,r7c2,20,c2,r7c2,r7c2_Cell_Intensity_Average,r7c2,cell
+R7,r7c2,20,c2,r7c2,r7c2_Cytoplasm_Intensity_Average,r7c2,cytoplasm
+R7,r7c2,20,c2,r7c2,r7c2_Nucleus_Intensity_Average,r7c2,nucleus
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Cell_Intensity_Average,PDGFR,cell
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Cytoplasm_Intensity_Average,PDGFR,cytoplasm
+R7,PDGFR,1500,c3,pdgfr,PDGFR_Nucleus_Intensity_Average,PDGFR,nucleus
+R7,MMP9,1500,c4,mmp9,MMP9_Cell_Intensity_Average,MMP9,cell
+R7,MMP9,1500,c4,mmp9,MMP9_Cytoplasm_Intensity_Average,MMP9,cytoplasm
+R7,MMP9,1500,c4,mmp9,MMP9_Nucleus_Intensity_Average,MMP9,nucleus
+R7,GATA3,1500,c5,gata3,GATA3_Cell_Intensity_Average,GATA3,cell
+R7,GATA3,1500,c5,gata3,GATA3_Cytoplasm_Intensity_Average,GATA3,cytoplasm
+R7,GATA3,1500,c5,gata3,GATA3_Nucleus_Intensity_Average,GATA3,nucleus
+R8,r8c2,25,c2,r8c2,r8c2_Cell_Intensity_Average,r8c2,cell
+R8,r8c2,25,c2,r8c2,r8c2_Cytoplasm_Intensity_Average,r8c2,cytoplasm
+R8,r8c2,25,c2,r8c2,r8c2_Nucleus_Intensity_Average,r8c2,nucleus
+R8,CD11c,1500,c3,cd11c,CD11c_Cell_Intensity_Average,CD11c,cell
+R8,CD11c,1500,c3,cd11c,CD11c_Cytoplasm_Intensity_Average,CD11c,cytoplasm
+R8,CD11c,1500,c3,cd11c,CD11c_Nucleus_Intensity_Average,CD11c,nucleus
+R8,Sting,1000,c4,sting,Sting_Cell_Intensity_Average,Sting,cell
+R8,Sting,1000,c4,sting,Sting_Cytoplasm_Intensity_Average,Sting,cytoplasm
+R8,Sting,1000,c4,sting,Sting_Nucleus_Intensity_Average,Sting,nucleus
+R8,CD11b,1500,c5,cd11b,CD11b_Cell_Intensity_Average,CD11b,cell
+R8,CD11b,1500,c5,cd11b,CD11b_Cytoplasm_Intensity_Average,CD11b,cytoplasm
+R8,CD11b,1500,c5,cd11b,CD11b_Nucleus_Intensity_Average,CD11b,nucleus
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a077677c4caa31d810fb588009576304825a6079
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/not_intensities.csv
@@ -0,0 +1,12 @@
+Marker
+Cell_Size
+Nuc_X
+Nuc_Y_Inv
+ROI_index
+Nucleus_Size
+Nucleus_Roundness
+Sample_ID
+Round
+Exp
+Channel
+Target
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b2a7d5f6a6a53ff6f91726b06a8d441ffb6d482f
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/round_color_data.csv
@@ -0,0 +1,10 @@
+rgb,hex,Round
+"(0.28685356234627135, 0.13009829239513535, 0.23110332132624437)",#49213b,R0
+"(0.36541462435986094, 0.2025447048359916, 0.37693310021636883)",#5d3460,R1
+"(0.40867533458903105, 0.2940761173840091, 0.5166711878800253)",#684b84,R2
+"(0.42890613750051265, 0.4082290173220481, 0.6335348887063806)",#6d68a2,R3
+"(0.4444462906865238, 0.5264664993764805, 0.7056321892616532)",#7186b4,R4
+"(0.47707206309601013, 0.6427061780374552, 0.7418477948908153)",#7aa4bd,R5
+"(0.5414454866716836, 0.7466759172596551, 0.7572905778378964)",#8abec1,R6
+"(0.6414710091647722, 0.8321551072276492, 0.7746773027952071)",#a4d4c6,R7
+"(0.7684256891219349, 0.8992667116749021, 0.8171383269422353)",#c4e5d0,R8
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4606e80d25e013c6fbbb2aa9ef230d35d7f0e436
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/sample_color_data.csv
@@ -0,0 +1,5 @@
+rgb,hex,Sample_ID
+"(0.9677975592919913, 0.44127456009157356, 0.5358103155058701)",#f77189,DD3S1.csv
+"(0.5920891529639701, 0.6418467016378244, 0.1935069134991043)",#97a431,DD3S2.csv
+"(0.21044753832183283, 0.6773105080456748, 0.6433941168468681)",#36ada4,DD3S3.csv
+"(0.5019607843137255, 0.5019607843137255, 0.5019607843137255)",#808080,TMA.csv
diff --git a/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv b/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c3a56032e25138d0214afe3facbca21ec86ca8ca
--- /dev/null
+++ b/wetransfer_data-zip_2024-05-17_1431/test_metadata/short_to_full_column_names.csv
@@ -0,0 +1,109 @@
+short_name,full_name
+AF488_Cell,AF488_Cell_Intensity_Average
+AF488_Cytoplasm,AF488_Cytoplasm_Intensity_Average
+AF488_Nucleus,AF488_Nucleus_Intensity_Average
+AF555_Cell,AF555_Cell_Intensity_Average
+AF555_Cytoplasm,AF555_Cytoplasm_Intensity_Average
+AF555_Nucleus,AF555_Nucleus_Intensity_Average
+AF647_Cell,AF647_Cell_Intensity_Average
+AF647_Cytoplasm,AF647_Cytoplasm_Intensity_Average
+AF647_Nucleus,AF647_Nucleus_Intensity_Average
+AF750_Cell,AF750_Cell_Intensity_Average
+AF750_Cytoplasm,AF750_Cytoplasm_Intensity_Average
+AF750_Nucleus,AF750_Nucleus_Intensity_Average
+aSMA_Cell,aSMA_Cell_Intensity_Average
+aSMA_Cytoplasm,aSMA_Cytoplasm_Intensity_Average
+aSMA_Nucleus,aSMA_Nucleus_Intensity_Average
+AXL_Cell,AXL_Cell_Intensity_Average
+AXL_Cytoplasm,AXL_Cytoplasm_Intensity_Average
+AXL_Nucleus,AXL_Nucleus_Intensity_Average
+B7H4_Cell,B7H4_Cell_Intensity_Average
+B7H4_Cytoplasm,B7H4_Cytoplasm_Intensity_Average
+B7H4_Nucleus,B7H4_Nucleus_Intensity_Average
+CA9_Cell,CA9_Cell_Intensity_Average
+CA9_Cytoplasm,CA9_Cytoplasm_Intensity_Average
+CA9_Nucleus,CA9_Nucleus_Intensity_Average
+CD4_Cell,CD4_Cell_Intensity_Average
+CD4_Cytoplasm,CD4_Cytoplasm_Intensity_Average
+CD4_Nucleus,CD4_Nucleus_Intensity_Average
+CD8_Cell,CD8_Cell_Intensity_Average
+CD8_Cytoplasm,CD8_Cytoplasm_Intensity_Average
+CD8_Nucleus,CD8_Nucleus_Intensity_Average
+CD11b_Cell,CD11b_Cell_Intensity_Average
+CD11b_Cytoplasm,CD11b_Cytoplasm_Intensity_Average
+CD11b_Nucleus,CD11b_Nucleus_Intensity_Average
+CD11c_Cell,CD11c_Cell_Intensity_Average
+CD11c_Cytoplasm,CD11c_Cytoplasm_Intensity_Average
+CD11c_Nucleus,CD11c_Nucleus_Intensity_Average
+CD20_Cell,CD20_Cell_Intensity_Average
+CD20_Cytoplasm,CD20_Cytoplasm_Intensity_Average
+CD20_Nucleus,CD20_Nucleus_Intensity_Average
+CD31_Cell,CD31_Cell_Intensity_Average
+CD31_Cytoplasm,CD31_Cytoplasm_Intensity_Average
+CD31_Nucleus,CD31_Nucleus_Intensity_Average
+CD44_Cell,CD44_Cell_Intensity_Average
+CD44_Cytoplasm,CD44_Cytoplasm_Intensity_Average
+CD44_Nucleus,CD44_Nucleus_Intensity_Average
+CD45_Cell,CD45_Cell_Intensity_Average
+CD45_Cytoplasm,CD45_Cytoplasm_Intensity_Average
+CD45_Nucleus,CD45_Nucleus_Intensity_Average
+CD68_Cell,CD68_Cell_Intensity_Average
+CD68_Cytoplasm,CD68_Cytoplasm_Intensity_Average
+CD68_Nucleus,CD68_Nucleus_Intensity_Average
+CD163_Cell,CD163_Cell_Intensity_Average
+CD163_Cytoplasm,CD163_Cytoplasm_Intensity_Average
+CD163_Nucleus,CD163_Nucleus_Intensity_Average
+CKs_Cell,CKs_Cell_Intensity_Average
+CKs_Cytoplasm,CKs_Cytoplasm_Intensity_Average
+CKs_Nucleus,CKs_Nucleus_Intensity_Average
+ColVI_Cell,ColVI_Cell_Intensity_Average
+ColVI_Cytoplasm,ColVI_Cytoplasm_Intensity_Average
+ColVI_Nucleus,ColVI_Nucleus_Intensity_Average
+Desmin_Cell,Desmin_Cell_Intensity_Average
+Desmin_Cytoplasm,Desmin_Cytoplasm_Intensity_Average
+Desmin_Nucleus,Desmin_Nucleus_Intensity_Average
+Ecad_Cell,Ecad_Cell_Intensity_Average
+Ecad_Cytoplasm,Ecad_Cytoplasm_Intensity_Average
+Ecad_Nucleus,Ecad_Nucleus_Intensity_Average
+Fibronectin_Cell,Fibronectin_Cell_Intensity_Average
+Fibronectin_Cytoplasm,Fibronectin_Cytoplasm_Intensity_Average
+Fibronectin_Nucleus,Fibronectin_Nucleus_Intensity_Average
+FOXP3_Cell,FOXP3_Cell_Intensity_Average
+FOXP3_Cytoplasm,FOXP3_Cytoplasm_Intensity_Average
+FOXP3_Nucleus,FOXP3_Nucleus_Intensity_Average
+GATA3_Cell,GATA3_Cell_Intensity_Average
+GATA3_Cytoplasm,GATA3_Cytoplasm_Intensity_Average
+GATA3_Nucleus,GATA3_Nucleus_Intensity_Average
+HLA_Cell,HLA_Cell_Intensity_Average
+HLA_Cytoplasm,HLA_Cytoplasm_Intensity_Average
+HLA_Nucleus,HLA_Nucleus_Intensity_Average
+Ki67_Cell,Ki67_Cell_Intensity_Average
+Ki67_Cytoplasm,Ki67_Cytoplasm_Intensity_Average
+Ki67_Nucleus,Ki67_Nucleus_Intensity_Average
+MMP9_Cell,MMP9_Cell_Intensity_Average
+MMP9_Cytoplasm,MMP9_Cytoplasm_Intensity_Average
+MMP9_Nucleus,MMP9_Nucleus_Intensity_Average
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+ * @license  MIT
+ */
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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding 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t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var 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sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return 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i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return 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Ra=function(t,e){this.uniformNames=e.map((function(t){return"u_"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};Ra.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},Ra.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)},Ra.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new Ca(t,e):new Sa(t,e)};var Fa=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};Fa.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new pi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},Fa.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},Fa.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=za(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))}},Fa.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))},Fa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ba=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};Ba.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new pi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new pi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ba.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)},Ba.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=za(r),a=za(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))}},Ba.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))},Ba.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ba.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ba.prototype.getBinding=function(t,e,r){return new Sa(t,e)};var Na=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};Na.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)},Na.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Na.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],c=n[o];if(s&&l&&c)for(var u=t;u<e;u++)this.zoomInPaintVertexArray.emplace(u,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(u,l.tl[0],l.tl[1],l.br[0],l.br[1],c.tl[0],c.tl[1],c.br[0],c.br[1],l.pixelRatio,c.pixelRatio)}},Na.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,ma.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,ma.members,this.expression.isStateDependent))},Na.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var ja=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Ra&&n.setConstantPatternPositions(t,e)}},ja.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Fa||h instanceof Ba||h instanceof Na)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},ja.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Da||r instanceof Ra)&&t.push.apply(t,r.uniformNames.map((function(t){return"#define HAS_UNIFORM_"+t})))}return t},ja.prototype.getPaintVertexBuffers=function(){return this._buffers},ja.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Da||i instanceof Ra||i instanceof Ba)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},ja.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},ja.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Na){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Fa||r instanceof Ba)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},ja.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Fa||r instanceof Ba||r instanceof Na)&&r.upload(t)}this.updatePaintBuffers()},ja.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Fa||e instanceof Ba||e instanceof Na)&&e.destroy()}};var Ua=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new ja(o,r,n,t)}this.needsUpload=!1,this._featureMap=new _a,this._bufferOffset=0};function Va(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(e+"-","").replace(/-/g,"_")]}function Ha(t,e,r){var n={color:{source:ta,composite:ea},number:{source:Zi,composite:ta}},i=function(t){return{"line-pattern":{source:Bi,composite:Bi},"fill-pattern":{source:Bi,composite:Bi},"fill-extrusion-pattern":{source:Bi,composite:Bi}}[t]}(t);return i&&i[r]||n[e][r]}Ua.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ua.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ua.prototype.get=function(t){return this.programConfigurations[t]},Ua.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ua.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Nn("ConstantBinder",Da),Nn("CrossFadedConstantBinder",Ra),Nn("SourceExpressionBinder",Fa),Nn("CrossFadedCompositeBinder",Na),Nn("CompositeExpressionBinder",Ba),Nn("ProgramConfiguration",ja,{omit:["_buffers"]}),Nn("ProgramConfigurationSet",Ua);var qa,Ga=(qa=15,{min:-1*Math.pow(2,qa-1),max:Math.pow(2,qa-1)-1});function Ya(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Ga.min||o.x>Ga.max||o.y<Ga.min||o.y>Ga.max)&&(_("Geometry exceeds allowed extent, reduce your vector tile buffer size"),o.x=l(o.x,Ga.min,Ga.max),o.y=l(o.y,Ga.min,Ga.max))}return r}function Wa(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Xa=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 zi,this.indexArray=new Yi,this.segments=new pa,this.programConfigurations=new Ua(ha,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Za(t,e){for(var r=0;r<t.length;r++)if(io(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(io(t,e[n]))return!0;return!!$a(t,e)}function Ja(t,e,r){return!!io(t,e)||!!eo(e,t,r)}function Ka(t,e){if(1===t.length)return no(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(io(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(no(e,t[a]))return!0;for(var o=0;o<e.length;o++)if($a(t,e[o]))return!0;return!1}function Qa(t,e,r){if(t.length>1){if($a(t,e))return!0;for(var n=0;n<e.length;n++)if(eo(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(eo(t[i],e,r))return!0;return!1}function $a(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++){if(to(n,i,e[a],e[a+1]))return!0}return!1}function to(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function eo(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++){if(ro(t,e[i-1],e[i])<n)return!0}return!1}function ro(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function no(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function io(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function ao(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function oo(t,e,r){var n=e.paint.get(t).value;return"constant"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function so(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function lo(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);"viewport"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var c=t[l];s.push(c.sub(o))}return s}Xa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;"circle"===n.type&&(a=n.layout.get("circle-sort-key"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Ya(c):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),d,r)){p||(d.geometry=Ya(c));var m=a?a.evaluate(d,{},r):void 0,g={id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:m};i.push(g)}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=x,_=b.geometry,w=b.index,T=b.sourceLayerIndex,k=t[w].feature;this.addFeature(x,_,w,r),e.featureIndex.insert(k,_,w,T,this.index)}},Xa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Xa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Xa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Xa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ha),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Xa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Xa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Wa(this.layoutVertexArray,c,u,-1,-1),Wa(this.layoutVertexArray,c,u,1,-1),Wa(this.layoutVertexArray,c,u,1,1),Wa(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Nn("CircleBucket",Xa,{omit:["layers"]});var co=new Si({"circle-sort-key":new Ti(Lt.layout_circle["circle-sort-key"])}),uo={paint:new Si({"circle-radius":new Ti(Lt.paint_circle["circle-radius"]),"circle-color":new Ti(Lt.paint_circle["circle-color"]),"circle-blur":new Ti(Lt.paint_circle["circle-blur"]),"circle-opacity":new Ti(Lt.paint_circle["circle-opacity"]),"circle-translate":new wi(Lt.paint_circle["circle-translate"]),"circle-translate-anchor":new wi(Lt.paint_circle["circle-translate-anchor"]),"circle-pitch-scale":new 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c=e[s]*r,u=s<l-1?e[s+1]*r:t.length;o-=Math.abs(ls(t,c,u,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)},Fo.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},Do.default=Ro;var gs=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 zi,this.indexArray=new Yi,this.indexArray2=new Qi,this.programConfigurations=new Ua(zo,t.layers,t.zoom),this.segments=new pa,this.segments2=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};gs.prototype.populate=function(t,e,r){this.hasPattern=ds("fill",this.layers,e);for(var n=this.layers[0].layout.get("fill-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r,e.availableImages):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("fill",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},gs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},gs.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)}},gs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},gs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},gs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,zo),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0},gs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())},gs.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray),h=f.vertexLength,p=[],d=[],m=0,g=s;m<g.length;m+=1){var v=g[m];if(0!==v.length){v!==s[0]&&d.push(p.length/2);var y=this.segments2.prepareSegment(v.length,this.layoutVertexArray,this.indexArray2),x=y.vertexLength;this.layoutVertexArray.emplaceBack(v[0].x,v[0].y),this.indexArray2.emplaceBack(x+v.length-1,x),p.push(v[0].x),p.push(v[0].y);for(var b=1;b<v.length;b++)this.layoutVertexArray.emplaceBack(v[b].x,v[b].y),this.indexArray2.emplaceBack(x+b-1,x+b),p.push(v[b].x),p.push(v[b].y);y.vertexLength+=v.length,y.primitiveLength+=v.length}}for(var _=Do(p,d),w=0;w<_.length;w+=3)this.indexArray.emplaceBack(h+_[w],h+_[w+1],h+_[w+2]);f.vertexLength+=l,f.primitiveLength+=_.length/3}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillBucket",gs,{omit:["layers","patternFeatures"]});var vs=new Si({"fill-sort-key":new Ti(Lt.layout_fill["fill-sort-key"])}),ys={paint:new Si({"fill-antialias":new wi(Lt.paint_fill["fill-antialias"]),"fill-opacity":new Ti(Lt.paint_fill["fill-opacity"]),"fill-color":new Ti(Lt.paint_fill["fill-color"]),"fill-outline-color":new Ti(Lt.paint_fill["fill-outline-color"]),"fill-translate":new wi(Lt.paint_fill["fill-translate"]),"fill-translate-anchor":new wi(Lt.paint_fill["fill-translate-anchor"]),"fill-pattern":new ki(Lt.paint_fill["fill-pattern"])}),layout:vs},xs=function(t){function e(e){t.call(this,e,ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r);var n=this.paint._values["fill-outline-color"];"constant"===n.value.kind&&void 0===n.value.value&&(this.paint._values["fill-outline-color"]=this.paint._values["fill-color"])},e.prototype.createBucket=function(t){return new gs(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,a,o){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var 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wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var 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a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 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O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error("Unimplemented type: "+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else 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n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn("ImagePosition",Sl),Nn("ImageAtlas",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack="",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g="right"===s?1:"left"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn["Arabic Supplement"](Y)||Yn["Arabic Extended-A"](Y)||Yn["Arabic Presentation Forms-A"](Y)||Yn["Arabic Presentation Forms-B"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.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(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of images 6401")}else _("Can't add FormattedSection with an empty image.")},Il.