MAINT: Check for shadowing and mutable defaults

doc/changes/devel/bugfix.rst

@@ -0,0 +1 @@
+Fix bug where :func:`mne.preprocessing.compute_proj_ecg` and :func:`mne.preprocessing.compute_proj_eog` could modify the default ``reject`` and ``flat`` arguments on multiple calls based on channel types present, by `Eric Larson`_.

mne/_fiff/open.py

@@ -268,7 +268,7 @@ def show_fiff(
     return out
 
 
-def _find_type(value, fmts=["FIFF_"], exclude=["FIFF_UNIT"]):
+def _find_type(value, fmts=("FIFF_",), exclude=("FIFF_UNIT",)):
     """Find matching values."""
     value = int(value)
     vals = [

mne/_fiff/pick.py

@@ -259,7 +259,7 @@ def channel_type(info, idx):
 
 
 @verbose
-def pick_channels(ch_names, include, exclude=[], ordered=None, *, verbose=None):
+def pick_channels(ch_names, include, exclude=(), ordered=None, *, verbose=None):
     """Pick channels by names.
 
     Returns the indices of ``ch_names`` in ``include`` but not in ``exclude``.
@@ -706,7 +706,7 @@ def _has_kit_refs(info, picks):
 
 @verbose
 def pick_channels_forward(
-    orig, include=[], exclude=[], ordered=None, copy=True, *, verbose=None
+    orig, include=(), exclude=(), ordered=None, copy=True, *, verbose=None
 ):
     """Pick channels from forward operator.
 
@@ -797,8 +797,8 @@ def pick_types_forward(
     seeg=False,
     ecog=False,
     dbs=False,
-    include=[],
-    exclude=[],
+    include=(),
+    exclude=(),
 ):
     """Pick by channel type and names from a forward operator.
 
@@ -893,7 +893,7 @@ def channel_indices_by_type(info, picks=None):
 
 @verbose
 def pick_channels_cov(
-    orig, include=[], exclude="bads", ordered=None, copy=True, *, verbose=None
+    orig, include=(), exclude="bads", ordered=None, copy=True, *, verbose=None
 ):
     """Pick channels from covariance matrix.
 

mne/channels/tests/test_channels.py

@@ -438,8 +438,8 @@ def test_1020_selection():
     raw = raw.rename_channels(dict(zip(raw.ch_names, montage.ch_names)))
     raw.set_montage(montage)
 
-    for input in ("a_string", 100, raw, [1, 2]):
-        pytest.raises(TypeError, make_1020_channel_selections, input)
+    for input_ in ("a_string", 100, raw, [1, 2]):
+        pytest.raises(TypeError, make_1020_channel_selections, input_)
 
     sels = make_1020_channel_selections(raw.info)
     # are all frontal channels placed before all occipital channels?

mne/commands/mne_setup_source_space.py

@@ -120,7 +120,7 @@ def run():
     subjects_dir = options.subjects_dir
     spacing = options.spacing
     ico = options.ico
-    oct = options.oct
+    oct_ = options.oct
     surface = options.surface
     n_jobs = options.n_jobs
     add_dist = options.add_dist
@@ -130,20 +130,22 @@ def run():
     overwrite = True if options.overwrite is not None else False
 
     # Parse source spacing option
-    spacing_options = [ico, oct, spacing]
+    spacing_options = [ico, oct_, spacing]
     n_options = len([x for x in spacing_options if x is not None])
+    use_spacing = "oct6"
     if n_options > 1:
         raise ValueError("Only one spacing option can be set at the same time")
     elif n_options == 0:
         # Default to oct6
-        use_spacing = "oct6"
+        pass
     elif n_options == 1:
         if ico is not None:
             use_spacing = "ico" + str(ico)
-        elif oct is not None:
-            use_spacing = "oct" + str(oct)
+        elif oct_ is not None:
+            use_spacing = "oct" + str(oct_)
         elif spacing is not None:
             use_spacing = spacing
+    del ico, oct_, spacing
     # Generate filename
     if fname is None:
         if subject_to is None:

