Run ruff fix.

src/fmripost_aroma/cli/parser.py

@@ -356,21 +356,21 @@ def _bids_filter(value, parser):
         ),
     )
     g_aroma.add_argument(
-        "--denoising-method",
-        action="store",
-        nargs="+",
-        choices=["aggr", "nonaggr", "orthaggr"],
+        '--denoising-method',
+        action='store',
+        nargs='+',
+        choices=['aggr', 'nonaggr', 'orthaggr'],
         default=None,
-        help="Denoising method to apply, if any.",
+        help='Denoising method to apply, if any.',
     )
     g_aroma.add_argument(
-        "--orthogonalize",
-        action="store_true",
+        '--orthogonalize',
+        action='store_true',
         default=False,
         help=(
-            "If True, the AROMA-flagged noise components will be orthogonalized with respect to "
-            "the non-noise components in the MELODIC mixing matrix. "
-            "This can serve as an alternative to non-aggressive denoising."
+            'If True, the AROMA-flagged noise components will be orthogonalized with respect to '
+            'the non-noise components in the MELODIC mixing matrix. '
+            'This can serve as an alternative to non-aggressive denoising.'
         ),
     )
 

src/fmripost_aroma/interfaces/aroma.py

@@ -18,16 +18,16 @@
 
 
 class _AROMAClassifierInputSpec(BaseInterfaceInputSpec):
-    motpars = File(exists=True, desc="motion parameters or general confounds file")
-    mixing = File(exists=True, desc="mixing matrix")
-    component_maps = File(exists=True, desc="thresholded z-statistic component maps")
-    TR = traits.Float(desc="repetition time in seconds")
+    motpars = File(exists=True, desc='motion parameters or general confounds file')
+    mixing = File(exists=True, desc='mixing matrix')
+    component_maps = File(exists=True, desc='thresholded z-statistic component maps')
+    TR = traits.Float(desc='repetition time in seconds')
 
 
 class _AROMAClassifierOutputSpec(TraitedSpec):
-    aroma_features = File(exists=True, desc="output confounds file extracted from ICA-AROMA")
-    aroma_noise_ics = File(exists=True, desc="ICA-AROMA noise components")
-    aroma_metadata = traits.Dict(desc="metadata for the ICA-AROMA confounds")
+    aroma_features = File(exists=True, desc='output confounds file extracted from ICA-AROMA')
+    aroma_noise_ics = File(exists=True, desc='ICA-AROMA noise components')
+    aroma_metadata = traits.Dict(desc='metadata for the ICA-AROMA confounds')
 
 
 class AROMAClassifier(SimpleInterface):
@@ -38,58 +38,58 @@ class AROMAClassifier(SimpleInterface):
 
     def _run_interface(self, runtime):
         TR = self.inputs.TR
-        motion_params = utils.load_motpars(self.inputs.motpars, source="fmriprep")
+        motion_params = utils.load_motpars(self.inputs.motpars, source='fmriprep')
         mixing = np.loadtxt(self.inputs.mixing)  # T x C
         component_maps = nb.load(self.inputs.component_maps)  # X x Y x Z x C
         if mixing.shape[1] != component_maps.shape[3]:
             raise ValueError(
-                f"Number of columns in mixing matrix ({mixing.shape[1]}) does not match "
-                f"fourth dimension of component maps file ({component_maps.shape[3]})."
+                f'Number of columns in mixing matrix ({mixing.shape[1]}) does not match '
+                f'fourth dimension of component maps file ({component_maps.shape[3]}).'
             )
 
         if mixing.shape[0] != motion_params.shape[0]:
             raise ValueError(
-                f"Number of rows in mixing matrix ({mixing.shape[0]}) does not match "
-                f"number of rows in motion parameters ({motion_params.shape[0]})."
+                f'Number of rows in mixing matrix ({mixing.shape[0]}) does not match '
+                f'number of rows in motion parameters ({motion_params.shape[0]}).'
             )
 
-        config.loggers.interface.info("  - extracting the CSF & Edge fraction features")
+        config.loggers.interface.info('  - extracting the CSF & Edge fraction features')
         metric_metadata = {}
         features_df = pd.DataFrame()
         spatial_df, spatial_metadata = features.feature_spatial(component_maps)
         features_df = pd.concat([features_df, spatial_df], axis=1)
         metric_metadata.update(spatial_metadata)
 
-        config.loggers.interface.info("  - extracting the Maximum RP correlation feature")
+        config.loggers.interface.info('  - extracting the Maximum RP correlation feature')
         ts_df, ts_metadata = features.feature_time_series(mixing, motion_params)
         features_df = pd.concat([features_df, ts_df], axis=1)
         metric_metadata.update(ts_metadata)
 
-        config.loggers.interface.info("  - extracting the High-frequency content feature")
+        config.loggers.interface.info('  - extracting the High-frequency content feature')
         # Should probably check that the frequencies match up with MELODIC's outputs
         mixing_fft, _ = utils.get_spectrum(mixing, TR)
         freq_df, freq_metadata = features.feature_frequency(mixing_fft, TR, f_hp=0.01)
         features_df = pd.concat([features_df, freq_df], axis=1)
         metric_metadata.update(freq_metadata)
 
-        config.loggers.interface.info("  - classification")
+        config.loggers.interface.info('  - classification')
         clf_df, clf_metadata = utils.classification(features_df)
         features_df = pd.concat([features_df, clf_df], axis=1)
         metric_metadata.update(clf_metadata)
 
