Sort imports (#12097)

Author: cbrnr

examples/datasets/hf_sef_data.py

@@ -14,8 +14,9 @@
 # %%
 
 
-import mne
 import os
+
+import mne
 from mne.datasets import hf_sef
 
 fname_evoked = os.path.join(hf_sef.data_path(), "MEG/subject_b/hf_sef_15min-ave.fif")

examples/datasets/limo_data.py

@@ -38,14 +38,13 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
+from mne import combine_evoked
 from mne.datasets.limo import load_data
 from mne.stats import linear_regression
-from mne.viz import plot_events, plot_compare_evokeds
-from mne import combine_evoked
-
+from mne.viz import plot_compare_evokeds, plot_events
 
 print(__doc__)
 

examples/datasets/opm_data.py

@@ -13,6 +13,7 @@
 # sphinx_gallery_thumbnail_number = 4
 
 import numpy as np
+
 import mne
 
 data_path = mne.datasets.opm.data_path()

examples/datasets/spm_faces_dataset_sgskip.py

@@ -27,10 +27,10 @@
 import matplotlib.pyplot as plt
 
 import mne
+from mne import combine_evoked, io
 from mne.datasets import spm_face
+from mne.minimum_norm import apply_inverse, make_inverse_operator
 from mne.preprocessing import ICA, create_eog_epochs
-from mne import io, combine_evoked
-from mne.minimum_norm import make_inverse_operator, apply_inverse
 
 print(__doc__)
 

examples/decoding/decoding_csp_eeg.py

@@ -20,18 +20,17 @@
 # %%
 
 
-import numpy as np
 import matplotlib.pyplot as plt
-
-from sklearn.pipeline import Pipeline
+import numpy as np
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.model_selection import ShuffleSplit, cross_val_score
+from sklearn.pipeline import Pipeline
 
-from mne import Epochs, pick_types, events_from_annotations
+from mne import Epochs, events_from_annotations, pick_types
 from mne.channels import make_standard_montage
-from mne.io import concatenate_raws, read_raw_edf
 from mne.datasets import eegbci
 from mne.decoding import CSP
+from mne.io import concatenate_raws, read_raw_edf
 
 print(__doc__)
 

examples/decoding/decoding_csp_timefreq.py

@@ -21,20 +21,19 @@
 # %%
 
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.model_selection import StratifiedKFold, cross_val_score
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder
 
 from mne import Epochs, create_info, events_from_annotations
-from mne.io import concatenate_raws, read_raw_edf
 from mne.datasets import eegbci
 from mne.decoding import CSP
+from mne.io import concatenate_raws, read_raw_edf
 from mne.time_frequency import AverageTFR
 
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.model_selection import StratifiedKFold, cross_val_score
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import LabelEncoder
-
 # %%
 # Set parameters and read data
 event_id = dict(hands=2, feet=3)  # motor imagery: hands vs feet

examples/decoding/decoding_rsa_sgskip.py

@@ -29,21 +29,19 @@
 
 # %%
 
+import matplotlib.pyplot as plt
 import numpy as np
 from pandas import read_csv
-import matplotlib.pyplot as plt
-
+from sklearn.linear_model import LogisticRegression
+from sklearn.manifold import MDS
+from sklearn.metrics import roc_auc_score
 from sklearn.model_selection import StratifiedKFold
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import roc_auc_score
-from sklearn.manifold import MDS
 
 import mne
-from mne.io import read_raw_fif, concatenate_raws
 from mne.datasets import visual_92_categories
-
+from mne.io import concatenate_raws, read_raw_fif
 
 print(__doc__)
 

examples/decoding/decoding_spatio_temporal_source.py

@@ -21,17 +21,16 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
-
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import StandardScaler
+import numpy as np
 from sklearn.feature_selection import SelectKBest, f_classif
 from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
 
 import mne
+from mne.decoding import LinearModel, SlidingEstimator, cross_val_multiscore, get_coef
 from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
-from mne.decoding import cross_val_multiscore, LinearModel, SlidingEstimator, get_coef
 
 print(__doc__)
 

examples/decoding/decoding_spoc_CMC.py

@@ -24,15 +24,14 @@
 
 # %%
 import matplotlib.pyplot as plt
+from sklearn.linear_model import Ridge
+from sklearn.model_selection import KFold, cross_val_predict
+from sklearn.pipeline import make_pipeline
 
 import mne
 from mne import Epochs
-from mne.decoding import SPoC
 from mne.datasets.fieldtrip_cmc import data_path
-
-from sklearn.pipeline import make_pipeline
-from sklearn.linear_model import Ridge
-from sklearn.model_selection import KFold, cross_val_predict
+from mne.decoding import SPoC
 
 # Define parameters
 fname = data_path() / "SubjectCMC.ds"

examples/decoding/decoding_time_generalization_conditions.py

@@ -20,10 +20,9 @@
 # %%
 
 import matplotlib.pyplot as plt
-
+from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.linear_model import LogisticRegression
 
 import mne
 from mne.datasets import sample