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var zl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},Dl={};function Rl(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function Fl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Bl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function Nl(t,e,r,n,i,a){for(var o=null,s=Fl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=Fl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function jl(t,e,r,n,i,a,o){if("point"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,v=e.bottom*a;return"height"===r||"both"===r?(u=i[1]+g-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(g+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}Dl[10]=!0,Dl[32]=!0,Dl[38]=!0,Dl[40]=!0,Dl[41]=!0,Dl[43]=!0,Dl[45]=!0,Dl[47]=!0,Dl[173]=!0,Dl[183]=!0,Dl[8203]=!0,Dl[8208]=!0,Dl[8211]=!0,Dl[8231]=!0;var ql=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);Nn("Anchor",ql);function Gl(t,e){var r=e.expression;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new pi(t+1))};if("source"===r.kind)return{kind:"source"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return"composite"===r.kind?{kind:"composite",minZoom:s,maxZoom:l,interpolationType:i}:{kind:"camera",minZoom:s,maxZoom:l,minSize:r.evaluate(new pi(s)),maxSize:r.evaluate(new pi(l)),interpolationType:i}}function Yl(t,e,r){var n=e.uSize,i=e.uSizeT,a=r.lowerSize,o=r.upperSize;return"source"===t.kind?a/128:"composite"===t.kind?qe(a/128,o/128,i):n}function Wl(t,e){var r=0,n=0;if("constant"===t.kind)n=t.layoutSize;else if("source"!==t.kind){var i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?l(or.interpolationFactor(i,e,a,o),0,1):0;"camera"===t.kind?n=qe(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Xl=Object.freeze({__proto__:null,getSizeData:Gl,evaluateSizeForFeature:Yl,evaluateSizeForZoom:Wl,SIZE_PACK_FACTOR:128});function Zl(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],c=0;s<r/2;){var u=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=u.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),c+=p;s-l[0].distance>n;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=f.dist(h)}return!0}function Jl(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Kl(t,e,r){return t?.6*e*r:0}function Ql(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function $l(t,e,r,n,i,a){for(var o=Kl(r,i,a),s=Ql(r,n)*a,l=0,c=Jl(t)/2,u=0;u<t.length-1;u++){var f=t[u],h=t[u+1],p=f.dist(h);if(l+p>c){var d=(c-l)/p,m=qe(f.x,h.x,d),g=qe(f.y,h.y,d),v=new ql(m,g,h.angleTo(f),u);return v._round(),!o||Zl(t,v,s,o,e)?v:void 0}l+=p}}function tc(t,e,r,n,i,a,o,s,l){var c=Kl(n,a,o),u=Ql(n,i),f=u*o,h=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-f<e/4&&(e=f+e/4),function t(e,r,n,i,a,o,s,l,c){for(var u=o/2,f=Jl(e),h=0,p=r-n,d=[],m=0;m<e.length-1;m++){for(var g=e[m],v=e[m+1],y=g.dist(v),x=v.angleTo(g);p+n<h+y;){var b=((p+=n)-h)/y,_=qe(g.x,v.x,b),w=qe(g.y,v.y,b);if(_>=0&&_<c&&w>=0&&w<c&&p-u>=0&&p+u<=f){var T=new ql(_,w,x,m);T._round(),i&&!Zl(e,T,o,i,a)||d.push(T)}}h+=y}l||d.length||s||(d=t(e,h/2,n,i,a,o,s,!0,c));return d}(t,h?e/2*s%e:(u/2+2*a)*o*s%e,e,c,r,f,h,!1,l)}function ec(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l=t[s],c=void 0,u=0;u<l.length-1;u++){var f=l[u],h=l[u+1];f.x<e&&h.x<e||(f.x<e?f=new i(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round():h.x<e&&(h=new i(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round()),f.y<r&&h.y<r||(f.y<r?f=new i(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round():h.y<r&&(h=new i(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round()),f.x>=n&&h.x>=n||(f.x>=n?f=new i(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round():h.x>=n&&(h=new i(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round()),f.y>=a&&h.y>=a||(f.y>=a?f=new i(f.x+(h.x-f.x)*((a-f.y)/(h.y-f.y)),a)._round():h.y>=a&&(h=new i(f.x+(h.x-f.x)*((a-f.y)/(h.y-f.y)),a)._round()),c&&f.equals(c[c.length-1])||(c=[f],o.push(c)),c.push(h)))))}return o}function rc(t,e,r,n){var a=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2,c=o.paddedRect.h-2,u=t.right-t.left,f=t.bottom-t.top,h=o.stretchX||[[0,l]],p=o.stretchY||[[0,c]],d=function(t,e){return t+e[1]-e[0]},m=h.reduce(d,0),g=p.reduce(d,0),v=l-m,y=c-g,x=0,b=m,_=0,w=g,T=0,k=v,A=0,M=y;if(o.content&&n){var S=o.content;x=nc(h,0,S[0]),_=nc(p,0,S[1]),b=nc(h,S[0],S[2]),w=nc(p,S[1],S[3]),T=S[0]-x,A=S[1]-_,k=S[2]-S[0]-b,M=S[3]-S[1]-w}var E=function(n,a,l,c){var h=ac(n.stretch-x,b,u,t.left),p=oc(n.fixed-T,k,n.stretch,m),d=ac(a.stretch-_,w,f,t.top),v=oc(a.fixed-A,M,a.stretch,g),y=ac(l.stretch-x,b,u,t.left),S=oc(l.fixed-T,k,l.stretch,m),E=ac(c.stretch-_,w,f,t.top),L=oc(c.fixed-A,M,c.stretch,g),C=new i(h,d),P=new i(y,d),I=new i(y,E),O=new i(h,E),z=new i(p/s,v/s),D=new i(S/s,L/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=n[n.length-1];n.push({fixed:s-c.stretch,stretch:c.stretch}),n.push({fixed:s-c.stretch,stretch:c.stretch+(l-s)})}return n.push({fixed:e+1,stretch:r}),n}function ac(t,e,r,n){return t/e*r+n}function oc(t,e,r,n){return t-e*r/n}var sc=function(t,e,r,n,a,o,s,l,c,u){if(this.boxStartIndex=t.length,c){var f=o.top,h=o.bottom,p=o.collisionPadding;p&&(f-=p[1],h+=p[3]);var d=h-f;d>0&&(d=Math.max(10,d),this.circleDiameter=d)}else{var m=o.top*s-l,g=o.bottom*s+l,v=o.left*s-l,y=o.right*s+l,x=o.collisionPadding;if(x&&(v-=x[0]*s,m-=x[1]*s,y+=x[2]*s,g+=x[3]*s),u){var b=new i(v,m),_=new i(y,m),w=new i(v,g),T=new i(y,g),k=u*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),v=Math.min(b.x,_.x,w.x,T.x),y=Math.max(b.x,_.x,w.x,T.x),m=Math.min(b.y,_.y,w.y,T.y),g=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,v,m,y,g,r,n,a)}this.boxEndIndex=t.length},lc=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new hc(b.p.x+d,b.p.y+d,d,t)),x+=4)}return r&&(console.log("num probes: "+x),console.log("best distance: "+y.d)),y.p}function fc(t,e){return e.max-t.max}function hc(t,e,r,n){this.p=new i(t,e),this.h=r,this.d=function(t,e){for(var r=!1,n=1/0,i=0;i<e.length;i++)for(var a=e[i],o=0,s=a.length,l=s-1;o<s;l=o++){var c=a[o],u=a[l];c.y>t.y!=u.y>t.y&&t.x<(u.x-c.x)*(t.y-c.y)/(u.y-c.y)+c.x&&(r=!r),n=Math.min(n,ro(t,c,u))}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}lc.prototype.push=function(t){this.data.push(t),this.length++,this._up(this.length-1)},lc.prototype.pop=function(){if(0!==this.length){var t=this.data[0],e=this.data.pop();return this.length--,this.length>0&&(this.data[0]=e,this._down(0)),t}},lc.prototype.peek=function(){return this.data[0]},lc.prototype._up=function(t){for(var e=this.data,r=this.compare,n=e[t];t>0;){var i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},lc.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case"top-right":case"top-left":case"top":i=r-7;break;case"bottom-right":case"bottom-left":case"bottom":i=7-r}switch(t){case"top-right":case"bottom-right":case"right":n=-e;break;case"top-left":case"bottom-left":case"left":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case"top-right":case"top-left":n=i-7;break;case"bottom-right":case"bottom-left":n=7-i;break;case"bottom":n=7-e;break;case"top":n=e-7}switch(t){case"top-right":case"bottom-right":r=-i;break;case"top-left":case"bottom-left":r=i;break;case"left":r=e;break;case"right":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'):"composite"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,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=wc,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=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,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=x,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=tl,t.config=F,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=Wr,t.createLayout=Ii,t.createStyleLayer=function(t){return"custom"===t.type?new Dc(t):new Rc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if("object"==typeof e&&null!==e&&null!==r){if("object"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=On,t.endsWith=g,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Yl,t.evaluateSizeForZoom=Wl,t.evaluateVariableOffset=dc,t.evented=ci,t.extend=u,t.featureFilter=sn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Ul,t.getAnchorJustification=mc,t.getArrayBuffer=xt,t.getImage=Tt,t.getJSON=function(t,e){return yt(u(t,{type:"json"}),e)},t.getRTLTextPluginStatus=ui,t.getReferrer=gt,t.getVideo=function(t,e){var r,n,i=self.document.createElement("video");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement("source");r=t[a],n=void 0,(n=self.document.createElement("a")).href=r,(n.protocol!==self.document.location.protocol||n.host!==self.document.location.host)&&(i.crossOrigin="Anonymous"),o.src=t[a],i.appendChild(o)}return{cancel:function(){}}},t.identity=ho,t.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t):null},t.isChar=Yn,t.isMapboxURL=q,t.keysDifference=function(t,e){var r=[];for(var n in t)n in e||r.push(n);return r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if("composite"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c["text-size"].possiblyEvaluate(new 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i,a=t[n],o=a.type;if("Point"===o||"MultiPoint"===o||"LineString"===o)i=mt(a.geometry,e);else if("MultiLineString"===o||"Polygon"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if("MultiPolygon"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON 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 At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},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 At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},At.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},At.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},At.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},At.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},At.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},At.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},At.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},At.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},At.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},At.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},At.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error("RTL Text Plugin failed to import scripts from "+i),a)}}catch(t){n(t.toString())}},At.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},At.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},At.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t),this.getAvailableImages(t))}return this.workerSources[t][e][r]},At.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},At.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},"undefined"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new At(t.window)),At})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return"undefined"==typeof window||"undefined"==typeof document?"not a 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e=t.getContext("2d");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?function(t){void 0===n[t]&&(n[t]=function(t){var r=function(t){var r=document.createElement("canvas"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext("webgl",n)||r.probablySupportsContext("experimental-webgl",n):r.supportsContext?r.supportsContext("webgl",n)||r.supportsContext("experimental-webgl",n):r.getContext("webgl",n)||r.getContext("experimental-webgl",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);if(!n||r.isContextLost())return!1;return r.shaderSource(n,"void main() {}"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS)}(t));return n[t]}(t&&t.failIfMajorPerformanceCaveat)?void 0:"insufficient WebGL support":"insufficient Canvas/getImageData support":"insufficient ArrayBuffer support":"insufficient Uint8ClampedArray support":"insufficient worker support":"insufficient JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}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 c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();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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this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchX" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchY" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "content" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. 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o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var 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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 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o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(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 F(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(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(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 c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);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 B(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 N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.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},N.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},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.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},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.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},N.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},N.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 j=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};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var H=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};H.prototype.get=function(){return this.current},H.prototype.set=function(t){},H.prototype.getDefault=function(){return this.default},H.prototype.setDefault=function(){this.set(this.default)};var q=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),G=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(H),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(H),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(H),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(H),J=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return{func:this.gl.ALWAYS,ref:0,mask:255}},e.prototype.set=function(t){var e=this.current;(t.func!==e.func||t.ref!==e.ref||t.mask!==e.mask||this.dirty)&&(this.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t,this.dirty=!1)},e}(H),K=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[t.KEEP,t.KEEP,t.KEEP]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||this.dirty)&&(this.gl.stencilOp(t[0],t[1],t[2]),this.current=t,this.dirty=!1)},e}(H),Q=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t,this.dirty=!1}},e}(H),$=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[0,1]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.depthRange(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.CULL_FACE):e.disable(e.CULL_FACE),this.current=t,this.dirty=!1}},e}(H),st=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.BACK},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.cullFace(t),this.current=t,this.dirty=!1)},e}(H),lt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.CCW},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.frontFace(t),this.current=t,this.dirty=!1)},e}(H),ct=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)},e}(H),ut=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.TEXTURE0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)},e}(H),ft=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),ht=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(H),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(H),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(H),mt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}},e}(H),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){var e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1},e}(H),vt=function(t){function e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(H),yt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(H),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(H),wt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+("map"===i.paint.get("circle-pitch-scale")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get("circle-translate"),i.paint.get("circle-translate-anchor")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V="composite"===I.kind||"camera"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get("icon-size").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X="none"!==n.layout.get("icon-text-fit")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" "+p+"kb"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),"$debug",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if("translucent"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get("text-variable-anchor")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c="map"===a,u="map"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b="none"!==n.layout.get("icon-text-fit")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. 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+ */
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t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":255,"./lib/epsilon":256,"./lib/geojson":257,"./lib/intersecter":258,"./lib/segment-chainer":260,"./lib/segment-selector":261}],255:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return n("new_seg",{seg:t,primary:e})},segmentRemove:function(t){return n("rem_seg",{seg:t})},tempStatus:function(t,e,r){return n("temp_status",{seg:t,above:e,below:r})},rewind:function(t){return n("rewind",{seg:t})},status:function(t,e,r){return n("status",{seg:t,above:e,below:r})},vert:function(e){return e===r?t:(r=e,n("vert",{x:e}))},log:function(t){return"string"!=typeof t&&(t=JSON.stringify(t,!1,"  ")),n("log",{txt:t})},reset:function(){return n("reset")},selected:function(t){return n("selected",{segs:t})},chainStart:function(t){return n("chain_start",{seg:t})},chainRemoveHead:function(t,e){return n("chain_rem_head",{index:t,pt:e})},chainRemoveTail:function(t,e){return n("chain_rem_tail",{index:t,pt:e})},chainNew:function(t,e){return n("chain_new",{pt1:t,pt2:e})},chainMatch:function(t){return n("chain_match",{index:t})},chainClose:function(t){return n("chain_close",{index:t})},chainAddHead:function(t,e){return n("chain_add_head",{index:t,pt:e})},chainAddTail:function(t,e){return n("chain_add_tail",{index:t,pt:e})},chainConnect:function(t,e){return n("chain_con",{index1:t,index2:e})},chainReverse:function(t){return n("chain_rev",{index:t})},chainJoin:function(t,e){return n("chain_join",{index1:t,index2:e})},done:function(){return n("done")}}}},{}],256:[function(t,e,r){e.exports=function(t){"number"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return"number"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){return Math.abs(e[0]-r[0])<t},pointsSameY:function(e,r){return Math.abs(e[1]-r[1])<t},pointsSame:function(t,r){return e.pointsSameX(t,r)&&e.pointsSameY(t,r)},pointsCompare:function(t,r){return e.pointsSameX(t,r)?e.pointsSameY(t,r)?0:t[1]<r[1]?-1:1:t[0]<r[0]?-1:1},pointsCollinear:function(e,r,n){var i=e[0]-r[0],a=e[1]-r[1],o=r[0]-n[0],s=r[1]-n[1];return Math.abs(i*s-o*a)<t},linesIntersect:function(e,r,n,i){var a=r[0]-e[0],o=r[1]-e[1],s=i[0]-n[0],l=i[1]-n[1],c=a*l-o*s;if(Math.abs(c)<t)return!1;var u=e[0]-n[0],f=e[1]-n[1],h=(s*f-l*u)/c,p=(a*f-o*u)/c,d={alongA:0,alongB:0,pt:[e[0]+h*a,e[1]+h*o]};return d.alongA=h<=-t?-2:h<t?-1:h-1<=-t?0:h-1<t?1:2,d.alongB=p<=-t?-2:p<t?-1:p-1<=-t?0:p-1<t?1:2,d},pointInsideRegion:function(e,r){for(var n=e[0],i=e[1],a=r[r.length-1][0],o=r[r.length-1][1],s=!1,l=0;l<r.length;l++){var c=r[l][0],u=r[l][1];u-i>t!=o-i>t&&(a-c)*(i-u)/(o-u)+c-n>t&&(s=!s),a=c,o=u}return s}};return e}},{}],257:[function(t,e,r){var n={toPolygon:function(t,e){function r(e){if(e.length<=0)return t.segments({inverted:!1,regions:[]});function r(e){var r=e.slice(0,e.length-1);return t.segments({inverted:!1,regions:[r]})}for(var n=r(e[0]),i=1;i<e.length;i++)n=t.selectDifference(t.combine(n,r(e[i])));return n}if("Polygon"===e.type)return t.polygon(r(e.coordinates));if("MultiPolygon"===e.type){for(var n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error("PolyBool: Cannot convert GeoJSON object to PolyBool polygon")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++){if(n(e,(s=t.children[r]).region))return void o(s,e)}var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function c(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:"Polygon",coordinates:[]}:1==u.length?