Remove extraneous hash symbols (nilearn#4096)

Remi-Gau · Nov 9, 2023 · 7cc1d7d · 7cc1d7d
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diff --git a/examples/02_decoding/plot_haxby_glm_decoding.py b/examples/02_decoding/plot_haxby_glm_decoding.py
@@ -93,7 +93,7 @@
     memory="nilearn_cache",
 )
 
-# %%#
+# %%
 # Run the :term:`GLM` on data from each session
 # ---------------------------------------------
 events[session].trial_type.unique()

diff --git a/examples/02_decoding/plot_haxby_multiclass.py b/examples/02_decoding/plot_haxby_multiclass.py
@@ -42,7 +42,7 @@
 # Sort the conditions by the order of appearance
 unique_conditions = unique_conditions[np.argsort(order)]
 
-# %%#
+# %%
 # Prepare the :term:`fMRI` data
 # -----------------------------
 from nilearn.maskers import NiftiMasker

diff --git a/examples/02_decoding/plot_miyawaki_encoding.py b/examples/02_decoding/plot_miyawaki_encoding.py
@@ -95,7 +95,7 @@
 plt.title(f"Run {3}, Stimulus {102}")
 plt.subplots_adjust(wspace=0.5)
 
-# %%#
+# %%
 # We now stack the :term:`fMRI` and stimulus data and remove an offset in the
 # beginning/end.
 

diff --git a/examples/02_decoding/plot_oasis_vbm.py b/examples/02_decoding/plot_oasis_vbm.py
@@ -108,7 +108,7 @@
 # decoding process
 mask = nifti_masker.inverse_transform(variance_threshold.get_support())
 
-# %%#
+# %%
 # Prediction pipeline with :term:`ANOVA` and SVR using
 # :class:`nilearn.decoding.DecoderRegressor` Object
 #

diff --git a/examples/03_connectivity/plot_compare_decomposition.py b/examples/03_connectivity/plot_compare_decomposition.py
@@ -25,7 +25,7 @@
       `preprint <https://hal.inria.fr/hal-00489507/>`_
 
 """
-# %%##
+# %%
 # Load brain development :term:`fMRI` dataset
 # -------------------------------------------
 from nilearn import datasets

diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -37,7 +37,7 @@
 in the documentation section :ref:`parcellating_brain`.
 """
 
-# %%####
+# %%
 # Download a brain development fMRI dataset and turn it to a data matrix
 # ----------------------------------------------------------------------
 #

diff --git a/examples/03_connectivity/plot_group_level_connectivity.py b/examples/03_connectivity/plot_group_level_connectivity.py
@@ -16,7 +16,7 @@
 
 """
 
-# %%##
+# %%
 # Load brain development :term:`fMRI` dataset and MSDL atlas
 # ----------------------------------------------------------
 # We study only 30 subjects from the dataset, to save computation time.

diff --git a/examples/03_connectivity/plot_signal_extraction.py b/examples/03_connectivity/plot_signal_extraction.py
@@ -46,7 +46,7 @@
 fmri_filenames = data.func[0]
 reduced_confounds = data.confounds[0]  # This is a preselected set of confounds
 
-# %%#
+# %%
 # Extract signals on a :term:`parcellation` defined by labels
 # -----------------------------------------------------------
 # Using the NiftiLabelsMasker

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -26,7 +26,7 @@
 
 """
 
-# %%#
+# %%
 # Retrieve the brain development :term:`fMRI` dataset
 # ---------------------------------------------------
 #

diff --git a/examples/04_glm_first_level/plot_bids_features.py b/examples/04_glm_first_level/plot_bids_features.py
@@ -24,7 +24,7 @@
 """
 
 
-# %%#
+# %%
 # Fetch openneuro :term:`BIDS` dataset
 # ------------------------------------
 # We download one subject from the stopsignal task

diff --git a/examples/04_glm_first_level/plot_localizer_surface_analysis.py b/examples/04_glm_first_level/plot_localizer_surface_analysis.py
@@ -55,7 +55,7 @@
 events_file = data.events
 events = pd.read_table(events_file)
 
