update HIBALL winterschool tutorial for 1.0a14

HIBALL-winterschool-2023.ipynb

@@ -20,7 +20,7 @@
     "assert Version(siibra.__version__) >= Version('1.0a08')\n",
     "\n",
     "import matplotlib\n",
-    "%matplotlib notebook\n",
+    "from nilearn import plotting\n",
     "\n",
     "# ignore the following lines at this point - we just touch some objects to trigger data loading \n",
     "# while we still take the introduction\n"
@@ -41,7 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "julich_brain = siibra.parcellations.get('julich 2.9')\n",
+    "julich_brain = siibra.parcellations.get('julich 3.1')\n",
     "julich_pmaps = julich_brain.get_map('mni152', 'statistical')\n",
     "julich_pmaps.fetch(region='4a left')\n",
     "siibra.warm_cache()"
@@ -251,37 +251,6 @@
     "print(amygdala.tree2str())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "111a852f",
-   "metadata": {},
-   "source": [
-    "Regions have several functions. Just as an example, we can request to compute its centroids in a given reference space, and plot them using nilearn's plotting library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8fd48b3c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "centroids = amygdala.compute_centroids(\n",
-    "    space=siibra.spaces.MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f78d5927",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nilearn import plotting\n",
-    "plotting.view_markers(centroids.as_list(), marker_color='r')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "b6d7a255",
@@ -832,11 +801,11 @@
    "outputs": [],
    "source": [
     "# We can plot the MNI coordinates to localize the measures and verify they are located in V1.\n",
-    "locations = siibra.PointSet(features[0].data.mni_xyz, space=\"mni152\")\n",
+    "locations = siibra.PointSet(features[0].data.mni_xyz.tolist(), space=\"mni152\")\n",
     "\n",
     "from nilearn import plotting\n",
-    "mask = v1.fetch_regional_map(\"mni152\")\n",
-    "display = plotting.plot_glass_brain(mask, cmap='viridis')\n",
+    "mask = v1.get_regional_map(\"mni152\")\n",
+    "display = plotting.plot_glass_brain(mask.fetch(), cmap='viridis')\n",
     "display.add_markers(locations.as_list(), marker_size=2) "
    ]
   },
@@ -952,28 +921,17 @@
     "To access full resolution data of BigBrain, we specify a bounding box in the physical space. For now, we just define a volume of interest from two corner points in the histological space. We specify the points with a string representation, which could be conveniently copy pasted from the interactive viewer siibra explorer. Note that the coordinates can be specified by 3-tuples, and in other ways."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "941867c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "space.get_template().fetch().get_bounding_box()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "4814d107",
    "metadata": {},
    "outputs": [],
    "source": [
-    "space = siibra.spaces.get('bigbrain')\n",
-    "space.\n",
-    "voi = space.bou(\n",
+    "voi = siibra.locations.BoundingBox(\n",
     "    point1=\"-30.590mm, 3.270mm, 47.814mm\",\n",
-    "    point2=\"-26.557mm, 6.277mm, 50.631mm\"\n",
+    "    point2=\"-26.557mm, 6.277mm, 50.631mm\",\n",
+    "    space='bigbrain'\n",
     ")\n",
     "\n",
     "# Note: we can reuse the template object from above,\n",
@@ -1142,7 +1100,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,