use copy=False and filter warnings now that PRs are open in VTK and nitime

Author: mscheltienne

mne/_fiff/_digitization.py

@@ -335,7 +335,7 @@ def _get_data_as_dict_from_dig(dig, exclude_ref_channel=True):
             f"Only single coordinate frame in dig is supported, got {dig_coord_frames}"
         )
     dig_ch_pos_location = np.array(dig_ch_pos_location)
-    dig_ch_pos_location = dig_ch_pos_location.reshape(-1, 3)  # empty will be (0, 3)
+    dig_ch_pos_location = dig_ch_pos_location.reshape((-1, 3), copy=False)
     return Bunch(
         nasion=fids.get("nasion", None),
         lpa=fids.get("lpa", None),

mne/_fiff/tag.py

@@ -177,7 +177,7 @@ def _read_matrix(fid, tag, shape, rlims):
             data = data.view(">c8")
         elif matrix_type == FIFF.FIFFT_COMPLEX_DOUBLE:
             data = data.view(">c16")
-        data = data.reshape(dims)
+        data = data.reshape(dims, copy=False)
     else:
         # Find dimensions and return to the beginning of tag data
         ndim = int(np.frombuffer(fid.read(4), dtype=">i4").item())

mne/beamformer/_rap_music.py

@@ -68,9 +68,9 @@ def _apply_rap_music(
     phi_sig = eig_vectors[:, -n_dipoles:]
 
     n_orient = 3 if is_free_ori else 1
-    G = G.reshape(G.shape[0], -1, n_orient)
+    G = G.reshape((G.shape[0], -1, n_orient), copy=False)
     gain = forward["sol"]["data"].copy()
-    gain = gain.reshape(G.shape)
+    gain = gain.reshape(G.shape, copy=False)
     n_channels = G.shape[0]
     A = np.empty((n_channels, n_dipoles))
     gain_dip = np.empty((n_channels, n_dipoles))
@@ -122,7 +122,7 @@ def _apply_rap_music(
     sol = linalg.lstsq(A, M)[0]
     if n_orient == 3:
         X = sol[:, np.newaxis] * oris[:, :, np.newaxis]
-        X = X.reshape(-1, len(times))
+        X = X.reshape((-1, len(times)), copy=False)
     else:
         X = sol
 

mne/beamformer/tests/test_dics.py

@@ -269,7 +269,7 @@ def test_make_dics(tmp_path, _load_forward, idx, whiten):
         exp=None,
         noise_cov=noise_cov,
     )
-    G = G.reshape(n_channels, n_verts, n_orient)
+    G = G.reshape((n_channels, n_verts, n_orient), copy=False)
     G = G.transpose(1, 2, 0).conj()  # verts, orient, ch
     _assert_weight_norm(filters, G)
 

mne/beamformer/tests/test_external.py

@@ -98,7 +98,7 @@ def test_lcmv_fieldtrip(_get_bf_data, bf_type, weight_norm, pick_ori, pwr):
     ft_fname = ft_data_path / ("ft_source_" + bf_type + "-vol.mat")
     stc_ft_data = pymatreader.read_mat(ft_fname)["stc"]
     if stc_ft_data.ndim == 1:
-        stc_ft_data = stc_ft_data.reshape(stc_ft_data.size, 1)
+        stc_ft_data = stc_ft_data.reshape((stc_ft_data.size, 1), copy=False)
 
     if stc_mne.data.ndim == 2:
         signs = np.sign((stc_mne.data * stc_ft_data).sum(-1, keepdims=True))

mne/beamformer/tests/test_lcmv.py

@@ -1185,7 +1185,7 @@ def test_unit_noise_gain_formula(pick_ori, weight_norm, reg, inversion):
     )
     n_channels, n_sources = G.shape
     n_sources //= 3
-    G = G.reshape(n_channels, n_sources, 3)
+    G = G.reshape((n_channels, n_sources, 3), copy=False)
     G = G.transpose(1, 2, 0)  # verts, orient, ch
     _assert_weight_norm(filters, G)
 

mne/channels/montage.py

@@ -973,9 +973,9 @@ def read_dig_hpts(fname, unit="mm"):
         label[ii]: this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == "eeg"
     }
     hpi = np.array([this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == "hpi"])
-    hpi = hpi.reshape(-1, 3)  # in case it's empty
+    hpi = hpi.reshape((-1, 3), copy=False)  # in case it's empty
     hsp = np.array([this_xyz for ii, this_xyz in enumerate(xyz) if kind[ii] == "extra"])
-    hsp = hsp.reshape(-1, 3)  # in case it's empty
+    hsp = hsp.reshape((-1, 3), copy=False)  # in case it's empty
     return make_dig_montage(ch_pos=ch_pos, **fid, hpi=hpi, hsp=hsp)
 
 

mne/chpi.py

@@ -117,7 +117,7 @@ def read_head_pos(fname):
     """
     _check_fname(fname, must_exist=True, overwrite="read")
     data = np.loadtxt(fname, skiprows=1)  # first line is header, skip it
-    data = data.reshape(-1, 10)  # ensure it's the right size even if empty
+    data = data.reshape((-1, 10), copy=False)  # ensure it's the right size even if empty
     if np.isnan(data).any():  # make sure we didn't do something dumb
         raise RuntimeError(f"positions could not be read properly from {fname}")
     return data
@@ -1390,7 +1390,7 @@ def compute_chpi_locs(
     )
     fwd = _magnetic_dipole_field_vec(guesses, meg_coils, too_close)
     fwd = np.dot(fwd, whitener.T)
-    fwd = fwd.reshape(guesses.shape[0], 3, -1)
+    fwd = fwd.reshape((guesses.shape[0], 3, -1), copy=False)
     fwd = np.linalg.svd(fwd, full_matrices=False)[2]
     guesses = dict(rr=guesses, whitened_fwd_svd=fwd)
     del fwd, R

mne/conftest.py

@@ -207,6 +207,9 @@ def pytest_configure(config: pytest.Config):
     ignore:^'.*' deprecated - use '.*'$:DeprecationWarning
     # dipy
     ignore:'where' used without 'out', expect .*:UserWarning
+    # VTK <-> NumPy 2.5 (https://gitlab.kitware.com/vtk/vtk/-/merge_requests/12796)
+    # nitime <-> NumPy 2.5 (https://github.com/nipy/nitime/pull/236)
+    ignore:Setting the shape on a NumPy array has been deprecated.*:DeprecationWarning
     """  # noqa: E501
     for warning_line in warning_lines.split("\n"):
         warning_line = warning_line.strip()

mne/decoding/receptive_field.py

@@ -361,7 +361,7 @@ def predict(self, X):
         else:
             extra = 1
         shape = shape[: self._y_dim + extra]
-        y_pred = y_pred.reshape(shape)
+        y_pred = y_pred.reshape(shape, copy=False)
         return y_pred
 
     def score(self, X, y):