{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden"
   },
   "source": [
    "# Vitessce Data Preparation Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export data to local files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import dependencies\n",
    "\n",
    "We need to import the classes and functions that we will be using from the corresponding packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from urllib.parse import quote_plus\n",
    "from os.path import join, isfile, isdir\n",
    "from urllib.request import urlretrieve\n",
    "from anndata import read_h5ad\n",
    "import scanpy as sc\n",
    "\n",
    "from vitessce import (\n",
    "    VitessceWidget,\n",
    "    VitessceConfig,\n",
    "    Component as cm,\n",
    "    CoordinationType as ct,\n",
    "    AnnDataWrapper,\n",
    ")\n",
    "from vitessce.data_utils import (\n",
    "    optimize_adata,\n",
    "    VAR_CHUNK_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download and process 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": [
    "adata_filepath = join(\"data\", \"habib17.processed.h5ad\")\n",
    "if not isfile(adata_filepath):\n",
    "    os.makedirs(\"data\", exist_ok=True)\n",
    "    urlretrieve('https://covid19.cog.sanger.ac.uk/habib17.processed.h5ad', adata_filepath)\n",
    "\n",
    "adata = read_h5ad(adata_filepath)\n",
    "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zarr_filepath = join(\"data\", \"habib17.processed.zarr\")\n",
    "if not isdir(zarr_filepath):\n",
    "    adata = optimize_adata(\n",
    "        adata,\n",
    "        obs_cols=[\"CellType\"],\n",
    "        obsm_keys=[\"X_umap\"],\n",
    "        var_cols=[\"top_highly_variable\"],\n",
    "        optimize_X=True,\n",
    "    )\n",
    "    adata.write_zarr(zarr_filepath, chunks=[adata.shape[0], VAR_CHUNK_SIZE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create the Vitessce configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the configuration by adding the views and datasets of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VitessceConfig(schema_version=\"1.0.15\", name='Habib et al', description='COVID-19 Healthy Donor Brain')\n",
    "dataset = vc.add_dataset(name='Brain').add_object(AnnDataWrapper(\n",
    "        adata_path=zarr_filepath,\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",
    "))\n",
    "scatterplot = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"X_umap\")\n",
    "cell_sets = vc.add_view(cm.OBS_SETS, dataset=dataset)\n",
    "genes = vc.add_view(cm.FEATURE_LIST, dataset=dataset)\n",
    "heatmap = vc.add_view(cm.HEATMAP, dataset=dataset)\n",
    "vc.layout((scatterplot | (cell_sets / genes)) / heatmap);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Export files to a local directory\n",
    "\n",
    "The `.export(to='files')` method on the view config instance will export files to the specified directory `out_dir`. The `base_url` parameter is required so that the file URLs in the view config point to the location where you ultimately intend to serve the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_dict = vc.export(to='files', base_url='http://localhost:3000', out_dir='./test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Serve the files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the files have been saved to the `./test` directory, they can be served by any static web server.\n",
    "\n",
    "If you would like to serve the files locally, we recommend [http-server](https://github.com/http-party/http-server) which can be installed with NPM or Homebrew:\n",
    "```sh\n",
    "cd test\n",
    "http-server ./ --cors -p 3000\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. View on vitessce.io\n",
    "\n",
    "The returned view config dict can be converted to a URL, and if the files are served on the internet (rather than locally), this URL can be used to share the interactive visualizations with colleagues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vitessce_url = \"http://vitessce.io/?url=data:,\" + quote_plus(json.dumps(config_dict))\n",
    "import webbrowser\n",
    "webbrowser.open(vitessce_url)"
   ]
  }
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