{type:"Polygon",coordinates:u[0]}:{type:"MultiPolygon",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t("./linked-list");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error("PolyBool: Zero-length segment detected; your epsilon is probably too small or too large");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{"./linked-list":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return 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p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,"stream-parser":301}],263:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":262}],265:[function(t,e,r){"use 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n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":262}],267:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":262}],268:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":262}],269:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.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 c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":262,"../exif_utils":263}],270:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":262}],271:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":262}],272:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\s/,o=/[^-]\bwidth="([^%]+?)"|[^-]\bwidth='([^%]+?)'/,s=/\bheight="([^%]+?)"|\bheight='([^%]+?)'/,l=/\bview[bB]ox="(.+?)"|\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:"px"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e="",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[""])[0];if(a.test(c)){var f=function(t){var e=t.match(o),r=t.match(s),n=t.match(l);return{width:e&&(e[1]||e[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}(c),h=parseFloat(f.width),p=parseFloat(f.height);if(f.width&&f.height){if(!n(h)||!n(p))return;return{width:h,height:p,type:"svg",mime:"image/svg+xml",wUnits:u(f.width),hUnits:u(f.height)}}var d=(f.viewbox||"").split(" 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e=77===t[0],r=h(t,4,e)-8;if(!(r<0)){var n=r+8;if(!(t.length-n<2)){var a=12*f(t,n+0,e);if(!(a<=0||(n+=2,t.length-n<a))){var o,s,l,d;for(o=0;o<a;o+=12)256===(d=f(t,n+o,e))?s=p(t,n+o,e):257===d&&(l=p(t,n+o,e));return s&&l?{width:s,height:l,type:"tiff",mime:"image/tiff",wUnits:"px",hUnits:"px"}:void 0}}}}}},{"../common":262}],274:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=t("../common").readUInt32LE,s=t("../exif_utils"),l=n("RIFF"),c=n("WEBP");function u(t,e){if(157===t[e+3]&&1===t[e+4]&&42===t[e+5])return{width:16383&a(t,e+6),height:16383&a(t,e+8),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}function f(t,e){if(47===t[e]){var r=o(t,e+1);return{width:1+(16383&r),height:1+(r>>14&16383),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}}function h(t,e){return{width:1+(t[e+6]<<16|t[e+5]<<8|t[e+4]),height:1+(t[e+9]<<e|t[e+8]<<8|t[e+7]),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}e.exports=function(t){if(!(t.length<16)&&(i(t,0,l)||i(t,8,c))){var e=12,r=null,n=0,a=o(t,4)+8;if(!(a>t.length)){for(;e+8<a;)if(0!==t[e]){var p=String.fromCharCode.apply(null,t.slice(e,e+4)),d=o(t,e+4);"VP8 "===p&&d>=10?r=r||u(t,e+8):"VP8L"===p&&d>=9?r=r||f(t,e+8):"VP8X"===p&&d>=10?r=r||h(t,e+8):"EXIF"===p&&(n=s.get_orientation(t.slice(e+8,e+8+d)),e=1/0),e+=8+d}else e++;if(r)return n>0&&(r.orientation=n),r}}}},{"../common":262,"../exif_utils":263}],275:[function(t,e,r){"use 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opacity;\n\t\t}\n\t\t",uniforms:{range:t.prop("range"),lineWidth:t.prop("lineWidth"),capSize:t.prop("capSize"),opacity:t.prop("opacity"),scale:t.prop("scale"),translate:t.prop("translate"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{color:{buffer:d,offset:function(t,e){return 4*e.offset},divisor:1},position:{buffer:c,offset:function(t,e){return 8*e.offset},divisor:1},positionFract:{buffer:p,offset:function(t,e){return 8*e.offset},divisor:1},error:{buffer:m,offset:function(t,e){return 16*e.offset},divisor:1},direction:{buffer:g,stride:24,offset:0},lineOffset:{buffer:g,stride:24,offset:8},capOffset:{buffer:g,stride:24,offset:16}},primitive:"triangles",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 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n=t("color-normalize"),i=t("array-bounds"),a=t("object-assign"),o=t("glslify"),s=t("pick-by-alias"),l=t("flatten-vertex-data"),c=t("earcut"),u=t("array-normalize"),f=t("to-float32"),h=f.float32,p=f.fract32,d=t("es6-weak-map"),m=t("parse-rect"),g=t("array-find-index");function v(t,e){if(!(this instanceof v))return new v(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle 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color;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\n\t// the order is important\n\treturn position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n}\n\nvoid main() {\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineOffset = lineTop * 2. - 1.;\n\n\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\n\ttangent = normalize(diff * scale * viewport.zw);\n\tvec2 normal = vec2(-tangent.y, tangent.x);\n\n\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\n\t\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\n\n\t\t+ thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * 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:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),l.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:"dynamic"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if("number"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,"uint8");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t("object-assign");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{"@plotly/point-cluster":59,"array-bounds":71,"color-id":87,"color-normalize":89,"flatten-vertex-data":191,glslify:227,"is-iexplorer":233,"object-assign":247,"parse-rect":249,"pick-by-alias":253,"to-float32":313,"update-diff":329}],282:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("pick-by-alias"),a=t("array-bounds"),o=t("raf"),s=t("array-range"),l=t("parse-rect"),c=t("flatten-vertex-data");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if("number"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:"data items columns rows values dimensions samples x",snap:"snap cluster",size:"sizes size radius",color:"colors color fill fill-color fillColor",opacity:"opacity alpha transparency opaque",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",borderColor:"borderColors borderColor bordercolor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range ranges databox dataBox",viewport:"viewport viewBox viewbox",domain:"domain domains area areas",padding:"pad padding paddings pads margin margins",transpose:"transpose transposed",diagonal:"diagonal diag showDiagonal",upper:"upper 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:"Fruitbat",21:"Anchovy"};n.calendars.discworld=a},{"../main":346,"object-assign":247}],335:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":346,"object-assign":247}],336:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new 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a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":346,"object-assign":247}],337:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":346,"object-assign":247}],338:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":346,"object-assign":247}],339:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":346,"object-assign":247}],340:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":346,"object-assign":247}],341:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":503,"../color":366}],353:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":531,"fast-isnumeric":190}],354:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"./attributes":349,"./common_defaults":352}],355:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":503,"../../lib/setcursor":524,"../../lib/svg_text_utils":529,"../../plot_api/plot_template":543,"../../plots/cartesian/axes":554,"../../plots/plots":619,"../../registry":638,"../color":366,"../dragelement":385,"../drawing":388,"../fx":406,"./draw_arrow_head":356,"@plotly/d3":58}],356:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":503,"../color":366,"./arrow_paths":348,"@plotly/d3":58}],357:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":567,"./attributes":349,"./calc_autorange":350,"./click":351,"./convert_coords":353,"./defaults":354,"./draw":355}],358:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../annotations/attributes":349}],359:[function(t,e,r){"use 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V,H,q,G,Y,W,X,Z,J,K,Q,$,tt,et=[],rt=[],nt={hLinePoint:null,vLinePoint:null},it=!1;if(Array.isArray(e))for(P="array",q=0;q<e.length;q++)(Y=t.calcdata[e[q].curveNumber||0])&&(W=Y[0].trace,"skip"!==Y[0].trace.hoverinfo&&(rt.push(Y),"h"===W.orientation&&(it=!0)));else{for(G=0;G<t.calcdata.length;G++)Y=t.calcdata[G],"skip"!==(W=Y[0].trace).hoverinfo&&v.isTraceInSubplots(W,l)&&(rt.push(Y),"h"===W.orientation&&(it=!0));var at,ot;if(!s)at="xpx"in e?e.xpx:_[0]._length/2,ot="ypx"in e?e.ypx:w[0]._length/2;else{if(!1===c.triggerHandler(t,"plotly_beforehover",e))return;var st=s.getBoundingClientRect();at=e.clientX-st.left,ot=e.clientY-st.top,u._calcInverseTransform(t);var lt=o.apply3DTransform(u._invTransform)(at,ot);if(at=lt[0],ot=lt[1],at<0||at>_[0]._length||ot<0||ot>w[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=at+_[0]._offset,e.pointerY=ot+w[0]._offset,V="xval"in e?v.flat(l,e.xval):v.p2c(_,at),H="yval"in e?v.flat(l,e.yval):v.p2c(w,ot),!i(V[0])||!i(H[0]))return o.warn("Fx.hover 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var e=i.isD3Selection(t)?t:n.select(t);e.selectAll("g.hovertext").remove(),e.selectAll(".spikeline").remove()},click:t("./click")}},{"../../lib":503,"../dragelement":385,"./attributes":397,"./calc":398,"./click":399,"./constants":400,"./defaults":401,"./helpers":402,"./hover":403,"./layout_attributes":407,"./layout_defaults":408,"./layout_global_defaults":409,"@plotly/d3":58}],407:[function(t,e,r){"use strict";var n=t("./constants"),i=t("../../plots/font_attributes"),a=i({editType:"none"});a.family.dflt=n.HOVERFONT,a.size.dflt=n.HOVERFONTSIZE,e.exports={clickmode:{valType:"flaglist",flags:["event","select"],dflt:"event",editType:"plot",extras:["none"]},dragmode:{valType:"enumerated",values:["zoom","pan","select","lasso","drawclosedpath","drawopenpath","drawline","drawrect","drawcircle","orbit","turntable",!1],dflt:"zoom",editType:"modebar"},hovermode:{valType:"enumerated",values:["x","y","closest",!1,"x unified","y 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":472,"../../plot_api/plot_template":543,"../../plots/cartesian/constants":561}],412:[function(t,e,r){"use 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r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var c=s[l],u=o[c];if("zoom"===i){var f=u.projection.scale,h="in"===a?2*f:.5*f;n.call("_guiRelayout",t,c+".projection.scale",h)}}"reset"===i&&x(t,"geo")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has("cartesian")?e._isHoriz?"y":"x":"closest")}function v(t){var e=g(t);n.call("_guiRelayout",t,"hovermode",e)}function y(t,e){for(var r=e.currentTarget.getAttribute("data-val"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],c=i[l].zoom,u="in"===r?1.05*c:c/1.05;o[l+".zoom"]=u}n.call("_guiRelayout",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,c=Object.keys(l),u=0;u<c.length;u++){var f=c[u];a[s+"."+f]=l[f]}n.call("_guiRelayout",t,a)}u.toImage={name:"toImage",title:function(t){var 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hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":482,"../../lib":503,"../../plots/cartesian/axis_ids":558,"../../plots/plots":619,"../../registry":638,"../shapes/draw":450}],427:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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n=t("../../registry"),i=t("../../lib"),a=t("../../plots/cartesian/axes"),o=t("./draw_newshape/helpers").readPaths,s=t("./draw_newshape/display_outlines"),l=t("../../plots/cartesian/handle_outline").clearOutlineControllers,c=t("../color"),u=t("../drawing"),f=t("../../plot_api/plot_template").arrayEditor,h=t("../dragelement"),p=t("../../lib/setcursor"),d=t("./constants"),m=t("./helpers");function g(t){var e=t._fullLayout;for(var r in e._shapeUpperLayer.selectAll("path").remove(),e._shapeLowerLayer.selectAll("path").remove(),e._plots){var n=e._plots[r].shapelayer;n&&n.selectAll("path").remove()}for(var i=0;i<e.shapes.length;i++)e.shapes[i].visible&&x(t,i)}function v(t){return!!t._fullLayout._drawing}function y(t){return!t._context.edits.shapePosition}function x(t,e){t._fullLayout._paperdiv.selectAll('.shapelayer [data-index="'+e+'"]').remove();var 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":i,"data-notex":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return c.positionText(l,n,g),l}}function 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h=e.length-c;if(l===h&&"date"!==r.type)r.dtick<1?t=n-.5*r.dtick:(t-=.5)+r.dtick<n&&(t+=r.dtick);else if(s<.1*h&&(o>.3*h||u(n)||u(a))){var p=r.dtick/2;t+=t+p<n?p:-p}return t}(y,t,l,c,u))+(1+Math.floor((u-y)/v))*v;else for("M"===l.dtick.charAt(0)&&(y=function(t,e,r,n,i){var a=s.findExactDates(e,i);if(a.exactDays>.8){var o=Number(r.substr(1));a.exactYears>.8&&o%12==0?t=H.tickIncrement(t,"M6","reverse")+1.5*M:a.exactMonths>.8?t=H.tickIncrement(t,"M1","reverse")+15.5*M:t-=S;var l=H.tickIncrement(t,r);if(l<=n)return l}return t}(y,t,v,c,a)),g=y,0;g<=u;)g=H.tickIncrement(g,v,!1,a);return{start:e.c2r(y,0,a),end:e.c2r(g,0,a),size:v,_dataSpan:u-c}},H.prepMinorTicks=function(t,e,r){if(!e.minor.dtick){delete t.dtick;var n,a=e.dtick&&i(e._tmin);if(a){var o=H.tickIncrement(e._tmin,e.dtick,!0);n=[e._tmin,.99*o+.01*e._tmin]}else{var l=s.simpleMap(e.range,e.r2l);n=[l[0],.8*l[0]+.2*l[1]]}if(t.range=s.simpleMap(n,e.l2r),t._isMinor=!0,H.prepTicks(t,r),a){var c=i(e.dtick),u=i(t.dtick),f=c?e.dtick:+e.dtick.substring(1),h=u?t.dtick:+t.dtick.substring(1);c&&u?$(f,h)?f===2*A&&h===2*M&&(t.dtick=A):f===2*A&&h===3*M?t.dtick=A:f!==A||(e._input.minor||{}).nticks?tt(f/h,2.5)?t.dtick=f/2:t.dtick=f:t.dtick=M:"M"===String(e.dtick).charAt(0)?u?t.dtick="M1":$(f,h)?f>=12&&2===h&&(t.dtick="M3"):t.dtick=e.dtick:"L"===String(t.dtick).charAt(0)?"L"===String(e.dtick).charAt(0)?$(f,h)||(t.dtick=tt(f/h,2.5)?e.dtick/2:e.dtick):t.dtick="D1":"D2"===t.dtick&&+e.dtick>1&&(t.dtick=1)}t.range=e.range}void 0===e.minor._tick0Init&&(t.tick0=e.tick0)},H.prepTicks=function(t,e){var r=s.simpleMap(t.range,t.r2l,void 0,void 0,e);if("auto"===t.tickmode||!t.dtick){var n,a=t.nticks;a||("category"===t.type||"multicategory"===t.type?(n=t.tickfont?s.bigFont(t.tickfont.size||12):15,a=t._length/n):(n="y"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),"radialaxis"===t._name&&(a*=2)),t.minor&&"array"!==t.minor.tickmode||"array"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,H.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}"period"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||"M"!==t.dtick.charAt(0))}var n=r(),a=H.getTickFormat(t);if(a){var 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D=!z;z?(t._dtickInit=t.dtick,t._tick0Init=t.tick0):(t.minor._dtickInit=t.minor.dtick,t.minor._tick0Init=t.minor.tick0);var R=z?t:s.extendFlat({},t,t.minor);if(D?H.prepMinorTicks(R,t,e):H.prepTicks(R,e),"array"!==R.tickmode){var F=Z(u),B=F[0],N=F[1],j=i(R.dtick),U="log"===a&&!(j||"L"===R.dtick.charAt(0)),V=H.tickFirst(R,e);if(z){if(t._tmin=V,V<B!==f)break;"category"!==a&&"multicategory"!==a||(N=f?Math.max(-.5,N):Math.min(t._categories.length-.5,N))}var q,G,Y=null,W=V;if(z)j?G=t.dtick:"date"===a?"string"==typeof t.dtick&&"M"===t.dtick.charAt(0)&&(G=T*t.dtick.substring(1)):G=t._roughDTick,q=Math.round((t.r2l(W)-t.r2l(t.tick0))/G)-1;var X=R.dtick;for(R.rangebreaks&&R._tick0Init!==R.tick0&&(W=Ct(W,t),f||(W=H.tickIncrement(W,X,!f,o))),z&&c&&(W=H.tickIncrement(W,X,!f,o),q--);f?W>=N:W<=N;W=H.tickIncrement(W,X,f,o)){if(z&&q++,R.rangebreaks&&!f){if(W<B)continue;if(R.maskBreaks(W)===I&&Ct(W,R)>=p)break}if(C.length>d||W===Y)break;Y=W;var J={value:W};z?(U&&W!==(0|W)&&(J.simpleLabel=!0),l>1&&q%l&&(J.skipLabel=!0),C.push(J)):(J.minor=!0,P.push(J))}}else z?(C=[],m=rt(t)):(P=[],L=rt(t))}if(O&&!("inside"===t.minor.ticks&&"outside"===t.ticks||"outside"===t.minor.ticks&&"inside"===t.ticks)){for(var K=C.map((function(t){return t.value})),Q=[],$=0;$<P.length;$++){var tt=P[$],et=tt.value;if(-1===K.indexOf(et)){for(var nt=!1,it=0;!nt&&it<C.length;it++)1e7+C[it].value===1e7+et&&(nt=!0);nt||Q.push(tt)}}P=Q}if(c&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,c=t[o].value,u=Math.abs(c-l),f=r||u,h=0;f>=y?h=u>=y&&u<=g?u:v:r===b&&f>=_?h=u>=_&&u<=x?u:b:f>=k?h=u>=k&&u<=w?u:T:r===A&&f>=A?h=A:f>=M?h=M:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=u&&(h=u,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,m=0;m<84;m++){var L=(m+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==I&&d++}(h*=d/84)||(t[n].drop=!0),s&&u>A&&(h=u)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var at="y"===t._id.charAt(0),ot=1;"auto"===t.tickmode&&(ot=t.tickfont?t.tickfont.size:12);var st=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=Ct(C[r].value,t);var lt=t.c2p(C[r].value);(at?st>lt-ot:st<lt+ot)?C.splice(f?r+1:r,1):st=lt}}Lt(t)&&360===Math.abs(u[1]-u[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead="",t._inCalcTicks=!0;var ct,ut,ft=function(e){e.text="",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var ht=C[r].minor,pt=C[r].value;ht?L.push({x:pt,minor:!0}):(n=t._prevDateHead,ct=H.tickText(t,pt,!1,C[r].simpleLabel),void 0!==(ut=C[r].periodX)&&(ct.periodX=ut,(ut>p||ut<h)&&(ut>p&&(ct.periodX=p),ut<h&&(ct.periodX=h),ft(ct))),C[r].skipLabel&&ft(ct),m.push(ct))}return m=m.concat(L),t._inCalcTicks=!1,c&&m.length&&(m[0].noTick=!0),m};var nt=[2,5,10],it=[1,2,3,6,12],at=[1,2,5,10,15,30],ot=[1,2,3,7,14],st=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],lt=[-.301,0,.301,.699,1],ct=[15,30,45,90,180];function ut(t,e,r){return e*s.roundUp(t/e,r)}function ft(t){var e=t.dtick;if(t._tickexponent=0,i(e)||"string"==typeof e||(e=1),"category"!==t.type&&"multicategory"!==t.type||(t._tickround=null),"date"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,""),a=n.length;if("M"===String(e).charAt(0))a>10||"01-01"!==n.substr(5)?t._tickround="d":t._tickround=+e.substr(1)%12==0?"y":"m";else if(e>=M&&a<=10||e>=15*M)t._tickround="d";else if(e>=L&&a<=16||e>=E)t._tickround="M";else if(e>=C&&a<=19||e>=L)t._tickround="S";else{var o=t.l2r(r+e).replace(/^-/,"").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||"L"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),c=Math.floor(Math.log(l)/Math.LN10+.01),u=void 0===t.minexponent?3:t.minexponent;Math.abs(c)>u&&(dt(t.exponentformat)&&!mt(c)?t._tickexponent=3*Math.round((c-1)/3):t._tickexponent=c)}else t._tickround=null}function ht(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||"",fontSize:n.size,font:n.family,fontColor:n.color}}H.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if("date"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>v)e/=v,n=a(10),t.dtick="M"+12*ut(e,n,nt);else if(o>T)e/=T,t.dtick="M"+ut(e,1,it);else if(o>M){if(t.dtick=ut(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ot),!r){var l=H.getTickFormat(t),c="period"===t.ticklabelmode;c&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),c&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=ut(e,E,it):o>L?t.dtick=ut(e,L,at):o>C?t.dtick=ut(e,C,at):(n=a(10),t.dtick=ut(e,n,nt))}else if("log"===t.type){t.tick0=0;var u=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(u[1]-u[0])<1){var f=1.5*Math.abs((u[1]-u[0])/e);e=Math.abs(Math.pow(10,u[1])-Math.pow(10,u[0]))/f,n=a(10),t.dtick="L"+ut(e,n,nt)}else t.dtick=e>.3?"D2":"D1"}else"category"===t.type||"multicategory"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Lt(t)?