Manually apply ruff/flake8-implicit-str-concat rule ISC001

	ISC001 Implicitly concatenated string literals on one line

This rule is currently disabled because it conflicts with the formatter:
	astral-sh/ruff#8272

mne/_fiff/meas_info.py

@@ -3350,7 +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

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:

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/conftest.py

@@ -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/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/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/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/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")

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

@@ -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]

mne/io/besa/besa.py

@@ -109,7 +109,7 @@ def _read_evoked_besa_avr(fname, verbose):
         fields["DI"] = float(fields["DI"])
     else:
         raise RuntimeError(
-            'No "DI" field present. Could not determine ' "sampling frequency."
+            'No "DI" field present. Could not determine sampling frequency.'
         )
     if "TSB" in fields:
         fields["TSB"] = float(fields["TSB"])

mne/io/ctf/ctf.py

@@ -113,7 +113,7 @@ def __init__(
         )
         if not directory.endswith(".ds"):
             raise TypeError(
-                'directory must be a directory ending with ".ds", ' f"got {directory}"
+                f'directory must be a directory ending with ".ds", got {directory}'
             )
         _check_option("system_clock", system_clock, ["ignore", "truncate"])
         logger.info(f"ds directory : {directory}")

mne/io/edf/edf.py

@@ -1547,7 +1547,7 @@ def _find_exclude_idx(ch_names, exclude, include=None):
     if include:  # find other than include channels
         if exclude:
             raise ValueError(
-                "'exclude' must be empty if 'include' is assigned. " f"Got {exclude}."
+                f"'exclude' must be empty if 'include' is assigned. Got {exclude}."
             )
         if isinstance(include, str):  # regex for channel names
             indices_include = []

mne/io/fieldtrip/utils.py

@@ -98,7 +98,7 @@ def _remove_missing_channels_from_trial(trial, missing_chan_idx):
             trial = np.delete(trial, missing_chan_idx, axis=0)
     else:
         raise ValueError(
-            '"trial" field of the FieldTrip structure ' "has an unknown format."
+            '"trial" field of the FieldTrip structure has an unknown format.'
         )
 
     return trial

mne/io/kit/kit.py

@@ -276,7 +276,7 @@ def _set_stimchannels(inst, info, stim, stim_code):
                 stim = picks
             else:
                 raise ValueError(
-                    "stim needs to be list of int, '>' or " f"'<', not {str(stim)!r}"
+                    f"stim needs to be list of int, '>' or '<', not {str(stim)!r}"
                 )
         else:
             stim = np.asarray(stim, int)

mne/io/persyst/persyst.py

@@ -77,7 +77,7 @@ def __init__(self, fname, preload=False, verbose=None):
         curr_path, lay_fname = op.dirname(fname), op.basename(fname)
         if not op.exists(fname):
             raise FileNotFoundError(
-                f"The path you specified, " f'"{lay_fname}",does not exist.'
+                f'The path you specified, "{lay_fname}",does not exist.'
             )
 
         # sections and subsections currently unused

mne/label.py

@@ -2693,7 +2693,7 @@ def write_labels_to_annot(
         for fname in annot_fname:
             if op.exists(fname):
                 raise ValueError(
-                    f'File {fname} exists. Use "overwrite=True" to ' "overwrite it"
+                    f'File {fname} exists. Use "overwrite=True" to overwrite it'
                 )
 
     # prepare container for data to save:

mne/minimum_norm/inverse.py

@@ -1764,7 +1764,7 @@ def _prepare_forward(
         exp = float(exp)
         if exp < 0:
             raise ValueError(
-                "depth exponent should be greater than or " f"equal to 0, got {exp}"
+                f"depth exponent should be greater than or equal to 0, got {exp}"
             )
         exp = exp or None  # alias 0. -> None
 

mne/morph_map.py

@@ -102,7 +102,7 @@ def read_morph_map(
             return _read_morph_map(fname, subject_from, subject_to)
     # if file does not exist, make it
     logger.info(
-        f'Morph map "{fname}" does not exist, creating it and saving it to ' "disk"
+        f'Morph map "{fname}" does not exist, creating it and saving it to disk'
     )
     logger.info(log_msg % (subject_from, subject_to))
     mmap_1 = _make_morph_map(subject_from, subject_to, subjects_dir, xhemi)

mne/preprocessing/_csd.py

@@ -136,7 +136,7 @@ def compute_current_source_density(
     n_legendre_terms = _ensure_int(n_legendre_terms, "n_legendre_terms")
     if n_legendre_terms < 1:
         raise ValueError(
-            "n_legendre_terms must be greater than 0, " f"got {n_legendre_terms}"
+            f"n_legendre_terms must be greater than 0, got {n_legendre_terms}"
         )
 
     if isinstance(sphere, str) and sphere == "auto":
@@ -148,14 +148,14 @@ def compute_current_source_density(
         x, y, z, radius = sphere
     except Exception:
         raise ValueError(
-            f'sphere must be "auto" or array-like with shape (4,), ' f"got {sphere}"
+            f'sphere must be "auto" or array-like with shape (4,), got {sphere}'
         )
     _validate_type(x, "numeric", "x")
     _validate_type(y, "numeric", "y")
     _validate_type(z, "numeric", "z")
     _validate_type(radius, "numeric", "radius")
     if radius <= 0:
-        raise ValueError("sphere radius must be greater than 0, " f"got {radius}")
+        raise ValueError("sphere radius must be greater than 0, got {radius}")
 
     pos = np.array([inst.info["chs"][pick]["loc"][:3] for pick in picks])
     if not np.isfinite(pos).all() or np.isclose(pos, 0.0).all(1).any():

mne/preprocessing/ica.py

@@ -560,7 +560,7 @@ def __repr__(self):
         """ICA fit information."""
         infos = self._get_infos_for_repr()
 
-        s = f'{infos.fit_on or "no"} decomposition, ' f"method: {infos.fit_method}"
+        s = f'{infos.fit_on or "no"} decomposition, method: {infos.fit_method}'
 
         if infos.fit_on is not None:
             s += (

mne/preprocessing/ieeg/_volume.py

@@ -62,7 +62,7 @@ def warp_montage(montage, moving, static, reg_affine, sdr_morph, verbose=None):
             ]
         )
         raise RuntimeError(
-            "Coordinate frame not supported, expected " f'"mri", got {bad_coord_frames}'
+            f'Coordinate frame not supported, expected "mri", got {bad_coord_frames}'
         )
     ch_names = list(ch_dict["ch_pos"].keys())
     ch_coords = np.array([ch_dict["ch_pos"][name] for name in ch_names])
@@ -192,7 +192,7 @@ def make_montage_volume(
             ]
         )
         raise RuntimeError(
-            "Coordinate frame not supported, expected " f'"mri", got {bad_coord_frames}'
+            f'Coordinate frame not supported, expected "mri", got {bad_coord_frames}'
         )
 
     ch_names = list(ch_dict["ch_pos"].keys())

mne/preprocessing/stim.py

@@ -82,7 +82,7 @@ def fix_stim_artifact(
     s_end = int(np.ceil(inst.info["sfreq"] * tmax))
     if (mode == "window") and (s_end - s_start) < 4:
         raise ValueError(
-            "Time range is too short. Use a larger interval " 'or set mode to "linear".'
+            'Time range is too short. Use a larger interval or set mode to "linear".'
         )
     window = None
     if mode == "window":