Sort imports (#12097)

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

examples/decoding/decoding_unsupervised_spatial_filter.py

@@ -17,15 +17,14 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.decomposition import PCA, FastICA
 
 import mne
 from mne.datasets import sample
 from mne.decoding import UnsupervisedSpatialFilter
 
-from sklearn.decomposition import PCA, FastICA
-
 print(__doc__)
 
 # Preprocess data

examples/decoding/decoding_xdawn_eeg.py

@@ -16,20 +16,18 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
-
-from sklearn.model_selection import StratifiedKFold
-from sklearn.pipeline import make_pipeline
+import numpy as np
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.model_selection import StratifiedKFold
+from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import MinMaxScaler
 
-from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info
+from mne import Epochs, EvokedArray, create_info, io, pick_types, read_events
 from mne.datasets import sample
-from mne.preprocessing import Xdawn
 from mne.decoding import Vectorizer
-
+from mne.preprocessing import Xdawn
 
 print(__doc__)
 

examples/decoding/ems_filtering.py

@@ -25,14 +25,14 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import StratifiedKFold
 
 import mne
-from mne import io, EvokedArray
+from mne import EvokedArray, io
 from mne.datasets import sample
 from mne.decoding import EMS, compute_ems
-from sklearn.model_selection import StratifiedKFold
 
 print(__doc__)
 

examples/decoding/linear_model_patterns.py

@@ -22,17 +22,16 @@
 
 # %%
 
-import mne
-from mne import io, EvokedArray
-from mne.datasets import sample
-from mne.decoding import Vectorizer, get_coef
-
-from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+import mne
+from mne import EvokedArray, io
+from mne.datasets import sample
 
 # import a linear classifier from mne.decoding
-from mne.decoding import LinearModel
+from mne.decoding import LinearModel, Vectorizer, get_coef
 
 print(__doc__)
 

examples/decoding/receptive_field_mtrf.py

@@ -28,16 +28,16 @@
 # %%
 # sphinx_gallery_thumbnail_number = 3
 
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.io import loadmat
 from os.path import join
 
-import mne
-from mne.decoding import ReceptiveField
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.io import loadmat
 from sklearn.model_selection import KFold
 from sklearn.preprocessing import scale
 
+import mne
+from mne.decoding import ReceptiveField
 
 # %%
 # Load the data from the publication

examples/decoding/ssd_spatial_filters.py

@@ -21,6 +21,7 @@
 
 
 import matplotlib.pyplot as plt
+
 import mne
 from mne import Epochs
 from mne.datasets.fieldtrip_cmc import data_path

examples/forward/forward_sensitivity_maps.py

@@ -18,12 +18,13 @@
 
 # %%
 
+import matplotlib.pyplot as plt
 import numpy as np
+
 import mne
 from mne.datasets import sample
-from mne.source_space import compute_distance_to_sensors
 from mne.source_estimate import SourceEstimate
-import matplotlib.pyplot as plt
+from mne.source_space import compute_distance_to_sensors
 
 print(__doc__)
 

examples/inverse/compute_mne_inverse_epochs_in_label.py

@@ -14,13 +14,12 @@
 
 # %%
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 import mne
 from mne.datasets import sample
-from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
-from mne.minimum_norm import apply_inverse
+from mne.minimum_norm import apply_inverse, apply_inverse_epochs, read_inverse_operator
 
 print(__doc__)
 

examples/inverse/compute_mne_inverse_volume.py

@@ -14,11 +14,11 @@
 
 # %%
 
-from nilearn.plotting import plot_stat_map
 from nilearn.image import index_img
+from nilearn.plotting import plot_stat_map
 
-from mne.datasets import sample
 from mne import read_evokeds
+from mne.datasets import sample
 from mne.minimum_norm import apply_inverse, read_inverse_operator
 
 print(__doc__)

examples/inverse/custom_inverse_solver.py

@@ -23,11 +23,11 @@
 
 import numpy as np
 from scipy import linalg
+
 import mne
 from mne.datasets import sample
 from mne.viz import plot_sparse_source_estimates
 
-
 data_path = sample.data_path()
 meg_path = data_path / "MEG" / "sample"
 fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
@@ -95,10 +95,10 @@ def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):
     """
     # Import the necessary private functions
     from mne.inverse_sparse.mxne_inverse import (
+        _make_sparse_stc,
         _prepare_gain,
-        is_fixed_orient,
         _reapply_source_weighting,
-        _make_sparse_stc,
+        is_fixed_orient,
     )
 
     all_ch_names = evoked.ch_names

examples/inverse/dics_epochs.py

@@ -17,10 +17,11 @@
 # License: BSD-3-Clause
 
 import numpy as np
+
 import mne
+from mne.beamformer import apply_dics_tfr_epochs, make_dics
 from mne.datasets import somato
-from mne.time_frequency import tfr_morlet, csd_tfr
-from mne.beamformer import make_dics, apply_dics_tfr_epochs
+from mne.time_frequency import csd_tfr, tfr_morlet
 
 print(__doc__)
 

examples/inverse/dics_source_power.py

@@ -21,10 +21,11 @@
 # %%
 
 import numpy as np
+
 import mne
+from mne.beamformer import apply_dics_csd, make_dics
 from mne.datasets import somato
 from mne.time_frequency import csd_morlet
-from mne.beamformer import make_dics, apply_dics_csd
 
 print(__doc__)