(t.tick0=0,n=1,t.dtick=ut(e,n,ct)):(t.tick0=0,n=a(10),t.dtick=ut(e,n,nt));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&"string"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,"ax.dtick error: "+String(h)}},H.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),c=o*Number(e.substr(1));if("M"===l)return s.incrementMonth(t,c,a);if("L"===l)return Math.log(Math.pow(10,t)+c)/Math.LN10;if("D"===l){var u="D2"===e?lt:st,f=t+.01*o,h=s.roundUp(s.mod(f,1),u,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw"unrecognized dtick 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a,o=ht(t,e),l="array"===t.tickmode,c=r||l,u=t.type,f="category"===u?t.d2l_noadd:t.d2l;if(l&&Array.isArray(t.ticktext)){var h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return"none"===n;var i={first:t._tmin,last:t._tmax}[n];return"all"!==n&&e!==i}var m=r?"never":"none"!==t.exponentformat&&d(t.showexponent)?"hide":"";if("date"===u?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||H.getTickFormat(t);n&&(a=i(a)?4:{y:"m",m:"d",d:"M",M:"S",S:4}[a]);var l,c=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),u=c.indexOf("\n");-1!==u&&(l=c.substr(u+1),c=c.substr(0,u));n&&("00:00:00"===c||"00:00"===c?(c=l,l=""):8===c.length&&(c=c.replace(/:00$/,"")));if(l)if(r)"d"===a?c+=", "+l:c=l+(c?", "+c:"");else if(t._inCalcTicks&&t._prevDateHead===l){var 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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,c),d="left",g="middle";if(s.fixedrange)return;m?(g="n"===m?"top":"bottom","right"===s.side&&(d="right")):"e"===v&&(d="right"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(u.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 zt(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),c=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=c*tt,Tt.b=(1-l)*et,Tt.t=(1-c)*et,Lt.attr("d",G(Tt))}else 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b="ns"===at?-e[1]*a._length/O._length:Zt(a,y,{n:"top",s:"bottom"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/I._length:Zt(o,x,{e:"right",w:"left"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var 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e=t.searchInfo.cd[0].trace,r=t.pointNumber,n=t.pointNumbers,i=n.length>0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function U(t,e,r){var n,a,o,s;for(n=0;n<e.length;n++){var l=e[n].cd[0].trace._fullInput,c=t._fullLayout._tracePreGUI[l.uid]||{};void 0===c.selectedpoints&&(c.selectedpoints=l._input.selectedpoints||null)}if(r){var u=r.points||[];for(n=0;n<e.length;n++)(s=e[n].cd[0].trace)._input.selectedpoints=s._fullInput.selectedpoints=[],s._fullInput!==s&&(s.selectedpoints=[]);for(n=0;n<u.length;n++){var f=u[n],h=f.data,p=f.fullData;f.pointIndices?([].push.apply(h.selectedpoints,f.pointIndices),s._fullInput!==s&&[].push.apply(p.selectedpoints,f.pointIndices)):(h.selectedpoints.push(f.pointIndex),s._fullInput!==s&&p.selectedpoints.push(f.pointIndex))}}else for(n=0;n<e.length;n++)delete(s=e[n].cd[0].trace).selectedpoints,delete s._input.selectedpoints,s._fullInput!==s&&delete s._fullInput.selectedpoints;var 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s,l,c=t.r2l(t[a][0],o),u=t.r2l(t[a][1],o),f="y"===p;if((f?(t._offset=n.t+(1-t.domain[1])*n.h,t._length=n.h*(t.domain[1]-t.domain[0]),t._m=t._length/(c-u),t._b=-t._m*u):(t._offset=n.l+t.domain[0]*n.w,t._length=n.w*(t.domain[1]-t.domain[0]),t._m=t._length/(u-c),t._b=-t._m*c),t._rangebreaks=[],t._lBreaks=0,t._m2=0,t._B=[],t.rangebreaks)&&(t._rangebreaks=t.locateBreaks(Math.min(c,u),Math.max(c,u)),t._rangebreaks.length)){for(s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._lBreaks+=Math.abs(l.max-l.min);var h=f;c>u&&(h=!h),h&&t._rangebreaks.reverse();var d=h?-1:1;for(t._m2=d*t._length/(Math.abs(u-c)-t._lBreaks),t._B.push(-t._m2*(f?u:c)),s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._B.push(t._B[t._B.length-1]-d*t._m2*(l.max-l.min));for(s=0;s<t._rangebreaks.length;s++)(l=t._rangebreaks[s]).pmin=B(l.min),l.pmax=B(l.max)}if(!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw e._replotting=!1,new Error("Something went wrong with axis scaling")},t.maskBreaks=function(e){var 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n=t("@plotly/d3"),i=t("d3-geo"),a=i.geoPath,o=i.geoDistance,s=t("d3-geo-projection"),l=t("../../registry"),c=t("../../lib"),u=c.strTranslate,f=t("../../components/color"),h=t("../../components/drawing"),p=t("../../components/fx"),d=t("../plots"),m=t("../cartesian/axes"),g=t("../cartesian/autorange").getAutoRange,v=t("../../components/dragelement"),y=t("../cartesian/select").prepSelect,x=t("../cartesian/select").clearSelect,b=t("../cartesian/select").selectOnClick,_=t("./zoom"),w=t("./constants"),T=t("../../lib/geo_location_utils"),k=t("../../lib/topojson_utils"),A=t("topojson-client").feature;function M(t){this.id=t.id,this.graphDiv=t.graphDiv,this.container=t.container,this.topojsonURL=t.topojsonURL,this.isStatic=t.staticPlot,this.topojsonName=null,this.topojson=null,this.projection=null,this.scope=null,this.viewInitial=null,this.fitScale=null,this.bounds=null,this.midPt=null,this.hasChoropleth=!1,this.traceHash={},this.layers={},this.basePaths={},this.dataPaths={},this.dataPoints={},this.clipDef=null,this.clipRect=null,this.bgRect=null,this.makeFramework()}var S=M.prototype;function E(t,e){var r=w.clipPad,n=t[0]+r,i=t[1]-r,a=e[0]+r,o=e[1]-r;n>0&&i<0&&(i+=360);var s=(i-n)/4;return{type:"Polygon",coordinates:[[[n,a],[n,o],[n+s,o],[n+2*s,o],[n+3*s,o],[i,o],[i,a],[i-s,a],[i-2*s,a],[i-3*s,a],[n,a]]]}}e.exports=function(t){return new M(t)},S.plot=function(t,e,r){var n=this,i=e[this.id],a=[],o=!1;for(var s in w.layerNameToAdjective)if("frame"!==s&&i["show"+s]){o=!0;break}for(var l=0;l<t.length;l++)if(t[0][0].trace.locationmode){o=!0;break}if(o){var 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i=t[n],a=i[0].trace;"choropleth"===a.type&&(this.hasChoropleth=!0),!0===a.visible&&a._length>0&&a._module.calcGeoJSON(i,e)}if(!this.updateProjection(t,e)){this.viewInitial&&this.scope===r.scope||this.saveViewInitial(r),this.scope=r.scope,this.updateBaseLayers(e,r),this.updateDims(e,r),this.updateFx(e,r),d.generalUpdatePerTraceModule(this.graphDiv,this,t,r);var o=this.layers.frontplot.select(".scatterlayer");this.dataPoints.point=o.selectAll(".point"),this.dataPoints.text=o.selectAll("text"),this.dataPaths.line=o.selectAll(".js-line");var s=this.layers.backplot.select(".choroplethlayer");this.dataPaths.choropleth=s.selectAll("path"),this.render()}},S.updateProjection=function(t,e){var r=this.graphDiv,n=e[this.id],l=e._size,u=n.domain,f=n.projection,h=n.lonaxis,p=n.lataxis,d=h._ax,m=p._ax,v=this.projection=function(t){var e=t.projection,r=e.type,n=w.projNames[r];n="geo"+c.titleCase(n);for(var l=(i[n]||s[n])(),u=t._isSatellite?180*Math.acos(1/e.distance)/Math.PI:t._isClipped?w.lonaxisSpan[r]/2:null,f=["center","rotate","parallels","clipExtent"],h=function(t){return t?l:[]},p=0;p<f.length;p++){var d=f[p];"function"!=typeof l[d]&&(l[d]=h)}l.isLonLatOverEdges=function(t){if(null===l(t))return!0;if(u){var e=l.rotate();return o(t,[-e[0],-e[1]])>u*Math.PI/180}return!1},l.getPath=function(){return a().projection(l)},l.getBounds=function(t){return l.getPath().bounds(t)},l.precision(w.precision),t._isSatellite&&l.tilt(e.tilt).distance(e.distance);u&&l.clipAngle(u-w.clipPad);return l}(n),y=[[l.l+l.w*u.x[0],l.t+l.h*(1-u.y[1])],[l.l+l.w*u.x[1],l.t+l.h*(1-u.y[0])]],x=n.center||{},b=f.rotation||{},_=h.range||[],T=p.range||[];if(n.fitbounds){d._length=y[1][0]-y[0][0],m._length=y[1][1]-y[0][1],d.range=g(r,d),m.range=g(r,m);var k=(d.range[0]+d.range[1])/2,A=(m.range[0]+m.range[1])/2;if(n._isScoped)x={lon:k,lat:A};else if(n._isClipped){x={lon:k,lat:A},b={lon:k,lat:A,roll:b.roll};var M=f.type,S=w.lonaxisSpan[M]/2||180,L=w.lataxisSpan[M]/2||90;_=[k-S,k+S],T=[A-L,A+L]}else x={lon:k,lat:A},b={lon:k,lat:b.lat,roll:b.roll}}v.center([x.lon-b.lon,x.lat-b.lat]).rotate([-b.lon,-b.lat,b.roll]).parallels(f.parallels);var C=E(_,T);v.fitExtent(y,C);var P=this.bounds=v.getBounds(C),I=this.fitScale=v.scale(),O=v.translate();if(n.fitbounds){var z=v.getBounds(E(d.range,m.range)),D=Math.min((P[1][0]-P[0][0])/(z[1][0]-z[0][0]),(P[1][1]-P[0][1])/(z[1][1]-z[0][1]));isFinite(D)?v.scale(D*I):c.warn("Something went wrong during"+this.id+"fitbounds computations.")}else v.scale(f.scale*I);var R=this.midPt=[(P[0][0]+P[1][0])/2,(P[0][1]+P[1][1])/2];if(v.translate([O[0]+(R[0]-O[0]),O[1]+(R[1]-O[1])]).clipExtent(P),n._isAlbersUsa){var F=v([x.lon,x.lat]),B=v.translate();v.translate([B[0]-(F[0]-B[0]),B[1]-(F[1]-B[1])])}},S.updateBaseLayers=function(t,e){var r=this,i=r.topojson,a=r.layers,o=r.basePaths;function s(t){return"lonaxis"===t||"lataxis"===t}function l(t){return Boolean(w.lineLayers[t])}function c(t){return Boolean(w.fillLayers[t])}var u=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter((function(t){return l(t)||c(t)?e["show"+t]:!s(t)||e[t].showgrid})),p=r.framework.selectAll(".layer").data(u,String);p.exit().each((function(t){delete a[t],delete o[t],n.select(this).remove()})),p.enter().append("g").attr("class",(function(t){return"layer "+t})).each((function(t){var e=a[t]=n.select(this);"bg"===t?r.bgRect=e.append("rect").style("pointer-events","all"):s(t)?o[t]=e.append("path").style("fill","none"):"backplot"===t?e.append("g").classed("choroplethlayer",!0):"frontplot"===t?e.append("g").classed("scatterlayer",!0):l(t)?o[t]=e.append("path").style("fill","none").style("stroke-miterlimit",2):c(t)&&(o[t]=e.append("path").style("stroke","none"))})),p.order(),p.each((function(r){var n=o[r],a=w.layerNameToAdjective[r];"frame"===r?n.datum(w.sphereSVG):l(r)||c(r)?n.datum(A(i,i.objects[r])):s(r)&&n.datum(function(t,e,r){var n,i,a,o=e[t],s=w.scopeDefaults[e.scope];"lonaxis"===t?(n=s.lonaxisRange,i=s.lataxisRange,a=function(t,e){return[t,e]}):"lataxis"===t&&(n=s.lataxisRange,i=s.lonaxisRange,a=function(t,e){return[e,t]});var l={type:"linear",range:[n[0],n[1]-1e-6],tick0:o.tick0,dtick:o.dtick};m.setConvert(l,r);var c=m.calcTicks(l);e.isScoped||"lonaxis"!==t||c.pop();for(var u=c.length,f=new Array(u),h=0;h<u;h++)for(var p=c[h].x,d=f[h]=[],g=i[0];g<i[1]+2.5;g+=2.5)d.push(a(p,g));return{type:"MultiLineString",coordinates:f}}(r,e,t)).call(f.stroke,e[r].gridcolor).call(h.dashLine,e[r].griddash,e[r].gridwidth),l(r)?n.call(f.stroke,e[a+"color"]).call(h.dashLine,"",e[a+"width"]):c(r)&&n.call(f.fill,e[a+"color"])}))},S.updateDims=function(t,e){var r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;"select"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:"lasso"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};"pan"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on("dblclick.zoom",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i="clip"+r._uid+t.id;t.clipDef=r._clips.append("clipPath").attr("id",i),t.clipRect=t.clipDef.append("rect"),t.framework=n.select(t.container).append("g").attr("class","geo "+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:"x",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:"y",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,"projection.scale":n.scale},e=t._isScoped?{"center.lon":r.lon,"center.lat":r.lat}:t._isClipped?{"projection.rotation.lon":i.lon,"projection.rotation.lat":i.lat}:{"center.lon":r.lon,"center.lat":r.lat,"projection.rotation.lon":i.lon},c.extendFlat(this.viewInitial,e)},S.render=function(){var t,e=this.projection,r=e.getPath();function n(t){var r=e(t.lonlat);return r?u(r[0],r[1]):null}function i(t){return e.isLonLatOverEdges(t.lonlat)?"none":null}for(t in this.basePaths)this.basePaths[t].attr("d",r);for(t in this.dataPaths)this.dataPaths[t].attr("d",(function(t){return r(t.geojson)}));for(t in this.dataPoints)this.dataPoints[t].attr("display",i).attr("transform",n)}},{"../../components/color":366,"../../components/dragelement":385,"../../components/drawing":388,"../../components/fx":406,"../../lib":503,"../../lib/geo_location_utils":496,"../../lib/topojson_utils":532,"../../registry":638,"../cartesian/autorange":553,"../cartesian/axes":554,"../cartesian/select":575,"../plots":619,"./constants":587,"./zoom":592,"@plotly/d3":58,"d3-geo":114,"d3-geo-projection":113,"topojson-client":315}],589:[function(t,e,r){"use strict";var n=t("../../plots/get_data").getSubplotCalcData,i=t("../../lib").counterRegex,a=t("./geo"),o="geo",s=i(o),l={};l.geo={valType:"subplotid",dflt:o,editType:"calc"},e.exports={attr:o,name:o,idRoot:o,idRegex:s,attrRegex:s,attributes:l,layoutAttributes:t("./layout_attributes"),supplyLayoutDefaults:t("./layout_defaults"),plot:function(t){for(var e=t._fullLayout,r=t.calcdata,i=e._subplots.geo,s=0;s<i.length;s++){var l=i[s],c=n(r,o,l),u=e[l]._subplot;u||(u=a({id:l,graphDiv:t,container:e._geolayer.node(),topojsonURL:t._context.topojsonURL,staticPlot:t._context.staticPlot}),e[l]._subplot=u),u.plot(c,e,t._promises)}},updateFx:function(t){for(var e=t._fullLayout,r=e._subplots.geo,n=0;n<r.length;n++){var i=e[r[n]];i._subplot.updateFx(e,i)}},clean:function(t,e,r,n){for(var i=n._subplots.geo||[],a=0;a<i.length;a++){var o=i[a],s=n[o]._subplot;!e[o]&&s&&(s.framework.remove(),s.clipDef.remove())}}}},{"../../lib":503,"../../plots/get_data":593,"./geo":588,"./layout_attributes":590,"./layout_defaults":591}],590:[function(t,e,r){"use strict";var n=t("../../components/color/attributes"),i=t("../domain").attributes,a=t("../../components/drawing/attributes").dash,o=t("./constants"),s=t("../../plot_api/edit_types").overrideAll,l=t("../../lib/sort_object_keys"),c={range:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},showgrid:{valType:"boolean",dflt:!1},tick0:{valType:"number",dflt:0},dtick:{valType:"number"},gridcolor:{valType:"color",dflt:n.lightLine},gridwidth:{valType:"number",min:0,dflt:1},griddash:a};(e.exports=s({domain:i({name:"geo"},{}),fitbounds:{valType:"enumerated",values:[!1,"locations","geojson"],dflt:!1,editType:"plot"},resolution:{valType:"enumerated",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:"enumerated",values:l(o.scopeDefaults),dflt:"world"},projection:{type:{valType:"enumerated",values:l(o.projNames)},rotation:{lon:{valType:"number"},lat:{valType:"number"},roll:{valType:"number"}},tilt:{valType:"number",dflt:0},distance:{valType:"number",min:1.001,dflt:2},parallels:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},scale:{valType:"number",min:0,dflt:1}},center:{lon:{valType:"number"},lat:{valType:"number"}},visible:{valType:"boolean",dflt:!0},showcoastlines:{valType:"boolean"},coastlinecolor:{valType:"color",dflt:n.defaultLine},coastlinewidth:{valType:"number",min:0,dflt:1},showland:{valType:"boolean",dflt:!1},landcolor:{valType:"color",dflt:o.landColor},showocean:{valType:"boolean",dflt:!1},oceancolor:{valType:"color",dflt:o.waterColor},showlakes:{valType:"boolean",dflt:!1},lakecolor:{valType:"color",dflt:o.waterColor},showrivers:{valType:"boolean",dflt:!1},rivercolor:{valType:"color",dflt:o.waterColor},riverwidth:{valType:"number",min:0,dflt:1},showcountries:{valType:"boolean"},countrycolor:{valType:"color",dflt:n.defaultLine},countrywidth:{valType:"number",min:0,dflt:1},showsubunits:{valType:"boolean"},subunitcolor:{valType:"color",dflt:n.defaultLine},subunitwidth:{valType:"number",min:0,dflt:1},showframe:{valType:"boolean"},framecolor:{valType:"color",dflt:n.defaultLine},framewidth:{valType:"number",min:0,dflt:1},bgcolor:{valType:"color",dflt:n.background},lonaxis:c,lataxis:c},"plot","from-root")).uirevision={valType:"any",editType:"none"}},{"../../components/color/attributes":365,"../../components/drawing/attributes":387,"../../lib/sort_object_keys":526,"../../plot_api/edit_types":536,"../domain":584,"./constants":587}],591:[function(t,e,r){"use 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k=o[w+"Span"],A=(k[h]||k["*"])/2,M=r("projection.rotation."+w.substr(0,3),f.projRotate[b]);_=[M-A,M+A]}var S=r(w+".range",_);r(w+".tick0"),r(w+".dtick",T),r(w+".showgrid",!!x&&void 0)&&(r(w+".gridcolor"),r(w+".gridwidth"),r(w+".griddash")),e[w]._ax={type:"linear",_id:w.slice(0,3),_traceIndices:s,setScale:n.identity,c2l:n.identity,r2l:n.identity,autorange:!0,range:S.slice(),_m:1,_input:{}}}var E=e.lonaxis.range,L=e.lataxis.range,C=E[0],P=E[1];C>0&&P<0&&(P+=360);var I,O,z,D=(C+P)/2;if(!p){var R=d?f.projRotate:[D,0,0];I=r("projection.rotation.lon",R[0]),r("projection.rotation.lat",R[1]),r("projection.rotation.roll",R[2]),r("showcoastlines",!d&&x)&&(r("coastlinecolor"),r("coastlinewidth")),r("showocean",!!x&&void 0)&&r("oceancolor")}(p?(O=-96.6,z=38.7):(O=d?D:I,z=(L[0]+L[1])/2),r("center.lon",O),r("center.lat",z),m&&(r("projection.tilt"),r("projection.distance")),g)&&r("projection.parallels",f.projParallels||[0,60]);r("projection.scale"),r("showland",!!x&&void 0)&&r("landcolor"),r("showlakes",!!x&&void 0)&&r("lakecolor"),r("showrivers",!!x&&void 0)&&(r("rivercolor"),r("riverwidth")),r("showcountries",d&&"usa"!==u&&x)&&(r("countrycolor"),r("countrywidth")),("usa"===u||"north america"===u&&50===c)&&(r("showsubunits",x),r("subunitcolor"),r("subunitwidth")),d||r("showframe",x)&&(r("framecolor"),r("framewidth")),r("bgcolor"),r("fitbounds")&&(delete e.projection.scale,d?(delete e.center.lon,delete e.center.lat):v?(delete e.center.lon,delete e.center.lat,delete e.projection.rotation.lon,delete e.projection.rotation.lat,delete e.lonaxis.range,delete e.lataxis.range):(delete e.center.lon,delete e.center.lat,delete e.projection.rotation.lon))}e.exports=function(t,e,r){i(t,e,r,{type:"geo",attributes:s,handleDefaults:c,fullData:r,partition:"y"})}},{"../../lib":503,"../get_data":593,"../subplot_defaults":632,"./constants":587,"./layout_attributes":590}],592:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=t("../../registry"),o=Math.PI/180,s=180/Math.PI,l={cursor:"pointer"},c={cursor:"auto"};function u(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],c=o._fullLayout,u=c[n],f={},h={};function p(t,e){f[n+"."+t]=i.nestedProperty(l,t).get(),a.call("_storeDirectGUIEdit",s,c._preGUI,f);var r=i.nestedProperty(u,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+"."+t]=e)}r(p),p("projection.scale",e.scale()/t.fitScale),p("fitbounds",!1),o.emit("plotly_relayout",h)}function h(t,e){var r=u(0,e);function i(r){var n=e.invert(t.midPt);r("center.lon",n[0]),r("center.lat",n[1])}return r.on("zoomstart",(function(){n.select(this).style(l)})).on("zoom",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render();var r=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":r[0],"geo.center.lat":r[1]})})).on("zoomend",(function(){n.select(this).style(c),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,m,g=u(0,e);function v(t){return e.invert(t)}function y(r){var n=e.rotate(),i=e.invert(t.midPt);r("projection.rotation.lon",-n[0]),r("center.lon",i[0]),r("center.lat",i[1])}return g.on("zoomstart",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=v(r)})).on("zoom",(function(){if(h=n.mouse(this),function(t){var r=v(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?v(h)&&(d=v(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=v(r=h),m=!0,t.render();var l=e.rotate(),c=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":c[0],"geo.center.lat":c[1],"geo.projection.rotation.lon":-l[0]})})).on("zoomend",(function(){n.select(this).style(c),m&&f(t,e,y)})),g}function d(t,e){var r,i={r:e.rotate(),k:e.scale()},a=u(0,e),o=function(t){var e=0,r=arguments.length,i=[];for(;++e<r;)i.push(arguments[e]);var a=n.dispatch.apply(null,i);return a.of=function(e,r){return function(i){var o;try{o=i.sourceEvent=n.event,i.target=t,n.event=i,a[i.type].apply(e,r)}finally{n.event=o}}},a}(a,"zoomstart","zoom","zoomend"),s=0,h=a.on;function p(t){s++||t({type:"zoomstart"})}function d(t){t({type:"zoom"})}function b(t){--s||t({type:"zoomend"})}function _(t){var r=e.rotate();t("projection.rotation.lon",-r[0]),t("projection.rotation.lat",-r[1])}return a.on("zoomstart",(function(){n.select(this).style(l);var t=n.mouse(this),s=e.rotate(),c=s,u=e.translate(),f=g(s);r=m(e,t),h.call(a,"zoom",(function(){var a=n.mouse(this);if(e.scale(i.k=n.event.scale),r){if(m(e,a)){e.rotate(s).translate(u);var l=m(e,a),h=y(r,l),p=T(v(f,h)),g=i.r=x(p,r,c);isFinite(g[0])&&isFinite(g[1])&&isFinite(g[2])||(g=c),e.rotate(g),c=g}}else r=m(e,t=a);d(o.of(this,arguments))})),p(o.of(this,arguments))})).on("zoomend",(function(){n.select(this).style(c),h.call(a,"zoom",null),b(o.of(this,arguments)),f(t,e,_)})).on("zoom.redraw",(function(){t.render();var r=e.rotate();t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.projection.rotation.lon":-r[0],"geo.projection.rotation.lat":-r[1]})})),n.rebind(a,o,"on")}function m(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t){var e=.5*t[0]*o,r=.5*t[1]*o,n=.5*t[2]*o,i=Math.sin(e),a=Math.cos(e),s=Math.sin(r),l=Math.cos(r),c=Math.sin(n),u=Math.cos(n);return[a*l*u+i*s*c,i*l*u-a*s*c,a*s*u+i*l*c,a*l*c-i*s*u]}function v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],c=e[3];return[r*o-n*s-i*l-a*c,r*s+n*o+i*c-a*l,r*l-n*c+i*o+a*s,r*c+n*l-i*s+a*o]}function y(t,e){if(t&&e){var r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(k(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,k(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function x(t,e,r){var n=w(e,2,t[0]);n=w(n,1,t[1]),n=w(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var m=180-a-2*p,g=(Math.atan2(h,u)-Math.atan2(c,i))*s,v=(Math.atan2(h,u)-Math.atan2(c,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],m,v)?