View03_DistributionView.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "baac801b-103c-4f8d-999d-54d59241fe55",
   "metadata": {},
   "source": [
    "# Distribution View\n",
    "\n",
    "The distribution view allows to explore large-scale live-cell imaging data and focus on single-cell property distributions to find and investigate rare events or temporal developments. Therefore, we provide cell size, length, and age distributions to filter interesting candidates that are visualized as overlays on the microscope image. Moreover, the temporal development of the selected and not selected cells by the filter are visualized in terms of total cell area and count.\n",
    "\n",
    "Have fun 🚀 executing the notebook yourself, experience the freedom of customization in jupyter notebooks, and have a look at the video below 👇 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "368dd99a-1915-4c33-b8b2-dc608d1062f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/qScXv45kyOU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f0ca4436b30>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "\n",
    "YouTubeVideo('qScXv45kyOU', width=800, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692529ed-410a-4e58-a42a-adb489f6c158",
   "metadata": {},
   "source": [
    "## 1. Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc182cb4-8423-43de-b20b-7afc4754ce30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting git+https://jugit.fz-juelich.de/publications/acia-pre.git@c989af4c3007882239dd724a2f3a8b13715d0c88#acia\n",
      "  Cloning https://jugit.fz-juelich.de/publications/acia-pre.git (to revision c989af4c3007882239dd724a2f3a8b13715d0c88) to /tmp/pip-req-build-yf_xbyyg\n",
      "  Running command git clone --filter=blob:none --quiet https://jugit.fz-juelich.de/publications/acia-pre.git /tmp/pip-req-build-yf_xbyyg\n",
      "  Running command git rev-parse -q --verify 'sha^c989af4c3007882239dd724a2f3a8b13715d0c88'\n",
      "  Running command git fetch -q https://jugit.fz-juelich.de/publications/acia-pre.git c989af4c3007882239dd724a2f3a8b13715d0c88\n",
      "  Resolved https://jugit.fz-juelich.de/publications/acia-pre.git to commit c989af4c3007882239dd724a2f3a8b13715d0c88\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: imagecodecs==2023.9.18 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (2023.9.18)\n",
      "Requirement already satisfied: igraph==0.11.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (0.11.3)\n",
      "Requirement already satisfied: dash==2.14.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash[diskcache]==2.14.1) (2.14.1)\n",
      "Requirement already satisfied: dash-bootstrap-components==1.5.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (1.5.0)\n",
      "Requirement already satisfied: multiprocess==0.70.15 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (0.70.15)\n",
      "Requirement already satisfied: moviepy==1.0.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (1.0.3)\n",
      "Requirement already satisfied: ipywidgets==8.1.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (8.1.1)\n",
      "Requirement already satisfied: numpy in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from imagecodecs==2023.9.18) (1.26.2)\n",
      "Requirement already satisfied: texttable>=1.6.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from igraph==0.11.3) (1.7.0)\n",
      "Requirement already satisfied: Flask<3.1,>=1.0.4 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (3.0.0)\n",
      "Requirement already satisfied: Werkzeug<3.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (3.0.1)\n",
      "Requirement already satisfied: plotly>=5.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (5.18.0)\n",
      "Requirement already satisfied: dash-html-components==2.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (2.0.0)\n",
      "Requirement already satisfied: dash-core-components==2.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (2.0.0)\n",
      "Requirement already satisfied: dash-table==5.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (5.0.0)\n",
      "Requirement already satisfied: typing-extensions>=4.1.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (4.8.0)\n",
      "Requirement already satisfied: requests in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (2.31.0)\n",
      "Requirement already satisfied: retrying in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (1.3.4)\n",
      "Requirement already satisfied: ansi2html in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (1.8.0)\n",
      "Requirement already satisfied: nest-asyncio in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (1.5.8)\n",
      "Requirement already satisfied: setuptools in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (68.2.2)\n",
      "Requirement already satisfied: importlib-metadata in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash==2.14.1->dash[diskcache]==2.14.1) (6.8.0)\n",
      "Requirement already satisfied: dill>=0.3.7 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from multiprocess==0.70.15) (0.3.7)\n",
      "Requirement already satisfied: decorator<5.0,>=4.0.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from moviepy==1.0.3) (4.4.2)\n",
      "Requirement already satisfied: tqdm<5.0,>=4.11.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from moviepy==1.0.3) (4.66.1)\n",
      "Requirement already satisfied: proglog<=1.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from moviepy==1.0.3) (0.1.10)\n",
      "Requirement already satisfied: imageio<3.0,>=2.5 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from moviepy==1.0.3) (2.33.0)\n",
      "Requirement already satisfied: imageio-ffmpeg>=0.2.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from moviepy==1.0.3) (0.4.9)\n",
      "Requirement already satisfied: comm>=0.1.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipywidgets==8.1.1) (0.2.0)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipywidgets==8.1.1) (8.17.2)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipywidgets==8.1.1) (5.13.0)\n",
      "Requirement already satisfied: widgetsnbextension~=4.0.9 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipywidgets==8.1.1) (4.0.9)\n",
      "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipywidgets==8.1.1) (3.0.9)\n",
      "Requirement already satisfied: diskcache>=5.2.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash[diskcache]==2.14.1) (5.6.3)\n",
      "Requirement already satisfied: psutil>=5.8.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from dash[diskcache]==2.14.1) (5.9.6)\n",
      "Collecting pycococreatortools@ git+https://github.com/waspinator/pycococreator.git@0.2.0 (from acia==0.2.32)\n",
      "  Cloning https://github.com/waspinator/pycococreator.git (to revision 0.2.0) to /tmp/pip-install-86sdp08i/pycococreatortools_055652b21cdc4dafa3c86c03fba8fe93\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/waspinator/pycococreator.git /tmp/pip-install-86sdp08i/pycococreatortools_055652b21cdc4dafa3c86c03fba8fe93\n",
      "  Running command git checkout -q fba8f4098f3c7aaa05fe119dc93bbe4063afdab8\n",