-# %%##
+# %%
 # Project the :term:`fMRI` image to the surface
 # ---------------------------------------------
 #
@@ -66,7 +66,7 @@
 
 fsaverage = nilearn.datasets.fetch_surf_fsaverage()
 
-# %%##
+# %%
 # The projection function simply takes the :term:`fMRI` data and the mesh.
 # Note that those correspond spatially, are they are both in :term:`MNI` space.
 from nilearn import surface
@@ -202,7 +202,7 @@
 # Note that re-creating the above analysis for the left hemisphere requires
 # little additional code!
 
-# %%##
+# %%
 # We project the :term:`fMRI` data to the mesh.
 texture = surface.vol_to_surf(fmri_img, fsaverage.pial_left)
 

diff --git a/examples/05_glm_second_level/plot_oasis.py b/examples/05_glm_second_level/plot_oasis.py
@@ -109,15 +109,15 @@
     design_matrix=design_matrix,
 )
 
-# %%##
+# %%
 # Estimating the :term:`contrast` is very simple.
 # We can just provide the column name of the design matrix.
 z_map = second_level_model.compute_contrast(
     second_level_contrast=[1, 0, 0],
     output_type="z_score",
 )
 
-# %%##
+# %%
 # We threshold the second level :term:`contrast`
 # at FDR-corrected p < 0.05 and plot it.
 from nilearn.glm import threshold_stats_img

diff --git a/examples/05_glm_second_level/plot_proportion_activated_voxels.py b/examples/05_glm_second_level/plot_proportion_activated_voxels.py
@@ -50,7 +50,7 @@
     second_level_input, design_matrix=design_matrix
 )
 
-# %%#
+# %%
 # To estimate the :term:`contrast` is very simple.
 # We can just provide the column name of the design matrix.
 z_map = second_level_model.compute_contrast(output_type="z_score")

diff --git a/examples/05_glm_second_level/plot_second_level_association_test.py b/examples/05_glm_second_level/plot_second_level_association_test.py
@@ -71,7 +71,7 @@
 model = SecondLevelModel(smoothing_fwhm=5.0)
 model.fit(contrast_map_filenames, design_matrix=design_matrix)
 
-# %%#
+# %%
 # To estimate the :term:`contrast` is very simple.
 # We can just provide the column name of the design matrix.
 z_map = model.compute_contrast("fluency", output_type="z_score")

diff --git a/examples/05_glm_second_level/plot_second_level_one_sample_test.py b/examples/05_glm_second_level/plot_second_level_one_sample_test.py
@@ -94,7 +94,7 @@
     output_type="z_score",
 )
 
-# %%##
+# %%
 # We threshold the second level :term:`contrast`
 # at uncorrected p < 0.001 and plot it.
 from scipy.stats import norm

diff --git a/examples/05_glm_second_level/plot_second_level_two_sample_test.py b/examples/05_glm_second_level/plot_second_level_two_sample_test.py
@@ -108,7 +108,7 @@
     second_level_input, design_matrix=paired_design_matrix
 )
 
-# %%#
+# %%
 # Estimating the :term:`contrast` is simple. To do so, we provide the column
 # name of the design matrix. The argument 'output_type' is set to return all
 # available outputs so that we can compare differences in the effect size,

diff --git a/examples/06_manipulating_images/plot_compare_mean_image.py b/examples/06_manipulating_images/plot_compare_mean_image.py
@@ -9,7 +9,7 @@
 """
 
 
-# %%##
+# %%
 # Fetching 2 subject movie watching brain development :term:`fMRI` datasets.
 from nilearn import datasets
 

diff --git a/examples/06_manipulating_images/plot_extract_rois_smith_atlas.py b/examples/06_manipulating_images/plot_extract_rois_smith_atlas.py
@@ -9,7 +9,7 @@
 using :class:`nilearn.regions.RegionExtractor` from regions module
 """
 
