Run ruff

docs/conf.py

@@ -369,7 +369,7 @@
 linkcode_resolve = make_linkcode_resolve(
     'xcp_d',
     (
-        'https://github.com/pennlinc/xcp_d/blob/' '{revision}/{package}/{path}#L{lineno}'  # noqa: FS003
+        'https://github.com/pennlinc/xcp_d/blob/{revision}/{package}/{path}#L{lineno}'  # noqa: FS003
     ),
 )
 

xcp_d/__about__.py

@@ -10,8 +10,7 @@
 __packagename__ = 'xcp_d'
 __copyright__ = 'Copyright 2020, PennLINC and DCAN labs'
 __credits__ = (
-    'Contributors: please check the ``.zenodo.json`` file at the top-level folder'
-    'of the repository'
+    'Contributors: please check the ``.zenodo.json`` file at the top-level folderof the repository'
 )
 __url__ = 'https://github.com/PennLINC/xcp_d'
 

xcp_d/_warnings.py

@@ -13,7 +13,7 @@ def _warn(message, category=None, stacklevel=1, source=None):
         category = type(category).__name__
         category = category.replace('type', 'WARNING')
 
-    logging.getLogger('py.warnings').warning(f"{category or 'WARNING'}: {message}")
+    logging.getLogger('py.warnings').warning(f'{category or "WARNING"}: {message}')
 
 
 def _showwarning(message, category, filename, lineno, file=None, line=None):

xcp_d/cli/parser.py

@@ -119,11 +119,11 @@ def _build_parser():
         default=None,
         metavar='FILE',
         help=(
-            "A JSON file describing custom BIDS input filters using PyBIDS. "
-            "For further details, please check out "
-            "https://xcp-d.readthedocs.io/en/"
-            f"{currentv.base_version if is_release else 'latest'}/usage.html#"
-            "filtering-inputs-with-bids-filter-files"
+            'A JSON file describing custom BIDS input filters using PyBIDS. '
+            'For further details, please check out '
+            'https://xcp-d.readthedocs.io/en/'
+            f'{currentv.base_version if is_release else "latest"}/usage.html#'
+            'filtering-inputs-with-bids-filter-files'
         ),
     )
     g_bids.add_argument(
@@ -868,7 +868,7 @@ def parse_args(args=None, namespace=None):
 
     # Ensure input and output folders are not the same
     if output_dir == fmri_dir:
-        rec_path = fmri_dir / 'derivatives' / f"xcp_d-{version.split('+')[0]}"
+        rec_path = fmri_dir / 'derivatives' / f'xcp_d-{version.split("+")[0]}'
         parser.error(
             'The selected output folder is the same as the input BIDS folder. '
             f'Please modify the output path (suggestion: {rec_path}).'
@@ -897,8 +897,8 @@ def parse_args(args=None, namespace=None):
     missing_subjects = participant_label - set(all_subjects)
     if missing_subjects:
         parser.error(
-            "One or more participant labels were not found in the BIDS directory: "
-            f"{', '.join(missing_subjects)}."
+            'One or more participant labels were not found in the BIDS directory: '
+            f'{", ".join(missing_subjects)}.'
         )
 
     config.execution.participant_label = sorted(participant_label)
@@ -1105,8 +1105,7 @@ def _validate_parameters(opts, build_log, parser):
         and (opts.high_pass > 0 and opts.low_pass > 0)
     ):
         parser.error(
-            f"'--lower-bpf' ({opts.high_pass}) must be lower than "
-            f"'--upper-bpf' ({opts.low_pass})."
+            f"'--lower-bpf' ({opts.high_pass}) must be lower than '--upper-bpf' ({opts.low_pass})."
         )
     elif not opts.bandpass_filter:
         build_log.warning('Bandpass filtering is disabled. ALFF outputs will not be generated.')