      "  Resolved https://github.com/waspinator/pycococreator.git to commit fba8f4098f3c7aaa05fe119dc93bbe4063afdab8\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: shapely<2.0.0,>=1.8.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (1.8.5.post1)\n",
      "Requirement already satisfied: opencv-python-headless==4.5.5.64 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (4.5.5.64)\n",
      "Requirement already satisfied: rtree>=0.9.7 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (1.1.0)\n",
      "Requirement already satisfied: pillow>=8.3.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (10.1.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (1.3.2)\n",
      "Requirement already satisfied: scikit-image>=0.19.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (0.20.0)\n",
      "Requirement already satisfied: seaborn>=0.11.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (0.13.0)\n",
      "Requirement already satisfied: roifile>=2021.6.6 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (2023.8.30)\n",
      "Requirement already satisfied: tifffile==2020.11.18 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (2020.11.18)\n",
      "Requirement already satisfied: pycocotools==2.0.7 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (2.0.7)\n",
      "Requirement already satisfied: pint>=0.18 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (0.22)\n",
      "Requirement already satisfied: papermill>=2.4.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (2.5.0)\n",
      "Requirement already satisfied: rasterio>=1.3.6 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from acia==0.2.32) (1.3.9)\n",
      "Requirement already satisfied: matplotlib>=2.1.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from pycocotools==2.0.7->acia==0.2.32) (3.8.2)\n",
      "Requirement already satisfied: Jinja2>=3.1.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from Flask<3.1,>=1.0.4->dash==2.14.1->dash[diskcache]==2.14.1) (3.1.2)\n",
      "Requirement already satisfied: itsdangerous>=2.1.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from Flask<3.1,>=1.0.4->dash==2.14.1->dash[diskcache]==2.14.1) (2.1.2)\n",
      "Requirement already satisfied: click>=8.1.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from Flask<3.1,>=1.0.4->dash==2.14.1->dash[diskcache]==2.14.1) (8.1.7)\n",
      "Requirement already satisfied: blinker>=1.6.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from Flask<3.1,>=1.0.4->dash==2.14.1->dash[diskcache]==2.14.1) (1.7.0)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (0.19.1)\n",
      "Requirement already satisfied: matplotlib-inline in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (0.1.6)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (3.0.41)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (2.17.2)\n",
      "Requirement already satisfied: stack-data in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (0.6.3)\n",
      "Requirement already satisfied: exceptiongroup in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (1.2.0)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets==8.1.1) (4.8.0)\n",
      "Requirement already satisfied: pyyaml in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from papermill>=2.4.0->acia==0.2.32) (6.0.1)\n",
      "Requirement already satisfied: nbformat>=5.1.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from papermill>=2.4.0->acia==0.2.32) (5.9.2)\n",
      "Requirement already satisfied: nbclient>=0.2.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from papermill>=2.4.0->acia==0.2.32) (0.9.0)\n",
      "Requirement already satisfied: entrypoints in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from papermill>=2.4.0->acia==0.2.32) (0.4)\n",
      "Requirement already satisfied: tenacity>=5.0.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from papermill>=2.4.0->acia==0.2.32) (8.2.3)\n",
      "Requirement already satisfied: packaging in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from plotly>=5.0.0->dash==2.14.1->dash[diskcache]==2.14.1) (23.2)\n",
      "Requirement already satisfied: affine in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (2.4.0)\n",
      "Requirement already satisfied: attrs in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (23.1.0)\n",
      "Requirement already satisfied: certifi in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (2023.11.17)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (0.7.2)\n",
      "Requirement already satisfied: snuggs>=1.4.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (1.4.7)\n",
      "Requirement already satisfied: click-plugins in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from rasterio>=1.3.6->acia==0.2.32) (1.1.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from requests->dash==2.14.1->dash[diskcache]==2.14.1) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from requests->dash==2.14.1->dash[diskcache]==2.14.1) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from requests->dash==2.14.1->dash[diskcache]==2.14.1) (2.1.0)\n",
      "Requirement already satisfied: scipy>=1.8 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-image>=0.19.1->acia==0.2.32) (1.11.4)\n",
      "Requirement already satisfied: networkx>=2.8 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-image>=0.19.1->acia==0.2.32) (3.2.1)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-image>=0.19.1->acia==0.2.32) (1.5.0)\n",
      "Requirement already satisfied: lazy_loader>=0.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-image>=0.19.1->acia==0.2.32) (0.3)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-learn>=1.0->acia==0.2.32) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from scikit-learn>=1.0->acia==0.2.32) (3.2.0)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from seaborn>=0.11.2->acia==0.2.32) (2.1.3)\n",
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      "Requirement already satisfied: six>=1.7.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from retrying->dash==2.14.1->dash[diskcache]==2.14.1) (1.16.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets==8.1.1) (0.8.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7->acia==0.2.32) (1.2.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7->acia==0.2.32) (1.4.5)\n",
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      "Requirement already satisfied: fastjsonschema in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from nbformat>=5.1.2->papermill>=2.4.0->acia==0.2.32) (2.19.0)\n",
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      "Requirement already satisfied: pytz>=2020.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from pandas>=1.2->seaborn>=0.11.2->acia==0.2.32) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from pandas>=1.2->seaborn>=0.11.2->acia==0.2.32) (2023.3)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets==8.1.1) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets==8.1.1) (0.2.12)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets==8.1.1) (2.0.1)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets==8.1.1) (2.4.1)\n",
