v2.5.0

.github/workflows/check_tags.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.10"]
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}

.github/workflows/django.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ['3.8', '3.9']
+        python-version: ['3.9', '3.10']
 
     steps:
     - uses: actions/checkout@v3

.github/workflows/linters.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}

BUILD

@@ -1 +1 @@
-4bbb195
+72de749

VERSION

@@ -1 +1 @@
-2.4.0
+2.5.0

src/manage.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 """Django's command-line utility for administrative tasks."""
+
 import os
 import sys
 

src/tags.json

@@ -13,5 +13,6 @@
   "hra": "Human Reference Atlas data",
   "sc": "Single cell analysis",
   "R": "Template written in R",
-  "zarr": "Using zarr file format"
+  "zarr": "Using zarr file format",
+  "cde": "Cell Distance Explorer"
 }

src/user_templates_api/templates/jupyter_lab/templates/hra_cde_tutorial/metadata.json

@@ -0,0 +1,13 @@
+{
+  "title": "Human Reference Atlas (HRA) Cell Distance Explorer (CDE) Tutorial",
+  "description": "This notebook shows users how to get a sample dataset for the CDE and visualize it with the node-dist-vis and CDE Jupyter widget.",
+  "tags": [
+    "hra",
+    "api",
+    "cde"
+  ],
+  "is_multi_dataset_template": true,
+  "template_format": "jinja",
+  "is_hidden": false,
+  "last_modified_unix_timestamp": 1770060963
+}

src/user_templates_api/templates/jupyter_lab/templates/hra_cde_tutorial/render.py

@@ -0,0 +1,6 @@
+from user_templates_api.templates.jupyter_lab.render import JupyterLabRender
+
+
+class JupyterLabExampleJinjaRender(JupyterLabRender):
+    def __init__(self):
+        pass

src/user_templates_api/templates/jupyter_lab/templates/hra_cde_tutorial/template.ipynb

@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "879b7334",
+   "metadata": {},
+   "source": [
+    "# A notebook to demonstrate usage of the Jupyter Widget for the Cell Distance Explorer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b4cdba9",
+   "metadata": {},
+   "source": "In this notebook, we download a sample dataset with 3D cell positions from the [Cell Distance Explorer (CDE)](https://apps.humanatlas.io/cde/) and then visualize it with the `node-dist-vis` and `CdeVisualization` Jupyter widgets."
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a124cb13",
+   "metadata": {},
+   "source": [
+    "# Install and import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d88ba163",
+   "metadata": {},
+   "source": [
+    "# Import native packages\n",
+    "import os\n",
+    "from pprint import pprint"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "8919f99f",
+   "metadata": {},
+   "source": [
+    "#Install and import external packages\n",
+    "%pip install requests pandas ipywidgets hra_jupyter_widgets\n",
+    "\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "# Import hra-jupyter-widgets. For documentation, please see https://github.com/x-atlas-consortia/hra-jupyter-widgets/blob/main/usage.ipynb\n",
+    "from hra_jupyter_widgets import (\n",
+    "    # BodyUi,\n",
+    "    CdeVisualization, # in this example, we will use this one...\n",
+    "    # Eui,\n",
+    "    # EuiOrganInformation,\n",
+    "    # FtuExplorer,\n",
+    "    # FtuExplorerSmall,\n",
+    "    # MedicalIllustration,\n",
+    "    # ModelViewer,\n",
+    "    NodeDistVis, # ...and this one, but all of them are usable for different purposes!\n",
+    "    # Rui,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9d5ff",
+   "metadata": {},
+   "source": [
+    "# Global variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "572c2376",
+   "metadata": {},
+   "source": [
+    "output_file = \"sample_data.csv\"\n",
+    "output_folder = \"data\"\n",
+    "path = os.path.join(output_folder, output_file)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ede91205",
+   "metadata": {},
+   "source": [
+    "# Download data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "19495224",
+   "metadata": {},
+   "source": [
+    "def download_public_sheet_as_csv(file_id, gid, path):\n",
+    "    \n",
+    "    # ensure output folder exists\n",
+    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
+    "    \n",
+    "    url = (\n",
+    "        f\"https://docs.google.com/spreadsheets/d/{file_id}/export?format=csv&gid={gid}\"\n",
+    "    )\n",
+    "    response = requests.get(url)\n",
+    "    if response.status_code == 200:\n",
+    "        with open(path, \"wb\") as f:\n",
+    "            f.write(response.content)\n",
+    "        print(f\"Success: saved to {path}\")\n",
+    "    else:\n",
+    "        print(f\"Error {response.status_code}: could not download sheet\")\n",
+    "\n",
+    "\n",
+    "# Your sheet’s IDs\n",
+    "file_id = \"1EUf7CUZb0NprgxBeX3nS3GXyUM89edIRH6NSeUsR3nY\"\n",
+    "gid = \"47698780\"\n",
+    "\n",
+    "download_public_sheet_as_csv(file_id, gid,path)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc56fffb",
+   "metadata": {},
+   "source": [
+    "## Read data as DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ab2354e9",
+   "metadata": {},
+   "source": [
+    "# Read the CSV file and convert it to a df\n",
+    "df = pd.read_csv(path)\n",
+    "df"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab66bee",
+   "metadata": {},
+   "source": "## Visualize with CDE"
+  },
+  {
+   "cell_type": "code",
+   "id": "e92f490f",
+   "metadata": {},
+   "source": [
+    "# Next, let's define a function that turns a DataFrame into a node list that can then be passed into the CdeVisualization or NodeDistVis widget\n",
+    "def make_node_list(df: pd.DataFrame, is_3d: bool = False):\n",
+    "    \"\"\"Turn a DataFrame into a list of dicts for passing them into a HRA widget\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): A DataFrame with cells\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # If the df does not have a z-axis column, let's add one and set all cells to 0\n",
+    "    if not is_3d:\n",
+    "        df.loc[:, (\"z\")] = 0\n",
+    "    \n",
+    "    node_list = [\n",
+    "        {\"x\": row[\"x\"], \"y\": row[\"y\"], \"z\": row[\"z\"], \"Cell Type\": row[\"Cell Type\"]}\n",
+    "        for index, row in df.iterrows()\n",
+    "    ]\n",
+    "\n",
+    "    return node_list"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "9d51eff4",
+   "metadata": {},
+   "source": [
+    "# Prepare df_cells for visualization with NodeDistVis widget\n",
+    "node_list = make_node_list(df, True)\n",
+    "\n",
+    "# Let's inspect the first 5 rows\n",
+    "pprint(node_list[:5])"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "7614f172",
+   "metadata": {},
+   "source": [
+    "# Let's instantiate the NodeDistVis class with some parameters. We pass in the node_list, indicate Endothelial cells as targets for the edges.\n",
+    "# As we are not supplying an edge list, we need to provide a max_edge_distance, which is set to 1000 (generously)\n",
+    "node_dist_vis = NodeDistVis(\n",
+    "    nodes=node_list,\n",
+    "    node_target_key=\"Cell Type\",\n",
+    "    node_target_value=\"Endothelial\",\n",
+    "    max_edge_distance=1000,\n",
+    ")\n",
+    "\n",
+    "# Display our new widget\n",
+    "display(node_dist_vis)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "9e9d46d5",
+   "metadata": {},
+   "source": [
+    "# Finally, let's instantiate the CDEVisualization class with our node_list as parameter.\n",
+    "cde = CdeVisualization(nodes=node_list)\n",
+    "\n",
+    "# Display our new widget\n",
+    "display(cde)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}