STY: Apply ruff/flake8-implicit-str-concat rule ISC001 (#12602)

.github/actions/rename_towncrier/rename_towncrier.py

@@ -11,22 +11,22 @@
 from github import Github
 from tomllib import loads
 
-event_name = os.getenv('GITHUB_EVENT_NAME', 'pull_request')
-if not event_name.startswith('pull_request'):
-    print(f'No-op for {event_name}')
+event_name = os.getenv("GITHUB_EVENT_NAME", "pull_request")
+if not event_name.startswith("pull_request"):
+    print(f"No-op for {event_name}")
     sys.exit(0)
-if 'GITHUB_EVENT_PATH' in os.environ:
-    with open(os.environ['GITHUB_EVENT_PATH'], encoding='utf-8') as fin:
+if "GITHUB_EVENT_PATH" in os.environ:
+    with open(os.environ["GITHUB_EVENT_PATH"], encoding="utf-8") as fin:
         event = json.load(fin)
-    pr_num = event['number']
-    basereponame = event['pull_request']['base']['repo']['full_name']
+    pr_num = event["number"]
+    basereponame = event["pull_request"]["base"]["repo"]["full_name"]
     real = True
 else:  # local testing
     pr_num = 12318  # added some towncrier files
     basereponame = "mne-tools/mne-python"
     real = False
 
-g = Github(os.environ.get('GITHUB_TOKEN'))
+g = Github(os.environ.get("GITHUB_TOKEN"))
 baserepo = g.get_repo(basereponame)
 
 # Grab config from upstream's default branch
@@ -45,9 +45,7 @@
 assert directory.endswith("/"), directory
 
 file_re = re.compile(rf"^{directory}({type_pipe})\.rst$")
-found_stubs = [
-    f for f in modified_files if file_re.match(f)
-]
+found_stubs = [f for f in modified_files if file_re.match(f)]
 for stub in found_stubs:
     fro = stub
     to = file_re.sub(rf"{directory}{pr_num}.\1.rst", fro)

examples/inverse/mixed_norm_inverse.py

@@ -90,7 +90,7 @@
 t = 0.083
 tidx = evoked.time_as_index(t).item()
 for di, dip in enumerate(dipoles, 1):
-    print(f"Dipole #{di} GOF at {1000 * t:0.1f} ms: " f"{float(dip.gof[tidx]):0.1f}%")
+    print(f"Dipole #{di} GOF at {1000 * t:0.1f} ms: {float(dip.gof[tidx]):0.1f}%")
 
 # %%
 # Plot dipole activations

mne/_fiff/meas_info.py

@@ -3205,9 +3205,7 @@ def create_info(ch_names, sfreq, ch_types="misc", verbose=None):
         _validate_type(ch_name, "str", "each entry in ch_names")
         _validate_type(ch_type, "str", "each entry in ch_types")
         if ch_type not in ch_types_dict:
-            raise KeyError(
-                f"kind must be one of {list(ch_types_dict)}, " f"not {ch_type}"
-            )
+            raise KeyError(f"kind must be one of {list(ch_types_dict)}, not {ch_type}")
         this_ch_dict = ch_types_dict[ch_type]
         kind = this_ch_dict["kind"]
         # handle chpi, where kind is a *list* of FIFF constants:
@@ -3352,7 +3350,7 @@ def _force_update_info(info_base, info_target):
     all_infos = np.hstack([info_base, info_target])
     for ii in all_infos:
         if not isinstance(ii, Info):
-            raise ValueError("Inputs must be of type Info. " f"Found type {type(ii)}")
+            raise ValueError(f"Inputs must be of type Info. Found type {type(ii)}")
     for key, val in info_base.items():
         if key in exclude_keys:
             continue

