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

for more information, see https://pre-commit.ci

examples/general/plot_22_quality.py

@@ -170,7 +170,7 @@
     scores,
     times,
     threshold=0.7,
-    title="Scalp Coupling Index " "Quality Evaluation",
+    title="Scalp Coupling Index Quality Evaluation",
 )
 
 # %%

mne_nirs/channels/_channels.py

@@ -95,7 +95,7 @@ def drop_sources(raw, sources):
         all_str = all([isinstance(src, int) for src in sources])
     except TypeError:
         raise ValueError(
-            "'ch_names' must be iterable, got " f"type {type(sources)} ({sources})."
+            f"'ch_names' must be iterable, got type {type(sources)} ({sources})."
         )
 
     if not all_str:
@@ -139,7 +139,7 @@ def drop_detectors(raw, detectors):
         all_str = all([isinstance(det, int) for det in detectors])
     except TypeError:
         raise ValueError(
-            "'ch_names' must be iterable, got " f"type {type(detectors)} ({detectors})."
+            f"'ch_names' must be iterable, got type {type(detectors)} ({detectors})."
         )
 
     if not all_str:
@@ -183,7 +183,7 @@ def pick_sources(raw, sources):
         all_str = all([isinstance(src, int) for src in sources])
     except TypeError:
         raise ValueError(
-            "'ch_names' must be iterable, got " f"type {type(sources)} ({sources})."
+            f"'ch_names' must be iterable, got type {type(sources)} ({sources})."
         )
 
     if not all_str:
@@ -227,7 +227,7 @@ def pick_detectors(raw, detectors):
         all_str = all([isinstance(det, int) for det in detectors])
     except TypeError:
         raise ValueError(
-            "'ch_names' must be iterable, got " f"type {type(detectors)} ({detectors})."
+            f"'ch_names' must be iterable, got type {type(detectors)} ({detectors})."
         )
 
     if not all_str:

mne_nirs/channels/_roi.py

@@ -45,7 +45,7 @@ def picks_pair_to_idx(raw, sd_pairs, on_missing="error"):
         pair_name = "S" + str(pair[0]) + "_D" + str(pair[1]) + " "
         pair_picks = np.where([pair_name in ch for ch in ch_names])[0]
         if len(pair_picks) == 0:
-            msg = "No matching channels found for source %s " "detector %s" % (
+            msg = "No matching channels found for source %s detector %s" % (
                 pair[0],
                 pair[1],
             )

mne_nirs/datasets/audio_or_visual_speech/_audio_or_visual_speech.py

@@ -56,8 +56,7 @@ def data_path(
     .. footbibliography::
     """
     dataset_params = dict(
-        archive_name="2021-fNIRS-Audio-visual-speech-"
-        "Broad-vs-restricted-regions.zip",
+        archive_name="2021-fNIRS-Audio-visual-speech-Broad-vs-restricted-regions.zip",
         hash="md5:16cac6565880dae6aed9b69100399d0b",
         url="https://osf.io/xwerv/download?version=1",
         folder_name="fNIRS-audio-visual-speech",
@@ -77,7 +76,7 @@ def data_path(
 
     # Do some wrangling to deal with nested directories
     bad_name = os.path.join(
-        dpath, "2021-fNIRS-Audio-visual-speech-" "Broad-vs-restricted-regions"
+        dpath, "2021-fNIRS-Audio-visual-speech-Broad-vs-restricted-regions"
     )
     if os.path.isdir(bad_name):
         os.rename(bad_name, dpath + ".true")

mne_nirs/datasets/block_speech_noise/_block_speech_noise.py

@@ -75,7 +75,7 @@ def data_path(
     dpath = str(dpath)
 
