MNT fix flake8 and add comments

Author: TomDLT

.github/workflows/run_tests.yml

@@ -26,13 +26,14 @@ jobs:
 
     - uses: actions/cache@v3
       with:
-        path: ~/.voxelwise_tutorials_data/shortclips
-        key: ${{ runner.os }}-shortclips
+        path: ~/voxelwise_tutorials_data/shortclips
+        key: shortclips-dataset
 
     - name: Install dependencies
       run: |
         pip install -U setuptools
         pip install -U wheel
+        # install pycortex from source to avoid using the cached wheel
         pip install git+https://github.com/gallantlab/pycortex.git
         pip install -e ."[github]"
         $CONDA/bin/conda install -c conda-forge git-annex=*=alldep*
@@ -47,7 +48,7 @@ jobs:
 
     - name: Config git-annex
       run: |
-        # change of path not in effect before next action
+        # note: change of path not in effect before next action
         echo "${CONDA}/bin" >> $GITHUB_PATH
         # add back python to avoid using conda's
         echo $(dirname $(which python)) >> $GITHUB_PATH

tutorials/shortclips/01_plot_explainable_variance.py

@@ -41,6 +41,7 @@
 # Path of the data directory
 # --------------------------
 from voxelwise_tutorials.io import get_data_home
+
 directory = get_data_home(dataset="shortclips")
 print(directory)
 
@@ -59,6 +60,7 @@
 # First, we load the fMRI responses on the test set, which contains brain
 # responses to ten (10) repeats of the same stimulus.
 import os
+
 file_name = os.path.join(directory, 'responses', f'{subject}_responses.hdf')
 Y_test = load_hdf5_array(file_name, key="Y_test")
 print("(n_repeats, n_samples_test, n_voxels) =", Y_test.shape)
@@ -67,6 +69,7 @@
 # Then, we compute the explainable variance for each voxel.
 
 from voxelwise_tutorials.utils import explainable_variance
+
 ev = explainable_variance(Y_test)
 print("(n_voxels,) =", ev.shape)
 
@@ -190,6 +193,7 @@
 # with a dot product ``@`` (equivalent to ``np.dot``).
 
 from voxelwise_tutorials.io import load_hdf5_sparse_array
+
 voxel_to_fsaverage = load_hdf5_sparse_array(mapper_file,
                                             key='voxel_to_fsaverage')
 ev_projected = voxel_to_fsaverage @ ev

tutorials/shortclips/04_plot_hemodynamic_response.py

@@ -29,6 +29,7 @@
 # Path of the data directory
 # --------------------------
 from voxelwise_tutorials.io import get_data_home
+
 directory = get_data_home(dataset="shortclips")
 print(directory)
 
@@ -108,6 +109,7 @@
 from voxelwise_tutorials.delayer import Delayer
 from himalaya.kernel_ridge import KernelRidgeCV
 from himalaya.backend import set_backend
+
 backend = set_backend("torch_cuda", on_error="warn")
 
 X_train = X_train.astype("float32")
@@ -126,6 +128,7 @@
 
 ###############################################################################
 from sklearn import set_config
+
 set_config(display='diagram')  # requires scikit-learn 0.23
 pipeline
 
@@ -182,23 +185,23 @@
 # function.
 
 import matplotlib.pyplot as plt
-fig, axs = plt.subplots(6, 1, figsize=(8, 6.5), constrained_layout=True, 
-        sharex=True)
-times = np.arange(n_trs)*TR
+
+fig, axs = plt.subplots(6, 1, figsize=(8, 6.5), constrained_layout=True,
+                        sharex=True)
+times = np.arange(n_trs) * TR
 
 axs[0].plot(times, y, color="r")
 axs[0].set_title("BOLD response")
 for i, (ax, xx) in enumerate(zip(axs.flat[1:], x_delayed.T)):
-  ax.plot(times, xx, color='k')
-  ax.set_title("$x(t - {0:.0f})$ (feature delayed by {1} sample{2})".format(
-      i*TR, i, "" if i == 1 else "s"))
+    ax.plot(times, xx, color='k')
+    ax.set_title("$x(t - {0:.0f})$ (feature delayed by {1} sample{2})".format(
+        i * TR, i, "" if i == 1 else "s"))
 for ax in axs.flat:
-  ax.axvline(40, color='gray')
-  ax.set_yticks([])
+    ax.axvline(40, color='gray')
+    ax.set_yticks([])
 _ = axs[-1].set_xlabel("Time [s]")
 plt.show()
 
-
 ###############################################################################
 # Compare with a model without delays
 # -----------------------------------

voxelwise_tutorials/regression_toy.py

@@ -85,7 +85,7 @@ def plot_2d(X, y, w, flat=True, alpha=None, show_noiseless=True):
     #####################
     # left plot: y = f(x)
 
-    try:  # computed_zorder is only available in matplotlib >= 3.4
+    try:  # computed_zorder is only available in matplotlib >= 3.4
         ax = fig.add_subplot(121, projection='3d', computed_zorder=False)
     except AttributeError:
         ax = fig.add_subplot(121, projection='3d')

voxelwise_tutorials/tests/test_mappers.py

@@ -20,6 +20,7 @@
 
 subject = "S01"
 directory = get_data_home(dataset="shortclips")
+print(f"\n\n{directory}\n\n")
 file_name = os.path.join("mappers", f'{subject}_mappers.hdf')
 mapper_file = os.path.join(directory, file_name)