[pre-commit.ci] auto fixes from pre-commit.com hooks

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larsoner · Feb 2, 2024 · 51579f8 · 51579f8
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diff --git a/doc/conf.py b/doc/conf.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 #
 # project-template documentation build configuration file, created by
 # sphinx-quickstart on Mon Jan 18 14:44:12 2016.
@@ -12,19 +11,21 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-from datetime import datetime, timezone
-import sys
 import os
+import sys
 import warnings
+from datetime import datetime, timezone
+
 import sphinx.util.logging
 from sphinx_gallery.sorting import FileNameSortKey
 
 sys.path.append("../")
 import mne
 from mne.fixes import _compare_version
-import mne_nirs
 from mne.tests.test_docstring_parameters import error_ignores
 
+import mne_nirs
+
 sphinx_logger = sphinx.util.logging.getLogger("mne")
 
 # -- Path setup --------------------------------------------------------------
@@ -291,8 +292,8 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    ('index', 'project-template.tex', u'project-template Documentation',
-     u'Robert Luke', 'manual'),
+    ('index', 'project-template.tex', 'project-template Documentation',
+     'Robert Luke', 'manual'),
 ]
 
 
@@ -301,8 +302,8 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    ('index', 'project-template', u'project-template Documentation',
-     [u'Robert Luke'], 1)
+    ('index', 'project-template', 'project-template Documentation',
+     ['Robert Luke'], 1)
 ]
 
 
@@ -312,8 +313,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    ('index', 'project-template', u'project-template Documentation',
-     u'Robert Luke', 'project-template', 'One line description of project.',
+    ('index', 'project-template', 'project-template Documentation',
+     'Robert Luke', 'project-template', 'One line description of project.',
      'Miscellaneous'),
 ]
 

diff --git a/examples/general/plot_01_data_io.py b/examples/general/plot_01_data_io.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 r"""
 .. _tut-importing-fnirs-data:
 
@@ -166,9 +165,10 @@
 # %%
 
 import os.path as op
+
+import mne
 import numpy as np
 import pandas as pd
-import mne
 
 # sphinx_gallery_thumbnail_number = 2
 

diff --git a/examples/general/plot_05_datasets.py b/examples/general/plot_05_datasets.py
@@ -25,9 +25,9 @@
 # License: BSD (3-clause)
 
 
-import mne_nirs
 import mne_bids.stats
 
+import mne_nirs
 
 # %%
 # *******************

diff --git a/examples/general/plot_06_gowerlabs.py b/examples/general/plot_06_gowerlabs.py
@@ -1,12 +1,11 @@
-# -*- coding: utf-8 -*-
 r"""
 .. _tut-gowerlabs-data:
 
 ========================
 Read Gowerlabs LUMO data
 ========================
 
-`LUMO <https://www.gowerlabs.co.uk/lumo>`__ is a modular, wearable, 
+`LUMO <https://www.gowerlabs.co.uk/lumo>`__ is a modular, wearable,
 high-density diffuse optical tomography (HD-DOT) system produced by
 `Gowerlabs <https://www.gowerlabs.co.uk>`__. This tutorial demonstrates
 how to load data from LUMO, and how to utilise 3D digitisation
@@ -31,12 +30,11 @@
 # License: BSD (3-clause)
 
 import os.path as op
+
 import mne
 from mne.datasets.testing import data_path
-
 from mne.viz import set_3d_view
 
-
 # %%
 # Import Gowerlabs Example File
 # -----------------------------
@@ -81,7 +79,7 @@
 #
 # We observe valid data in each channel, and note that the file includes a
 # number of event annotations.
-# Annotations are a flexible tool to represent events in your experiment. 
+# Annotations are a flexible tool to represent events in your experiment.
 # They can also be used to annotate other useful information such as bad
 # segments of data, participant movements, etc. We can inspect the
 # annotations to ensure they match what we expect from our experiment.
@@ -93,8 +91,8 @@
 # The implementation of annotations varies between manufacturers. Rather
 # than recording the onset and duration of a stimulus condition, LUMO records
 # discrete event markers which have a nominal one second duration. Each
-# marker can consist of an arbitrary character or string. In this sample, 
-# there were six `A` annotations, one `Cat` annotation, and two `Dog` 
+# marker can consist of an arbitrary character or string. In this sample,
+# there were six `A` annotations, one `Cat` annotation, and two `Dog`
 # annotations. We can view the specific data for each annotation by converting
 # the annotations to a dataframe.
 