mne/cov.py

@@ -310,7 +310,7 @@ def __iadd__(self, cov):
     def plot(
         self,
         info,
-        exclude=[],
+        exclude=(),
         colorbar=True,
         proj=False,
         show_svd=True,

mne/datasets/sleep_physionet/tests/test_physionet.py

@@ -46,12 +46,12 @@ def _check_mocked_function_calls(mocked_func, call_fname_hash_pairs, base_path):
     # order.
     for idx, current in enumerate(call_fname_hash_pairs):
         _, call_kwargs = mocked_func.call_args_list[idx]
-        hash_type, hash = call_kwargs["known_hash"].split(":")
+        hash_type, hash_ = call_kwargs["known_hash"].split(":")
         assert call_kwargs["url"] == _get_expected_url(current["name"]), idx
         assert Path(call_kwargs["path"], call_kwargs["fname"]) == _get_expected_path(
             base_path, current["name"]
         )
-        assert hash == current["hash"]
+        assert hash_ == current["hash"]
         assert hash_type == "sha1"
 
 

mne/decoding/tests/test_ssd.py

@@ -19,7 +19,7 @@
 
 
 def simulate_data(
-    freqs_sig=[9, 12],
+    freqs_sig=(9, 12),
     n_trials=100,
     n_channels=20,
     n_samples=500,

mne/epochs.py

@@ -3662,7 +3662,7 @@ def _is_good(
     reject,
     flat,
     full_report=False,
-    ignore_chs=[],
+    ignore_chs=(),
     verbose=None,
 ):
     """Test if data segment e is good according to reject and flat.
@@ -4631,7 +4631,7 @@ def make_fixed_length_epochs(
     reject_by_annotation=True,
     proj=True,
     overlap=0.0,
-    id=1,
+    id=1,  # noqa: A002
     verbose=None,
 ):
     """Divide continuous raw data into equal-sized consecutive epochs.

mne/event.py

@@ -925,7 +925,13 @@ def shift_time_events(events, ids, tshift, sfreq):
 
 @fill_doc
 def make_fixed_length_events(
-    raw, id=1, start=0, stop=None, duration=1.0, first_samp=True, overlap=0.0
+    raw,
+    id=1,  # noqa: A002
+    start=0,
+    stop=None,
+    duration=1.0,
+    first_samp=True,
+    overlap=0.0,
 ):
     """Make a set of :term:`events` separated by a fixed duration.
 

mne/evoked.py

@@ -549,7 +549,7 @@ def plot_topo(
         scalings=None,
         title=None,
         proj=False,
-        vline=[0.0],
+        vline=(0.0,),
         fig_background=None,
         merge_grads=False,
         legend=True,

mne/export/_export.py

@@ -211,9 +211,9 @@ def _infer_check_export_fmt(fmt, fname, supported_formats):
 
     if fmt not in supported_formats:
         supported = []
-        for format, extensions in supported_formats.items():
+        for fmt, extensions in supported_formats.items():
             ext_str = ", ".join(f"*.{ext}" for ext in extensions)
-            supported.append(f"{format} ({ext_str})")
+            supported.append(f"{fmt} ({ext_str})")
 
         supported_str = ", ".join(supported)
         raise ValueError(

mne/inverse_sparse/mxne_optim.py

@@ -799,7 +799,7 @@ def __call__(self, x):  # noqa: D105
         else:
             return np.hstack([x @ op for op in self.ops]) / np.sqrt(self.n_dicts)
 
-    def norm(self, z, ord=2):
+    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(

mne/io/bti/bti.py

@@ -78,7 +78,7 @@ def __init__(self, target):
     def __enter__(self):  # noqa: D105
         return self.target
 
-    def __exit__(self, type, value, tb):  # noqa: D105
+    def __exit__(self, exception_type, value, tb):  # noqa: D105
         pass
 