      "Requirement already satisfied: pure-eval in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets==8.1.1) (0.2.2)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat>=5.1.2->papermill>=2.4.0->acia==0.2.32) (2023.11.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat>=5.1.2->papermill>=2.4.0->acia==0.2.32) (0.31.0)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jsonschema>=2.6->nbformat>=5.1.2->papermill>=2.4.0->acia==0.2.32) (0.13.1)\n",
      "Requirement already satisfied: pyzmq>=23.0 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jupyter-client>=6.1.12->nbclient>=0.2.0->papermill>=2.4.0->acia==0.2.32) (25.1.1)\n",
      "Requirement already satisfied: tornado>=6.2 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jupyter-client>=6.1.12->nbclient>=0.2.0->papermill>=2.4.0->acia==0.2.32) (6.3.3)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /home/johannes/miniconda3/envs/viz-lab/lib/python3.10/site-packages (from jupyter-core!=5.0.*,>=4.12->nbclient>=0.2.0->papermill>=2.4.0->acia==0.2.32) (4.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install imagecodecs==2023.9.18 igraph==0.11.3 dash[diskcache]==2.14.1 dash-bootstrap-components==1.5.0 multiprocess==0.70.15 \"git+https://jugit.fz-juelich.de/publications/acia-pre.git@c989af4c3007882239dd724a2f3a8b13715d0c88#acia\" moviepy==1.0.3 ipywidgets==8.1.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd4977c-d650-4016-89d4-2c0cf8a1a7f3",
   "metadata": {},
   "source": [
    "## 2. Load segmentation & tracking information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e764beae-97eb-4ff5-9f44-d89e79a0400d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-02-26 15:21:00--  https://fz-juelich.sciebo.de/s/3Ng8f6LG8Ed02zi/download\n",
      "Resolving fz-juelich.sciebo.de (fz-juelich.sciebo.de)... 128.176.1.2\n",
      "Connecting to fz-juelich.sciebo.de (fz-juelich.sciebo.de)|128.176.1.2|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/zip]\n",
      "Saving to: ‘data.zip’\n",
      "\n",
      "data.zip                [  <=>               ] 575,91M  12,7MB/s    in 47s     \n",
      "\n",
      "2024-02-26 15:21:48 (12,2 MB/s) - ‘data.zip’ saved [603882800]\n",
      "\n",
      "mkdir: cannot create directory ‘data’: File exists\n",
      "Archive:  data.zip\n",
      " extracting: data/03/image_stack.tiff  \n",
      " extracting: data/03/simpleTracking.json  \n",
      "mkdir: cannot create directory ‘assets’: File exists\n"
     ]
    }
   ],
   "source": [
    "# download data\n",
    "!wget -O data.zip https://fz-juelich.sciebo.de/s/3Ng8f6LG8Ed02zi/download\n",
    "\n",
    "!mkdir data\n",
    "!unzip -o data.zip -d data\n",
    "!mkdir assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b878656a-4d99-4156-85c7-f0e13d8a8e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Meta-information\n",
    "\n",
    "pixel_size = 0.072 # micrometer/pixel\n",
    "frame_step = 1/60 # 1 frame/minute\n",
    "\n",
    "image_path = \"data/03/image_stack.tiff\"\n",
    "seg_and_track_file = \"data/03/simpleTracking.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "015cd9e5-827c-4f9b-987f-03bd20fe3629",
   "metadata": {},
   "outputs": [],
   "source": [
    "from acia.tracking.formats import parse_simple_tracking\n",
    "\n",
    "# load segmentation and tracking information from file\n",
    "with open(seg_and_track_file, \"r\") as tf:\n",
    "    ov, tracking_graph = parse_simple_tracking(tf.read())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ab6c00f-b59f-46c0-8152-b04ae4fa544b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91cf38191bcb4bcc9dfaa4214b118cab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/238364 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "from shapely.geometry import Polygon\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def pairwise_distances(points: np.ndarray):\n",
    "    distances = []\n",
    "\n",
    "    if len(points) == 0:\n",
    "        return distances\n",
    "\n",
    "    for a, b in zip(points, points[1:]):\n",
    "        distances.append(np.linalg.norm(a - b))\n",
    "\n",
    "    return distances\n",
    "\n",
    "# load single-cell information into pandas data frame\n",
    "cell_df = pd.DataFrame([\n",
    "    {\n",
    "        \"id\": cont.id,\n",
    "        \"frame\": cont.frame,\n",
    "        \"area\": cont.area * (pixel_size **2),\n",
    "        \"length\": np.max(pairwise_distances(np.array(cont.polygon.minimum_rotated_rectangle.exterior.coords))) * pixel_size,\n",
    "        \"contour_coordinates\": cont.coordinates,\n",
    "        \"time\": cont.frame * frame_step\n",
    "    } for cont in tqdm(ov)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d59acb4-bf36-495f-8545-07dbfe2ee9c3",
   "metadata": {},
   "outputs": [
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       "                                      contour_coordinates       time  \n",
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       "\n",
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     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "cell_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c70af03-c811-48da-9f91-9b831df1ff59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from acia.tracking.utils import life_cycle_lineage\n",
    "\n",
    "# create lifecycle lineage (one cell life one node in the graph, in contrast to one cell detection one node in the graph)\n",
    "lc_lineage = life_cycle_lineage(tracking_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "353009b7-627f-4044-bd23-b8528c32e9f9",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4916</th>\n",
       "      <td>4916</td>\n",
       "      <td>[4915]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[206669, 208043, 209415, 210790, 212164, 21355...</td>\n",
       "      <td>777</td>\n",
       "      <td>799</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4917</th>\n",
       "      <td>4917</td>\n",
       "      <td>[4915]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[206667, 208042, 209414, 210789, 212163, 21355...</td>\n",
       "      <td>777</td>\n",
       "      <td>799</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4918 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      track_id predecessors    successors  \\\n",
       "0            0           []            []   \n",
       "1            1           []            []   \n",
       "2            2           []            []   \n",
       "3            3           []            []   \n",
       "4            4           []            []   \n",
       "...        ...          ...           ...   \n",
       "4913      4913       [4912]            []   \n",
       "4914      4914       [4912]            []   \n",
       "4915      4915       [4911]  [4916, 4917]   \n",
       "4916      4916       [4915]            []   \n",
       "4917      4917       [4915]            []   \n",
       "\n",
       "                                                  cycle  start_frame  \\\n",
       "0                                              [237595]          799   \n",
       "1                                              [237594]          799   \n",