     # Do some wrangling to deal with nested directories
-    bad_name = os.path.join(dpath, "2021-fNIRS-Analysis-Methods-" "Passive-Auditory")
+    bad_name = os.path.join(dpath, "2021-fNIRS-Analysis-Methods-Passive-Auditory")
     if os.path.isdir(bad_name):
         os.rename(bad_name, dpath + ".true")
         shutil.rmtree(dpath)

mne_nirs/io/snirf/_snirf.py

@@ -46,17 +46,17 @@ def write_raw_snirf(raw, fname, add_montage=False):
     """
     supported_types = ["fnirs_cw_amplitude", "fnirs_od", "hbo", "hbr"]
     picks = _picks_to_idx(raw.info, supported_types, exclude=[])
-    assert len(picks) == len(
-        raw.ch_names
-    ), "Data must only be of type fnirs_cw_amplitude,  fnirs_od, hbo or hbr"
+    assert len(picks) == len(raw.ch_names), (
+        "Data must only be of type fnirs_cw_amplitude,  fnirs_od, hbo or hbr"
+    )
     if ("fnirs_cw_amplitude" in raw) or ("fnirs_od" in raw):
-        assert (
-            len(np.unique(raw.info.get_channel_types())) == 1
-        ), "All channels must be of the same type"
+        assert len(np.unique(raw.info.get_channel_types())) == 1, (
+            "All channels must be of the same type"
+        )
     elif ("hbo" in raw) or ("hbr" in raw):
-        assert (
-            len(np.unique(raw.info.get_channel_types())) <= 2
-        ), "Channels must be of type hbo and hbr"
+        assert len(np.unique(raw.info.get_channel_types())) <= 2, (
+            "Channels must be of type hbo and hbr"
+        )
 
     with h5py.File(fname, "w") as f:
         nirs = f.create_group("/nirs")

mne_nirs/preprocessing/_mayer.py

@@ -88,7 +88,7 @@ def quantify_mayer_fooof(
         # or raw data, I just haven't tried this. Let me know if this works
         # for you and we can ease this restriction.
         raise RuntimeError(
-            "Mayer wave estimation should be run on " "haemoglobin concentration data."
+            "Mayer wave estimation should be run on haemoglobin concentration data."
         )
 
     df = pd.DataFrame()

mne_nirs/signal_enhancement/_negative_correlation.py

@@ -43,7 +43,7 @@ def enhance_negative_correlation(raw):
 
     if (not len(hbo_channels)) & (not len(hbr_channels)):
         raise RuntimeError(
-            "enhance_negative_correlation should " "be run on haemoglobin data."
+            "enhance_negative_correlation should be run on haemoglobin data."
         )
 
     if len(hbo_channels) != len(hbr_channels):

mne_nirs/simulation/_simulation.py

@@ -70,10 +70,10 @@ def simulate_nirs_raw(
     frame_times = np.arange(sig_dur * sfreq) / sfreq
 
     assert len(amplitude) == len(annot_desc), (
-        "Same number of amplitudes as " "annotations required."
+        "Same number of amplitudes as annotations required."
     )
     assert len(amplitude) == len(stim_dur), (
-        "Same number of amplitudes as " "durations required."
+        "Same number of amplitudes as durations required."
     )
 
     onset = 0.0

mne_nirs/statistics/_glm_level_first.py

@@ -116,7 +116,7 @@ def save(self, fname, overwrite=False):
             fname = str(fname)
         if not fname.endswith("glm.h5"):
             raise OSError(
-                "The filename must end with glm.h5, " f"instead received {fname}"
+                f"The filename must end with glm.h5, instead received {fname}"
             )
         write_hdf5(fname, self._get_state(), overwrite=overwrite, title="mnepython")
 
@@ -240,7 +240,7 @@ def data(self, data):
                 raise TypeError("Data names and channel names do not match")
         for d in data:
             if type(data[d]) is not nilearn.glm.regression.RegressionResults:
-                raise TypeError("Dictionary items must be" " nilearn RegressionResults")
+                raise TypeError("Dictionary items must be nilearn RegressionResults")
 