[a,g,r[2]]:[m,v,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return 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s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},{"../registry":638,"./cartesian/constants":561}],594:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{"../cartesian/constants":561,"has-passive-events":229,"mouse-change":241,"mouse-event-offset":242,"mouse-wheel":244}],595:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/str2rgbarray");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":528,"../cartesian/axes":554}],596:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../plot_api/edit_types":536,"../cartesian":568,"../cartesian/attributes":552,"../cartesian/constants":561,"../get_data":593,"../layout_attributes":610,"./scene2d":597}],597:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1124,"../../components/dragelement/helpers":384,"../../components/fx":406,"../../lib/show_no_webgl_msg":525,"../../plots/cartesian/axes":554,"../../registry":638,"../cartesian/autorange":553,"../cartesian/constants":561,"../cartesian/constraints":562,"./camera":594,"./convert":595,"webgl-context":331}],598:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../lib":503,"../../plot_api/edit_types":536,"../get_data":593,"./layout/attributes":599,"./layout/defaults":603,"./layout/layout_attributes":604,"./scene":608}],599:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],600:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":366,"../../../lib/extend":493,"../../../plot_api/edit_types":536,"../../cartesian/layout_attributes":569}],601:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":503,"../../../plot_api/plot_template":543,"../../cartesian/axis_defaults":556,"../../cartesian/type_defaults":582,"./axis_attributes":600,tinycolor2:312}],602:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open 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o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":503,"../../../lib/str2rgbarray":528}],603:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":366,"../../../lib":503,"../../../registry":638,"../../get_data":593,"../../subplot_defaults":632,"./axis_defaults":601,"./layout_attributes":604}],604:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":503,"../../../lib/extend":493,"../../domain":584,"./axis_attributes":600}],605:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":528}],606:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":503,"../../cartesian/axes":554}],607:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated 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B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d",{willReadFrequently:!0}),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1124,"../../components/fx":406,"../../lib":503,"../../lib/show_no_webgl_msg":525,"../../lib/str2rgbarray":528,"../../plots/cartesian/axes":554,"../../registry":638,"./layout/convert":602,"./layout/spikes":605,"./layout/tick_marks":606,"./project":607,"has-passive-events":229,"webgl-context":331}],609:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],610:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:"string",editType:"layoutstyle"},font:n({editType:"layoutstyle"}),xref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},yref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},x:{valType:"number",min:0,max:1,dflt:.5,editType:"layoutstyle"},y:{valType:"number",min:0,max:1,dflt:"auto",editType:"layoutstyle"},xanchor:{valType:"enumerated",dflt:"auto",values:["auto","left","center","right"],editType:"layoutstyle"},yanchor:{valType:"enumerated",dflt:"auto",values:["auto","top","middle","bottom"],editType:"layoutstyle"},pad:l(s({editType:"layoutstyle"}),{}),editType:"layoutstyle"},uniformtext:{mode:{valType:"enumerated",values:[!1,"hide","show"],dflt:!1,editType:"plot"},minsize:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"},autosize:{valType:"boolean",dflt:!1,editType:"none"},width:{valType:"number",min:10,dflt:700,editType:"plot"},height:{valType:"number",min:10,dflt:450,editType:"plot"},margin:{l:{valType:"number",min:0,dflt:80,editType:"plot"},r:{valType:"number",min:0,dflt:80,editType:"plot"},t:{valType:"number",min:0,dflt:100,editType:"plot"},b:{valType:"number",min:0,dflt:80,editType:"plot"},pad:{valType:"number",min:0,dflt:0,editType:"plot"},autoexpand:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},computed:{valType:"any",editType:"none"},paper_bgcolor:{valType:"color",dflt:a.background,editType:"plot"},plot_bgcolor:{valType:"color",dflt:a.background,editType:"layoutstyle"},autotypenumbers:{valType:"enumerated",values:["convert types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:a.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:l({},i.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:n({editType:"layoutstyle"})}}},{"../components/color/attributes":365,"../components/shapes/draw_newshape/attributes":451,"../lib/extend":493,"./animation_attributes":548,"./font_attributes":585,"./pad_attributes":618}],611:[function(t,e,r){"use strict";var n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl 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s=e[r[i]],h=s.domain,d=s._subplot.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":d,x:n.l+n.w*h.x[0],y:n.t+n.h*(1-h.y[1]),width:n.w*(h.x[1]-h.x[0]),height:n.h*(h.y[1]-h.y[0]),preserveAspectRatio:"none"});var m=c.select(s._subplot.div);if(!(null===m.select(".mapboxgl-ctrl-logo").node().offsetParent)){var g=e._glimages.append("g");g.attr("transform",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append("path").attr("d",p.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),g.append("path").attr("d",p.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),g.append("path").attr("d",p.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),g.append("polygon").attr("points",p.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var v=m.select(".mapboxgl-ctrl-attrib").text().replace("Improve this 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t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},l.dispose=function(){var t=this.subplot.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer),t.getSource(this.idSource)&&t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new s(t,e);return n.update(r),n}},{"../../lib":503,"../../lib/svg_text_utils":529,"./constants":611,"./convert_text_opts":612}],615:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color").defaultLine,a=t("../domain").attributes,o=t("../font_attributes"),s=t("../../traces/scatter/attributes").textposition,l=t("../../plot_api/edit_types").overrideAll,c=t("../../plot_api/plot_template").templatedArray,u=t("./constants"),f=o({});f.family.dflt="Open Sans Regular, Arial Unicode MS 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n=t("../../lib").extendFlat,i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../ohlc/attributes"),o=t("../box/attributes");function s(t){return{line:{color:n({},o.line.color,{dflt:t}),width:o.line.width,editType:"style"},fillcolor:o.fillcolor,editType:"style"}}e.exports={xperiod:a.xperiod,xperiod0:a.xperiod0,xperiodalignment:a.xperiodalignment,xhoverformat:i("x"),yhoverformat:i("y"),x:a.x,open:a.open,high:a.high,low:a.low,close:a.close,line:{width:n({},o.line.width,{}),editType:"style"},increasing:s(a.increasing.line.color.dflt),decreasing:s(a.decreasing.line.color.dflt),text:a.text,hovertext:a.hovertext,whiskerwidth:n({},o.whiskerwidth,{dflt:0}),hoverlabel:a.hoverlabel}},{"../../lib":503,"../../plots/cartesian/axis_format_attributes":557,"../box/attributes":673,"../ohlc/attributes":872}],686:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/cartesian/align_period"),o=t("../ohlc/calc").calcCommon;function 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n=t("../../lib/extend").extendFlat,i=t("../../lib/extend").extendDeep,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../components/color/attributes"),l=t("../../plots/domain").attributes,c=t("../../plots/cartesian/layout_attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../constants/delta.js"),h=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,p=o({editType:"plot",colorEditType:"plot"}),d={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:s.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},m={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},g=u("step",i({},d,{range:m}));e.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:l({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:n({},p,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:h("value")},font:n({},p,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:h("value")},increasing:{symbol:{valType:"string",dflt:f.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:f.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:n({},p,{}),editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:i({},d,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:s.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:a({range:m,visible:n({},c.visible,{dflt:!0}),tickmode:c.tickmode,nticks:c.nticks,tick0:c.tick0,dtick:c.dtick,tickvals:c.tickvals,ticktext:c.ticktext,ticks:n({},c.ticks,{dflt:"outside"}),ticklen:c.ticklen,tickwidth:c.tickwidth,tickcolor:c.tickcolor,ticklabelstep:c.ticklabelstep,showticklabels:c.showticklabels,tickfont:o({}),tickangle:c.tickangle,tickformat:c.tickformat,tickformatstops:c.tickformatstops,tickprefix:c.tickprefix,showtickprefix:c.showtickprefix,ticksuffix:c.ticksuffix,showticksuffix:c.showticksuffix,separatethousands:c.separatethousands,exponentformat:c.exponentformat,minexponent:c.minexponent,showexponent:c.showexponent,editType:"plot"},"plot"),steps:g,threshold:{line:{color:n({},d.line.color,{}),width:n({},d.line.width,{dflt:1}),editType:"plot"},thickness:n({},d.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}},{"../../components/color/attributes":365,"../../constants/delta.js":473,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/cartesian/axis_format_attributes":557,"../../plots/cartesian/layout_attributes":569,"../../plots/domain":584,"../../plots/font_attributes":585}],856:[function(t,e,r){"use 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n=t("../../lib"),i=t("./attributes"),a=t("../../plots/domain").defaults,o=t("../../plot_api/plot_template"),s=t("../../plots/array_container_defaults"),l=t("./constants.js"),c=t("../../plots/cartesian/tick_value_defaults"),u=t("../../plots/cartesian/tick_mark_defaults"),f=t("../../plots/cartesian/tick_label_defaults"),h=t("../../plots/cartesian/prefix_suffix_defaults");function p(t,e){function r(r,a){return n.coerce(t,e,i.gauge.steps,r,a)}r("color"),r("line.color"),r("line.width"),r("range"),r("thickness")}e.exports={supplyDefaults:function(t,e,r,d){function m(r,a){return n.coerce(t,e,i,r,a)}a(e,d,m),m("mode"),e._hasNumber=-1!==e.mode.indexOf("number"),e._hasDelta=-1!==e.mode.indexOf("delta"),e._hasGauge=-1!==e.mode.indexOf("gauge");var g=m("value");e._range=[0,"number"==typeof g?1.5*g:1];var v,y,x,b,_,w,T=new Array(2);function k(t,e){return n.coerce(x,b,i.gauge,t,e)}function A(t,e){return 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m,E,L,C,P,I=e[0].trace,O=n.select(this),z=I._hasGauge,D=I._isAngular,R=I._isBullet,F=I.domain,B={w:v._size.w*(F.x[1]-F.x[0]),h:v._size.h*(F.y[1]-F.y[0]),l:v._size.l+v._size.w*F.x[0],r:v._size.r+v._size.w*(1-F.x[1]),t:v._size.t+v._size.h*(1-F.y[1]),b:v._size.b+v._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,H=I.align||"center";if(E=j,z){if(D&&(m=N,E=j+U/2,L=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*V)}),R){var q=h.bulletPadding,G=1-h.bulletNumberDomainSize+q;m=B.l+(G+(1-G)*b[H])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-q)*B.w,B.h)}}}else m=B.l+b[H]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var c,u,h,m=r[0].trace,g=i.numbersX,v=i.numbersY,T=m.align||"center",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,"g","numbers"),P=[];m._hasNumber&&P.push("number");m._hasDelta&&(P.push("delta"),"left"===m.delta.position&&P.reverse());var I=C.selectAll("text").data(P);function O(e,r,n,i){if(!e.match("s")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace("s","f").replace(/\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}I.enter().append("text"),I.attr("text-anchor",(function(){return A})).attr("class",(function(t){return t})).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),I.exit().remove();var z,D=m.mode+m.align;m._hasDelta&&(z=function(){var e=k(t,{tickformat:m.delta.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=function(t){return m.delta.relative?t.relativeDelta:t.delta},s=function(t,e){return 0===t||"number"!=typeof t||isNaN(t)?"-":(t>0?m.delta.increasing.symbol:m.delta.decreasing.symbol)+e(t)},l=function(t){return t.delta>=0?m.delta.increasing.color:m.delta.decreasing.color};void 0===m._deltaLastValue&&(m._deltaLastValue=o(r[0]));var c=C.select("text.delta");function h(){c.text(s(o(r[0]),i)).call(y.fill,l(r[0])).call(p.convertToTspans,t)}return c.call(f.font,m.delta.font).call(y.fill,l({delta:m._deltaLastValue})),w(E)?c.transition().duration(E.duration).ease(E.easing).tween("text",(function(){var t=n.select(this),e=o(r[0]),c=m._deltaLastValue,u=O(m.delta.valueformat,i,c,e),f=a(c,e);return m._deltaLastValue=e,function(e){t.text(s(f(e),u)),t.call(y.fill,l({delta:f(e)}))}})).each("end",(function(){h(),L&&L()})).each("interrupt",(function(){h(),L&&L()})):h(),u=M(s(o(r[0]),i),m.delta.font,A,t),c}(),D+=m.delta.position+m.delta.font.size+m.delta.font.family+m.delta.valueformat,D+=m.delta.increasing.symbol+m.delta.decreasing.symbol,h=u);m._hasNumber&&(!function(){var e=k(t,{tickformat:m.number.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=m.number.suffix,s=m.number.prefix,l=C.select("text.number");function u(){var e="number"==typeof r[0].y?s+i(r[0].y)+o:"-";l.text(e).call(f.font,m.number.font).call(p.convertToTspans,t)}w(E)?l.transition().duration(E.duration).ease(E.easing).each("end",(function(){u(),L&&L()})).each("interrupt",(function(){u(),L&&L()})).attrTween("text",(function(){var t=n.select(this),e=a(r[0].lastY,r[0].y);m._lastValue=r[0].y;var l=O(m.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(s+l(e(r))+o)}})):u(),c=M(s+i(r[0].y)+o,m.number.font,A,t)}(),D+=m.number.font.size+m.number.font.family+m.number.valueformat+m.number.suffix+m.number.prefix,h=c);if(m._hasDelta&&m._hasNumber){var R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*m.delta.font.size;"left"===m.delta.position&&(R=S(m,"deltaPos",0,-1*(c.width*b[m.align]+u.width*(1-b[m.align])+j),D,Math.min),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"right"===m.delta.position&&(R=S(m,"deltaPos",0,c.width*(1-b[m.align])+u.width*b[m.align]+j,D,Math.max),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"bottom"===m.delta.position&&(R=null,F=u.height,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),"top"===m.delta.position&&(R=null,F=c.top,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),z.attr({dx:R,dy:F})}(m._hasNumber||m._hasDelta)&&C.attr("transform",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?"middle":r>0?"start":"end"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h="inside"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:"M"+h*R+",0h"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+"rotate("+-c(e)+")"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll("g.bg-arc").data(F);B.enter().append("g").classed("bg-arc",!0).append("path"),B.select("path").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll("g.value-arc").data([p.gauge.bar]);j.enter().append("g").classed("value-arc",!0).append("path");var U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":366,"../../components/drawing":388,"../../constants/alignment":471,"../../lib":503,"../../lib/svg_text_utils":529,"../../plots/cartesian/axes":554,"../../plots/cartesian/axis_defaults":556,"../../plots/cartesian/layout_attributes":569,"../../plots/cartesian/position_defaults":572,"./constants":858,"@plotly/d3":58,"d3-interpolate":116}],862:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 0},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633,"../mesh3d/attributes":867}],863:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc"),i=t("../streamtube/calc").processGrid,a=t("../streamtube/calc").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374,"../streamtube/calc":1045}],864:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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n=t("@plotly/d3"),i=t("../../components/drawing"),a=t("../../components/color");e.exports=function(t,e,r){var o=r||n.select(t).selectAll("g.ohlclayer").selectAll("g.trace");o.style("opacity",(function(t){return t[0].trace.opacity})),o.each((function(t){var e=t[0].trace;n.select(this).selectAll("path").each((function(t){if(!t.empty){var r=e[t.dir].line;n.select(this).style("fill","none").call(a.stroke,r.color).call(i.dashLine,r.dash,r.width).style("opacity",e.selectedpoints&&!t.selected?.3:1)}}))}))}},{"../../components/color":366,"../../components/drawing":388,"@plotly/d3":58}],881:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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i=n._has&&n._has("parcats"),a=e._has&&e._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":593,"./plot":887}],883:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap,i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),o=t("../../lib/filter_unique.js"),s=t("../../components/drawing"),l=t("../../lib"),c=t("fast-isnumeric");function u(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}e.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,m,g=r.map((function(t){var e;if("trace"===t.categoryorder)e=null;else if("array"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!c(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),"category descending"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,g[e])}));var v,y=e.line;y?(i(e,"line")&&a(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=g.map((function(t){return t.inds}));for(m=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],m+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,m)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=g[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,m))}},{"../../components/colorscale/calc":374,"../../components/colorscale/helpers":377,"../../components/drawing":388,"../../lib":503,"../../lib/filter_unique.js":494,"../../lib/gup":500,"fast-isnumeric":190}],884:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("../parcoords/merge_length");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o="array");var u=r("categoryorder",o);"array"===u?