 

mne/io/curry/curry.py

@@ -197,10 +197,10 @@ def _read_curry_parameters(fname):
             if any(var_name in line for var_name in var_names):
                 key, val = line.replace(" ", "").replace("\n", "").split("=")
                 param_dict[key.lower().replace("_", "")] = val
-            for type in CHANTYPES:
-                if "DEVICE_PARAMETERS" + CHANTYPES[type] + " START" in line:
+            for key, type_ in CHANTYPES.items():
+                if f"DEVICE_PARAMETERS{type_} START" in line:
                     data_unit = next(fid)
-                    unit_dict[type] = (
+                    unit_dict[key] = (
                         data_unit.replace(" ", "").replace("\n", "").split("=")[-1]
                     )
 

mne/io/edf/edf.py

@@ -1454,7 +1454,9 @@ def _read_gdf_header(fname, exclude, include=None):
 
 
 def _check_stim_channel(
-    stim_channel, ch_names, tal_ch_names=["EDF Annotations", "BDF Annotations"]
+    stim_channel,
+    ch_names,
+    tal_ch_names=("EDF Annotations", "BDF Annotations"),
 ):
     """Check that the stimulus channel exists in the current datafile."""
     DEFAULT_STIM_CH_NAMES = ["status", "trigger"]

mne/io/fieldtrip/tests/helpers.py

@@ -185,7 +185,7 @@ def get_epochs(system):
         else:
             event_id = [int(cfg_local["eventvalue"])]
 
-        event_id = [id for id in event_id if id in events[:, 2]]
+        event_id = [id_ for id_ in event_id if id_ in events[:, 2]]
 
     epochs = mne.Epochs(
         raw_data,

mne/io/fieldtrip/tests/test_fieldtrip.py

@@ -254,18 +254,18 @@ def test_one_channel_elec_bug(version):
 @pytest.mark.filterwarnings("ignore:.*number of bytes.*:RuntimeWarning")
 @pytest.mark.parametrize("version", all_versions)
 @pytest.mark.parametrize("type", ["averaged", "epoched", "raw"])
-def test_throw_exception_on_cellarray(version, type):
+def test_throw_exception_on_cellarray(version, type_):
     """Test for a meaningful exception when the data is a cell array."""
-    fname = get_data_paths("cellarray") / f"{type}_{version}.mat"
+    fname = get_data_paths("cellarray") / f"{type_}_{version}.mat"
     info = get_raw_info("CNT")
     with pytest.raises(
         RuntimeError, match="Loading of data in cell arrays " "is not supported"
     ):
-        if type == "averaged":
+        if type_ == "averaged":
             mne.read_evoked_fieldtrip(fname, info)
-        elif type == "epoched":
+        elif type_ == "epoched":
             mne.read_epochs_fieldtrip(fname, info)
-        elif type == "raw":
+        elif type_ == "raw":
             mne.io.read_raw_fieldtrip(fname, info)
 
 

mne/preprocessing/nirs/_tddr.py

@@ -111,7 +111,6 @@ def _TDDR(signal, sample_rate):
     tune = 4.685
     D = np.sqrt(np.finfo(signal.dtype).eps)
     mu = np.inf
-    iter = 0
 
     # Step 1. Compute temporal derivative of the signal
     deriv = np.diff(signal_low)
@@ -120,8 +119,7 @@ def _TDDR(signal, sample_rate):
     w = np.ones(deriv.shape)
 
     # Step 3. Iterative estimation of robust weights
-    while iter < 50:
-        iter = iter + 1
+    for _ in range(50):
         mu0 = mu
 
         # Step 3a. Estimate weighted mean

mne/preprocessing/ssp.py

@@ -13,7 +13,7 @@
 from .._fiff.reference import make_eeg_average_ref_proj
 from ..epochs import Epochs
 from ..proj import compute_proj_epochs, compute_proj_evoked
-from ..utils import logger, verbose, warn
+from ..utils import _validate_type, logger, verbose, warn
 from .ecg import find_ecg_events
 from .eog import find_eog_events
 