xcp_d/cli/workflow.py

@@ -30,11 +30,11 @@ def build_workflow(config_file, retval):
     notice_path = data.load.readable('NOTICE')
     if notice_path.exists():
         banner[0] += '\n'
-        banner += [f"License NOTICE {'#' * 50}"]
+        banner += [f'License NOTICE {"#" * 50}']
         banner += [f'XCP-D {version}']
         banner += notice_path.read_text().splitlines(keepends=False)[1:]
         banner += ['#' * len(banner[1])]
-    build_log.log(25, f"\n{' ' * 9}".join(banner))
+    build_log.log(25, f'\n{" " * 9}'.join(banner))
 
     # warn if older results exist: check for dataset_description.json in output folder
     msg = check_pipeline_version(
@@ -95,7 +95,7 @@ def build_workflow(config_file, retval):
     if config.execution.datasets:
         init_msg += [f'Searching for derivatives and atlases: {config.execution.datasets}.']
 
-    build_log.log(25, f"\n{' ' * 11}* ".join(init_msg))
+    build_log.log(25, f'\n{" " * 11}* '.join(init_msg))
 
     retval['workflow'] = init_xcpd_wf()
 

xcp_d/config.py

@@ -201,7 +201,7 @@
             if _proc_oc_kbytes.exists():
                 _oc_limit = _proc_oc_kbytes.read_text().strip()
             if _oc_limit in ('0', 'n/a') and Path('/proc/sys/vm/overcommit_ratio').exists():
-                _oc_limit = f"{Path('/proc/sys/vm/overcommit_ratio').read_text().strip()}%"
+                _oc_limit = f'{Path("/proc/sys/vm/overcommit_ratio").read_text().strip()}%'
 except Exception:  # noqa: S110, BLE001
     pass
 
@@ -416,7 +416,7 @@ class execution(_Config):
     """Folder where derivatives will be stored."""
     atlases = []
     """Selection of atlases to apply to the data."""
-    run_uuid = f"{strftime('%Y%m%d-%H%M%S')}_{uuid4()}"
+    run_uuid = f'{strftime("%Y%m%d-%H%M%S")}_{uuid4()}'
     """Unique identifier of this particular run."""
     participant_label = None
     """List of participant identifiers that are to be preprocessed."""

xcp_d/interfaces/censoring.py

@@ -39,8 +39,7 @@ class _RemoveDummyVolumesInputSpec(BaseInterfaceInputSpec):
         None,
         mandatory=False,
         desc=(
-            'TSV file with selected confounds for denoising. '
-            'May be None if denoising is disabled.'
+            'TSV file with selected confounds for denoising. May be None if denoising is disabled.'
         ),
     )
     confounds_images = traits.Either(
@@ -71,8 +70,7 @@ class _RemoveDummyVolumesOutputSpec(TraitedSpec):
         File(exists=True),
         None,
         desc=(
-            'TSV file with selected confounds for denoising. '
-            'May be None if denoising is disabled.'
+            'TSV file with selected confounds for denoising. May be None if denoising is disabled.'
         ),
     )
     confounds_images_dropped_TR = traits.Either(
@@ -386,8 +384,7 @@ def _run_interface(self, runtime):
             censoring_df.loc[random_censor, column_name] = 0
             temporal_mask_metadata[column_name] = {
                 'Description': (
-                    f'Randomly selected low-motion volumes to retain exactly {exact_scan} '
-                    'volumes.'
+                    f'Randomly selected low-motion volumes to retain exactly {exact_scan} volumes.'
                 ),
                 'Levels': {
                     '0': 'Retained or high-motion volume',
@@ -905,9 +902,9 @@ def _run_interface(self, runtime):
         signal_regressors = [c for c in new_confound_df.columns if c.startswith('signal__')]
         if signal_regressors:
             LOGGER.warning(
-                "Signal regressors detected. "
-                "Orthogonalizing nuisance regressors w.r.t. the following signal regressors: "
-                f"{', '.join(signal_regressors)}"
+                'Signal regressors detected. '
+                'Orthogonalizing nuisance regressors w.r.t. the following signal regressors: '
+                f'{", ".join(signal_regressors)}'
             )
             noise_regressors = [c for c in new_confound_df.columns if not c.startswith('signal__')]
 
