{
 "cells": [
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Vitessce Widget Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of 3k PBMC reference from Remote Zarr Store"
   ]
  },
  {
   "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",
    "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. Set the URL for the Remote Dataset\n",
    "\n",
    "For this example, we already have uploaded the `pbmc3k` dataset as a zarr store from the [scanpy docs](https://scanpy.readthedocs.io/en/stable/api/scanpy.datasets.pbmc3k.html) to the cloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://storage.googleapis.com/vitessce-demo-data/anndata-test/pbmc3k_processed.zarr/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create a Vitessce view config\n",
    "\n",
    "Define the data and views you would like to include in the widget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VitessceConfig(schema_version=\"1.0.15\", name='PBMC Reference')\n",
    "dataset = vc.add_dataset(name='PBMC 3k').add_object(AnnDataWrapper(adata_url=url, obs_set_paths=[\"obs/louvain\"], obs_set_names=[\"Louvain\"], obs_embedding_paths=[\"obsm/X_umap\", \"obsm/X_pca\"], obs_embedding_names=[\"UMAP\", \"PCA\"], obs_feature_matrix_path=\"X\"))\n",
    "\n",
    "umap = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\n",
    "pca = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"PCA\")\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",
    "\n",
    "vc.layout((umap / pca) | ((cell_sets | genes) / heatmap));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Create the Vitessce widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A widget can be created with the `.widget()` method on the config instance. Here, the `proxy=True` parameter allows this widget to be used in a cloud notebook environment, such as Binder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vw = vc.widget()\n",
    "vw"
   ]
  }
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