{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d974a397",
   "metadata": {},
   "source": [
    "## Animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3b56b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import unist\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dd348e",
   "metadata": {},
   "source": [
    "The datasets used for this tutorial is the reconstruction of human metastatic lymph node and can be downloaded at:\n",
    "\n",
    "https://drive.google.com/file/d/1FtQ-yUSH418SYTPFRgR-ONolvwmYXgJh/view?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7c6b851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 831406 × 0\n",
       "    obs: 'pred_label'\n",
       "    obsm: 'spatial'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read in data\n",
    "adata = sc.read(\"/Users/shuilan/Library/CloudStorage/OneDrive-InsideMDAnderson/3D spatial transcriptomics/data/3D/OpenST_predicted.h5ad\")\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2eb4359c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from unist.downstream.vis import three_d_plot, construct_pc, rotate_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7a34e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "anno_colors = {\n",
    "    'T_cell': '#E64B35',\n",
    "    'Cytotoxic_IFN_signaling': '#D73027',\n",
    "    'Dendritic_cells': '#F0E442',\n",
    "    'Macrophages': '#A6CEE3',\n",
    "    'M1_macrophages': '#1F78B4',\n",
    "    'Mast_cells': '#FF7F00',\n",
    "    'Germinal_Center_Plasma_IgM_B_cell': '#FDB462',\n",
    "    'Plasma_IgA': '#B3DE69',\n",
    "    'Plasma_IgG': '#33A02C',\n",
    "    'Fibroblasts': '#CAB2D6',\n",
    "    'CAF': '#6A3D9A',\n",
    "    'CAM': '#8E0152',\n",
    "    'High_Endothelial_Venules': '#4575B4',\n",
    "    'Cortex_CCL21': '#91CF60',\n",
    "    'Tumor': '#999999',\n",
    "    'Tumor_Keratin_Pearl': '#252525'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e397668",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = adata[adata.obs[\"pred_label\"] != \"unknown\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b04e8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_model, plot_cmap = construct_pc(\n",
    "    adata=adata.copy(),\n",
    "    spatial_key=\"spatial\",\n",
    "    groupby=\"pred_label\",\n",
    "    key_added=\"pred_label\",\n",
    "    colormap=anno_colors\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25aa68e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotate_model(model=pc_model, angle=(90, 0, 0), inplace=True)\n",
    "rotate_model(model=pc_model, angle=(0, 90, 0), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a63f918",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "os.chdir(f\"/Users/shuilan/Library/CloudStorage/OneDrive-InsideMDAnderson/3D spatial transcriptomics/data\")\n",
    "out_image_path = f\"animations\"\n",
    "Path(out_image_path).mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c8fab88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imageio[ffmpeg] in /opt/anaconda3/envs/unist/lib/python3.10/site-packages (2.37.2)\n",
      "Requirement already satisfied: numpy in /opt/anaconda3/envs/unist/lib/python3.10/site-packages (from imageio[ffmpeg]) (1.26.4)\n",
      "Requirement already satisfied: pillow>=8.3.2 in /opt/anaconda3/envs/unist/lib/python3.10/site-packages (from imageio[ffmpeg]) (12.1.1)\n",
      "Collecting imageio-ffmpeg (from imageio[ffmpeg])\n",
      "  Downloading imageio_ffmpeg-0.6.0-py3-none-macosx_10_9_intel.macosx_10_9_x86_64.whl.metadata (1.5 kB)\n",
      "Requirement already satisfied: psutil in /opt/anaconda3/envs/unist/lib/python3.10/site-packages (from imageio[ffmpeg]) (7.2.2)\n",
      "Downloading imageio_ffmpeg-0.6.0-py3-none-macosx_10_9_intel.macosx_10_9_x86_64.whl (24.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.9/24.9 MB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m  \u001b[33m0:00:00\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: imageio-ffmpeg\n",
      "Successfully installed imageio-ffmpeg-0.6.0\n"
     ]
    }
   ],
   "source": [
    "! pip install imageio[ffmpeg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5adfcc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_text = 'PC model for metastasis lymph node'\n",
    "\n",
    "three_d_plot(\n",
    "    pc_model,\n",
    "    key=\"pred_label\",\n",
    "    model_size=4,\n",
    "    filename=os.path.join(out_image_path, f\"whole.mp4\"),\n",
    "    window_size=(1500, 1500),\n",
    "    view_up=(0, 0, 1),\n",
    "    cpo=\"yz\",\n",
    "    show_legend=False,\n",
    "    off_screen=False,\n",
    "    model_style=\"points\",\n",
    "    opacity=1,\n",
    "    jupyter=\"static\",\n",
    "    background=\"white\",\n",
    "    framerate=10,\n",
    "    pad_ratio=0.3,\n",
    "    text=plot_text,\n",
    "    text_kwargs={\n",
    "        \"font_size\": 40,\n",
    "        \"font_family\": \"arial\",\n",
    "        \"color\": \"black\",\n",
    "        \"position\": \"upper_left\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd9e1bd",
   "metadata": {},
   "source": [
    "## Animation Demo\n",
    "\n",
    "<video width=\"700\" controls autoplay loop muted>\n",
    "  <source src=\"_static/whole.mp4\" type=\"video/mp4\">\n",
    "</video>"
   ]
  }
 ],
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  "kernelspec": {
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