[MAINT] run isort and flake8 on the whole nilearn code base in CI

.flake8

@@ -2,12 +2,14 @@
 exclude =
     .git,
     __pycache__,
-	env,
-	venv,
+	auto_examples,
     build,
     dist,
+	env,
 	nilearn/externals/tempita
+	nilearn/externals/
 	nilearn_cache
+	venv,
 --select = D,E,F,W,C90
 docstring-convention = numpy
 max-line-length = 79

.github/workflows/flake8.yml

@@ -36,21 +36,4 @@ jobs:
 
       - name: "Run Flake8 on whole file"
         shell: bash {0}
-        run: |
-          flake8 --verbose \
-            examples/0[0234567]* \
-            maint_tools \
-            nilearn/_utils \
-            nilearn/connectome \
-            nilearn/datasets \
-            nilearn/de* \
-            nilearn/glm \
-            nilearn/image \
-            nilearn/interfaces \
-            nilearn/input_data \
-            nilearn/maskers \
-            nilearn/mass_univariate \
-            nilearn/regions \
-            nilearn/reporting \
-            nilearn/surface \
-            nilearn/*.py
+        run: flake8 --verbose .

.github/workflows/isort.yml

@@ -29,22 +29,8 @@ jobs:
           python -m pip install --upgrade pip isort
 
       - name: "Run isort"
-        run: |
-          isort --diff --check --settings-path pyproject.toml \
+        run: isort --diff --check --settings-path pyproject.toml \
             examples/0[0234567]* \
             maint_tools \
-            nilearn/_utils \
-            nilearn/connectome \
-            nilearn/datasets \
-            nilearn/de* \
-            nilearn/glm \
-            nilearn/image \
-            nilearn/interfaces \
-            nilearn/input_data \
-            nilearn/maskers \
-            nilearn/mass_univariate \
-            nilearn/plotting \
-            nilearn/regions \
-            nilearn/rerporting \
-            nilearn/surface \
-            nilearn/*.py
+            nilearn \
+            doc

doc/conf.py

@@ -15,6 +15,7 @@
 import sys
 
 import sphinx
+
 from nilearn._utils import _compare_version
 
 # ----------------------------------------------------------------------------

examples/00_tutorials/plot_single_subject_single_run.py

@@ -123,6 +123,7 @@
 # Formally, we have taken the first design matrix, because the model is
 # implictily meant to for multiple runs.
 import matplotlib.pyplot as plt
+
 from nilearn.plotting import plot_design_matrix
 
 plot_design_matrix(design_matrix)

examples/02_decoding/plot_haxby_different_estimators.py

@@ -77,9 +77,10 @@
 # classifiers
 import time
 
-from nilearn.decoding import Decoder
 from sklearn.model_selection import LeaveOneGroupOut
 
+from nilearn.decoding import Decoder
+
 cv = LeaveOneGroupOut()
 classifiers_data = {}
 

examples/02_decoding/plot_haxby_frem.py

@@ -67,9 +67,10 @@
 # ------------------------------------
 
 import numpy as np
-from nilearn import plotting
 from sklearn.metrics import confusion_matrix
 
+from nilearn import plotting
+
 # Calculate the confusion matrix
 matrix = confusion_matrix(
     y_test,

examples/02_decoding/plot_haxby_full_analysis.py

@@ -65,11 +65,12 @@
 # The classifier used here is a support vector classifier (svc). We use
 # class:`nilearn.decoding.Decoder` and specify the classifier.
 import numpy as np
-from nilearn.decoding import Decoder
 
 # Make a data splitting object for cross validation
 from sklearn.model_selection import LeaveOneGroupOut
 
+from nilearn.decoding import Decoder
+
 cv = LeaveOneGroupOut()
 
 ##############################################################
@@ -126,6 +127,7 @@
 # We make a simple bar plot to summarize the results
 # --------------------------------------------------
 import matplotlib.pyplot as plt
+
 from nilearn.plotting import show
 
 plt.figure()

examples/02_decoding/plot_haxby_glm_decoding.py

@@ -22,6 +22,7 @@
 # By default 2nd subject will be fetched
 import numpy as np
 import pandas as pd
+
 from nilearn import datasets
 
 haxby_dataset = datasets.fetch_haxby()
@@ -161,9 +162,10 @@
 # corresponding conditions labels and session labels
 # (for the cross validation).
 
