fix notebook 4

fix notebook (convert to pdf & upload as artefact)

.github/workflows/test-paper-notebooks.yml

@@ -1,10 +1,10 @@
-name: '[test] test siibra-paper notebooks'
+name: "[test] test siibra-paper notebooks"
 
 on:
   push:
-    branches: [ 'main', 'refactor_attr' ]
+    branches: ["main", "refactor_attr"]
   pull_request:
-    branches: [ '*' ]
+    branches: ["*"]
 
 jobs:
   test_tutorials:
@@ -21,9 +21,22 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install -U .
-          pip install -r requirements.txt
-          pip install matplotlib
-          pip install pytest nbmake
-      
+          pip install matplotlib jupyter
+          apt-get install pandoc
+
       - name: Run notebooks with nbmake
-        run: pytest --nbmake --nbmake-timeout=1200 paper_notebookes
+        run: |
+          for notebook in $(find ./paper_notebooks -type f)
+          do
+            echo "jupyter execute $notebook --allow-errors --inplace"
+            jupyter execute $notebook --allow-errors --inplace
+
+            echo "jupyter nbconvert --to pdf $notebook"
+            jupyter nbconvert --to pdf $notebook
+          done
+
+      - name: Archive code coverage results
+        uses: actions/upload-artifact@v4
+        with:
+          name: Notebook PDFs
+          path: paper_notebooks/*.pdf

paper_notebookes/4-probabilistic-assignment.ipynb

@@ -1,6 +1,5 @@
 {
- "cells": [
-  {
+ "cells": [{
    "cell_type": "code",
    "execution_count": null,
    "id": "51571a53",
@@ -233,6 +232,7 @@
    "outputs": [],
    "source": [
     "\n",
+    "region = siibra.get_region('3.0.3', region.name)\n",
     "features = siibra.find_features(region, siibra.modality_vocab.modality.STREAMLINECOUNTS)\n",
     "\n",
     "ft = features[0]\n",
@@ -254,120 +254,6 @@
     "named_sorted_series = sorted_series.rename(index={r:shortname(r) for r in sorted_series.index})\n",
     "named_sorted_series[:15].plot(kind=\"bar\")\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15eed6e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "    features[0].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3238c1e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fts = siibra.features.get(region, siibra.features.connectivity.StreamlineCounts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "035d099b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fts[0].get_element(\"000\").plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc05ad7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shortname(region):\n",
-    "    return (\n",
-    "        re.sub('\\s*\\(.*\\)\\s*|\\s*Area\\s*', ' ', region.name)\n",
-    "        .replace('left', 'L')\n",
-    "        .replace('right', 'R')\n",
-    "        .strip()\n",
-    "    )\n",
-    "\n",
-    "fts = siibra.features.get(region, siibra.features.connectivity.StreamlineCounts)\n",
-    "conn = fts[0].get_element(\"000\")\n",
-    "conndata = conn.get_profile(region).data\n",
-    "conndata.rename(index={r:shortname(r) for r in conndata.index}, inplace=True)\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(3.5, 3.0))\n",
-    "conndata[:15].plot(kind='bar', rot=45, ax=ax)\n",
-    "plt.xticks(ha='right')\n",
-    "plt.tight_layout()\n",
-    "plt.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ed0cbeb7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8ec0cc8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# find cluster peaks\n",
-    "peak_accuracy_mm = 2\n",
-    "clusterpeaks = clustermap.find_peaks(mindist=10, sigma_mm=peak_accuracy_mm)\n",
-    "print(clusterpeaks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6981e626",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get one cluster peak\n",
-    "peak_id = 0\n",
-    "peakmap = siibra.volumes.from_pointset(clusterpeaks, label=peak_id, target=clustermap, min_num_points=1)\n",
-    "view = plotting.plot_glass_brain(peakmap.fetch(), alpha=1, threshold=0, cmap='RdBu')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b71082e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "matches = (\n",
-    "    pmaps\n",
-    "    .assign(clustermap)\n",
-    "    .query(f\"correlation >= {min_correlation}\", engine='python')\n",
-    "    .sort_values('correlation', ascending=False)\n",
-    ")\n",
-    "matches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ca50d55",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -391,4 +277,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}