       "2                      [233165, 234585, 236010, 237438]          795   \n",
       "3                              [231898, 233321, 234737]          795   \n",
       "4     [228280, 229699, 231119, 232523, 233947, 23537...          792   \n",
       "...                                                 ...          ...   \n",
       "4913  [222060, 223475, 224900, 226332, 227759, 22918...          788   \n",
       "4914  [222027, 223441, 224865, 226297, 227723, 22914...          788   \n",
       "4915  [126950, 128085, 129227, 130372, 131530, 13269...          714   \n",
       "4916  [206669, 208043, 209415, 210790, 212164, 21355...          777   \n",
       "4917  [206667, 208042, 209414, 210789, 212163, 21355...          777   \n",
       "\n",
       "      end_frame       age  \n",
       "0           799  0.016667  \n",
       "1           799  0.016667  \n",
       "2           798  0.066667  \n",
       "3           797  0.050000  \n",
       "4           799  0.133333  \n",
       "...         ...       ...  \n",
       "4913        799  0.200000  \n",
       "4914        799  0.200000  \n",
       "4915        776  1.050000  \n",
       "4916        799  0.383333  \n",
       "4917        799  0.383333  \n",
       "\n",
       "[4918 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load tracking (temporal) information into pandas data frame\n",
    "lc_df = pd.DataFrame([{\"track_id\": n, \"predecessors\": list(lc_lineage.predecessors(n)), \"successors\": list(lc_lineage.successors(n)), \"cycle\": lc_lineage.nodes[n][\"cycle\"], \"start_frame\": lc_lineage.nodes[n][\"start_frame\"], \"end_frame\": lc_lineage.nodes[n][\"end_frame\"], \"age\": len(lc_lineage.nodes[n][\"cycle\"])  * frame_step} for n in lc_lineage.nodes])\n",
    "\n",
    "lc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d3341cf-07b1-4529-8f21-d1c5dc198721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 1094, 938, 3)\n"
     ]
    }
   ],
   "source": [
    "import tifffile\n",
    "import numpy as np\n",
    "\n",
    "# cache the image data\n",
    "images = tifffile.imread(image_path)\n",
    "# make them rgb\n",
    "if len(images.shape) == 3 or (len(images.shape) == 4 and images.shape[-1] == 1):\n",
    "    images = np.stack([images] * 3, axis=-1)\n",
    "print(images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10906863-53a6-44b7-911a-5338fa25c330",
   "metadata": {},
   "source": [
    "## 3. Utility functionalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8220ee7c-efcb-4edb-b349-90b1eeb43a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_lookup = {det_id: track_id for track_id in lc_lineage.nodes() for det_id in lc_lineage.nodes[track_id][\"cycle\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18720203-8fcf-4f07-9a21-ec43613e4cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from acia.base import Overlay\n",
    "\n",
    "def get_frame_overlay(frame: int):\n",
    "    \n",
    "    # operate on the dataframe\n",
    "    return cell_df[cell_df[\"frame\"] == frame] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01980364-7aba-4b22-be88-86c4be24338f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_figure(frame:int, selection=None, cell_info=None):\n",
    "    \"\"\"\n",
    "    Creates a plotly figure with images and selected/unselected cell overlay\n",
    "    \"\"\"\n",
    "    \n",
    "    # use only the first channel for the image\n",
    "    image = images[frame][...,0]\n",
    "    \n",
    "    # Render image into figure\n",
    "    compression_level=8\n",
    "    fig = px.imshow(image, binary_string=True, binary_compression_level=compression_level)\n",
    "\n",
    "    if selection is not None:\n",
    "        selection = set(selection)\n",
    "    \n",
    "    fig.update_layout(autosize=True)\n",
    "\n",
    "    # obtain the segmentation of the frame\n",
    "    frame_df = get_frame_overlay(frame)\n",
    "    \n",
    "    # loop over all contours in this frame and create an overlay trace in plotly\n",
    "    for _,contour in frame_df.iterrows():\n",
    "        \n",
    "        coordinates = np.concatenate([contour[\"contour_coordinates\"], contour[\"contour_coordinates\"][0:1]])\n",
    "        x_array = np.array(coordinates[:,0])\n",
    "        y_array = np.array(coordinates[:,1])\n",
    "\n",
    "        color = (255,255,0)\n",
    "        if selection is not None:\n",
    "            if contour.id in selection:\n",
    "                color = (255, 0, 0)\n",
    "\n",
    "        text = f'I am cell {contour.id}'\n",
    "        if cell_info is not None:\n",
    "            text = cell_info[contour.id]\n",
    "\n",
    "        # Adding a trace with a fill, setting opacity to 0\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                x=x_array, \n",
    "                y=y_array, \n",
    "                fill=\"toself\",\n",
    "                fillcolor=f'rgba{tuple([*color, 0.5])}',\n",
    "                mode='lines',\n",
    "                line=dict(\n",
    "                    color=f\"rgb{color}\",\n",
    "                    width=2\n",
    "                ),\n",
    "                name=f'Cell id: {contour.id}',\n",
    "                text=text,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c8697be-0628-4898-bbaa-3612bb23e080",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_selection(data, min, max, mode):\n",
    "    low = np.quantile(data, min / 100)\n",
    "    high = np.quantile(data, max / 100)\n",
    "    \n",
    "    data = np.array(data)\n",
    "    \n",
    "    print(low, high)    \n",
    "    \n",
    "    selection = (low <= data) & (data <= high)\n",
    "    if mode == \"Exclusive\":\n",
    "        selection = ~selection # invert the selection\n",
    "        \n",
    "    return selection, low, high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1656ca7c-aebe-42b1-9179-93508b36083f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "import dash\n",
    "import uuid\n",
    "import cv2\n",
    "import moviepy.editor as mpy\n",
    "\n",
    "\n",
    "def render_video(cell_df, selection_mask=None):\n",
    "    \n",
    "    uid = uuid.uuid4()\n",
    "    thickness = 2\n",
    "    \n",
    "    selected_df = cell_df.iloc[selection_mask]\n",
    "    unselected_df = cell_df.iloc[~selection_mask]\n",
    "\n",
    "    video_images = []\n",
    "    for frame in tqdm(np.unique(cell_df[\"frame\"])):\n",
    "        frame_image = np.copy(images[frame])#source.get_frame(frame).raw\n",
    "        \n",
    "        font = cv2.FONT_HERSHEY_SIMPLEX \n",
    "\n",
    "        # org \n",
    "        org = (50, 50) \n",
    "\n",
    "        # fontScale \n",
    "        fontScale = 0.5\n",
    "\n",
    "        # Blue color in RGB \n",
    "        color = (255, 255, 255) \n",
    "\n",
    "        # Line thickness of 2 px \n",
    "        thickness = 2\n",
    "        \n",
    "        cv2.putText(frame_image, f\"Frame {frame:02d}\", org, font,  \n",
    "                   fontScale, color, thickness, cv2.LINE_AA)\n",
    "        \n",
    "        sel_frame_df = selected_df[selected_df[\"frame\"] == frame]\n",
