{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden"
   },
   "source": [
    "# Vitessce Widget Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of a Loom file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join, isfile, isdir\n",
    "from urllib.request import urlretrieve\n",
    "from anndata import read_loom\n",
    "import numpy as np\n",
    "\n",
    "from vitessce import (\n",
    "    VitessceConfig,\n",
    "    Component as cm,\n",
    "    CoordinationType as ct,\n",
    "    AnnDataWrapper,\n",
    ")\n",
    "from vitessce.data_utils import (\n",
    "    optimize_adata,\n",
    "    to_diamond,\n",
    "    VAR_CHUNK_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download data\n",
    "\n",
    "Download `osmFISH_SScortex_mouse_all_cells.loom` from http://loom.linnarssonlab.org/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loom_filepath = join(\"data\", \"osmFISH_SScortex_mouse_all_cells.loom\")\n",
    "if not isfile(loom_filepath):\n",
    "    os.makedirs(\"data\", exist_ok=True)\n",
    "    urlretrieve('http://loom.linnarssonlab.org/clone/osmFISH/osmFISH_SScortex_mouse_all_cells.loom', loom_filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Open Loom file with AnnData's read_loom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = read_loom(loom_filepath, obsm_names={\"tSNE\": [\"_tSNE_1\", \"_tSNE_2\"], \"spatial\": [\"X\", \"Y\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate pseudo-segmentations as diamond-shaped polygons centered on the spatial coordinate of each cell, and store in `adata.obsm[\"segmentations\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cells = adata.obs.shape[0]\n",
    "adata.obsm[\"segmentations\"] = np.zeros((num_cells, 4, 2))\n",
    "radius = 100\n",
    "for i in range(num_cells):\n",
    "    adata.obsm[\"segmentations\"][i, :, :] = to_diamond(adata.obsm['spatial'][i, 0], adata.obsm['spatial'][i, 1], radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the AnnData object to a Zarr store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zarr_filepath = join(\"data\", \"osmFISH_SScortex_mouse_all_cells.zarr\")\n",
    "if not isdir(zarr_filepath) or True:\n",
    "    adata = optimize_adata(\n",
    "        adata,\n",
    "        obs_cols=[\"ClusterName\"],\n",
    "        obsm_keys=[\"tSNE\", \"spatial\", \"segmentations\"],\n",
    "        optimize_X=True,\n",
    "    )\n",
    "    adata.write_zarr(zarr_filepath, chunks=[adata.shape[0], VAR_CHUNK_SIZE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Configure Vitessce\n",
    "\n",
    "Create a Vitessce view config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VitessceConfig(schema_version=\"1.0.15\", name='Loom Example', description='osmFISH dataset of the mouse cortex including all cells')\n",
    "w = AnnDataWrapper(adata_path=zarr_filepath, obs_set_paths=[\"obs/ClusterName\"], obs_set_names=[\"Clusters\"], obs_locations_path=\"obsm/spatial\", obs_segmentations_path=\"obsm/segmentations\", obs_embedding_paths=[\"obsm/tSNE\"])\n",
    "dataset = vc.add_dataset(name='SScortex').add_object(w)\n",
    "\n",
    "tsne = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"tSNE\")\n",
    "cell_sets = vc.add_view(cm.OBS_SETS, dataset=dataset)\n",
    "spatial = vc.add_view(cm.SPATIAL, dataset=dataset)\n",
    "\n",
    "spatial_segmentation_layer_value = {\n",
    "  \"opacity\": 1,\n",
    "  \"radius\": 0,\n",
    "  \"visible\": True,\n",
    "  \"stroked\": False\n",
    "}\n",
    "\n",
    "vc.link_views([spatial], [ct.SPATIAL_ZOOM, ct.SPATIAL_TARGET_X, ct.SPATIAL_TARGET_Y, ct.SPATIAL_SEGMENTATION_LAYER], [-6.43, 10417.69, 24885.55, spatial_segmentation_layer_value])\n",
    "vc.layout(spatial | (tsne / cell_sets));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Render the widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A widget can be created with the `.widget()` method on the config instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vw = vc.widget()\n",
    "vw"
   ]
  }
 ],
 "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}