@@ -112,7 +112,10 @@ def _compute_exg_proj(
     my_info["bads"] += bads
 
     # Handler rejection parameters
+    _validate_type(reject, (None, dict), "reject")
+    _validate_type(flat, (None, dict), "flat")
     if reject is not None:  # make sure they didn't pass None
+        reject = reject.copy()  # must make a copy or we modify default!
         if (
             len(
                 pick_types(
@@ -170,6 +173,7 @@ def _compute_exg_proj(
         ):
             _safe_del_key(reject, "eog")
     if flat is not None:  # make sure they didn't pass None
+        flat = flat.copy()
         if (
             len(
                 pick_types(
@@ -300,9 +304,9 @@ def compute_proj_ecg(
     filter_length="10s",
     n_jobs=None,
     ch_name=None,
-    reject=dict(grad=2000e-13, mag=3000e-15, eeg=50e-6, eog=250e-6),
+    reject=dict(grad=2000e-13, mag=3000e-15, eeg=50e-6, eog=250e-6),  # noqa: B006
     flat=None,
-    bads=[],
+    bads=(),
     avg_ref=False,
     no_proj=False,
     event_id=999,
@@ -461,9 +465,9 @@ def compute_proj_eog(
     average=True,
     filter_length="10s",
     n_jobs=None,
-    reject=dict(grad=2000e-13, mag=3000e-15, eeg=500e-6, eog=np.inf),
+    reject=dict(grad=2000e-13, mag=3000e-15, eeg=500e-6, eog=np.inf),  # noqa: B006
     flat=None,
-    bads=[],
+    bads=(),
     avg_ref=False,
     no_proj=False,
     event_id=998,

mne/preprocessing/tests/test_ssp.py

@@ -69,6 +69,9 @@ def test_compute_proj_ecg(short_raw, average):
     # XXX: better tests
 
     # without setting a bad channel, this should throw a warning
+    # (first with a call that makes sure we copy the mutable default "reject")
+    with pytest.warns(RuntimeWarning, match="longer than the signal"):
+        compute_proj_ecg(raw.copy().pick("mag"), l_freq=None, h_freq=None)
     with pytest.warns(RuntimeWarning, match="No good epochs found"):
         projs, events, drop_log = compute_proj_ecg(
             raw,

mne/report/report.py

@@ -2998,7 +2998,7 @@ def __enter__(self):
         """Do nothing when entering the context block."""
         return self
 
-    def __exit__(self, type, value, traceback):
+    def __exit__(self, exception_type, value, traceback):
         """Save the report when leaving the context block."""
         if self.fname is not None:
             self.save(self.fname, open_browser=False, overwrite=True)

mne/source_estimate.py

@@ -2476,7 +2476,7 @@ def save_as_volume(
         src,
         dest="mri",
         mri_resolution=False,
-        format="nifti1",
+        format="nifti1",  # noqa: A002
         *,
         overwrite=False,
         verbose=None,
@@ -2525,7 +2525,13 @@ def save_as_volume(
         )
         nib.save(img, fname)
 
-    def as_volume(self, src, dest="mri", mri_resolution=False, format="nifti1"):
+    def as_volume(
+        self,
+        src,
+        dest="mri",
+        mri_resolution=False,
+        format="nifti1",  # noqa: A002
+    ):
         """Export volume source estimate as a nifti object.
 
         Parameters

mne/source_space/_source_space.py

@@ -2047,11 +2047,12 @@ def _make_volume_source_space(
     volume_labels=None,
     do_neighbors=True,
     n_jobs=None,
-    vol_info={},
+    vol_info=None,
     single_volume=False,
 ):
     """Make a source space which covers the volume bounded by surf."""
     # Figure out the grid size in the MRI coordinate frame
+    vol_info = {} if vol_info is None else vol_info
     if "rr" in surf:
         mins = np.min(surf["rr"], axis=0)
         maxs = np.max(surf["rr"], axis=0)

mne/time_frequency/csd.py

@@ -35,7 +35,7 @@
 
 @verbose
 def pick_channels_csd(
-    csd, include=[], exclude=[], ordered=None, copy=True, *, verbose=None
+    csd, include=(), exclude=(), ordered=None, copy=True, *, verbose=None
 ):
     """Pick channels from cross-spectral density matrix.