    "        unsel_frame_df = unselected_df[unselected_df[\"frame\"] == frame]\n",
    "\n",
    "        cv2.polylines(frame_image, [np.array(cell_data[\"contour_coordinates\"]).astype(np.int32) for _,cell_data in sel_frame_df.iterrows()], True, (255, 0, 0), thickness)\n",
    "        cv2.polylines(frame_image, [np.array(cell_data[\"contour_coordinates\"]).astype(np.int32) for _,cell_data in unsel_frame_df.iterrows()], True, (255, 255, 0), thickness)\n",
    "\n",
    "        video_images.append(frame_image)\n",
    "        \n",
    "    clip =  mpy.ImageSequenceClip(video_images, fps=10)\n",
    "    crf=23\n",
    "    filename=f\"assets/test-{uid}.mp4\"\n",
    "    clip.write_videofile(\n",
    "            filename, \n",
    "            codec='vp9',ffmpeg_params=[\"-quality\", \"realtime\", \"-speed\", \"4\"] # preset=\"ultrafast\", ffmpeg_params=[\"-crf\", str(crf)],\n",
    "    )\n",
    "\n",
    "    return filename"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c3fa06-eb5d-4162-b402-359f2adb1550",
   "metadata": {},
   "source": [
    "## 4. Compute single-cell stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f413cd45-ed09-4794-b10e-676d1524d1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "track_lookup = {det_id: n for n in lc_lineage.nodes for det_id in lc_lineage.nodes[n][\"cycle\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83b7c8cf-2530-4e7a-9319-f831f749b20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add track id to every cell detection\n",
    "cell_df[\"track_id\"] = cell_df[\"id\"].map(lambda cid: track_lookup[cid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9911883-304f-4976-b6f5-7f9697f2e25a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add trajectory age to every cell detection\n",
    "cell_df[\"track_age\"] = cell_df[\"track_id\"].map(lambda track_id: lc_df[lc_df[\"track_id\"] == track_id].iloc[0][\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab947ff0-99d2-49bc-b827-078829cbb541",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the age of a cell at its observation\n",
    "cell_df['age'] = cell_df.apply(lambda x: lc_df[lc_df[\"track_id\"] == x[\"track_id\"]].iloc[0][\"cycle\"].index(x[\"id\"]) / 60, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "49c3c810-a96c-4434-8b1a-8c55fe2fa460",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make an information text for cell countours\n",
    "cell_df[\"cell_info\"] = cell_df.apply(lambda x: f\"I am cell {x['id']}<br />Area: {x['area']:.2f} micrometer**2<br />Length: {x['length']:.2f} micrometer<br />Age: {x['age']*60} minutes \", axis=1) #Cell detection info:<br />area: { x['area_smaller'] * 100:.1f }% <= {x['area']} <= {(1 - x['area_smaller']) * 100:.1f}%<br /><br />Cell track info:<br />age: {x['age_younger'] * 100:.1f}% <= {x['track_age']} <= {(1 - x['age_younger']) * 100:.1f}%\", axis=1)\n",
    "cell_info = {x['id']: x['cell_info'] for _, x in cell_df.iterrows()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "46518042-8146-44b4-b91f-71a53a3715ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>frame</th>\n",
       "      <th>area</th>\n",
       "      <th>length</th>\n",
       "      <th>contour_coordinates</th>\n",
       "      <th>time</th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_age</th>\n",
       "      <th>age</th>\n",
       "      <th>cell_info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.055456</td>\n",
       "      <td>2.117461</td>\n",
       "      <td>[[496.0, 410.0], [495.0, 411.0], [492.0, 411.0...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2101</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>I am cell 1&lt;br /&gt;Area: 2.06 micrometer**2&lt;br /...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.855872</td>\n",
       "      <td>1.996361</td>\n",
       "      <td>[[510.0, 410.0], [509.0, 411.0], [508.0, 411.0...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>I am cell 2&lt;br /&gt;Area: 1.86 micrometer**2&lt;br /...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.076192</td>\n",
       "      <td>2.140230</td>\n",
       "      <td>[[497.0, 409.0], [496.0, 410.0], [494.0, 410.0...</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>2101</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>I am cell 7&lt;br /&gt;Area: 2.08 micrometer**2&lt;br /...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.876608</td>\n",
       "      <td>2.028561</td>\n",
       "      <td>[[509.0, 410.0], [508.0, 411.0], [507.0, 411.0...</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>22</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>I am cell 8&lt;br /&gt;Area: 1.88 micrometer**2&lt;br /...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2.128032</td>\n",
       "      <td>2.208535</td>\n",
       "      <td>[[499.0, 408.0], [498.0, 409.0], [495.0, 409.0...</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2101</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>I am cell 12&lt;br /&gt;Area: 2.13 micrometer**2&lt;br ...</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>238359</th>\n",
       "      <td>239020</td>\n",
       "      <td>799</td>\n",
       "      <td>3.172608</td>\n",
       "      <td>3.342516</td>\n",
       "      <td>[[432.0, 1028.0], [431.0, 1029.0], [429.0, 102...</td>\n",
       "      <td>13.316667</td>\n",
       "      <td>2447</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>I am cell 239020&lt;br /&gt;Area: 3.17 micrometer**2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238360</th>\n",
       "      <td>239021</td>\n",
       "      <td>799</td>\n",
       "      <td>3.110400</td>\n",
       "      <td>3.461627</td>\n",
       "      <td>[[332.0, 1029.0], [331.0, 1030.0], [327.0, 103...</td>\n",
       "      <td>13.316667</td>\n",
       "      <td>2478</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>I am cell 239021&lt;br /&gt;Area: 3.11 micrometer**2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238361</th>\n",
       "      <td>239022</td>\n",
       "      <td>799</td>\n",
       "      <td>2.387232</td>\n",
       "      <td>2.765983</td>\n",
       "      <td>[[419.0, 1030.0], [417.0, 1032.0], [417.0, 103...</td>\n",
       "      <td>13.316667</td>\n",
       "      <td>2446</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>I am cell 239022&lt;br /&gt;Area: 2.39 micrometer**2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238362</th>\n",
       "      <td>239023</td>\n",
       "      <td>799</td>\n",
       "      <td>2.970432</td>\n",
       "      <td>3.233113</td>\n",
       "      <td>[[505.0, 1032.0], [504.0, 1033.0], [503.0, 103...</td>\n",
       "      <td>13.316667</td>\n",
       "      <td>2422</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>I am cell 239023&lt;br /&gt;Area: 2.97 micrometer**2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238363</th>\n",
       "      <td>239024</td>\n",
       "      <td>799</td>\n",
       "      <td>3.377376</td>\n",
       "      <td>3.600535</td>\n",
       "      <td>[[549.0, 1039.0], [548.0, 1040.0], [546.0, 104...</td>\n",
       "      <td>13.316667</td>\n",