@@ -936,15 +933,15 @@ def _run_interface(self, runtime):
             for col in noise_regressors:
                 desc_str = (
                     "This regressor is orthogonalized with respect to the 'signal' regressors "
-                    f"({', '.join(signal_regressors)}) after dummy scan removal, "
-                    "but prior to any censoring."
+                    f'({", ".join(signal_regressors)}) after dummy scan removal, '
+                    'but prior to any censoring.'
                 )
 
                 col_metadata = {}
                 if col in confounds_metadata.keys():
                     col_metadata = confounds_metadata.pop(col)
                     if 'Description' in col_metadata.keys():
-                        desc_str = f"{col_metadata['Description']} {desc_str}"
+                        desc_str = f'{col_metadata["Description"]} {desc_str}'
 
                 col_metadata['Description'] = desc_str
                 confounds_metadata[f'{col}_orth'] = col_metadata

xcp_d/interfaces/execsummary.py

@@ -328,7 +328,7 @@ def generate_report(self, out_file=None):
                     'LaTeX',
                     f"""<pre>{text}</pre>
 <h3>Bibliography</h3>
-<pre>{load_data("boilerplate.bib").read_text()}</pre>
+<pre>{load_data('boilerplate.bib').read_text()}</pre>
 """,
                 )
             )

xcp_d/interfaces/report.py

@@ -150,12 +150,12 @@ def _generate_segment(self):
         mean_relative_rms = str(round(qcfile['mean_relative_rms'][0], 4))
         max_relative_rms = str(round(qcfile['max_relative_rms'][0], 4))
         dvars = (
-            f"{round(qcfile['mean_dvars_initial'][0], 4)}, "
-            f"{round(qcfile['mean_dvars_final'][0], 4)}"
+            f'{round(qcfile["mean_dvars_initial"][0], 4)}, '
+            f'{round(qcfile["mean_dvars_final"][0], 4)}'
         )
         fd_dvars_correlation = (
-            f"{round(qcfile['fd_dvars_correlation_initial'][0], 4)}, "
-            f"{round(qcfile['fd_dvars_correlation_final'][0], 4)}"
+            f'{round(qcfile["fd_dvars_correlation_initial"][0], 4)}, '
+            f'{round(qcfile["fd_dvars_correlation_final"][0], 4)}'
         )
         num_vols_censored = str(round(qcfile['num_censored_volumes'][0], 4))
 

xcp_d/interfaces/workbench.py

@@ -1444,8 +1444,7 @@ class _CiftiSmoothInputSpec(_WBCommandInputSpec):
         exists=True,
         position=6,
         argstr='-left-corrected-areas %s',
-        desc='vertex areas (as a metric) to use instead of computing them from '
-        'the left surface.',
+        desc='vertex areas (as a metric) to use instead of computing them from the left surface.',
     )
     right_surf = File(
         exists=True,
@@ -1458,8 +1457,7 @@ class _CiftiSmoothInputSpec(_WBCommandInputSpec):
         exists=True,
         position=8,
         argstr='-right-corrected-areas %s',
-        desc='vertex areas (as a metric) to use instead of computing them from '
-        'the right surface',
+        desc='vertex areas (as a metric) to use instead of computing them from the right surface',
     )
     cerebellum_surf = File(
         exists=True,

xcp_d/tests/run_local_tests.py

@@ -60,7 +60,7 @@ def run_command(command, env=None):
 
     if process.returncode != 0:
         raise RuntimeError(
-            f'Non zero return code: {process.returncode}\n' f'{command}\n\n{process.stdout.read()}'
+            f'Non zero return code: {process.returncode}\n{command}\n\n{process.stdout.read()}'
         )
 