mne/_fiff/proj.py

@@ -491,7 +491,7 @@ def plot_projs_topomap(
                     _projs.remove(_proj)
             if len(_projs) == 0:
                 raise ValueError(
-                    "Nothing to plot (no projectors for channel " f"type {ch_type})."
+                    f"Nothing to plot (no projectors for channel type {ch_type})."
                 )
         # now we have non-empty _projs list with correct channel type(s)
         from ..viz.topomap import plot_projs_topomap
@@ -1100,7 +1100,7 @@ def _has_eeg_average_ref_proj(
     missing = [name for name in want_names if name not in found_names]
     if missing:
         if found_names:  # found some but not all: warn
-            warn(f"Incomplete {ch_type} projector, " f"missing channel(s) {missing}")
+            warn(f"Incomplete {ch_type} projector, missing channel(s) {missing}")
         return False
     return True
 

mne/_fiff/tests/test_constants.py

@@ -123,7 +123,7 @@ def test_constants(tmp_path):
     fname = "fiff.zip"
     dest = tmp_path / fname
     pooch.retrieve(
-        url="https://codeload.github.com/" f"{REPO}/fiff-constants/zip/{COMMIT}",
+        url=f"https://codeload.github.com/{REPO}/fiff-constants/zip/{COMMIT}",
         path=tmp_path,
         fname=fname,
         known_hash=None,

mne/_fiff/write.py

@@ -45,7 +45,7 @@ def _get_split_size(split_size):
     if isinstance(split_size, str):
         exp = dict(MB=20, GB=30).get(split_size[-2:], None)
         if exp is None:
-            raise ValueError("split_size has to end with either" '"MB" or "GB"')
+            raise ValueError('split_size has to end with either "MB" or "GB"')
         split_size = int(float(split_size[:-2]) * 2**exp)
 
     if split_size > 2147483648:
@@ -77,7 +77,7 @@ def write_int(fid, kind, data):
     max_val = data.max() if data.size > 0 else 0
     if max_val > INT32_MAX:
         raise TypeError(
-            f"Value {max_val} exceeds maximum allowed ({INT32_MAX}) for " f"tag {kind}"
+            f"Value {max_val} exceeds maximum allowed ({INT32_MAX}) for tag {kind}"
         )
     data = data.astype(">i4").T
     _write(fid, data, kind, data_size, FIFF.FIFFT_INT, ">i4")

mne/bem.py

@@ -1026,7 +1026,7 @@ def get_fitting_dig(info, dig_kinds="auto", exclude_frontal=True, verbose=None):
     _validate_type(info, "info")
     if info["dig"] is None:
         raise RuntimeError(
-            "Cannot fit headshape without digitization " ', info["dig"] is None'
+            'Cannot fit headshape without digitization, info["dig"] is None'
         )
     if isinstance(dig_kinds, str):
         if dig_kinds == "auto":

mne/channels/channels.py

@@ -1902,7 +1902,7 @@ def combine_channels(
     # Instantiate channel info and data
     new_ch_names, new_ch_types, new_data = [], [], []
     if not isinstance(keep_stim, bool):
-        raise TypeError('"keep_stim" must be of type bool, not ' f"{type(keep_stim)}.")
+        raise TypeError(f'"keep_stim" must be of type bool, not {type(keep_stim)}.')
     if keep_stim:
         stim_ch_idx = list(pick_types(inst.info, meg=False, stim=True))
         if stim_ch_idx:
@@ -1915,7 +1915,7 @@ def combine_channels(
     # Get indices of bad channels
     ch_idx_bad = []
     if not isinstance(drop_bad, bool):
-        raise TypeError('"drop_bad" must be of type bool, not ' f"{type(drop_bad)}.")
+        raise TypeError(f'"drop_bad" must be of type bool, not {type(drop_bad)}.')
     if drop_bad and inst.info["bads"]:
         ch_idx_bad = pick_channels(ch_names, inst.info["bads"])
 