         # Split out the motion components
         motion_components = features_df.loc[
-            features_df["classification"] == "rejected",
-            "classification",
+            features_df['classification'] == 'rejected',
+            'classification',
         ].index
         motion_components = motion_components.values
-        motion_components_file = os.path.abspath("aroma_motion_components.txt")
-        np.savetxt(motion_components_file, motion_components, fmt="%s")
+        motion_components_file = os.path.abspath('aroma_motion_components.txt')
+        np.savetxt(motion_components_file, motion_components, fmt='%s')
 
-        features_file = os.path.abspath("aroma_features.tsv")
-        features_df.to_csv(features_file, sep="\t", index=False)
+        features_file = os.path.abspath('aroma_features.tsv')
+        features_df.to_csv(features_file, sep='\t', index=False)
 
-        self._results["aroma_features"] = features_file
-        self._results["aroma_metadata"] = metric_metadata
-        self._results["aroma_noise_ics"] = motion_components_file
+        self._results['aroma_features'] = features_file
+        self._results['aroma_metadata'] = metric_metadata
+        self._results['aroma_noise_ics'] = motion_components_file
         return runtime

src/fmripost_aroma/interfaces/confounds.py

@@ -129,14 +129,14 @@ def _get_ica_confounds(ica_out_dir, skip_vols, newpath=None):
 
 
 class _ICADenoiseInputSpec(BaseInterfaceInputSpec):
-    method = traits.Enum("aggr", "nonaggr", "orthaggr", mandatory=True, desc="denoising method")
-    bold_file = File(exists=True, mandatory=True, desc="input file to denoise")
-    mask_file = File(exists=True, mandatory=True, desc="mask file")
-    confounds = File(exists=True, mandatory=True, desc="confounds file")
+    method = traits.Enum('aggr', 'nonaggr', 'orthaggr', mandatory=True, desc='denoising method')
+    bold_file = File(exists=True, mandatory=True, desc='input file to denoise')
+    mask_file = File(exists=True, mandatory=True, desc='mask file')
+    confounds = File(exists=True, mandatory=True, desc='confounds file')
 
 
 class _ICADenoiseOutputSpec(TraitedSpec):
-    denoised_file = File(exists=True, desc="denoised output file")
+    denoised_file = File(exists=True, desc='denoised output file')
 
 
 class ICADenoise(SimpleInterface):
@@ -160,12 +160,12 @@ def _run_interface(self, runtime):
 
         # Split up component time series into accepted and rejected components
         metrics_df = pd.read_table(metrics_file)
-        rejected_columns = metrics_df.loc[metrics_df["classification"] == "rejected", "Component"]
-        accepted_columns = metrics_df.loc[metrics_df["classification"] == "accepted", "Component"]
+        rejected_columns = metrics_df.loc[metrics_df['classification'] == 'rejected', 'Component']
+        accepted_columns = metrics_df.loc[metrics_df['classification'] == 'accepted', 'Component']
         rejected_components = confounds_df[rejected_columns].to_numpy()
         accepted_components = confounds_df[accepted_columns].to_numpy()
 
-        if method == "aggr":
+        if method == 'aggr':
             # Denoise the data with the motion components
             masker = NiftiMasker(
                 mask_img=self.inputs.mask_file,
@@ -184,7 +184,7 @@ def _run_interface(self, runtime):
 
             # Transform denoised data back into 4D space
             denoised_img = masker.inverse_transform(denoised_img_2d)
-        elif method == "orthaggr":
+        elif method == 'orthaggr':
             # Regress the good components out of the bad time series to get "pure evil" regressors
             betas = np.linalg.lstsq(accepted_components, rejected_components, rcond=None)[0]
             pred_bad_timeseries = np.dot(accepted_components, betas)
@@ -231,7 +231,7 @@ def _run_interface(self, runtime):
             # Save to file
             denoised_img = unmask(data_denoised, self.inputs.mask_file)
 
-        self._results["denoised_file"] = os.path.abspath("denoised.nii.gz")
-        denoised_img.to_filename(self._results["denoised_file"])
+        self._results['denoised_file'] = os.path.abspath('denoised.nii.gz')
+        denoised_img.to_filename(self._results['denoised_file'])
 
         return runtime

src/fmripost_aroma/interfaces/reportlets.py

@@ -188,16 +188,16 @@ class _ICAAROMAInputSpecRPT(nrb._SVGReportCapableInputSpec):
     in_file = File(
         exists=True,
         mandatory=True,
-        desc="BOLD series input to ICA-AROMA",
+        desc='BOLD series input to ICA-AROMA',
     )
     melodic_dir = Directory(
         exists=True,
         mandatory=True,
-        desc="MELODIC directory containing the ICA outputs",
+        desc='MELODIC directory containing the ICA outputs',
     )
     aroma_noise_ics = File(
         exists=True,
-        desc="Noise components estimated by ICA-AROMA, in a comma-separated values file.",
+        desc='Noise components estimated by ICA-AROMA, in a comma-separated values file.',
     )
     out_report = File(
         'ica_aroma_reportlet.svg',
@@ -237,7 +237,7 @@ def _generate_report(self):
         )
 
     def _post_run_hook(self, runtime):
-        NIWORKFLOWS_LOG.info("Generating report for ICA AROMA")
+        NIWORKFLOWS_LOG.info('Generating report for ICA AROMA')
 
         return super()._post_run_hook(runtime)