 
@@ -71,9 +71,7 @@ def run_tests(test_regex, test_mark, check_path):
 
     if check_path:
         run_str = (
-            'docker run --rm -ti '
-            '--entrypoint /bin/ls '
-            f'pennlinc/xcp_d:unstable {mounted_code}'
+            f'docker run --rm -ti --entrypoint /bin/ls pennlinc/xcp_d:unstable {mounted_code}'
         )
         try:
             run_command(run_str)

xcp_d/tests/utils.py

@@ -77,7 +77,7 @@ def download_test_data(dset, data_dir=None):
         return
 
     if dset not in URLS:
-        raise ValueError(f"dset ({dset}) must be one of: {', '.join(URLS.keys())}")
+        raise ValueError(f'dset ({dset}) must be one of: {", ".join(URLS.keys())}')
 
     if not data_dir:
         data_dir = os.path.join(os.path.dirname(get_test_data_path()), 'test_data')
@@ -238,7 +238,7 @@ def run_command(command, env=None):
 
     if process.returncode != 0:
         raise RuntimeError(
-            f'Non zero return code: {process.returncode}\n' f'{command}\n\n{process.stdout.read()}'
+            f'Non zero return code: {process.returncode}\n{command}\n\n{process.stdout.read()}'
         )
 
 

xcp_d/utils/atlas.py

@@ -169,7 +169,7 @@ def collect_atlases(datasets, atlases, file_format, bids_filters=None):
 
     for _atlas, atlas_info in atlas_cache.items():
         if not atlas_info['labels']:
-            raise FileNotFoundError(f"No TSV file found for {atlas_info['image']}")
+            raise FileNotFoundError(f'No TSV file found for {atlas_info["image"]}')
 
         # Check the contents of the labels file
         df = pd.read_table(atlas_info['labels'])

xcp_d/utils/bids.py

@@ -64,12 +64,10 @@ class BIDSError(ValueError):
     def __init__(self, message, bids_root):
         indent = 10
         header = (
-            f'{"".join(["-"] * indent)} BIDS root folder: "{bids_root}" '
-            f'{"".join(["-"] * indent)}'
+            f'{"".join(["-"] * indent)} BIDS root folder: "{bids_root}" {"".join(["-"] * indent)}'
         )
         self.msg = (
-            f"\n{header}\n{''.join([' '] * (indent + 1))}{message}\n"
-            f"{''.join(['-'] * len(header))}"
+            f'\n{header}\n{"".join([" "] * (indent + 1))}{message}\n{"".join(["-"] * len(header))}'
         )
         super().__init__(self.msg)
         self.bids_root = bids_root
@@ -132,13 +130,13 @@ def collect_participants(layout, participant_label=None, strict=False):
     found_label = sorted(set(participant_label) & all_participants)
     if not found_label:
         raise BIDSError(
-            f"Could not find participants [{', '.join(participant_label)}]",
+            f'Could not find participants [{", ".join(participant_label)}]',
             layout,
         )
 
     if notfound_label := sorted(set(participant_label) - all_participants):
         exc = BIDSError(
-            f"Some participants were not found: {', '.join(notfound_label)}",
+            f'Some participants were not found: {", ".join(notfound_label)}',
             layout,
         )
         if strict:
@@ -210,9 +208,9 @@ def collect_data(
             [f.path for f in layout.get(extension=['.nii.gz', '.dtseries.nii'])]
         )
         raise FileNotFoundError(
-            f"No BOLD data found in allowed spaces ({', '.join(allowed_spaces)}).\n\n"
-            f"Query: {queries['bold']}\n\n"
-            f"Found files:\n\n{filenames}"
+            f'No BOLD data found in allowed spaces ({", ".join(allowed_spaces)}).\n\n'
+            f'Query: {queries["bold"]}\n\n'
+            f'Found files:\n\n{filenames}'
         )
 