         self._data = data
 
@@ -430,11 +430,11 @@ def to_dataframe_region_of_interest(
 
         if isinstance(weighted, dict):
             if weighted.keys() != group_by.keys():
-                raise KeyError("Keys of group_by and weighted " "must be the same")
+                raise KeyError("Keys of group_by and weighted must be the same")
             for key in weighted.keys():
                 if len(weighted[key]) != len(group_by[key]):
                     raise ValueError(
-                        "The length of the keys for group_by " "and weighted must match"
+                        "The length of the keys for group_by and weighted must match"
                     )
                 if (np.array(weighted[key]) < 0).any():
                     raise ValueError("Weights must be positive values")
@@ -567,8 +567,8 @@ def surface_projection(
         df_use = df.query("Condition in @condition")
         if len(df_use) == 0:
             raise KeyError(
-                f'condition={repr(condition)} not found in conditions: '
-                f'{sorted(set(df["Condition"]))}'
+                f"condition={repr(condition)} not found in conditions: "
+                f"{sorted(set(df['Condition']))}"
             )
         df = df_use
         df = df.query("Chroma in @chroma").copy()
@@ -639,7 +639,7 @@ def data(self, data):
             raise TypeError("Data must be a nilearn glm contrast type")
         if data.effect.size != len(self.info.ch_names):
             raise TypeError(
-                "Data results must be the same length " "as the number of channels"
+                "Data results must be the same length as the number of channels"
             )
 
         self._data = data
@@ -853,9 +853,7 @@ def _state_to_glm(glm):
                     glm["data"][channel]["model"]["design"],
                 )
             else:
-                raise OSError(
-                    "Unknown model type " f"{glm['data'][channel]['modelname']}"
-                )
+                raise OSError(f"Unknown model type {glm['data'][channel]['modelname']}")
 
             for key in glm["data"][channel]["model"]:
                 model.__setattr__(key, glm["data"][channel]["model"][key])

mne_nirs/tests/test_examples.py

@@ -14,7 +14,7 @@
 def examples_path():
     if not os.path.isdir("BIDS-NIRS-Tapping"):
         os.system(
-            "git clone --depth 1 " "https://github.com/rob-luke/BIDS-NIRS-Tapping.git"
+            "git clone --depth 1 https://github.com/rob-luke/BIDS-NIRS-Tapping.git"
         )
 
     if os.path.isdir("examples"):

mne_nirs/visualisation/_plot_3d_montage.py

@@ -138,14 +138,11 @@ def plot_3d_montage(
         if bad:
             src_det_names = None
             logger.info(
-                "Could not automatically map source/detector names to "
-                "10-20 locations."
+                "Could not automatically map source/detector names to 10-20 locations."
             )
         else:
             src_det_names = locs
-            logger.info(
-                "Source-detector names automatically mapped to 10-20 " "locations"
-            )
+            logger.info("Source-detector names automatically mapped to 10-20 locations")
 
     head_mri_t = _get_trans(trans, "head", "mri")[0]
     del trans

mne_nirs/visualisation/_plot_GLM_surface_projection.py

@@ -101,9 +101,9 @@ def plot_glm_surface_projection(
     info = deepcopy(inst if isinstance(inst, Info) else inst.info)
     if not (info.ch_names == list(statsmodel_df["ch_name"].values)):
         raise RuntimeError(
-            'MNE data structure does not match dataframe '
-            f'results.\nMNE = {info.ch_names}.\n'
-            f'GLM = {list(statsmodel_df["ch_name"].values)}'
+            "MNE data structure does not match dataframe "
+            f"results.\nMNE = {info.ch_names}.\n"
+            f"GLM = {list(statsmodel_df['ch_name'].values)}"
         )
 
     ea = EvokedArray(np.tile(statsmodel_df[value].values.T, (1, 1)).T, info.copy())

mne_nirs/visualisation/_plot_GLM_topo.py

@@ -275,9 +275,9 @@ def plot_glm_group_topo(
     assert len(np.unique(statsmodel_df["Chroma"])) == 1, "Only one Chroma allowed"
 
     if "Condition" in statsmodel_df.columns:
-        assert (
-            len(np.unique(statsmodel_df["Condition"])) == 1
-        ), "Only one condition allowed"
+        assert len(np.unique(statsmodel_df["Condition"])) == 1, (
+            "Only one condition allowed"
+        )
         c = np.unique(statsmodel_df["Condition"])[0]
     else:
         c = "Contrast"