       "      <td>2423</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>I am cell 239024&lt;br /&gt;Area: 3.38 micrometer**2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238364 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  frame      area    length  \\\n",
       "0            1      0  2.055456  2.117461   \n",
       "1            2      0  1.855872  1.996361   \n",
       "2            7      1  2.076192  2.140230   \n",
       "3            8      1  1.876608  2.028561   \n",
       "4           12      2  2.128032  2.208535   \n",
       "...        ...    ...       ...       ...   \n",
       "238359  239020    799  3.172608  3.342516   \n",
       "238360  239021    799  3.110400  3.461627   \n",
       "238361  239022    799  2.387232  2.765983   \n",
       "238362  239023    799  2.970432  3.233113   \n",
       "238363  239024    799  3.377376  3.600535   \n",
       "\n",
       "                                      contour_coordinates       time  \\\n",
       "0       [[496.0, 410.0], [495.0, 411.0], [492.0, 411.0...   0.000000   \n",
       "1       [[510.0, 410.0], [509.0, 411.0], [508.0, 411.0...   0.000000   \n",
       "2       [[497.0, 409.0], [496.0, 410.0], [494.0, 410.0...   0.016667   \n",
       "3       [[509.0, 410.0], [508.0, 411.0], [507.0, 411.0...   0.016667   \n",
       "4       [[499.0, 408.0], [498.0, 409.0], [495.0, 409.0...   0.033333   \n",
       "...                                                   ...        ...   \n",
       "238359  [[432.0, 1028.0], [431.0, 1029.0], [429.0, 102...  13.316667   \n",
       "238360  [[332.0, 1029.0], [331.0, 1030.0], [327.0, 103...  13.316667   \n",
       "238361  [[419.0, 1030.0], [417.0, 1032.0], [417.0, 103...  13.316667   \n",
       "238362  [[505.0, 1032.0], [504.0, 1033.0], [503.0, 103...  13.316667   \n",
       "238363  [[549.0, 1039.0], [548.0, 1040.0], [546.0, 104...  13.316667   \n",
       "\n",
       "        track_id  track_age       age  \\\n",
       "0           2101   0.866667  0.000000   \n",
       "1             22   1.233333  0.000000   \n",
       "2           2101   0.866667  0.016667   \n",
       "3             22   1.233333  0.016667   \n",
       "4           2101   0.866667  0.033333   \n",
       "...          ...        ...       ...   \n",
       "238359      2447   0.350000  0.333333   \n",
       "238360      2478   0.683333  0.666667   \n",
       "238361      2446   0.350000  0.333333   \n",
       "238362      2422   0.600000  0.583333   \n",
       "238363      2423   0.600000  0.583333   \n",
       "\n",
       "                                                cell_info  \n",
       "0       I am cell 1<br />Area: 2.06 micrometer**2<br /...  \n",
       "1       I am cell 2<br />Area: 1.86 micrometer**2<br /...  \n",
       "2       I am cell 7<br />Area: 2.08 micrometer**2<br /...  \n",
       "3       I am cell 8<br />Area: 1.88 micrometer**2<br /...  \n",
       "4       I am cell 12<br />Area: 2.13 micrometer**2<br ...  \n",
       "...                                                   ...  \n",
       "238359  I am cell 239020<br />Area: 3.17 micrometer**2...  \n",
       "238360  I am cell 239021<br />Area: 3.11 micrometer**2...  \n",
       "238361  I am cell 239022<br />Area: 2.39 micrometer**2...  \n",
       "238362  I am cell 239023<br />Area: 2.97 micrometer**2...  \n",
       "238363  I am cell 239024<br />Area: 3.38 micrometer**2...  \n",
       "\n",
       "[238364 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc305d6-0f5a-49f1-ab32-0dbf1f1e3ef6",
   "metadata": {},
   "source": [
    "## 5. Create the interactive visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a502b220-f1de-4701-ba1f-5f9880b09554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# View 03: Explore single-cell distributions"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"1600\"\n",
       "            src=\"http://127.0.0.1:8052/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f0c59478ac0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Video, Markdown, display\n",
    "import dash_bootstrap_components as dbc\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "\n",
    "from dash import Dash, dcc, html, Input, Output, callback, no_update, State, ctx\n",
    "import os\n",
    "import json\n",
    "\n",
    "from PIL import Image\n",
    "from pandas import DataFrame\n",
    "import pandas as pd\n",
    "import plotly.figure_factory as ff\n",
    "from functools import reduce\n",
    "\n",
    "from dash.long_callback import DiskcacheLongCallbackManager\n",
    "\n",
    "## Create Diskcache (for long running video creation)\n",
    "import diskcache\n",
    "cache = diskcache.Cache(\"./cache\")\n",
    "long_callback_manager = DiskcacheLongCallbackManager(cache)\n",
    "\n",
    "min_frame = 0\n",
    "max_frame = len(images) - 1\n",
    "\n",
    "# extract single-cell properties for filtering\n",
    "areas = np.array(cell_df[\"area\"])\n",
    "ages = np.array(cell_df[\"age\"])\n",
    "lengths = np.array(cell_df[\"length\"])\n",
    "\n",
    "\n",
    "app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "#############################\n",
    "### Create the App layout ###\n",
    "#############################\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dcc.Store(id='memory'),\n",
    "    dbc.Row([\n",
    "        dbc.Col([\n",
    "            # graph for microscope image\n",
    "            dcc.Loading(dcc.Graph(id=\"image\")),\n",
    "            # frame slider\n",
    "            dbc.Col(dcc.Slider(min_frame, max_frame, step=1, value=5, id='frame-slider', tooltip={\"placement\": \"bottom\", \"always_visible\": True}, marks={0: '0', max_frame: f'{max_frame}'},), width=12),\n",
    "            # video download button\n",
    "            dbc.Col(html.Div(dbc.Button(\"Download Video\", id=\"btn_video\", color=\"primary\"), className=\"d-grid gap-2\"), width=12),\n",
    "            dcc.Download(id=\"download-video\")\n",
    "        ], width=4),\n",
    "        dbc.Col([\n",
    "            # normalize button\n",
    "            dbc.Row([\n",
    "                dbc.Checklist(\n",
    "                    options=[\n",
    "                        {\"label\": \"Normalize\", \"value\": \"normalize\"},\n",
    "                    ],\n",
    "                    value=[],\n",
    "                    id=\"normalized-input\",\n",
    "                    switch=True,\n",
    "                ),\n",
    "            ]),\n",
    "            # cell size graph\n",
    "            dbc.Row(dcc.Loading(dcc.Graph(id=\"lineage\", mathjax=True))),\n",
    "            # cell count graph\n",
    "            dbc.Row(dcc.Loading(dcc.Graph(id=\"counts\", mathjax=True))),\n",
    "        ], width=8),\n",
    "\n",
    "    ]),\n",
    "    dbc.Row([\n",
    "        # distribution for cell area\n",
    "        dbc.Col(dcc.Loading([\n",
    "            dcc.Graph(id=\"area-dist\", mathjax=True),\n",
    "            dcc.RadioItems(['Inclusive', 'Exclusive'], 'Inclusive', id=\"area-radio\"),\n",
