[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
@@ -36,7 +38,7 @@ per-file-ignores =
 	examples/*/*: D103, D205, D301, D400
     # - docstrings rules that should not be applied to doc
 	doc/*: D100, D103, F401
-ignore = D105, D107, E402, W503, W504, W605
+ignore = D105, D107, E402, W503, W504, W605, BLK100
 # for compatibility with black
 # https://black.readthedocs.io/en/stable/guides/using_black_with_other_tools.html#flake8
 extend-ignore = E203

.github/workflows/flake8.yml

@@ -31,26 +31,8 @@ jobs:
 
       - name: "Install Flake8"
         shell: bash {0}
-        run: |
-          python -m pip install --upgrade pip flake8 flake8-docstrings
+        run: python -m pip install --upgrade pip flake8 flake8-docstrings
 
       - 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

@@ -25,26 +25,7 @@ jobs:
 
       - name: "Install isort"
         shell: bash {0}
-        run: |
-          python -m pip install --upgrade pip isort
+        run: python -m pip install --upgrade pip isort
 
       - name: "Run isort"
-        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
+        run: isort --diff --check --settings-path pyproject.toml .

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/01_plotting/plot_3d_map_to_surface_projection.py

@@ -34,6 +34,7 @@
 # 1 if the curvature is positive, -1 if the curvature is negative.
 
 import numpy as np
+
 from nilearn import surface
 
 curv_right = surface.load_surf_data(fsaverage.curv_right)

examples/01_plotting/plot_carpet.py

@@ -37,6 +37,7 @@
 # Visualizing global patterns over time
 # -------------------------------------
 import matplotlib.pyplot as plt
+
 from nilearn.plotting import plot_carpet
 
 display = plot_carpet(adhd_dataset.func[0], mask_img, t_r=t_r)
@@ -49,6 +50,7 @@
 # Create a gray matter/white matter/cerebrospinal fluid mask from
 # ICBM152 tissue probability maps.
 import numpy as np
+
 from nilearn import image
 
 atlas = datasets.fetch_icbm152_2009()

examples/01_plotting/plot_colormaps.py

@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+
 from nilearn.plotting import show
 from nilearn.plotting.cm import _cmap_d as nilearn_cmaps
 

examples/01_plotting/plot_haxby_masks.py

@@ -7,6 +7,7 @@
 
 
 import matplotlib.pyplot as plt
+
 from nilearn import datasets
 
 haxby_dataset = datasets.fetch_haxby()

examples/01_plotting/plot_prob_atlas.py

@@ -22,8 +22,7 @@
 See :ref:`plotting` for more information to know how to tune the parameters.
 """
 # Load 4D probabilistic atlases
-from nilearn import plotting
-from nilearn import datasets
+from nilearn import datasets, plotting
 
 # Harvard Oxford Atlasf
 harvard_oxford = datasets.fetch_atlas_harvard_oxford("cort-prob-2mm")

examples/01_plotting/plot_surf_atlas.py

@@ -93,6 +93,7 @@
 # the mean location to obtain the coordinates.
 
 import numpy as np
+
 from nilearn import surface
 
 atlas = destrieux_atlas

examples/01_plotting/plot_surface_projection_strategies.py

@@ -25,6 +25,7 @@
 import matplotlib
 import numpy as np
 from matplotlib import pyplot as plt
+
 from nilearn.plotting import show
 from nilearn.surface import surface
 

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.
@@ -117,9 +118,10 @@
 # -----------------------
 # 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
 
+from nilearn.plotting import plot_matrix, show
+
 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)
@@ -36,9 +37,10 @@
 #########################################################################
 # Surface bold response
 # ---------------------
-from nilearn import datasets, surface
 from sklearn import neighbors
 
+from nilearn import datasets, surface
+
 # Fetch a coarse surface of the left hemisphere only for speed
 fsaverage = datasets.fetch_surf_fsaverage(mesh="fsaverage5")
 hemi = "left"
@@ -62,12 +64,13 @@
 #########################################################################
 # 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
 
+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