     if file_format == 'cifti':
@@ -521,9 +519,7 @@ def collect_morphometry_data(layout, participant_label, bids_filters):
         elif len(files) > 1:
             surface_str = '\n\t'.join(files)
             raise ValueError(
-                f'More than one {name} found.\n'
-                f'Surfaces found:\n\t{surface_str}\n'
-                f'Query: {query}'
+                f'More than one {name} found.\nSurfaces found:\n\t{surface_str}\nQuery: {query}'
             )
         else:
             morphometry_files[name] = None
@@ -726,8 +722,8 @@ def collect_confounds(
     for confound_name, confound_def in confound_spec['confounds'].items():
         if confound_def['dataset'] not in layout_dict.keys():
             raise ValueError(
-                f"Missing dataset required by confound spec: *{confound_def['dataset']}*. "
-                "Did you provide it with the `--datasets` flag?"
+                f'Missing dataset required by confound spec: *{confound_def["dataset"]}*. '
+                'Did you provide it with the `--datasets` flag?'
             )
 
         layout = layout_dict[confound_def['dataset']]

xcp_d/utils/boilerplate.py

@@ -104,12 +104,12 @@ def describe_censoring(*, motion_filter_type, head_radius, fd_thresh, exact_scan
     desc = ''
     if fd_thresh > 0:
         desc += (
-            "Framewise displacement was calculated from the "
-            f"{'filtered ' if motion_filter_type else ''}motion parameters using the formula from "
-            f"@power_fd_dvars, with a head radius of {head_radius} mm. "
-            f"Volumes with {'filtered ' if motion_filter_type else ''}framewise displacement "
-            f"greater than {fd_thresh} mm were flagged as high-motion outliers for the sake of "
-            "later censoring [@power_fd_dvars]."
+            'Framewise displacement was calculated from the '
+            f'{"filtered " if motion_filter_type else ""}motion parameters using the formula from '
+            f'@power_fd_dvars, with a head radius of {head_radius} mm. '
+            f'Volumes with {"filtered " if motion_filter_type else ""}framewise displacement '
+            f'greater than {fd_thresh} mm were flagged as high-motion outliers for the sake of '
+            'later censoring [@power_fd_dvars].'
         )
 
     if exact_scans and (fd_thresh > 0):

xcp_d/utils/modified_data.py

@@ -252,8 +252,7 @@ def calculate_exact_scans(exact_times, scan_length, t_r, bold_file):
 
     if non_float_times:
         LOGGER.warning(
-            f'Non-float values {non_float_times} in {os.path.basename(bold_file)} '
-            'will be ignored.'
+            f'Non-float values {non_float_times} in {os.path.basename(bold_file)} will be ignored.'
         )
 
     exact_scans = [int(t // t_r) for t in retained_exact_times]

xcp_d/utils/plotting.py

@@ -866,9 +866,9 @@ def plot_carpet(
     img = nb.load(func)
 
     if isinstance(img, nb.Cifti2Image):  # CIFTI
-        assert (
-            img.nifti_header.get_intent()[0] == 'ConnDenseSeries'
-        ), f'Not a dense timeseries: {img.nifti_header.get_intent()[0]}, {func}'
+        assert img.nifti_header.get_intent()[0] == 'ConnDenseSeries', (
+            f'Not a dense timeseries: {img.nifti_header.get_intent()[0]}, {func}'
+        )
 
         # Get required information
         data = img.get_fdata().T
@@ -917,9 +917,9 @@ def plot_carpet(
         order = seg_data.argsort(kind='stable')
         # Get color maps
         cmap = ListedColormap([plt.get_cmap('Paired').colors[i] for i in (1, 0, 7, 3)])
-        assert len(cmap.colors) == len(
-            struct_map
-        ), 'Mismatch between expected # of structures and colors'
+        assert len(cmap.colors) == len(struct_map), (
+            'Mismatch between expected # of structures and colors'
+        )
     else:
         # Order following segmentation labels
         order = np.argsort(seg_data)[::-1]