-from nilearn.decoding import Decoder
 from sklearn.model_selection import LeaveOneGroupOut
 
+from nilearn.decoding import Decoder
+
 decoder = Decoder(
     estimator="svc",
     mask=haxby_dataset.mask,

examples/02_decoding/plot_haxby_grid_search.py

@@ -196,6 +196,7 @@
 # Plot the prediction scores using matplotlib
 # -------------------------------------------
 from matplotlib import pyplot as plt
+
 from nilearn.plotting import show
 
 plt.figure(figsize=(6, 4))

examples/02_decoding/plot_haxby_multiclass.py

@@ -15,6 +15,7 @@
 
 import numpy as np
 import pandas as pd
+
 from nilearn import datasets
 
 # By default 2nd subject from haxby datasets will be fetched.
@@ -112,13 +113,14 @@
 plt.xticks([1, 2], ["One vs All", "One vs One"])
 plt.title("Prediction: accuracy score")
 
+from sklearn.metrics import confusion_matrix
+
 ##############################################################################
 # Plot a confusion matrix
 # -----------------------
 # We fit on the first 10 sessions and plot a confusion matrix on the
 # last 2 sessions
 from nilearn.plotting import plot_matrix, show
-from sklearn.metrics import confusion_matrix
 
 svc_ovo.fit(X[session < 10], y[session < 10])
 y_pred_ovo = svc_ovo.predict(X[session >= 10])

examples/02_decoding/plot_haxby_searchlight.py

@@ -15,6 +15,7 @@
 # Load Haxby dataset
 # ------------------
 import pandas as pd
+
 from nilearn import datasets
 from nilearn.image import get_data, load_img, new_img_like
 

examples/02_decoding/plot_haxby_searchlight_surface.py

@@ -13,6 +13,7 @@
 # Load Haxby dataset
 # ------------------
 import pandas as pd
+
 from nilearn import datasets
 
 # We fetch 2nd subject from haxby datasets (which is default)
@@ -33,11 +34,12 @@
 fmri_img = index_img(fmri_filename, condition_mask)
 y, session = y[condition_mask], session[condition_mask]
 
+from sklearn import neighbors
+
 #########################################################################
 # Surface bold response
 # ---------------------
 from nilearn import datasets, surface
-from sklearn import neighbors
 
 # Fetch a coarse surface of the left hemisphere only for speed
 fsaverage = datasets.fetch_surf_fsaverage(mesh="fsaverage5")
@@ -59,15 +61,16 @@
 nn = neighbors.NearestNeighbors(radius=radius)
 adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil()
 
-#########################################################################
-# Searchlight computation
-# -----------------------
-from nilearn.decoding.searchlight import search_light
 from sklearn.linear_model import RidgeClassifier
 from sklearn.model_selection import KFold
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
 
+#########################################################################
+# Searchlight computation
+# -----------------------
+from nilearn.decoding.searchlight import search_light
+
 # Simple linear estimator preceded by a normalization step
 estimator = make_pipeline(StandardScaler(), RidgeClassifier(alpha=10.0))
 

examples/02_decoding/plot_haxby_stimuli.py

@@ -8,6 +8,7 @@
 """
 
 import matplotlib.pyplot as plt
+
 from nilearn import datasets
 from nilearn.plotting import show
 

examples/02_decoding/plot_miyawaki_encoding.py

@@ -56,6 +56,7 @@
 # data, clean and mask it.
 
