MNT rename tutorials and simplify merged tutorials for colab

Author: mvdoc

tutorials/Makefile

@@ -23,23 +23,25 @@ NBDIR = notebooks/shortclips
 merge-notebooks:
 	python merge_notebooks.py \
 		$(NBDIR)/01_setup_colab.ipynb \
+		$(NBDIR)/02_download_shortclips.ipynb \
 		$(NBDIR)/03_compute_explainable_variance.ipynb \
 		$(NBDIR)/04_understand_ridge_regression.ipynb \
 		$(NBDIR)/05_fit_wordnet_model.ipynb \
 		$(NBDIR)/06_visualize_hemodynamic_response.ipynb \
 		$(NBDIR)/08_fit_motion_energy_model.ipynb \
 		$(NBDIR)/09_fit_banded_ridge_model.ipynb \
-		> $(NBDIR)/merged_for_colab.ipynb
-	echo "Saved in $(NBDIR)/merged_for_colab.ipynb"
+		> $(NBDIR)/vem_tutorials_merged_for_colab.ipynb
+	echo "Saved in $(NBDIR)/vem_tutorials_merged_for_colab.ipynb"
 
 	python merge_notebooks.py \
 		$(NBDIR)/01_setup_colab.ipynb \
+		$(NBDIR)/02_download_shortclips.ipynb \
 		$(NBDIR)/03_compute_explainable_variance.ipynb \
 		$(NBDIR)/05_fit_wordnet_model.ipynb \
 		$(NBDIR)/08_fit_motion_energy_model.ipynb \
 		$(NBDIR)/09_fit_banded_ridge_model.ipynb \
-		> $(NBDIR)/merged_for_colab_model_fitting.ipynb
-	echo "Saved in $(NBDIR)/merged_for_colab_model_fitting.ipynb"
+		> $(NBDIR)/vem_tutorials_merged_for_colab_model_fitting.ipynb
+	echo "Saved in $(NBDIR)/vem_tutorials_merged_for_colab_model_fitting.ipynb"
 
 build:
 	jupyter book build --all $(SOURCEDIR)

tutorials/_config.yml

@@ -13,8 +13,8 @@ execute:
   exclude_patterns:
     - notebooks/shortclips/00_setup_colab.ipynb
     - notebooks/shortclips/07_extract_motion_energy.ipynb
-    - notebooks/shortclips/merged_for_colab_model_fitting.ipynb
-    - notebooks/shortclips/merged_for_colab.ipynb
+    - notebooks/shortclips/vem_tutorials_merged_for_colab_model_fitting.ipynb
+    - notebooks/shortclips/vem_tutorials_merged_for_colab.ipynb
     - notebooks/vim2/00_download_vim2.ipynb
     - notebooks/vim2/01_extract_motion_energy.ipynb
     - notebooks/vim2/02_plot_ridge_model.ipynb

tutorials/merge_notebooks.py

@@ -5,8 +5,8 @@
 python merge_notebooks.py A.ipynb B.ipynb C.ipynb > merged.ipynb
 """
 import io
-import sys
 import os.path
+import sys
 
 import nbformat
 
@@ -19,13 +19,15 @@ def merge_notebooks(filenames):
         if merged is None:
             merged = nb
             # add a Markdown cell with the file name, then all cells
-            merged.cells = [cell_with_title(title=os.path.basename(fname))
-                            ] + nb.cells
+            # merged.cells = [cell_with_title(title=os.path.basename(fname))
+            #                 ] + nb.cells
+            # Do not add the title cell, use the title in each notebook
+            merged.cells = nb.cells
         else:
             # add a code cell resetting all variables
             merged.cells.append(cell_with_reset())
             # add a Markdown cell with the file name
-            merged.cells.append(cell_with_title(title=os.path.basename(fname)))
+            # merged.cells.append(cell_with_title(title=os.path.basename(fname)))
             # add all cells from current notebook
             merged.cells.extend(nb.cells)
 

tutorials/notebooks/shortclips/01_setup_colab.ipynb

@@ -34,10 +34,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Install all required dependencies and download the data\n",
+        "## Install all required dependencies\n",
         "\n",
-        "Uncomment and run the following cell to download the required packages.\n",
-        "\n"
+        "Uncomment and run the following cell to download the required packages."
       ]
     },
     {
@@ -99,42 +98,6 @@
         "                       \"on your machine.\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now run the following cell to download the data for the tutorials.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from voxelwise_tutorials.io import download_datalad\n",
-        "\n",
-        "DATAFILES = [\n",
-        "    \"features/motion_energy.hdf\",\n",
-        "    \"features/wordnet.hdf\",\n",
-        "    \"mappers/S01_mappers.hdf\",\n",
-        "    \"responses/S01_responses.hdf\",\n",
-        "]\n",
-        "\n",
-        "source = \"https://gin.g-node.org/gallantlab/shortclips\"\n",
-        "destination = \"/content/shortclips\"\n",
-        "\n",
-        "for datafile in DATAFILES:\n",
-        "    local_filename = download_datalad(\n",
-        "        datafile,\n",
-        "        destination=destination,\n",
-        "        source=source\n",
-        "    )"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

tutorials/notebooks/shortclips/04_understand_ridge_regression.ipynb

@@ -4,13 +4,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Understand ridge regression and cross-validation\n",
+        "# Understand ridge regression and hyperparameter selection\n",
         "\n",
         "In future examples, we will model the fMRI responses using a regularized linear\n",
         "regression known as *ridge regression*. This example explains why we use ridge\n",
         "regression, and how to use cross-validation to select the appropriate\n",
-        "regularization hyper-parameter.\n",
+        "regularization hyperparameter.\n",
         "\n",
         "Linear regression is a method to model the relation between some input\n",
         "variables $X \\in \\mathbb{R}^{(n \\times p)}$ (the features) and an output\n",

tutorials/notebooks/shortclips/05_fit_wordnet_model.ipynb

@@ -4,8 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Fit a ridge model with wordnet features\n",
+        "# Fit a voxelwise encoding model with WordNet features\n",
         "\n",
         "In this example, we model the fMRI responses with semantic \"wordnet\" features,\n",
         "manually annotated on each frame of the movie stimulus. The model is a\n",

tutorials/notebooks/shortclips/08_fit_motion_energy_model.ipynb

@@ -4,8 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Fit a ridge model with motion-energy features\n",
+        "# Fit a voxelwise encoding model with motion-energy features\n",
         "\n",
         "In this example, we model the fMRI responses with motion-energy features\n",
         "extracted from the movie stimulus. The model is a regularized linear regression\n",

tutorials/notebooks/shortclips/09_fit_banded_ridge_model.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Fit a banded ridge model with both wordnet and motion-energy features\n",
+        "# Fit a voxelwise encoding model with both WordNet and motion-energy features\n",
         "\n",
         "In this example, we model the fMRI responses with a *banded ridge regression* \n",
         "with two different feature spaces: motion energy and wordnet categories.\n",