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==u||(e.categoryorder="trace")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:u}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,"values",d),h("hoveron"),h("hovertemplate"),h("arrangement"),h("bundlecolors"),h("sortpaths"),h("counts");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,"labelfont",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,"tickfont",g)}},{"../../components/colorscale/defaults":376,"../../components/colorscale/helpers":377,"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/domain":584,"../parcoords/merge_length":898,"./attributes":881}],885:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":881,"./base_plot":882,"./calc":883,"./defaults":884,"./plot":887}],886:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolateNumber,a=t("../../plot_api/plot_api"),o=t("../../components/fx"),s=t("../../lib"),l=s.strTranslate,c=t("../../components/drawing"),u=t("tinycolor2"),f=t("../../lib/svg_text_utils");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[u],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function 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n=t("./parcats");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{"./parcats":886}],888:[function(t,e,r){"use strict";var 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k(t){t.on("mousemove",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on("mouseleave",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on("dragstart",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar="ns"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l["s"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on("drag",(function(t){w(this,t)})).on("dragend",(function(t){!function(t,e){var 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0!==(x=c[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{"../../components/color":366,"fast-isnumeric":190,tinycolor2:312}],904:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":503,"../../plots/domain":584,"../bar/defaults":652,"./attributes":901,"fast-isnumeric":190}],905:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":402}],906:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":503}],907:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":901,"./base_plot":902,"./calc":903,"./defaults":904,"./layout_attributes":908,"./layout_defaults":909,"./plot":910,"./style":911,"./style_one":912}],908:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],909:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":503,"./layout_attributes":908}],910:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr("transform",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*d.r+","+n*d.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var T=n.select(this).selectAll("g.titletext").data(m.title.text?[0]:[]);if(T.enter().append("g").classed("titletext",!0),T.exit().remove(),T.each((function(){var 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n=t("../scatter/attributes");e.exports={x:n.x,y:n.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:n.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"},transforms:void 0}},{"../scatter/attributes":927}],914:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_pointcloud2d,i=t("../../lib/str2rgbarray"),a=t("../../plots/cartesian/autorange").findExtremes,o=t("../scatter/get_trace_color");function s(t,e){this.scene=t,this.uid=e,this.type="pointcloud",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color="rgb(0, 0, 0)",this.name="",this.hoverinfo="all",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new 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r.update(e),r}},{"../../../stackgl_modules":1124,"../../lib/str2rgbarray":528,"../../plots/cartesian/autorange":553,"../scatter/get_trace_color":937}],915:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a("x"),a("y"),a("xbounds"),a("ybounds"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a("text"),a("marker.color",r),a("marker.opacity"),a("marker.blend"),a("marker.sizemin"),a("marker.sizemax"),a("marker.border.color",r),a("marker.border.arearatio"),e._length=null}},{"../../lib":503,"./attributes":913}],916:[function(t,e,r){"use strict";["*pointcloud* trace is deprecated!","Please consider switching to the *scattergl* trace type."].join(" 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n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,h=t("../../lib/extend").extendFlat,p=t("../../plot_api/edit_types").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:f("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),customdata:void 0,node:{label:{valType:"data_array",dflt:[]},groups:{valType:"info_array",impliedEdits:{x:[],y:[]},dimensions:2,freeLength:!0,dflt:[],items:{valType:"number",editType:"calc"}},x:{valType:"data_array",dflt:[]},y:{valType:"data_array",dflt:[]},color:{valType:"color",arrayOk:!0},customdata:{valType:"data_array",editType:"calc"},line:{color:{valType:"color",dflt:a.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:.5,arrayOk:!0}},pad:{valType:"number",arrayOk:!1,min:0,dflt:20},thickness:{valType:"number",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:"enumerated",values:["all","none","skip"],dflt:"all"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:["value","label"]})},link:{label:{valType:"data_array",dflt:[]},color:{valType:"color",arrayOk:!0},customdata:{valType:"data_array",editType:"calc"},line:{color:{valType:"color",dflt:a.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:0,arrayOk:!0}},source:{valType:"data_array",dflt:[]},target:{valType:"data_array",dflt:[]},value:{valType:"data_array",dflt:[]},hoverinfo:{valType:"enumerated",values:["all","none","skip"],dflt:"all"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:["value","label"]}),colorscales:u("concentrationscales",{editType:"calc",label:{valType:"string",editType:"calc",dflt:""},cmax:{valType:"number",editType:"calc",dflt:1},cmin:{valType:"number",editType:"calc",dflt:0},colorscale:h(c().colorscale,{dflt:[[0,"white"],[1,"black"]]})})}},"calc","nested")).transforms=void 0},{"../../components/color/attributes":365,"../../components/colorscale/attributes":373,"../../components/fx/attributes":397,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/domain":584,"../../plots/font_attributes":585,"../../plots/template_attributes":633}],918:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../components/fx/layout_attributes"),s=t("../../lib/setcursor"),l=t("../../components/dragelement"),c=t("../../plots/cartesian/select").prepSelect,u=t("../../lib"),f=t("../../registry");function h(t,e){var r=t._fullData[e],n=t._fullLayout,i=n.dragmode,a="pan"===n.dragmode?"move":"crosshair",o=r._bgRect;if("pan"!==i&&"zoom"!==i){s(o,a);var h={_id:"x",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:"y",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call("_guiRestyle",t,{"node.groups":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name="sankey",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},"plot","nested"),r.plot=function(t){var e=i(t.calcdata,"sankey")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var i=n._has&&n._has("sankey"),a=e._has&&e._has("sankey");i&&!a&&(n._paperdiv.selectAll(".sankey").remove(),n._paperdiv.selectAll(".bgsankey").remove())},r.updateFx=function(t){for(var e=0;e<t._fullData.length;e++)h(t,e)}},{"../../components/dragelement":385,"../../components/fx/layout_attributes":407,"../../lib":503,"../../lib/setcursor":524,"../../plot_api/edit_types":536,"../../plots/cartesian/select":575,"../../plots/get_data":593,"../../registry":638,"./plot":923}],919:[function(t,e,r){"use strict";var n=t("strongly-connected-components"),i=t("../../lib"),a=t("../../lib/gup").wrap,o=i.isArrayOrTypedArray,s=i.isIndex,l=t("../../components/colorscale");function c(t){var e,r=t.node,a=t.link,c=[],u=o(a.color),f=o(a.customdata),h={},p={},d=a.colorscales.length;for(e=0;e<d;e++){var m=a.colorscales[e],g=l.extractScale(m,{cLetter:"c"}),v=l.makeColorScaleFunc(g);p[m.label]=v}var y=0;for(e=0;e<a.value.length;e++)a.source[e]>y&&(y=a.source[e]),a.target[e]>y&&(y=a.target[e]);var x,b=y+1;t.node._count=b;var _=t.node.groups,w={};for(e=0;e<_.length;e++){var T=_[e];for(x=0;x<T.length;x++){var k=T[x],A=b+e;w.hasOwnProperty(k)?i.warn("Node "+k+" is already part of a group."):w[k]=A}}var M={source:[],target:[]};for(e=0;e<a.value.length;e++){var S=a.value[e],E=a.source[e],L=a.target[e];if(S>0&&s(E,b)&&s(L,b)&&(!w.hasOwnProperty(E)||!w.hasOwnProperty(L)||w[E]!==w[L])){w.hasOwnProperty(L)&&(L=w[L]),w.hasOwnProperty(E)&&(E=w[E]),L=+L,h[E=+E]=h[L]=!0;var C="";a.label&&a.label[e]&&(C=a.label[e]);var P=null;C&&p.hasOwnProperty(C)&&(P=p[C]),c.push({pointNumber:e,label:C,color:u?a.color[e]:a.color,customdata:f?a.customdata[e]:a.customdata,concentrationscale:P,source:E,target:L,value:+S}),M.source.push(E),M.target.push(L)}}var I=b+_.length,O=o(r.color),z=o(r.customdata),D=[];for(e=0;e<I;e++)if(h[e]){var R=r.label[e];D.push({group:e>b-1,childrenNodes:[],pointNumber:e,label:R,color:O?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var F=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,M.source,M.target)&&(F=!0),{circular:F,links:c,nodes:D,groups:_,groupLookup:w}}e.exports=function(t,e){var r=c(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},{"../../components/colorscale":378,"../../lib":503,"../../lib/gup":500,"strongly-connected-components":306}],920:[function(t,e,r){"use strict";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}},{}],921:[function(t,e,r){"use strict";var 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function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll("."+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||"",w+m.source.label,T+m.target.label,m.concentrationscale?k+a("%0.2f")(m.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(m.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<g[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":366,"../../components/fx":406,"../../lib":503,"./constants":920,"./render":924,"@plotly/d3":58}],924:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,m=t("../../lib/gup"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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n=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../../components/colorscale/attributes"),s=t("../../plots/font_attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../components/drawing/attributes").pattern,u=t("../../components/drawing"),f=t("./constants"),h=t("../../lib/extend").extendFlat;e.exports={x:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},x0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dx:{valType:"number",dflt:1,editType:"calc",anim:!0},y:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},y0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dy:{valType:"number",dflt:1,editType:"calc",anim:!0},xperiod:{valType:"any",dflt:0,editType:"calc"},yperiod:{valType:"any",dflt:0,editType:"calc"},xperiod0:{valType:"any",editType:"calc"},yperiod0:{valType:"any",editType:"calc"},xperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},yperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},xhoverformat:n("x"),yhoverformat:n("y"),stackgroup:{valType:"string",dflt:"",editType:"calc"},orientation:{valType:"enumerated",values:["v","h"],editType:"calc"},groupnorm:{valType:"enumerated",values:["","fraction","percent"],dflt:"",editType:"calc"},stackgaps:{valType:"enumerated",values:["infer 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("../../components/colorscale/helpers").hasColorscale,i=t("../../components/colorscale/calc"),a=t("./subtypes");e.exports=function(t,e){a.hasLines(e)&&n(e,"line")&&i(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),a.hasMarkers(e)&&(n(e,"marker")&&i(t,e,{vals:e.marker.color,containerStr:"marker",cLetter:"c"}),n(e,"marker.line")&&i(t,e,{vals:e.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}},{"../../components/colorscale/calc":374,"../../components/colorscale/helpers":377,"./subtypes":952}],931:[function(t,e,r){"use strict";e.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},{}],932:[function(t,e,r){"use strict";var n=t("./calc");function i(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return 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n=t("../../lib"),i=t("../../registry"),a=t("./attributes"),o=t("./constants"),s=t("./subtypes"),l=t("./xy_defaults"),c=t("./period_defaults"),u=t("./stack_defaults"),f=t("./marker_defaults"),h=t("./line_defaults"),p=t("./line_shape_defaults"),d=t("./text_defaults"),m=t("./fillcolor_defaults"),g=t("../../lib").coercePattern;e.exports=function(t,e,r,v){function y(r,i){return n.coerce(t,e,a,r,i)}var x=l(t,e,v,y);if(x||(e.visible=!1),e.visible){c(t,e,v,y),y("xhoverformat"),y("yhoverformat");var b=u(t,e,v,y),_=!b&&x<o.PTS_LINESONLY?"lines+markers":"lines";y("text"),y("hovertext"),y("mode",_),s.hasLines(e)&&(h(t,e,r,v,y),p(t,e,y),y("connectgaps"),y("line.simplify")),s.hasMarkers(e)&&f(t,e,r,v,y,{gradient:!0}),s.hasText(e)&&(y("texttemplate"),d(t,e,v,y));var w=[];(s.hasMarkers(e)||s.hasText(e))&&(y("cliponaxis"),y("marker.maxdisplayed"),w.push("points")),y("fill",b?b.fillDflt:"none"),"none"!==e.fill&&(m(t,e,r,y),s.hasLines(e)||p(t,e,y),g(y,"fillpattern",e.fillcolor,!1));var T=(e.line||{}).color,k=(e.marker||{}).color;"tonext"!==e.fill&&"toself"!==e.fill||w.push("fills"),y("hoveron",w.join("+")||"points"),"fills"!==e.hoveron&&y("hovertemplate");var A=i.getComponentMethod("errorbars","supplyDefaults");A(t,e,T||k||r,{axis:"y"}),A(t,e,T||k||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,y)}}},{"../../lib":503,"../../registry":638,"./attributes":927,"./constants":931,"./fillcolor_defaults":935,"./line_defaults":940,"./line_shape_defaults":942,"./marker_defaults":946,"./period_defaults":947,"./stack_defaults":950,"./subtypes":952,"./text_defaults":953,"./xy_defaults":954}],935:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../lib").isArrayOrTypedArray;e.exports=function(t,e,r,a){var o=!1;if(e.marker){var s=e.marker.color,l=(e.marker.line||{}).color;s&&!i(s)?o=s:l&&!i(l)&&(o=l)}a("fillcolor",n.addOpacity((e.line||{}).color||o||r,.5))}},{"../../components/color":366,"../../lib":503}],936:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axes");e.exports=function(t,e,r){var i={},a={_fullLayout:r},o=n.getFromTrace(a,e,"x"),s=n.getFromTrace(a,e,"y");return i.xLabel=n.tickText(o,o.c2l(t.x),!0).text,i.yLabel=n.tickText(s,s.c2l(t.y),!0).text,i}},{"../../plots/cartesian/axes":554}],937:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./subtypes");e.exports=function(t,e){var r,a;if("lines"===t.mode)return(r=t.line.color)&&n.opacity(r)?r:t.fillcolor;if("none"===t.mode)return t.fill?t.fillcolor:"";var o=e.mcc||(t.marker||{}).color,s=e.mlcc||((t.marker||{}).line||{}).color;return(a=o&&n.opacity(o)?o:s&&n.opacity(s)&&(e.mlw||((t.marker||{}).line||{}).width)?s:"")?n.opacity(a)<.3?n.addOpacity(a,.3):a:(r=(t.line||{}).color)&&n.opacity(r)&&i.hasLines(t)&&t.line.width?r:t.fillcolor}},{"../../components/color":366,"./subtypes":952}],938:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/fx"),a=t("../../registry"),o=t("./get_trace_color"),s=t("../../components/color"),l=n.fillText;e.exports=function(t,e,r,c){var u=t.cd,f=u[0].trace,h=t.xa,p=t.ya,d=h.c2p(e),m=p.c2p(r),g=[d,m],v=f.hoveron||"",y=-1!==f.mode.indexOf("markers")?3:.5,x=!!f.xperiodalignment,b=!!f.yperiodalignment;if(-1!==v.indexOf("points")){var _=function(t){var e=Math.max(y,t.mrc||0),r=h.c2p(t.x)-d,n=p.c2p(t.y)-m;return Math.max(Math.sqrt(r*r+n*n)-e,1-y/e)},w=i.getDistanceFunction(c,(function(t){if(x){var e=h.c2p(t.xStart),r=h.c2p(t.xEnd);return d>=Math.min(e,r)&&d<=Math.max(e,r)?0:1/0}var n=Math.max(3,t.mrc||0),i=1-1/n,a=Math.abs(h.c2p(t.x)-d);return a<n?i*a/n:a-n+i}),(function(t){if(b){var e=p.c2p(t.yStart),r=p.c2p(t.yEnd);return m>=Math.min(e,r)&&m<=Math.max(e,r)?0:1/0}var n=Math.max(3,t.mrc||0),i=1-1/n,a=Math.abs(p.c2p(t.y)-m);return a<n?i*a/n:a-n+i}),_);if(i.getClosest(u,w,t),!1!==t.index){var T=u[t.index],k=h.c2p(T.x,!0),A=p.c2p(T.y,!0),M=T.mrc||1;t.index=T.i;var S=u[0].t.orientation,E=S&&(T.sNorm||T.s),L="h"===S?E:void 0!==T.orig_x?T.orig_x:T.x,C="v"===S?E:void 0!==T.orig_y?T.orig_y:T.y;return n.extendFlat(t,{color:o(f,T),x0:k-M,x1:k+M,xLabelVal:L,y0:A-M,y1:A+M,yLabelVal:C,spikeDistance:_(T),hovertemplate:f.hovertemplate}),l(T,f,t),a.getComponentMethod("errorbars","hoverInfo")(T,f,t),[t]}}if(-1!==v.indexOf("fills")&&f._polygons){var P,I,O,z,D,R,F,B,N,j=f._polygons,U=[],V=!1,H=1/0,q=-1/0,G=1/0,Y=-1/0;for(P=0;P<j.length;P++)(O=j[P]).contains(g)&&(V=!V,U.push(O),G=Math.min(G,O.ymin),Y=Math.max(Y,O.ymax));if(V){var W=((G=Math.max(G,0))+(Y=Math.min(Y,p._length)))/2;for(P=0;P<U.length;P++)for(z=U[P].pts,I=1;I<z.length;I++)(B=z[I-1][1])>W!=(N=z[I][1])>=W&&(R=z[I-1][0],F=z[I][0],N-B&&(D=R+(F-R)*(W-B)/(N-B),H=Math.min(H,D),q=Math.max(q,D)));H=Math.max(H,0),q=Math.min(q,h._length);var X=s.defaultLine;return s.opacity(f.fillcolor)?X=f.fillcolor:s.opacity((f.line||{}).color)&&(X=f.line.color),n.extendFlat(t,{distance:t.maxHoverDistance,x0:H,x1:q,y0:W,y1:W,color:X,hovertemplate:!1}),delete t.index,f.text&&!Array.isArray(f.text)?t.text=String(f.text):t.text=f.name,[t]}}}},{"../../components/color":366,"../../components/fx":406,"../../lib":503,"../../registry":638,"./get_trace_color":937}],939:[function(t,e,r){"use strict";var n=t("./subtypes");e.exports={hasLines:n.hasLines,hasMarkers:n.hasMarkers,hasText:n.hasText,isBubble:n.isBubble,attributes:t("./attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("./cross_trace_defaults"),calc:t("./calc").calc,crossTraceCalc:t("./cross_trace_calc"),arraysToCalcdata:t("./arrays_to_calcdata"),plot:t("./plot"),colorbar:t("./marker_colorbar"),formatLabels:t("./format_labels"),style:t("./style").style,styleOnSelect:t("./style").styleOnSelect,hoverPoints:t("./hover"),selectPoints:t("./select"),animatable:!0,moduleType:"trace",name:"scatter",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","symbols","errorBarsOK","showLegend","scatter-like","zoomScale"],meta:{}}},{"../../plots/cartesian":568,"./arrays_to_calcdata":926,"./attributes":927,"./calc":928,"./cross_trace_calc":932,"./cross_trace_defaults":933,"./defaults":934,"./format_labels":936,"./hover":938,"./marker_colorbar":945,"./plot":948,"./select":949,"./style":951,"./subtypes":952}],940:[function(t,e,r){"use 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y=e.exports=h({x:n.x,y:n.y,z:{valType:"data_array"},text:f({},n.text,{}),texttemplate:s({},{}),hovertext:f({},n.hovertext,{}),hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),mode:f({},n.mode,{dflt:"lines+markers"}),surfaceaxis:{valType:"enumerated",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:"color"},projection:{x:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},y:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},z:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}}},connectgaps:n.connectgaps,line:v,marker:f({symbol:{valType:"enumerated",values:p(u),dflt:"circle",arrayOk:!0},size:f({},m.size,{dflt:8}),sizeref:m.sizeref,sizemin:m.sizemin,sizemode:m.sizemode,opacity:f({},m.opacity,{arrayOk:!1}),colorbar:m.colorbar,line:f({width:f({},g.width,{arrayOk:!1})},i("marker.line"))},i("marker")),textposition:f({},n.textposition,{dflt:"top 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n=t("../../../stackgl_modules").gl_line3d,i=t("../../../stackgl_modules").gl_scatter3d,a=t("../../../stackgl_modules").gl_error3d,o=t("../../../stackgl_modules").gl_mesh3d,s=t("../../../stackgl_modules").delaunay_triangulate,l=t("../../lib"),c=t("../../lib/str2rgbarray"),u=t("../../lib/gl_format_color").formatColor,f=t("../scatter/make_bubble_size_func"),h=t("../../constants/gl3d_dashes"),p=t("../../constants/gl3d_markers"),d=t("../../plots/cartesian/axes"),m=t("../../components/fx/helpers").appendArrayPointValue,g=t("./calc_errors");function v(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var y=v.prototype;function x(t){return null==t?0:t.indexOf("left")>-1?