diff --git a/examples/general/plot_10_hrf_simulation.py b/examples/general/plot_10_hrf_simulation.py
@@ -21,13 +21,15 @@
 #
 # License: BSD (3-clause)
 
-import mne
-import mne_nirs
 import matplotlib.pylab as plt
+import mne
 import numpy as np
+from nilearn.plotting import plot_design_matrix
+
+import mne_nirs
 from mne_nirs.experimental_design import make_first_level_design_matrix
 from mne_nirs.statistics import run_glm
-from nilearn.plotting import plot_design_matrix
+
 np.random.seed(1)
 
 

diff --git a/examples/general/plot_11_hrf_measured.py b/examples/general/plot_11_hrf_measured.py
@@ -32,20 +32,16 @@
 # License: BSD (3-clause)
 
 import os
-import numpy as np
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
 import mne
-import mne_nirs
+import numpy as np
+from nilearn.plotting import plot_design_matrix
 
+import mne_nirs
+from mne_nirs.channels import get_long_channels, get_short_channels, picks_pair_to_idx
 from mne_nirs.experimental_design import make_first_level_design_matrix
 from mne_nirs.statistics import run_glm
-from mne_nirs.channels import (get_long_channels,
-                               get_short_channels,
-                               picks_pair_to_idx)
-
-from nilearn.plotting import plot_design_matrix
-
 
 # %%
 # Import raw NIRS data
@@ -141,7 +137,7 @@
 fig, ax = plt.subplots(figsize=(15, 6), constrained_layout=True)
 ax.plot(raw_haemo.times, s)
 ax.legend(["Control", "Left", "Right"], loc="upper right")
-ax.set_xlabel("Time (s)");
+ax.set_xlabel("Time (s)")
 
 
 # %%

diff --git a/examples/general/plot_12_group_glm.py b/examples/general/plot_12_group_glm.py
@@ -74,34 +74,31 @@
 
 
 # Import common libraries
+import matplotlib as mpl
+
+# Import Plotting Library
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import seaborn as sns
 
-# Import MNE processing
-from mne.preprocessing.nirs import optical_density, beer_lambert_law
+# Import StatsModels
+import statsmodels.formula.api as smf
 
-# Import MNE-NIRS processing
-from mne_nirs.statistics import run_glm
-from mne_nirs.experimental_design import make_first_level_design_matrix
-from mne_nirs.statistics import statsmodels_to_results
-from mne_nirs.channels import get_short_channels, get_long_channels
-from mne_nirs.channels import picks_pair_to_idx
-from mne_nirs.visualisation import plot_glm_group_topo
-from mne_nirs.datasets import fnirs_motor_group
-from mne_nirs.visualisation import plot_glm_surface_projection
-from mne_nirs.io.fold import fold_channel_specificity
+# Import MNE processing
+from mne.preprocessing.nirs import beer_lambert_law, optical_density
 
 # Import MNE-BIDS processing
-from mne_bids import BIDSPath, read_raw_bids, get_entity_vals
-
-# Import StatsModels
-import statsmodels.formula.api as smf
+from mne_bids import BIDSPath, get_entity_vals, read_raw_bids
 
-# Import Plotting Library
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-import seaborn as sns
+from mne_nirs.channels import get_long_channels, get_short_channels, picks_pair_to_idx
+from mne_nirs.datasets import fnirs_motor_group
+from mne_nirs.experimental_design import make_first_level_design_matrix
+from mne_nirs.io.fold import fold_channel_specificity
 
+# Import MNE-NIRS processing
+from mne_nirs.statistics import run_glm, statsmodels_to_results
+from mne_nirs.visualisation import plot_glm_group_topo, plot_glm_surface_projection
 
 # %%
 # Set up directories

diff --git a/examples/general/plot_13_fir_glm.py b/examples/general/plot_13_fir_glm.py
@@ -64,28 +64,26 @@
 
 
 # Import common libraries
+# Import Plotting Library
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
-# Import MNE processing
-from mne.preprocessing.nirs import optical_density, beer_lambert_law
+# Import StatsModels
+import statsmodels.formula.api as smf
 