-# %%#
+# %%
 # Fetching the smith :term:`ICA` 10 RSN by importing datasets utilities
 from nilearn import datasets
 

diff --git a/examples/06_manipulating_images/plot_negate_image.py b/examples/06_manipulating_images/plot_negate_image.py
@@ -10,7 +10,7 @@
 # %%
 from nilearn import datasets, image, plotting
 
-# %%##
+# %%
 # Retrieve the data: a motor :term:`contrast` map.
 
 stat_img = datasets.load_sample_motor_activation_image()

diff --git a/examples/06_manipulating_images/plot_resample_to_template.py b/examples/06_manipulating_images/plot_resample_to_template.py
@@ -19,7 +19,7 @@
 
 stat_img = load_sample_motor_activation_image()
 
-# %%##
+# %%
 # Now, the motor :term:`contrast` map image can be resampled
 # to the :term:`MNI` template image.
 from nilearn.image import resample_to_img

diff --git a/examples/06_manipulating_images/plot_roi_extraction.py b/examples/06_manipulating_images/plot_roi_extraction.py
@@ -213,7 +213,7 @@
     cut_coords=cut_coords,
 )
 
-# %%#
+# %%
 # We can post-process the results obtained with simple operations
 # such as mask intersection and :term:`dilation<Dilation>`
 # to regularize the mask definition.

diff --git a/examples/07_advanced/plot_age_group_prediction_cross_val.py b/examples/07_advanced/plot_age_group_prediction_cross_val.py
@@ -16,7 +16,7 @@
 
 """
 
-# %%##
+# %%
 # Load brain development :term:`fMRI` dataset and MSDL atlas
 # ----------------------------------------------------------
 # We study only 60 subjects from the dataset, to save computation time.

diff --git a/examples/07_advanced/plot_bids_analysis.py b/examples/07_advanced/plot_bids_analysis.py
@@ -19,7 +19,7 @@
    images were already normalized to the same :term:`MNI` space.
 """
 
-# %%#
+# %%
 # Fetch example :term:`BIDS` dataset
 # ----------------------------------
 # We download a simplified :term:`BIDS` dataset made available for illustrative

diff --git a/examples/07_advanced/plot_ica_neurovault.py b/examples/07_advanced/plot_ica_neurovault.py
@@ -104,7 +104,7 @@
 X = np.vstack(X)
 term_weights = term_weights[is_usable, :]
 
-# %%##
+# %%
 # Run :term:`ICA` and map components to terms
 # -------------------------------------------
 

diff --git a/examples/07_advanced/plot_surface_bids_analysis.py b/examples/07_advanced/plot_surface_bids_analysis.py
@@ -18,7 +18,7 @@
 images were already normalized to the same :term:`MNI` space.
 """
 
-# %%#
+# %%
 # Fetch example :term:`BIDS` dataset
 # ----------------------------------
 # We download a simplified :term:`BIDS` dataset made available for illustrative

diff --git a/examples/08_experimental/plot_surface_image_and_maskers.py b/examples/08_experimental/plot_surface_image_and_maskers.py
@@ -67,7 +67,7 @@ def plot_surf_img(
 plot_surf_img(mean_img)
 plotting.show()
 
-# %%##
+# %%
 # Connectivity with a surface atlas and `SurfaceLabelsMasker`
 # -----------------------------------------------------------
 from nilearn import connectome, plotting
@@ -91,7 +91,7 @@ def plot_surf_img(
 plotting.show()
 
 
-# %%##
+# %%
 # Using the `Decoder`
 # -------------------
 import numpy as np
@@ -137,7 +137,7 @@ def check_embedded_nifti_masker(estimator, *args, **kwargs):
 plot_surf_img(decoder.coef_img_[0], threshold=1e-6)
 plotting.show()
 
-# %%##
+# %%
 # Decoding with a scikit-learn `Pipeline`
 # ---------------------------------------
 import numpy as np