-1:t.indexOf("right")>-1?1:0}function b(t){return 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U={x:E[o],y:L[o],z:C[o]},V={xLabel:D("xaxis",E[o]),yLabel:D("yaxis",L[o]),zLabel:D("zaxis",C[o])},H={};m(H,e,o);var q=e._meta||{};s[o]=l.texttemplateString(j(o),V,F,H,U,q)}}if(a={position:h,mode:e.mode,text:s},"line"in e&&(a.lineColor=u(S,1,P),a.lineWidth=S.width,a.lineDashes=S.dash),"marker"in e){var G=f(e);a.scatterColor=u(M,1,P),a.scatterSize=T(M.size,P,_,20,G),a.scatterMarker=T(M.symbol,P,w,"\u25cf"),a.scatterLineWidth=M.line.width,a.scatterLineColor=u(M.line,1,P),a.scatterAngle=0}"textposition"in e&&(a.textOffset=function(t){var e=[0,0];if(Array.isArray(t))for(var r=0;r<t.length;r++)e[r]=[0,0],t[r]&&(e[r][0]=x(t[r]),e[r][1]=b(t[r]));else e[0]=x(t),e[1]=b(t);return e}(e.textposition),a.textColor=u(e.textfont,1,P),a.textSize=T(e.textfont.size,P,l.identity,12),a.textFont=e.textfont.family,a.textAngle=0);var Y=["x","y","z"];for(a.project=[!1,!1,!1],a.projectScale=[1,1,1],a.projectOpacity=[1,1,1],o=0;o<3;++o){var W=e.projection[Y[o]];(a.project[o]=W.show)&&(a.projectOpacity[o]=W.opacity,a.projectScale[o]=W.scale)}a.errorBounds=g(e,v,p);var X=function(t){for(var e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&!1!==t[2].visible&&(a=t[2]),a&&a.visible&&(e[i]=a.width/2,r[i]=c(a.color),n[i]=a.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return a.errorColor=X.color,a.errorLineWidth=X.lineWidth,a.errorCapSize=X.capSize,a.delaunayAxis=e.surfaceaxis,a.delaunayColor=c(e.surfacecolor),a}function A(t){if(l.isArrayOrTypedArray(t)){var e=t[0];return l.isArrayOrTypedArray(e)&&(t=e),"rgb("+t.slice(0,3).map((function(t){return Math.round(255*t)}))+")"}return null}function M(t){return l.isArrayOrTypedArray(t)?4===t.length&&"number"==typeof t[0]?A(t):t.map(A):null}y.handlePick=function(t){if(t.object&&(t.object===this.linePlot||t.object===this.delaunayMesh||t.object===this.textMarkers||t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel="",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},y.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=k(this.scene,t);"mode"in p&&(this.mode=p.mode),"lineDashes"in p&&p.lineDashes in h&&(f=h[p.lineDashes]),this.color=M(p.scatterColor)||M(p.lineColor),this.dataPoints=p.position,e={gl:this.scene.glplot.gl,position:p.position,color:p.lineColor,lineWidth:p.lineWidth||1,dashes:f[0],dashScale:f[1],opacity:t.opacity,connectGaps:t.connectgaps},-1!==this.mode.indexOf("lines")?this.linePlot?this.linePlot.update(e):(this.linePlot=n(e),this.linePlot._trace=this,this.scene.glplot.add(this.linePlot)):this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose(),this.linePlot=null);var d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var m=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var f=u[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);m.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(m):(m.gl=u,this.delaunayMesh=o(m),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},y.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new v(t,e.uid);return r.update(e),r}},{"../../../stackgl_modules":1124,"../../components/fx/helpers":402,"../../constants/gl3d_dashes":476,"../../constants/gl3d_markers":477,"../../lib":503,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../../plots/cartesian/axes":554,"../scatter/make_bubble_size_func":944,"./calc_errors":957}],959:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],m=0;m<3;++m){var g="projection."+d[m];f(g+".show")&&(f(g+".opacity"),f(g+".scale"))}var v=n.getComponentMethod("errorbars","supplyDefaults");v(t,e,h||p||r,{axis:"z"}),v(t,e,h||p||r,{axis:"y",inherit:"z"}),v(t,e,h||p||r,{axis:"x",inherit:"z"})}else e.visible=!1}},{"../../lib":503,"../../registry":638,"../scatter/line_defaults":940,"../scatter/marker_defaults":946,"../scatter/subtypes":952,"../scatter/text_defaults":953,"./attributes":955}],960:[function(t,e,r){"use strict";e.exports={plot:t("./convert"),attributes:t("./attributes"),markerSymbols:t("../../constants/gl3d_markers"),supplyDefaults:t("./defaults"),colorbar:[{container:"marker",min:"cmin",max:"cmax"},{container:"line",min:"cmin",max:"cmax"}],calc:t("./calc"),moduleType:"trace",name:"scatter3d",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","symbols","showLegend","scatter-like"],meta:{}}},{"../../constants/gl3d_markers":477,"../../plots/gl3d":598,"./attributes":955,"./calc":956,"./convert":958,"./defaults":959}],961:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../plots/attributes"),a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../../plots/template_attributes").texttemplateAttrs,s=t("../../components/colorscale/attributes"),l=t("../../lib/extend").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:"string",editType:"calc"},a:{valType:"data_array",editType:"calc"},b:{valType:"data_array",editType:"calc"},mode:l({},n.mode,{dflt:"markers"}),text:l({},n.text,{}),texttemplate:o({editType:"plot"},{keys:["a","b","text"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:["linear","spline"]}),smoothing:u.smoothing,editType:"calc"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:["none","toself","tonext"],dflt:"none"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:"calc"},s("marker.line")),gradient:c.gradient,editType:"calc"},s("marker")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:["a","b","text","name"]}),hoveron:n.hoveron,hovertemplate:a()}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plots/attributes":550,"../../plots/template_attributes":633,"../scatter/attributes":927}],962:[function(t,e,r){"use 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),m=p("b"),g=Math.min(d.length,m.length);if(g){e._length=g,p("text"),p("texttemplate"),p("hovertext"),p("mode",g<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../lib/topojson_utils").getTopojsonFeatures,o=t("../../lib/geojson_utils"),s=t("../../lib/geo_location_utils"),l=t("../../plots/cartesian/autorange").findExtremes,c=t("../../constants/numerical").BADNUM,u=t("../scatter/calc").calcMarkerSize,f=t("../scatter/subtypes"),h=t("./style");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d="geojson-id"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m="geojson-id"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if("geojson"===o.fitbounds&&"geojson-id"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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s=o.calcTraceToLineCoords(e),c="none"!==a.fill?o.makePolygon(s):o.makeLine(s);r.selectAll("path.js-line").data([{geojson:c,trace:a}]).enter().append("path").classed("js-line",!0).style("stroke-miterlimit",2)}f.hasMarkers(a)&&r.selectAll("path.point").data(i.identity).enter().append("path").classed("point",!0).each((function(t){l(t,this)})),f.hasText(a)&&r.selectAll("g").data(i.identity).enter().append("g").append("text").each((function(t){l(t,this)})),h(t,e)}))}}},{"../../constants/numerical":479,"../../lib":503,"../../lib/geo_location_utils":496,"../../lib/geojson_utils":497,"../../lib/topojson_utils":532,"../../plots/cartesian/autorange":553,"../scatter/calc":928,"../scatter/subtypes":952,"./style":978,"@plotly/d3":58}],977:[function(t,e,r){"use strict";var n=t("../scatter/subtypes"),i=t("../../constants/numerical").BADNUM;e.exports=function(t,e){var 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n=t("../../plots/attributes"),i=t("../scatter/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../components/colorscale/attributes"),s=t("../../lib/sort_object_keys"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll,u=t("./constants").DASHES,f=i.line,h=i.marker,p=h.line,d=e.exports=c({x:i.x,x0:i.x0,dx:i.dx,y:i.y,y0:i.y0,dy:i.dy,xperiod:i.xperiod,yperiod:i.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:i.xperiodalignment,yperiodalignment:i.yperiodalignment,xhoverformat:a("x"),yhoverformat:a("y"),text:i.text,hovertext:i.hovertext,textposition:i.textposition,textfont:i.textfont,mode:{valType:"flaglist",flags:["lines","markers","text"],extras:["none"]},line:{color:f.color,width:f.width,shape:{valType:"enumerated",values:["linear","hv","vh","hvh","vhv"],dflt:"linear",editType:"plot"},dash:{valType:"enumerated",values:s(u),dflt:"solid"}},marker:l({},o("marker"),{symbol:h.symbol,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,opacity:h.opacity,colorbar:h.colorbar,line:l({},o("marker.line"),{width:p.width})}),connectgaps:i.connectgaps,fill:l({},i.fill,{dflt:"none"}),fillcolor:i.fillcolor,selected:{marker:i.selected.marker,textfont:i.selected.textfont},unselected:{marker:i.unselected.marker,textfont:i.unselected.textfont},opacity:n.opacity},"calc","nested");d.x.editType=d.y.editType=d.x0.editType=d.y0.editType="calc+clearAxisTypes",d.hovertemplate=i.hovertemplate,d.texttemplate=i.texttemplate},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../lib/sort_object_keys":526,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../scatter/attributes":927,"./constants":982}],980:[function(t,e,r){"use 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n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,m,g),g("line.shape")),l.hasMarkers(e)&&(f(t,e,r,m,g),g("marker.line.width",v||y?1:0)),l.hasText(e)&&(g("texttemplate"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":503,"../../registry":638,"../scatter/constants":931,"../scatter/fillcolor_defaults":935,"../scatter/line_defaults":940,"../scatter/marker_defaults":946,"../scatter/period_defaults":947,"../scatter/subtypes":952,"../scatter/text_defaults":953,"../scatter/xy_defaults":954,"./attributes":979,"./helpers":987}],985:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new 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v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var 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_=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if("toself"===s.fill||"tonext"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=y.lineOptions[e+1];if(g){var v=g.positions;if("tonexty"===s.fill){for(p=d.slice(),e=Math.floor(v.length/2);e--;){var x=v[2*e],b=v[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var _=y.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),y.fill2d.update(y.fillOptions))}var M=v.dragmode,S=l(M),E=v.clickmode.indexOf("select")>-1;for(m=0;m<_;m++){var L=r[m][0],C=L.trace,P=L.t,I=P.index,O=C._length,z=P.x,D=P.y;if(C.selectedpoints||S||E){if(S||(S=!0),C.selectedpoints){var R=y.selectBatch[I]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<O;g++)F[g]||B.push(g);y.unselectBatch[I]=B}var N=P.xpx=new Array(O),j=P.ypx=new Array(O);for(g=0;g<O;g++)N[g]=x.c2p(z[g]),j[g]=b.c2p(D[g])}else P.xpx=P.ypx=null}if(S){if(y.select2d||(y.select2d=n(v._glcanvas.data()[1].regl)),y.scatter2d){var U=new Array(_);for(m=0;m<_;m++)U[m]=y.selectBatch[m].length||y.unselectBatch[m].length?y.markerUnselectedOptions[m]:{};y.scatter2d.update(U)}y.select2d&&(y.select2d.update(y.markerOptions),y.select2d.update(y.markerSelectedOptions)),y.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else y.scatter2d&&y.scatter2d.update(y.markerOptions);var V={viewport:d(v,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},H=s.repeat(V,y.count);y.fill2d&&y.fill2d.update(H),y.line2d&&y.line2d.update(H),y.error2d&&y.error2d.update(H.concat(H)),y.scatter2d&&y.scatter2d.update(H),y.select2d&&y.select2d.update(H),y.glText&&y.glText.forEach((function(t){t.update(V)}))}else y.init()}}).reglPrecompiled=p},{"../../components/dragelement/helpers":384,"../../lib":503,"../../lib/prepare_regl":516,"../scatter/link_traces":943,"../scatter/subtypes":952,"./edit_style":985,"gl-text":225,"regl-error2d":279,"regl-line2d":280,"regl-scatter2d":281}],991:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{"../scatter/subtypes":952,"./edit_style":985}],993:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scattergeo/attributes"),o=t("../scatter/attributes"),s=t("../../plots/mapbox/layout_attributes"),l=t("../../plots/attributes"),c=t("../../components/colorscale/attributes"),u=t("../../lib/extend").extendFlat,f=t("../../plot_api/edit_types").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:"markers"}),text:u({},o.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c("marker")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:"string"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/mapbox/layout_attributes":615,"../../plots/template_attributes":633,"../scatter/attributes":927,"../scattergeo/attributes":969}],994:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("../../constants/numerical").BADNUM,o=t("../../lib/geojson_utils"),s=t("../../components/colorscale"),l=t("../../components/drawing"),c=t("../scatter/make_bubble_size_func"),u=t("../scatter/subtypes"),f=t("../../plots/mapbox/convert_text_opts"),h=t("../../components/fx/helpers").appendArrayPointValue,p=t("../../lib/svg_text_utils").NEWLINES,d=t("../../lib/svg_text_utils").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:"none"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return""}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b="none"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&"circle"===a.marker.symbol,A=w&&"circle"!==a.marker.symbol,M=m(),S=m(),E=m(),L=m(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility="visible",i.extendFlat(M.paint,{"fill-color":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility="visible",i.extendFlat(S.paint,{"line-width":a.line.width,"line-color":a.line.color,"line-opacity":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),m=i.isArrayOrTypedArray(f.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,"marker")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var b=t[e],_=b.lonlat;if(!y(_)){var w={};r&&(w.mcc=b.mcc=r(b.mc)),a&&(w.mrc=b.mrc=a(b.ms)),o&&(w.mo=o(b.mo)),h&&(w.selected=b.selected||0),x.push({type:"Feature",geometry:{type:"Point",coordinates:_},properties:w})}}if(h)for(v=l.makeSelectedPointStyleFns(u),e=0;e<x.length;e++){var T=x[e].properties;v.selectedOpacityFn&&(T.mo=g(v.selectedOpacityFn(T))),v.selectedColorFn&&(T.mcc=v.selectedColorFn(T)),v.selectedSizeFn&&(T.mrc=v.selectedSizeFn(T))}return{geojson:{type:"FeatureCollection",features:x},mcc:p||v&&v.selectedColorFn?{type:"identity",property:"mcc"}:f.color,mrc:d||v&&v.selectedSizeFn?{type:"identity",property:"mrc"}:(k=f.size,k/2),mo:m||v&&v.selectedOpacityFn?{type:"identity",property:"mo"}:g(f.opacity)};var k}(e);E.geojson=P.geojson,E.layout.visibility="visible",i.extendFlat(E.paint,{"circle-color":P.mcc,"circle-radius":P.mrc,"circle-opacity":P.mo})}if((A||T)&&(L.geojson=function(t,e){for(var r=e._fullLayout,n=t[0].trace,a=n.marker||{},o=a.symbol,s=a.angle,l="circle"!==o?g(o):v,c="auto"!==s?g(s,!0):v,f=u.hasText(n)?g(n.text):v,m=[],x=0;x<t.length;x++){var b=t[x];if(!y(b.lonlat)){var _,w=n.texttemplate;if(w){var T=Array.isArray(w)?w[x]||"":w,k=n._module.formatLabels(b,n,r),A={};h(A,n,b.i);var M=n._meta||{};_=i.texttemplateString(T,k,r._d3locale,A,b,M)}else 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n=t("../scatterpolar/attributes"),i=t("../scattergl/attributes"),a=t("../../plots/template_attributes").texttemplateAttrs;e.exports={mode:n.mode,r:n.r,theta:n.theta,r0:n.r0,dr:n.dr,theta0:n.theta0,dtheta:n.dtheta,thetaunit:n.thetaunit,text:n.text,texttemplate:a({editType:"plot"},{keys:["r","theta","text"]}),hovertext:n.hovertext,hovertemplate:n.hovertemplate,line:i.line,connectgaps:i.connectgaps,marker:i.marker,fill:i.fill,fillcolor:i.fillcolor,textposition:i.textposition,textfont:i.textfont,hoverinfo:n.hoverinfo,selected:n.selected,unselected:n.unselected}},{"../../plots/template_attributes":633,"../scattergl/attributes":979,"../scatterpolar/attributes":1002}],1010:[function(t,e,r){"use 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":503,"../scatter/subtypes":952,"./helpers":1038}],1044:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+="x"),s||g[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?m:"y"===t?g:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Y=j(e,r+1,i),W=j(e+1,r,i);if(O[q]*U<O[G]*U&&z[q]*V<z[Y]*V&&D[q]*H<D[W]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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r=t.data.data;r.color=e._hasColorscale?B(N[r.i]):F(N[r.i],r.id)})),w[0].hierarchy=D,w}},r._runCrossTraceCalc=function(t,e){var r=e._fullLayout,n=e.calcdata,i=r[t+"colorway"],a=r["_"+t+"colormap"];r["extend"+t+"colors"]&&(i=l(i,"icicle"===t?p:"treemap"===t?h:f));var o,s=0;function c(t){var e=t.data.data,r=e.id;!1===e.color&&(a[r]?e.color=a[r]:t.parent?t.parent.parent?e.color=t.parent.data.data.color:(a[r]=e.color=i[s%i.length],s++):e.color=o)}for(var u=0;u<n.length;u++){var d=n[u][0];d.trace.type===t&&d.hierarchy&&(o=d.trace.root.color,d.hierarchy.each(c))}},r.crossTraceCalc=function(t){return r._runCrossTraceCalc("sunburst",t)}},{"../../components/colorscale":378,"../../constants/numerical":479,"../../lib":503,"../pie/calc":903,"d3-hierarchy":115,"fast-isnumeric":190}],1052:[function(t,e,r){"use 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m=u("text");u("texttemplate"),e.texttemplate||u("textinfo",Array.isArray(m)?"text+label":"label"),u("hovertext"),u("hovertemplate");o(t,e,s,u,"auto",{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),u("insidetextorientation"),u("sort"),u("rotation"),u("root.color"),a(e,s,u),e._length=null}else e.visible=!1}},{"../../components/colorscale":378,"../../lib":503,"../../plots/domain":584,"../bar/defaults":652,"./attributes":1049}],1054:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../components/fx/helpers").appendArrayPointValue,o=t("../../components/fx"),s=t("../../lib"),l=t("../../lib/events"),c=t("./helpers"),u=t("../pie/helpers").formatPieValue;function f(t,e,r){for(var n=t.data.data,i={curveNumber:e.index,pointNumber:n.i,data:e._input,fullData:e},o=0;o<r.length;o++){var s=r[o];s in t&&(i[s]=t[s])}return"parentString"in 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n.castOption(e,a,"outsidetextfont."+t)||n.castOption(e,a,"textfont."+t)||i.size},r.isOutsideText=function(t,e){return!t._hasColorscale&&r.isHierarchyRoot(e)},r.determineTextFont=function(t,e,a,o){return r.isOutsideText(t,e)?function(t,e,n){return{color:r.getOutsideTextFontKey("color",t,e,n),family:r.getOutsideTextFontKey("family",t,e,n),size:r.getOutsideTextFontKey("size",t,e,n)}}(t,e,a):function(t,e,a,o){var s=(o||{}).onPathbar,l=e.data.data,c=l.i,u=n.castOption(t,c,(s?"pathbar.textfont":"insidetextfont")+".color");return!u&&t._input.textfont&&(u=n.castOption(t._input,c,"textfont.color")),{color:u||i.contrast(l.color),family:r.getInsideTextFontKey("family",t,e,a,o),size:r.getInsideTextFontKey("size",t,e,a,o)}}(t,e,a,o)},r.hasTransition=function(t){return!!(t&&t.duration>0)},r.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},r.isHeader=function(t,e){return!(r.isLeaf(t)||t.depth===e._maxDepth-1)},r.getParent=function(t,e){return r.findEntryWithLevel(t,s(e))},r.listPath=function(t,e){var n=t.parent;if(!n)return[];var i=e?[n.data[e]]:[n];return r.listPath(n,e).concat(i)},r.getPath=function(t){return r.listPath(t,"label").join("/")+"/"},r.formatValue=o.formatPieValue,r.formatPercent=function(t,e){var r=n.formatPercent(t,0);return"0%"===r&&(r=o.formatPiePercent(t,e)),r}},{"../../components/color":366,"../../lib":503,"../../lib/setcursor":524,"../pie/helpers":906}],1056:[function(t,e,r){"use 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D=null,R={};v&&_.each((function(t){R[b.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!D&&b.isEntry(t)&&(D=t)}));var F=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(M).descendants(),B=M.height+1,N=0,j=S;T.hasMultipleRoots&&b.isHierarchyRoot(M)&&(F=F.slice(1),B-=1,N=1,j+=1),F=F.filter((function(t){return t.y1<=j}));var U=p(k.rotation);U&&F.forEach((function(t){t.x0+=U,t.x1+=U}));var V=Math.min(B,S),H=function(t){return(t-N)/V*I},q=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},G=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,O,z)},Y=function(t){return O+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},W=function(t){return z+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(_=_.data(F,b.getPtId)).enter().append("g").classed("slice",!0),v?_.exit().transition().each((function(){var t=n.select(this);t.select("path.surface").transition().attrTween("d",(function(t){var e=function(t){var e,r=b.getPtId(t),n=R[r],i=R[b.getPtId(M)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+U;e=t.rpx1<i.rpx1?{x0:t.x0,x1:t.x1,rpx0:0,rpx1:0}:{x0:o,x1:o,rpx0:t.rpx0,rpx1:t.rpx1}}else{var s,l=b.getPtId(t.parent);_.each((function(t){if(b.getPtId(t)===l)return s=t}));var c,u=s.children;u.forEach((function(t,e){if(b.getPtId(t)===r)return c=e}));var f=u.