-# Import MNE-NIRS processing
-from mne_nirs.statistics import run_glm
-from mne_nirs.experimental_design import make_first_level_design_matrix
-from mne_nirs.statistics import statsmodels_to_results
-from mne_nirs.datasets import fnirs_motor_group
-from mne_nirs.channels import get_short_channels, get_long_channels
+# Import MNE processing
+from mne.preprocessing.nirs import beer_lambert_law, optical_density
 
 # Import MNE-BIDS processing
 from mne_bids import BIDSPath, read_raw_bids
 
-# Import StatsModels
-import statsmodels.formula.api as smf
-
-# Import Plotting Library
-import matplotlib.pyplot as plt
+from mne_nirs.channels import get_long_channels, get_short_channels
+from mne_nirs.datasets import fnirs_motor_group
+from mne_nirs.experimental_design import make_first_level_design_matrix
 
+# Import MNE-NIRS processing
+from mne_nirs.statistics import run_glm, statsmodels_to_results
 
 # %%
 # Define FIR analysis

diff --git a/examples/general/plot_14_glm_components.py b/examples/general/plot_14_glm_components.py
@@ -56,21 +56,24 @@
 
 # Import common libraries
 import os
-import numpy as np
-import mne
 
-# Import MNE-NIRS processing
-from mne_nirs.experimental_design import make_first_level_design_matrix, \
-    longest_inter_annotation_interval, drift_high_pass
+import matplotlib as mpl
+
+# Import Plotting Library
+import matplotlib.pyplot as plt
+import mne
+import numpy as np
 
 # Import Nilearn
 from nilearn.glm import first_level
 from nilearn.plotting import plot_design_matrix
 
-# Import Plotting Library
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-
+# Import MNE-NIRS processing
+from mne_nirs.experimental_design import (
+    drift_high_pass,
+    longest_inter_annotation_interval,
+    make_first_level_design_matrix,
+)
 
 # %%
 # Haemodynamic Response Function

diff --git a/examples/general/plot_16_waveform_group.py b/examples/general/plot_16_waveform_group.py
@@ -76,33 +76,36 @@
 # License: BSD (3-clause)
 
 # Import common libraries
-import pandas as pd
-from itertools import compress
 from collections import defaultdict
 from copy import deepcopy
+from itertools import compress
 from pprint import pprint
 
-# Import MNE processing
-from mne.viz import plot_compare_evokeds
+# Import Plotting Library
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+# Import StatsModels
+import statsmodels.formula.api as smf
 from mne import Epochs, events_from_annotations, set_log_level
+from mne.preprocessing.nirs import (
+    beer_lambert_law,
+    optical_density,
+    scalp_coupling_index,
+    temporal_derivative_distribution_repair,
+)
 
-# Import MNE-NIRS processing
-from mne_nirs.channels import get_long_channels
-from mne_nirs.channels import picks_pair_to_idx
-from mne_nirs.datasets import fnirs_motor_group
-from mne.preprocessing.nirs import beer_lambert_law, optical_density,\
-    temporal_derivative_distribution_repair, scalp_coupling_index
-from mne_nirs.signal_enhancement import enhance_negative_correlation
+# Import MNE processing
+from mne.viz import plot_compare_evokeds
 
 # Import MNE-BIDS processing
 from mne_bids import BIDSPath, read_raw_bids
 
-# Import StatsModels
-import statsmodels.formula.api as smf
-
-# Import Plotting Library
-import matplotlib.pyplot as plt
-import seaborn as sns
+# Import MNE-NIRS processing
+from mne_nirs.channels import get_long_channels, picks_pair_to_idx
+from mne_nirs.datasets import fnirs_motor_group
+from mne_nirs.signal_enhancement import enhance_negative_correlation
 
 # Set general parameters
 set_log_level("WARNING")  # Don't show info, as it is repetitive for many subjects
@@ -215,7 +218,7 @@ def individual_analysis(bids_path):
                              picks=pick, axes=axes[idx], show=False,
                              colors=[color], legend=False, ylim=lims, ci=0.95,
                              show_sensors=idx == 2)
-        axes[idx].set_title('{}'.format(evoked))
+        axes[idx].set_title(f'{evoked}')
 axes[0].legend(["Oxyhaemoglobin", "Deoxyhaemoglobin"])
 
 # %%