{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden"
   },
   "source": [
    "# Vitessce Widget Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The from_object shortcut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import dependencies\n",
    "\n",
    "Import the functions and classes that we will be using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "from urllib.request import urlretrieve\n",
    "from anndata import read_h5ad\n",
    "import scanpy as sc\n",
    "\n",
    "from vitessce import (\n",
    "    VitessceConfig,\n",
    "    Component as cm,\n",
    "    CoordinationType as ct,\n",
    "    AnnDataWrapper,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download the data\n",
    "\n",
    "For this example, we need to download a dataset from the COVID-19 Cell Atlas https://www.covid19cellatlas.org/index.healthy.html#habib17."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"data\", exist_ok=True)\n",
    "adata_filepath = join(\"data\", \"habib17.processed.h5ad\")\n",
    "urlretrieve('https://covid19.cog.sanger.ac.uk/habib17.processed.h5ad', adata_filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = read_h5ad(join(\"data\", \"habib17.processed.h5ad\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Preprocess the Data For Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_dispersion = adata.var[\"dispersions_norm\"][\n",
    "    sorted(\n",
    "        range(len(adata.var[\"dispersions_norm\"])),\n",
    "        key=lambda k: adata.var[\"dispersions_norm\"][k],\n",
    "    )[-51:][0]\n",
    "]\n",
    "adata.var[\"top_highly_variable\"] = (\n",
    "    adata.var[\"dispersions_norm\"] > top_dispersion\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With one line of code, you may create a Vitessce widget based on an automatically inferred configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vw = VitessceConfig.from_object(AnnDataWrapper(\n",
    "        adata,\n",
    "        obs_embedding_paths=[\"obsm/X_umap\"],\n",
    "        obs_embedding_names=[\"UMAP\"],\n",
    "        obs_set_paths=[\"obs/CellType\"],\n",
    "        obs_set_names=[\"Cell Type\"],\n",
    "        obs_feature_matrix_path=\"X\",\n",
    "        feature_filter_path=\"var/top_highly_variable\"\n",
    "), schema_version=\"1.0.15\").widget(height=800)\n",
    "vw"
   ]
  }
 ],
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