 import numpy as np
+
 from nilearn.maskers import MultiNiftiMasker
 
 masker = MultiNiftiMasker(

examples/02_decoding/plot_miyawaki_reconstruction.py

@@ -53,6 +53,7 @@
 # Then we prepare and mask the data
 # ---------------------------------
 import numpy as np
+
 from nilearn.maskers import MultiNiftiMasker
 
 sys.stderr.write("Preprocessing data...")
@@ -295,6 +296,7 @@ def split_multi_scale(y, y_shape):
 # ground truth
 
 from matplotlib import pyplot as plt
+
 from nilearn.plotting import show
 
 for i in range(6):

examples/02_decoding/plot_oasis_vbm.py

@@ -50,6 +50,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+
 from nilearn import datasets
 from nilearn.image import get_data
 from nilearn.maskers import NiftiMasker

examples/02_decoding/plot_oasis_vbm_space_net.py

@@ -14,6 +14,7 @@
 # Load the Oasis VBM dataset
 # --------------------------
 import numpy as np
+
 from nilearn import datasets
 
 n_subjects = 200  # increase this number if you have more RAM on your box

examples/02_decoding/plot_simulated_data.py

@@ -36,10 +36,7 @@
 
 import matplotlib.pyplot as plt
 import nibabel
-import nilearn.masking
 import numpy as np
-from nilearn import decoding
-from nilearn.plotting import show
 from scipy import linalg
 from scipy.ndimage import gaussian_filter
 from sklearn import linear_model, svm
@@ -49,6 +46,10 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils import check_random_state
 
+import nilearn.masking
+from nilearn import decoding
+from nilearn.plotting import show
+
 
 ##############################################################################
 # A function to generate data

examples/03_connectivity/plot_data_driven_parcellations.py

@@ -48,6 +48,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib import patches, ticker
+
 from nilearn import datasets, plotting
 from nilearn.image import get_data, index_img, mean_img
 from nilearn.regions import Parcellations

examples/03_connectivity/plot_multi_subject_connectome.py

@@ -9,6 +9,7 @@
 
 """
 import numpy as np
+
 from nilearn import plotting
 
 n_subjects = 4  # subjects to consider for group-sparse covariance (max: 40)

examples/03_connectivity/plot_probabilistic_atlas_extraction.py

@@ -78,6 +78,7 @@
 
 # Display the correlation matrix
 import numpy as np
+
 from nilearn import plotting
 
 # Mask out the major diagonal

examples/03_connectivity/plot_signal_extraction.py

@@ -75,6 +75,7 @@
 
 # Plot the correlation matrix
 import numpy as np
+
 from nilearn import plotting
 
 # Make a large figure

examples/04_glm_first_level/plot_adhd_dmn.py

@@ -20,6 +20,7 @@
 
 """
 import numpy as np
+
 from nilearn import datasets, plotting
 from nilearn.glm.first_level import (
     FirstLevelModel,

examples/04_glm_first_level/plot_bids_features.py

@@ -154,9 +154,10 @@
 )
 
 import matplotlib.pyplot as plt
-from nilearn import plotting
 from scipy.stats import norm
 
+from nilearn import plotting
+
 plotting.plot_glass_brain(
     z_map,
     colorbar=True,

examples/04_glm_first_level/plot_fir_model.py

@@ -21,6 +21,7 @@
 #########################################################################
 # At first, we grab the localizer data.
 import pandas as pd
+
 from nilearn.datasets import func
 
 data = func.fetch_localizer_first_level()
@@ -131,6 +132,7 @@
 #
 # We can now  proceed by estimating the contrasts and displaying them.
 import matplotlib.pyplot as plt
+
 from nilearn.plotting import plot_stat_map
 
 fig = plt.figure(figsize=(11, 3))

examples/04_glm_first_level/plot_hrf.py

@@ -128,6 +128,7 @@ def mion_time_derivative(tr, oversampling=16.0):
 # ----------------------------------
 
 import matplotlib.pyplot as plt
+
 from nilearn.glm.first_level import compute_regressor
 
 oversampling = 16

examples/04_glm_first_level/plot_predictions_residuals.py

@@ -17,6 +17,7 @@
 # Import modules
 # --------------
 import pandas as pd
+
 from nilearn import image, masking
 from nilearn.datasets import fetch_spm_auditory
 

examples/04_glm_first_level/plot_spm_multimodal_faces.py

@@ -71,6 +71,7 @@
 # Make the design matrices.
 import numpy as np
 import pandas as pd
+
 from nilearn.glm.first_level import make_first_level_design_matrix
 
 design_matrices = []