@@ -1937,7 +1937,7 @@ def combine_channels(
         this_picks = [idx for idx in this_picks if idx not in ch_idx_bad]
         if these_bads:
             logger.info(
-                "Dropped the following channels in group " f"{this_group}: {these_bads}"
+                f"Dropped the following channels in group {this_group}: {these_bads}"
             )
         #  Check if combining less than 2 channel
         if len(set(this_picks)) < 2:
@@ -2130,9 +2130,7 @@ def read_vectorview_selection(name, fname=None, info=None, verbose=None):
             # get the name of the selection in the file
             pos = line.find(":")
             if pos < 0:
-                logger.info(
-                    '":" delimiter not found in selections file, ' "skipping line"
-                )
+                logger.info('":" delimiter not found in selections file, skipping line')
                 continue
             sel_name_file = line[:pos]
             # search for substring match with name provided

mne/channels/tests/test_montage.py

@@ -1976,7 +1976,7 @@ def test_montage_add_fiducials():
 
     # check that adding MNI fiducials fails because we're in MRI
     with pytest.raises(
-        RuntimeError, match="Montage should be in the " '"mni_tal" coordinate frame'
+        RuntimeError, match='Montage should be in the "mni_tal" coordinate frame'
     ):
         montage.add_mni_fiducials(subjects_dir=subjects_dir)
 
@@ -1991,7 +1991,7 @@ def test_montage_add_fiducials():
     # which is the FreeSurfer RAS
     montage = make_dig_montage(ch_pos=test_ch_pos, coord_frame="mni_tal")
     with pytest.raises(
-        RuntimeError, match="Montage should be in the " '"mri" coordinate frame'
+        RuntimeError, match='Montage should be in the "mri" coordinate frame'
     ):
         montage.add_estimated_fiducials(subject=subject, subjects_dir=subjects_dir)
 

mne/commands/mne_coreg.py

@@ -73,7 +73,7 @@ def run():
         type=str,
         default=None,
         dest="interaction",
-        help='Interaction style to use, can be "trackball" or ' '"terrain".',
+        help='Interaction style to use, can be "trackball" or "terrain".',
     )
     _add_verbose_flag(parser)
 

mne/conftest.py

@@ -514,7 +514,7 @@ def _check_pyqtgraph(request):
     qt_version, api = _check_qt_version(return_api=True)
     if (not qt_version) or _compare_version(qt_version, "<", "5.12"):
         pytest.skip(
-            f"Qt API {api} has version {qt_version} " f"but pyqtgraph needs >= 5.12!"
+            f"Qt API {api} has version {qt_version} but pyqtgraph needs >= 5.12!"
         )
     try:
         import mne_qt_browser  # noqa: F401
@@ -525,10 +525,10 @@ def _check_pyqtgraph(request):
         f_name = request.function.__name__
         if lower_2_0 and m_name in pre_2_0_skip_modules:
             pytest.skip(
-                f'Test-Module "{m_name}" was skipped for' f" mne-qt-browser < 0.2.0"
+                f'Test-Module "{m_name}" was skipped for mne-qt-browser < 0.2.0'
             )
         elif lower_2_0 and f_name in pre_2_0_skip_funcs:
-            pytest.skip(f'Test "{f_name}" was skipped for ' f"mne-qt-browser < 0.2.0")
+            pytest.skip(f'Test "{f_name}" was skipped for mne-qt-browser < 0.2.0')
     except Exception:
         pytest.skip("Requires mne_qt_browser")
     else:

mne/coreg.py

@@ -1905,7 +1905,7 @@ def _orig_hsp_point_distance(self):
     def _log_dig_mri_distance(self, prefix):
         errs_nearest = self.compute_dig_mri_distances()
         logger.info(
-            f"{prefix} median distance: " f"{np.median(errs_nearest * 1000):6.2f} mm"
+            f"{prefix} median distance: {np.median(errs_nearest * 1000):6.2f} mm"
         )
 
     @property

mne/decoding/csp.py

@@ -139,13 +139,11 @@ def __init__(
         if transform_into == "average_power":
             if log is not None and not isinstance(log, bool):
                 raise ValueError(
-                    "log must be a boolean if transform_into == " '"average_power".'
+                    'log must be a boolean if transform_into == "average_power".'
                 )
         else:
             if log is not None:
-                raise ValueError(
-                    "log must be a None if transform_into == " '"csp_space".'
-                )
+                raise ValueError('log must be a None if transform_into == "csp_space".')
         self.log = log
 
