Update main tutorials to version 1.0a08

FZJ-INM1-BDA · Mar 12, 2024 · ea7d57a · ea7d57a
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diff --git a/01-BasicConcepts.ipynb b/01-BasicConcepts.ipynb
@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "import siibra\n",
-    "assert siibra.__version__ >= \"1.0a02\"\n",
+    "assert siibra.__version__ >= \"1.0a08\"\n",
     "import os\n",
     "import matplotlib\n",
     "%matplotlib notebook"
@@ -522,7 +522,7 @@
    "outputs": [],
    "source": [
     "area44r = julichbrain.get_region(\"44 right\")\n",
-    "bbox = area44r.get_bounding_box(\"bigbrain\")\n",
+    "bbox = area44r.get_boundingbox(\"bigbrain\")\n",
     "bbox"
    ]
   },

diff --git a/02-DataFeatures.ipynb b/02-DataFeatures.ipynb
@@ -22,49 +22,63 @@
    "outputs": [],
    "source": [
     "import siibra\n",
-    "assert siibra.__version__ >= \"1.0a02\""
+    "assert siibra.__version__ >= \"1.0a08\""
    ]
   },
   {
    "cell_type": "markdown",
    "id": "7b75cdf8",
    "metadata": {},
    "source": [
-    "We can choose different types of features from this module. The feature types are organized in a hierarchy under the most abstract type `siibra.features.Feature`. All other feature types are subclasses of it. The current hierarchy is\n",
-    "```\n",
-    "Feature\n",
-    "├── CompoundFeature\n",
-    "├── Tabular\n",
-    "│   ├── CorticalProfile\n",
-    "│   │   ├── BigBrainIntensityProfile\n",
-    "│   │   ├── CellDensityProfile\n",
-    "│   │   └── ReceptorDensityProfile\n",
-    "│   ├── GeneExpressions\n",
-    "│   │   └── ProxyFeature\n",
-    "│   ├── LayerwiseBigBrainIntensities\n",
-    "│   ├── LayerwiseCellDensity\n",
-    "│   ├── ReceptorDensityFingerprint\n",
-    "│   └── RegionalTimeseriesActivity\n",
-    "│       └── RegionalBOLD\n",
-    "├── RegionalConnectivity\n",
-    "│   ├── FunctionalConnectivity\n",
-    "│   ├── AnatomoFunctionalConnectivity\n",
-    "│   ├── StreamlineCounts\n",
-    "│   ├── StreamlineLengths\n",
-    "│   └── TracingConnectivity\n",
-    "├── Image\n",
-    "│   ├── CellBodyStainedVolumeOfInterest\n",
-    "│   ├── BlockfaceVolumeOfInterest\n",
-    "│   ├── DTIVolumeOfInterest\n",
-    "│   ├── PLIVolumeOfInterest\n",
-    "│   ├── MRIVolumeOfInterest\n",
-    "│   ├── XPCTVolumeOfInterest\n",
-    "│   ├── LSFMVolumeOfInterest\n",
-    "│   └── CellbodyStainedSection\n",
-    "└── EbrainsDataFeature\n",
-    "```\n",
-    "\n",
-    "Note that this list is provided by `siibra.features.Feature.get_ascii_tree()`."
+    "We can choose different types of features from this module. The feature types are organized in a hierarchy under the most abstract type `siibra.features.Feature`. All other feature types are subclasses of it. The current hierarchy can be obtained by"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "96143ef5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature\n",
+      "├── CompoundFeature\n",
+      "├── Tabular\n",
+      "│   ├── CorticalProfile\n",
+      "│   │   ├── BigBrainIntensityProfile\n",
+      "│   │   ├── CellDensityProfile\n",
+      "│   │   └── ReceptorDensityProfile\n",
+      "│   ├── GeneExpressions\n",
+      "│   │   ├── ProxyFeature\n",
+      "│   │   └── ProxyFeature\n",
+      "│   ├── LayerwiseBigBrainIntensities\n",
+      "│   ├── LayerwiseCellDensity\n",
+      "│   ├── ReceptorDensityFingerprint\n",
+      "│   └── RegionalTimeseriesActivity\n",
+      "│       └── RegionalBOLD\n",
+      "├── RegionalConnectivity\n",
+      "│   ├── FunctionalConnectivity\n",
+      "│   ├── AnatomoFunctionalConnectivity\n",
+      "│   ├── StreamlineCounts\n",
+      "│   ├── StreamlineLengths\n",
+      "│   └── TracingConnectivity\n",
+      "├── Image\n",
+      "│   ├── CellBodyStainedVolumeOfInterest\n",
+      "│   ├── BlockfaceVolumeOfInterest\n",
+      "│   ├── DTIVolumeOfInterest\n",
+      "│   ├── PLIVolumeOfInterest\n",
+      "│   ├── MRIVolumeOfInterest\n",
+      "│   ├── XPCTVolumeOfInterest\n",
+      "│   ├── LSFMVolumeOfInterest\n",
+      "│   └── CellbodyStainedSection\n",
+      "└── EbrainsDataFeature\n"
+     ]
+    }
+   ],
+   "source": [
+    "siibra.features.render_ascii_tree(\"Feature\")"
    ]
   },
   {
@@ -220,7 +234,7 @@
     "features = siibra.features.get(\n",
     "    v1, siibra.features.molecular.GeneExpressions, gene=[\"TAC1\", \"MAOA\", \"GABARAPL2\"]\n",
     ")\n",
-    "fig = features[0].plot()\n",
+    "fig = features[0].plot(title=f'Gene Expressions in {v1}')\n",
     "features[0].data"
    ]
   },