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(c/f),x1:h((c+1)/f)}}return a(n,e)}(t);return function(t){return G(e(t))}})),t.select("g.slicetext").attr("opacity",0)})).remove():_.exit().remove(),_.order();var X=null;if(v&&D){var Z=b.getPtId(D);_.each((function(t){null===X&&b.getPtId(t)===Z&&(X=t.x1)}))}var J=_;function K(t){var e=t.parent,r=R[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}v&&(J=J.transition().each("end",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),J.each((function(i){var c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../bar/style":662,"../bar/uniform_text":664,"../pie/helpers":906,"../pie/plot":910,"./constants":1052,"./fx":1054,"./helpers":1055,"./style":1060,"@plotly/d3":58,"d3-hierarchy":115,"d3-interpolate":116}],1060:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":366,"../../lib":503,"../bar/uniform_text":664,"@plotly/d3":58}],1061:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or 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0},{"../../components/color":366,"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633}],1062:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374}],1063:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),"lightposition"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../../stackgl_modules":1124,"../../components/colorscale":378,"../../lib":503,"../../lib/gl_format_color":499,"../../lib/str2rgbarray":528,"../heatmap/find_empties":798,"../heatmap/interp2d":801}],1064:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");function s(t,e,r,n){var i=n("opacityscale");"max"===i?e.opacityscale=[[0,.1],[1,1]]:"min"===i?e.opacityscale=[[0,1],[1,.1]]:"extremes"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":376,"../../lib":503,"../../registry":638,"./attributes":1061}],1065:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":598,"./attributes":1061,"./calc":1062,"./convert":1063,"./defaults":1064}],1066:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:20},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))}},"calc","from-root")).transforms=void 0},{"../../components/annotations/attributes":349,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/cartesian/axis_format_attributes":557,"../../plots/domain":584,"../../plots/font_attributes":585}],1067:[function(t,e,r){"use strict";var n=t("../../plots/get_data").getModuleCalcData,i=t("./plot");r.name="table",r.plot=function(t){var e=n(t.calcdata,"table")[0];e.length&&i(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("table"),a=e._has&&e._has("table");i&&!a&&n._paperdiv.selectAll(".table").remove()}},{"../../plots/get_data":593,"./plot":1074}],1068:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap;e.exports=function(){return n({})}},{"../../lib/gup":500}],1069:[function(t,e,r){"use strict";e.exports={cellPad:8,columnExtentOffset:10,columnTitleOffset:28,emptyHeaderHeight:16,latexCheck:/^\$.*\$$/,goldenRatio:1.618,lineBreaker:"<br>",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:"cubic-out",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:"cubic-out",uplift:5,wrapSpacer:" ",wrapSplitCharacter:" ",cn:{table:"table",tableControlView:"table-control-view",scrollBackground:"scroll-background",yColumn:"y-column",columnBlock:"column-block",scrollAreaClip:"scroll-area-clip",scrollAreaClipRect:"scroll-area-clip-rect",columnBoundary:"column-boundary",columnBoundaryClippath:"column-boundary-clippath",columnBoundaryRect:"column-boundary-rect",columnCells:"column-cells",columnCell:"column-cell",cellRect:"cell-rect",cellText:"cell-text",cellTextHolder:"cell-text-holder",scrollbarKit:"scrollbar-kit",scrollbar:"scrollbar",scrollbarSlider:"scrollbar-slider",scrollbarGlyph:"scrollbar-glyph",scrollbarCaptureZone:"scrollbar-capture-zone"}}},{}],1070:[function(t,e,r){"use strict";var n=t("./constants"),i=t("../../lib/extend").extendFlat,a=t("fast-isnumeric");function o(t){if(Array.isArray(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,o(t[r]));return e}return t}function s(t,e){return t+e}function l(t){var e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var m=d.concat(p(r).map((function(){return c((d[0]||[""]).length)}))),g=e.domain,v=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),y=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?m[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=m.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":493,"./constants":1069,"fast-isnumeric":190}],1071:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:"header",type:"header",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:"cells1",type:"cells",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:"cells2",type:"cells",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+("string"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e("columnorder",o)}(e,s),s("cells.values"),s("cells.format"),s("cells.align"),s("cells.prefix"),s("cells.suffix"),s("cells.height"),s("cells.line.width"),s("cells.line.color"),s("cells.fill.color"),n.coerceFont(s,"cells.font",n.extendFlat({},o.font)),e._length=null}},{"../../lib":503,"../../plots/domain":584,"./attributes":1066}],1073:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),moduleType:"trace",name:"table",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":1066,"./base_plot":1067,"./calc":1068,"./defaults":1072,"./plot":1074}],1074:[function(t,e,r){"use strict";var n=t("./constants"),i=t("@plotly/d3"),a=t("../../lib").numberFormat,o=t("../../lib/gup"),s=t("../../components/drawing"),l=t("../../lib/svg_text_utils"),c=t("../../lib").raiseToTop,u=t("../../lib").strTranslate,f=t("../../lib").cancelTransition,h=t("./data_preparation_helper"),p=t("./data_split_helpers"),d=t("../../components/color");function m(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return"clip"+t._fullLayout._uid+"_scrollAreaBottomClip_"+e.key}function v(t,e){return"clip"+t._fullLayout._uid+"_columnBoundaryClippath_"+e.calcdata.key+"_"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll("."+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append("g").classed(n.cn.scrollbarKit,!0).style("shape-rendering","geometricPrecision"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr("transform",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll("."+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append("g").classed(n.cn.scrollbar,!0);var l=s.selectAll("."+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append("g").classed(n.cn.scrollbarSlider,!0),l.attr("transform",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll("."+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append("line").classed(n.cn.scrollbarGlyph,!0).attr("stroke","black").attr("stroke-width",n.scrollbarWidth).attr("stroke-linecap","round").attr("y1",n.scrollbarWidth/2),c.attr("y2",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr("stroke-opacity",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr("stroke-opacity",0);var f=s.selectAll("."+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append("line").classed(n.cn.scrollbarCaptureZone,!0).attr("stroke","white").attr("stroke-opacity",.01).attr("stroke-width",n.scrollbarCaptureWidth).attr("stroke-linecap","butt").attr("y1",0).on("mousedown",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on("drag",L(e,t)).on("dragend",(function(){}))),f.attr("y2",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll("."+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append("g").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll("."+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append("g").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var 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s=0;s<t.length;s++)r=(a=t[s])._input,i(r,a,e,o)}}},{"../../components/color":366,"../../constants/delta.js":473,"../../lib":503,"../bar/defaults":652,"../scatter/period_defaults":947,"../scatter/xy_defaults":954,"./attributes":1106}],1111:[function(t,e,r){"use strict";e.exports=function(t,e){return t.x="xVal"in e?e.xVal:e.x,t.y="yVal"in e?e.yVal:e.y,"initial"in e&&(t.initial=e.initial),"delta"in e&&(t.delta=e.delta),"final"in e&&(t.final=e.final),e.xa&&(t.xaxis=e.xa),e.ya&&(t.yaxis=e.ya),t}},{}],1112:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axes").hoverLabelText,i=t("../../components/color").opacity,a=t("../bar/hover").hoverOnBars,o=t("../../constants/delta.js"),s=o.INCREASING.SYMBOL,l=o.DECREASING.SYMBOL;e.exports=function(t,e,r,o,c){var u=a(t,e,r,o,c);if(u){var f=u.cd,h=f[0].trace,p="h"===h.orientation,d=p?"x":"y",m=p?t.xa:t.ya,g=f[u.index],v=g.isSum?g.b+g.s:g.rawS;if(!g.isSum){u.initial=g.b+g.s-v,u.delta=v,u.final=u.initial+u.delta;var y=k(Math.abs(u.delta));u.deltaLabel=v<0?"("+y+")":y,u.finalLabel=k(u.final),u.initialLabel=k(u.initial)}var x=g.hi||h.hoverinfo,b=[];if(x&&"none"!==x&&"skip"!==x){var _="all"===x,w=x.split("+"),T=function(t){return _||-1!==w.indexOf(t)};g.isSum||(!T("final")||T(p?"x":"y")||b.push(u.finalLabel),T("delta")&&(v<0?b.push(u.deltaLabel+" "+l):b.push(u.deltaLabel+" "+s)),T("initial")&&b.push("Initial: "+u.initialLabel))}return b.length&&(u.extraText=b.join("<br>")),u.color=function(t,e){var r=t[e.dir].marker,n=r.color,a=r.line.color,o=r.line.width;if(i(n))return n;if(i(a)&&o)return a}(h,g),[u]}function k(t){return n(m,t,h[d+"hoverformat"])}}},{"../../components/color":366,"../../constants/delta.js":473,"../../plots/cartesian/axes":554,"../bar/hover":655}],1113:[function(t,e,r){"use 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+ * @license  MIT
+ */
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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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 v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){y.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function _(){y.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){y.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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p=l[f-2];if(p.left===h)if(h.left===i){if(!(d=p.right)||0!==d._color){if(p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).left===p?m.left=h:m.right=h;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){if(h.right=i.left,p._color=0,p.left=i.right,i._color=1,i.left=h,i.right=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).left===p?m.left=i:m.right=i;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else if(h.right===i){if(!(d=p.left)||0!==d._color){if(p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).right===p?m.right=h:m.left=h;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var m;if(h.left=i.right,p._color=0,p.right=i.left,i._color=1,i.right=h,i.left=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).right===p?m.right=i:m.left=i;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}}return l[0]._color=1,new s(r,l[0])},l.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return c(t,this.root);case 2:return u(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return f(e,r,this._compare,t,this.root)}},Object.defineProperty(l,"begin",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new h(this,t)}}),Object.defineProperty(l,"end",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new h(this,t)}}),l.at=function(t){if(t<0)return new h(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new h(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new h(this,[])},l.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function m(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,"valid",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,"node",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t("bit-twiddle"),i=t("gl-mat4/multiply"),a=t("split-polygon"),o=t("robust-orientation"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\n                                    0.0;\n}\n\nfloat roundTo(float a, float b) {\n  return float(b * floor((a + 0.5 * b) / b));\n}\n\nfloat look_round_n_directions(float a, int n) {\n  float b = positive_angle(a);\n  float div = TWO_PI / float(n);\n  float c = roundTo(b, div);\n  return look_upwards(c);\n}\n\nfloat applyAlignOption(float rawAngle, float delta) {\n  return\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\n                    rawAngle;                // otherwise return back raw input angle\n}\n\nbool isAxisTitle = (axis.x == 0.0) &&\n                   (axis.y == 0.0) &&\n                   (axis.z == 0.0);\n\nvoid main() {\n  //Compute world offset\n  float axisDistance = position.z;\n  vec3 dataPosition = axisDistance * axis + offset;\n\n  float beta = angle; // i.e. user defined attributes for each tick\n\n  float axisAngle;\n  float clipAngle;\n  float flip;\n\n  if (enableAlign) {\n    axisAngle = (isAxisTitle) ? HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use 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h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function 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must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_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"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.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 c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,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],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var 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=u[n],a=0;a<i.length;++a){var 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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() 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u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var 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(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.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 m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function 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 m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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}},{}],94:[function(t,e,r){e.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],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.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,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var 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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]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":32,"gl-fbo":86,ndarray:259,"typedarray-pool":308}],132:[function(t,e,r){"use strict";var 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radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof 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HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return 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p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=m(o,e.stride.slice()),c=0;"float32"===r?c=t.FLOAT:"float64"===r?(c=t.FLOAT,l=!1,r="float32"):"uint8"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r="uint8");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");d=t.RGBA}}c!==t.FLOAT||t.getExtension("OES_texture_float")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);"float32"!==r&&"float64"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=g(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension("EXT_texture_filter_anisotropic");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error("gl-texture2d: Must specify wrap mode for rows and columns");for(var 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 h(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return h(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,h(this,this._shape[0],t),t}}}),d.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},d.dispose=function(){this.gl.deleteTexture(this.handle)},d.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)},d.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var l=u(t)?t:t.raw;if(l){this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l)}else{if(!(t.shape&&t.stride&&t.data))throw new Error("gl-texture2d: Unsupported data type");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error("gl-texture2d: Texture dimensions are out of bounds");!function(t,e,r,o,s,l,c,u){var h=u.dtype,p=u.shape.slice();if(p.length<2||p.length>3)throw new Error("gl-texture2d: Invalid ndarray, must be 2d or 3d");var d=0,g=0,v=m(p,u.stride.slice());"float32"===h?d=t.FLOAT:"float64"===h?(d=t.FLOAT,v=!1,h="float32"):"uint8"===h?d=t.UNSIGNED_BYTE:(d=t.UNSIGNED_BYTE,v=!1,h="uint8");if(2===p.length)g=t.LUMINANCE,p=[p[0],p[1],1],u=n(u.data,p,[u.stride[0],u.stride[1],1],u.offset);else{if(3!==p.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===p[2])g=t.ALPHA;else if(2===p[2])g=t.LUMINANCE_ALPHA;else if(3===p[2])g=t.RGB;else{if(4!==p[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");g=t.RGBA}p[2]}g!==t.LUMINANCE&&g!==t.ALPHA||s!==t.LUMINANCE&&s!==t.ALPHA||(g=s);if(g!==s)throw new Error("gl-texture2d: Incompatible texture format for setPixels");var y=u.size,x=c.indexOf(o)<0;x&&c.push(o);if(d===l&&v)0===u.offset&&u.data.length===y?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data.subarray(u.offset,u.offset+y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data.subarray(u.offset,u.offset+y));else{var b;b=l===t.FLOAT?a.mallocFloat32(y):a.mallocUint8(y);var _=n(b,p,[p[2],p[2]*p[0],1]);d===t.FLOAT&&l===t.UNSIGNED_BYTE?f(_,u):i.assign(_,u),x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,b.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,b.subarray(0,y)),l===t.FLOAT?a.freeFloat32(b):a.freeUint8(b)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],147:[function(t,e,r){"use strict";e.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error("gl-vao: Too many vertex attributes");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,c=!!a.normalized,u=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,c,u,f)}else{if("number"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else if(2===a.length)t.vertexAttrib2f(i,a[0],a[1]);else if(3===a.length)t.vertexAttrib3f(i,a[0],a[1],a[2]);else{if(4!==a.length)throw new Error("gl-vao: Invalid vertex attribute");t.vertexAttrib4f(i,a[0],a[1],a[2],a[3])}t.disableVertexAttribArray(i)}}for(;i<n;++i)t.disableVertexAttribArray(i)}else{t.bindBuffer(t.ARRAY_BUFFER,null);for(i=0;i<n;++i)t.disableVertexAttribArray(i)}}},{}],148:[function(t,e,r){"use strict";var n=t("./do-bind.js");function 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3:t.vertexAttrib3f(this.location,this.a,this.b,this.c);break;case 4:t.vertexAttrib4f(this.location,this.a,this.b,this.c,this.d)}},a.prototype.bind=function(){this._ext.bindVertexArrayOES(this.handle);for(var t=0;t<this._attribs.length;++t)this._attribs[t].bind(this.gl)},a.prototype.unbind=function(){this._ext.bindVertexArrayOES(null)},a.prototype.dispose=function(){this._ext.deleteVertexArrayOES(this.handle)},a.prototype.update=function(t,e,r){if(this.bind(),n(this.gl,e,t),this.unbind(),this._attribs.length=0,t)for(var a=0;a<t.length;++a){var o=t[a];"number"==typeof o?this._attribs.push(new i(a,1,o)):Array.isArray(o)&&this._attribs.push(new i(a,o.length,o[0],o[1],o[2],o[3]))}this._useElements=!!e,this._elementsType=r||this.gl.UNSIGNED_SHORT},a.prototype.draw=function(t,e,r){r=r||0;var n=this.gl;this._useElements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t,e){return new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
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+/*!
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+ *
+ * Copyright (c) 2014-2015, Jon Schlinkert.
+ * Licensed under the MIT license.
+ */
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t.length=r,t}},{"cell-orientation":47,"compare-cell":56,"compare-oriented-cell":57}],277:[function(t,e,r){
+/*!
+ * repeat-string <https://github.com/jonschlinkert/repeat-string>
+ *
+ * Copyright (c) 2014-2015, Jon Schlinkert.
+ * Licensed under the MIT License.
+ */
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c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(t,e,r){"use strict";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){"use strict";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){"use strict";var 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