    "            dcc.RangeSlider(0, 100, value=[0, 100], id='area-slider', allowCross=False, tooltip={\"placement\": \"bottom\", \"always_visible\": True}, marks={0: '0', 100: f'100'},)\n",
    "        ]), width=4),\n",
    "        # distribution for cell length\n",
    "        dbc.Col(dcc.Loading([\n",
    "            dcc.Graph(id=\"end-area-ratio\", mathjax=True),#figure=age_fig, id=\"age-dist\"),\n",
    "            dcc.RadioItems(['Inclusive', 'Exclusive'], 'Inclusive', id=\"length-radio\"),\n",
    "            dcc.RangeSlider(0, 100, value=[0, 100], id='length-slider', allowCross=False, tooltip={\"placement\": \"bottom\", \"always_visible\": True}, marks={0: '0', 100: f'100'},),\n",
    "        ]), width=4),\n",
    "        # distribution for cell age\n",
    "        dbc.Col(dcc.Loading([\n",
    "            dcc.Graph(id=\"age-dist\", mathjax=True),\n",
    "            dcc.RadioItems(['Inclusive', 'Exclusive'], 'Inclusive', id=\"age-radio\"),\n",
    "            dcc.RangeSlider(0, 100, value=[0, 100], id='age-slider', allowCross=False, tooltip={\"placement\": \"bottom\", \"always_visible\": True}, marks={0: '0', 100: f'100'},),\n",
    "        ]), width=4),\n",
    "    ]),\n",
    "])\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"memory\", \"data\"),\n",
    "    Input(\"area-slider\", \"value\"),\n",
    "    Input(\"area-radio\", \"value\"),\n",
    "    Input(\"length-slider\", \"value\"),\n",
    "    Input(\"length-radio\", \"value\"),\n",
    "    Input(\"age-slider\", \"value\"),\n",
    "    Input(\"age-radio\", \"value\"),\n",
    ")\n",
    "def recompute_selection(area_slider, area_radio, length_slider, length_radio, age_slider, age_radio):\n",
    "    # compute area and length selections\n",
    "    area_selection, area_low, area_high = compute_selection(areas, area_slider[0], area_slider[1], area_radio)\n",
    "    length_mask, length_low, length_high = compute_selection(lengths, length_slider[0], length_slider[1], length_radio)\n",
    "    age_selection, age_low, age_high = compute_selection(ages, age_slider[0], age_slider[1], age_radio)\n",
    "    \n",
    "    \"\"\"\n",
    "    # compute age selection (a bit more difficult, because it affects full trajectories of cells)\n",
    "    age_low = np.quantile(lc_df[\"age\"], age_slider[0] / 100)\n",
    "    age_high = np.quantile(lc_df[\"age\"], age_slider[1] / 100)\n",
    "    \n",
    "    \n",
    "    inclusive_set_selection = (age_low <= ages) & (ages <= age_high)\n",
    "    print(inclusive_set_selection)\n",
    "    if age_radio == \"Inclusive\":\n",
    "        age_subset = ages[inclusive_set_selection]\n",
    "        age_selection = inclusive_set_selection\n",
    "    else:\n",
    "        age_subset = ages[~inclusive_set_selection]\n",
    "        age_selection = ~inclusive_set_selection\n",
    "\n",
    "    \"\"\"\n",
    "    # fuse all selections into a mask\n",
    "    total_selection_mask = area_selection & age_selection & length_mask\n",
    "    total_selection = set(cell_df[\"id\"][total_selection_mask])\n",
    "    \n",
    "    print(\"Finished recompute selection\")\n",
    "    \n",
    "    return {\n",
    "        \"selection\": list(total_selection),\n",
    "        \"mask\": total_selection_mask,\n",
    "        \"area_low\": area_low,\n",
    "        \"area_high\": area_high,\n",
    "        \"age_low\": age_low,\n",
    "        \"age_high\": age_high,\n",
    "        \"length_low\": length_low,\n",
    "        \"length_high\": length_high,\n",
    "    }\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"image\", \"figure\"),\n",
    "    Input(\"frame-slider\", \"value\"),\n",
    "    Input(\"memory\", \"data\"),\n",
    "    prevent_initial_call=True,\n",
    "    manager=long_callback_manager,\n",
    ")\n",
    "def update_microscopy_frame(frame_slider: int, data: dict):\n",
    "    \"\"\"Updates the microscopy frame in the visulization\n",
    "\n",
    "    Args:\n",
    "        frame_slider (int): frame slider position\n",
    "        data (dict): current data state including computed cell selection\n",
    "\n",
    "    Returns:\n",
    "        the microscopy image figure\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    # frame selected by the slider\n",
    "    target_frame = frame_slider\n",
    "\n",
    "    total_selection = set(data[\"selection\"])\n",
    "    \n",
    "    print(\"Make microscopy figure\")\n",
    "    \n",
    "    # create the image figure using the selection mask\n",
    "    end_fig = get_image_figure(target_frame, total_selection, cell_info)\n",
    "    end_fig.update_layout(\n",
    "        height=700,\n",
    "        showlegend=False\n",
    "    )\n",
    "    \n",
    "    print(\"Finished microscopy figure\")\n",
    "    \n",
    "    return end_fig\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"lineage\", \"figure\"),\n",
    "    Output(\"counts\", \"figure\"),\n",
    "    Input(\"memory\", \"data\"),\n",
    "    Input(\"normalized-input\", \"value\"),\n",
    "    prevent_initial_call=True,\n",
    "    manager=long_callback_manager,\n",
    ")\n",
    "def update_temporal_figures(data: dict, normalized_input):\n",
    "    \"\"\"Updates the temporal development of single-cell properties\n",
    "\n",
    "    Args:\n",
    "        data (dict): Current data state including cell selections\n",
    "        normalized_input (_type_): _description_\n",
    "\n",
    "    Returns:\n",
    "        figures for total cell area and cell counts\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    ##########################################\n",
    "    # Create area and count over time figure #\n",
    "    ##########################################\n",
    "    \n",
    "    area_df = cell_df[[\"id\", \"frame\", \"area\", \"time\"]]\n",
    "    length_df = cell_df[[\"id\", \"frame\", \"length\", \"time\"]]\n",
    "    age_df = cell_df[[\"id\", \"frame\", \"track_age\", \"time\"]]\n",
    "    \n",
    "    total_selection_mask = np.array(data[\"mask\"], dtype=bool)\n",
    "    \n",
    "    # selected cell areas\n",
    "    df_unselected = area_df[~total_selection_mask].groupby(\"time\").agg(\"sum\").reset_index()\n",
    "    df_selected = area_df[total_selection_mask].groupby(\"time\").agg(\"sum\").reset_index()\n",
    "    df_selected[\"type\"] = \"selected\"\n",
    "    df_unselected[\"type\"] = \"unselected\"\n",
    "    \n",
    "    # selected cell counts\n",
    "    df_unselected_sizes = area_df[~total_selection_mask].groupby(\"time\").size().reset_index(name=\"cell_count\")\n",
    "    df_selected_sizes = area_df[total_selection_mask].groupby(\"time\").size().reset_index(name=\"cell_count\")\n",
    "    df_selected_sizes[\"type\"] = \"selected\"\n",
    "    df_unselected_sizes[\"type\"] = \"unselected\"\n",
    "    \n",
    "    df_area = pd.concat([df_unselected, df_selected])\n",
    "    df_counts = pd.concat([df_unselected_sizes, df_selected_sizes])\n",
    "    \n",
    "    # compute normalized values\n",
    "    grouped = df_area.groupby(\"time\")\n",
    "    df_area['area_norm'] = grouped['area'].transform(lambda x: x/x.sum())\n",
    "\n",
    "    grouped = df_counts.groupby(\"time\")\n",