         _validate_type(norm_trace, bool, "norm_trace")

mne/decoding/transformer.py

@@ -115,14 +115,14 @@ def __init__(self, info=None, scalings=None, with_mean=True, with_std=True):
 
         if not (scalings is None or isinstance(scalings, (dict, str))):
             raise ValueError(
-                "scalings type should be dict, str, or None, " f"got {type(scalings)}"
+                f"scalings type should be dict, str, or None, got {type(scalings)}"
             )
         if isinstance(scalings, str):
             _check_option("scalings", scalings, ["mean", "median"])
         if scalings is None or isinstance(scalings, dict):
             if info is None:
                 raise ValueError(
-                    'Need to specify "info" if scalings is' f"{type(scalings)}"
+                    f'Need to specify "info" if scalings is {type(scalings)}'
                 )
             self._scaler = _ConstantScaler(info, scalings, self.with_std)
         elif scalings == "mean":
@@ -339,7 +339,7 @@ def inverse_transform(self, X):
         X = np.asarray(X)
         if X.ndim not in (2, 3):
             raise ValueError(
-                "X should be of 2 or 3 dimensions but has shape " f"{X.shape}"
+                f"X should be of 2 or 3 dimensions but has shape {X.shape}"
             )
         return X.reshape(X.shape[:-1] + self.features_shape_)
 
@@ -642,7 +642,7 @@ def __init__(self, estimator, average=False):
 
         if not isinstance(average, bool):
             raise ValueError(
-                "average parameter must be of bool type, got " f"{type(bool)} instead"
+                f"average parameter must be of bool type, got {type(bool)} instead"
             )
 
         self.estimator = estimator

mne/epochs.py

@@ -623,7 +623,7 @@ def __init__(
                 reject_tmin = self.tmin
             elif reject_tmin < tmin:
                 raise ValueError(
-                    f"reject_tmin needs to be None or >= tmin " f"(got {reject_tmin})"
+                    f"reject_tmin needs to be None or >= tmin (got {reject_tmin})"
                 )
 
         if reject_tmax is not None:
@@ -632,7 +632,7 @@ def __init__(
                 reject_tmax = self.tmax
             elif reject_tmax > tmax:
                 raise ValueError(
-                    f"reject_tmax needs to be None or <= tmax " f"(got {reject_tmax})"
+                    f"reject_tmax needs to be None or <= tmax (got {reject_tmax})"
                 )
 
         if (reject_tmin is not None) and (reject_tmax is not None):

mne/evoked.py

@@ -1769,7 +1769,7 @@ def _read_evoked(fname, condition=None, kind="average", allow_maxshield=False):
         # find string-based entry
         if isinstance(condition, str):
             if kind not in _aspect_dict.keys():
-                raise ValueError('kind must be "average" or ' '"standard_error"')
+                raise ValueError('kind must be "average" or "standard_error"')
 
             comments, aspect_kinds, t = _get_entries(fid, evoked_node, allow_maxshield)
             goods = np.isin(comments, [condition]) & np.isin(

mne/export/_egimff.py

@@ -54,7 +54,7 @@ def export_evokeds_mff(fname, evoked, history=None, *, overwrite=False, verbose=
     info = evoked[0].info
     if np.round(info["sfreq"]) != info["sfreq"]:
         raise ValueError(
-            "Sampling frequency must be a whole number. " f'sfreq: {info["sfreq"]}'
+            f'Sampling frequency must be a whole number. sfreq: {info["sfreq"]}'
         )
     sampling_rate = int(info["sfreq"])
 

mne/filter.py

@@ -1668,7 +1668,7 @@ def _mt_spectrum_proc(
     kind = "Detected" if line_freqs is None else "Removed"
     found_freqs = (
         "\n".join(
-            f"    {freq:6.2f} : " f"{counts[freq]:4d} window{_pl(counts[freq])}"
+            f"    {freq:6.2f} : {counts[freq]:4d} window{_pl(counts[freq])}"
             for freq in sorted(counts)
         )
         or "    None"