diff --git a/03-ProbabilisticAssignment.ipynb b/03-ProbabilisticAssignment.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import siibra\n",
-    "assert siibra.__version__ >= \"1.0a02\"\n",
+    "assert siibra.__version__ >= \"1.0a08\"\n",
     "from nilearn import plotting\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib notebook"

diff --git a/04-DifferentialGeneExpressions.ipynb b/04-DifferentialGeneExpressions.ipynb
@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "import siibra, siibra_jugex\n",
-    "assert siibra.__version__ >= \"1.0a02\"\n",
+    "assert siibra.__version__ >= \"1.0a08\"\n",
     "assert siibra_jugex.__version__ >= \"1.2\""
    ]
   },

diff --git a/05-CombinedWorkflow.ipynb b/05-CombinedWorkflow.ipynb
@@ -19,9 +19,9 @@
    "outputs": [],
    "source": [
     "import siibra\n",
-    "assert siibra.__version__ >= \"1.0a02\"\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib notebook"
+    "assert siibra.__version__ >= \"1.0a08\"\n",
    ]
   },
   {
@@ -47,6 +47,18 @@
     "]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "julich_mni_pmaps = julichbrain.get_map(\n",
+    "    space=siibra.spaces.MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC, \n",
+    "    maptype=siibra.MapType.STATISTICAL\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -56,11 +68,8 @@
     "# fetch and display their probability maps\n",
     "from nilearn import plotting\n",
     "for region in regions:\n",
-    "    pmap = region.get_regional_map(\n",
-    "        space=siibra.spaces.MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC, \n",
-    "        maptype=siibra.MapType.STATISTICAL\n",
-    "    )\n",
-    "    plotting.plot_stat_map(pmap.fetch(), title=region.name)"
+    "    pmap = julich_mni_pmaps.fetch(region)\n",
+    "    plotting.plot_stat_map(pmap, title=region.name)"
    ]
   },
   {

diff --git a/06-CorticalPatchSampling.ipynb b/06-CorticalPatchSampling.ipynb
@@ -8,6 +8,7 @@
    "outputs": [],
    "source": [
     "import siibra\n",
+    "assert siibra.__version__ >= \"1.0a08\"\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib notebook\n",
     "import numpy as np\n",