    "    df_counts['cell_count_norm'] = grouped['cell_count'].transform(lambda x: x/x.sum())\n",
    "    \n",
    "    # based on normalizer button choose which data column to use\n",
    "    y_post = ''\n",
    "    if \"normalize\" in normalized_input:\n",
    "        y_post = \"_norm\"\n",
    "        \n",
    "    color_discrete_map= {\"selected\": \"rgb(255, 0, 0)\", \"unselected\": \"rgb(255, 255, 0)\"}\n",
    "    \n",
    "    # create the figures\n",
    "    figures = [\n",
    "        px.area(df_area, x=\"time\", y=\"area\" + y_post, color=\"type\", title=\"Cell Area Over Time\", color_discrete_map=color_discrete_map),\n",
    "        px.area(df_counts, x=\"time\", y=\"cell_count\" + y_post, color=\"type\", title=\"Cell Count Over Time\", color_discrete_map=color_discrete_map),\n",
    "    ]\n",
    "    \n",
    "    for figure in figures:\n",
    "        figure.update_layout(\n",
    "            xaxis=dict(\n",
    "                spikemode  = 'across+toaxis', spikedash = 'solid'\n",
    "            ),\n",
    "            xaxis_title=\"Time [hour]\",\n",
    "            margin=dict(\n",
    "                r=0,\n",
    "                b=0,\n",
    "            ),\n",
    "        )\n",
    "        figure.update_traces(hovertemplate=None)\n",
    "        figure.update_layout(hovermode=\"x\")\n",
    "        \n",
    "    figures[0].update_layout(\n",
    "        yaxis_title=r\"$\\text{Total cell area [} \\mu m \\text{]}$\"\n",
    "    )\n",
    "    figures[1].update_layout(\n",
    "        yaxis_title=r\"$\\text{Total cell count}$\"\n",
    "    )\n",
    "    \n",
    "    print(\"Finished temporal figures\")\n",
    "    \n",
    "    return figures[0], figures[1]\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"area-dist\", \"figure\"),\n",
    "    Output(\"age-dist\", \"figure\"),\n",
    "    Output(\"end-area-ratio\", \"figure\"),\n",
    "    Input(\"memory\", \"data\"),\n",
    "    prevent_initial_call=True,\n",
    "    manager=long_callback_manager,\n",
    ")\n",
    "def update_histograms(data: dict):\n",
    "    \"\"\"Update the histograms\n",
    "\n",
    "    Args:\n",
    "        data (dict): Current selection state\n",
    "\n",
    "    Returns:\n",
    "        three figures for single-cell area, age and length\n",
    "    \"\"\"\n",
    "\n",
    "    ##########################\n",
    "    # Create histogram plots #\n",
    "    ##########################\n",
    "\n",
    "    total_selection_mask = np.array(data[\"mask\"], dtype=bool)\n",
    "    \n",
    "    area_low, area_high = data[\"area_low\"], data[\"area_high\"]\n",
    "    length_low, length_high = data[\"length_low\"], data[\"length_high\"]\n",
    "    age_low, age_high = data[\"age_low\"], data[\"age_high\"]\n",
    "    \n",
    "    #area_fig = px.histogram(cell_df, x=\"area\", nbins=100)\n",
    "\n",
    "    area_fig = go.Figure()\n",
    "\n",
    "    area_fig.add_trace(go.Histogram(x=cell_df[\"area\"].iloc[total_selection_mask], name='selected',nbinsx=100, marker_color='#FF0000',))\n",
    "    area_fig.add_trace(go.Histogram(x=cell_df[\"area\"].iloc[~total_selection_mask], name='unselected',nbinsx=100, marker_color='#FFFF00',))\n",
    "    \n",
    "    # The two histograms are drawn on top of another\n",
    "    area_fig.update_layout(barmode='stack')\n",
    "    \n",
    "    area_fig.update_layout(\n",
    "        title=\"Cell Area Distribution\",\n",
    "        autosize=True,\n",
    "        xaxis_title=r\"$\\text{Cell size [} \\mu m^2 \\text{]}$\",\n",
    "        yaxis_title=\"Frequency\",\n",
    "    )\n",
    "    \n",
    "    area_fig.add_vline(x=area_low, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "    area_fig.add_vline(x=area_high, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "            \n",
    "    age_fig = go.Figure() #px.histogram(lc_df, x=\"age\", nbins=100)\n",
    "\n",
    "    min_age = np.min(ages)\n",
    "    max_age = np.max(ages)\n",
    "\n",
    "    xbins=dict(start=min_age * 60, end=max_age * 60, size=1)\n",
    "    age_fig.add_trace(go.Histogram(x=ages[total_selection_mask] * 60, name='selected',marker_color='#FF0000',xbins=xbins))\n",
    "    age_fig.add_trace(go.Histogram(x=ages[~total_selection_mask] * 60, name='unselected', marker_color='#FFFF00', xbins=xbins))\n",
    "\n",
    "    # The two histograms are drawn on top of another\n",
    "    age_fig.update_layout(barmode='stack')\n",
    "\n",
    "    age_fig.add_vline(x=age_low*60, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "    age_fig.add_vline(x=age_high*60, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "\n",
    "    age_fig.update_layout(\n",
    "        title=\"Cell Age Distribution\",\n",
    "        xaxis_title=\"Cell age [minute]\",\n",
    "        yaxis_title=\"Frequency\",\n",
    "    )\n",
    "\n",
    "    length_fig = go.Figure()\n",
    "    length_fig.add_trace(go.Histogram(x=cell_df[\"length\"].iloc[total_selection_mask], name='selected',nbinsx=100, marker_color='#FF0000',))\n",
    "    length_fig.add_trace(go.Histogram(x=cell_df[\"length\"].iloc[~total_selection_mask], name='unselected',nbinsx=100, marker_color='#FFFF00',))\n",
    "    \n",
    "    # The two histograms are drawn on top of another\n",
    "    length_fig.update_layout(barmode='stack')\n",
    "    \n",
    "    #length_fig = px.histogram(cell_df, x=\"length\", nbins=100)\n",
    "    \n",
    "    length_fig.add_vline(x=length_low, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "    length_fig.add_vline(x=length_high, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "\n",
    "    \n",
    "    length_fig.update_layout(\n",
    "        title=\"Cell Length Distribution\",\n",
    "        autosize=True,\n",
    "        xaxis_title=r\"$\\text{Cell length [} \\mu m \\text{]}$\",\n",
    "        yaxis_title=\"Frequency\",\n",
    "    )\n",
    "    \n",
    "    return area_fig, age_fig, length_fig\n",
    "\n",
    "@app.long_callback(\n",
    "    Output(\"download-video\", \"data\"),\n",
    "    Input(\"btn_video\", \"n_clicks\"),\n",
    "    State(\"memory\", \"data\"),\n",
    "    prevent_initial_call=True,\n",
    "    manager=long_callback_manager,\n",
    ")\n",
    "def gen_video(num_clicks: int, data: dict):\n",
    "    \"\"\"Generate video of the microscopy sequence\n",
    "\n",
    "    Args:\n",
    "        num_clicks (int): just used to call the method when the button is pressed\n",
    "        data (_type_): current cell selection data\n",
    "\n",
    "    Returns:\n",
    "        _type_: _description_\n",
    "    \"\"\"\n",
    "    # 1. gen video file\n",
    "    print(\"Start generating video file...\")\n",
    "    output_file = render_video(cell_df, np.array(data[\"mask\"], bool))\n",
    "\n",
    "\n",
    "    # 2. send it to the browser\n",
    "    return dcc.send_file(\n",
    "        output_file\n",
    "    )\n",
    "\n",
    "\n",
    "display(Markdown(\"# View 03: Explore single-cell distributions\"))\n",
    "app.run_server(port=8052, jupyter_mode=\"inline\", jupyter_height=1600)"
   ]
  }
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