mne/forward/_field_interpolation.py

@@ -351,7 +351,7 @@ def _make_surface_mapping(
         raise KeyError('surf must have both "rr" and "nn"')
     if "coord_frame" not in surf:
         raise KeyError(
-            "The surface coordinate frame must be specified " 'in surf["coord_frame"]'
+            'The surface coordinate frame must be specified in surf["coord_frame"]'
         )
     _check_option("mode", mode, ["accurate", "fast"])
 

mne/forward/_make_forward.py

@@ -127,7 +127,7 @@ def _read_coil_def_file(fname, use_registry=True):
                 vals = np.fromstring(line, sep=" ")
                 if len(vals) != 7:
                     raise RuntimeError(
-                        f"Could not interpret line {p + 1} as 7 points:\n" f"{line}"
+                        f"Could not interpret line {p + 1} as 7 points:\n{line}"
                     )
                 # Read and verify data for each integration point
                 w.append(vals[0])

mne/gui/_coreg.py

@@ -689,7 +689,7 @@ def _lock_fids_changed(self, change=None):
             self._forward_widget_command(locked_widgets, "set_enabled", False)
             self._forward_widget_command(fits_widgets, "set_enabled", False)
             self._display_message(
-                "Placing MRI fiducials - " f"{self._current_fiducial.upper()}"
+                f"Placing MRI fiducials - {self._current_fiducial.upper()}"
             )
 
         self._set_sensors_visibility(self._lock_fids)
@@ -702,7 +702,7 @@ def _current_fiducial_changed(self, change=None):
         self._follow_fiducial_view()
         if not self._lock_fids:
             self._display_message(
-                "Placing MRI fiducials - " f"{self._current_fiducial.upper()}"
+                f"Placing MRI fiducials - {self._current_fiducial.upper()}"
             )
 
     @observe("_info_file")
@@ -953,7 +953,7 @@ def _omit_hsp(self):
         self._update_plot("hsp")
         self._update_distance_estimation()
         self._display_message(
-            f"{n_omitted} head shape points omitted, " f"{n_remaining} remaining."
+            f"{n_omitted} head shape points omitted, {n_remaining} remaining."
         )
 
     def _reset_omit_hsp_filter(self):

mne/inverse_sparse/mxne_inverse.py

@@ -460,7 +460,7 @@ def mixed_norm(
         _check_option("alpha", alpha, ("sure",))
     elif not 0.0 <= alpha < 100:
         raise ValueError(
-            'If not equal to "sure" alpha must be in [0, 100). ' f"Got alpha = {alpha}"
+            f'If not equal to "sure" alpha must be in [0, 100). Got alpha = {alpha}'
         )
     if n_mxne_iter < 1:
         raise ValueError(

mne/inverse_sparse/mxne_optim.py

@@ -755,9 +755,7 @@ def __call__(self, x):  # noqa: D105
     def norm(self, z, ord=2):  # noqa: A002
         """Squared L2 norm if ord == 2 and L1 norm if order == 1."""
         if ord not in (1, 2):
-            raise ValueError(
-                "Only supported norm order are 1 and 2. " f"Got ord = {ord}"
-            )
+            raise ValueError(f"Only supported norm order are 1 and 2. Got ord = {ord}")
         stft_norm = stft_norm1 if ord == 1 else stft_norm2
         norm = 0.0
         if len(self.n_coefs) > 1:

mne/io/base.py

@@ -1721,7 +1721,7 @@ def save(
         data_test = self[0, 0][0]
         if fmt == "short" and np.iscomplexobj(data_test):
             raise ValueError(
-                'Complex data must be saved as "single" or ' '"double", not "short"'
+                'Complex data must be saved as "single" or "double", not "short"'
             )
 
         # check for file existence and expand `~` if present
@@ -3007,7 +3007,7 @@ def _write_raw_buffer(fid, buf, cals, fmt):
             write_function = write_complex128
         else:
             raise ValueError(
-                'only "single" and "double" supported for ' "writing complex data"
+                'only "single" and "double" supported for writing complex data'
             )
 
     buf = buf / np.ravel(cals)[:, None]