BUG: Fix doc building bugs

doc/conf.py

@@ -37,6 +37,8 @@
 os.environ["MNE_BROWSER_OVERVIEW_MODE"] = "hidden"
 os.environ["MNE_BROWSER_THEME"] = "light"
 os.environ["MNE_3D_OPTION_THEME"] = "light"
+# https://numba.readthedocs.io/en/latest/reference/deprecation.html#deprecation-of-old-style-numba-captured-errors  # noqa: E501
+os.environ["NUMBA_CAPTURED_ERRORS"] = "new_style"
 sphinx_logger = sphinx.util.logging.getLogger("mne")
 
 # -- Path setup --------------------------------------------------------------

examples/decoding/receptive_field_mtrf.py

@@ -55,7 +55,7 @@
 data = loadmat(join(path, "speech_data.mat"))
 raw = data["EEG"].T
 speech = data["envelope"].T
-sfreq = float(data["Fs"])
+sfreq = float(data["Fs"].item())
 sfreq /= decim
 speech = mne.filter.resample(speech, down=decim, npad="auto")
 raw = mne.filter.resample(raw, down=decim, npad="auto")

examples/inverse/mixed_norm_inverse.py

@@ -87,7 +87,7 @@
 )
 
 t = 0.083
-tidx = evoked.time_as_index(t)
+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}%")
 

mne/conftest.py

@@ -104,6 +104,10 @@ def pytest_configure(config):
     if os.getenv("PYTEST_QT_API") is None and os.getenv("QT_API") is not None:
         os.environ["PYTEST_QT_API"] = os.environ["QT_API"]
 
+    # https://numba.readthedocs.io/en/latest/reference/deprecation.html#deprecation-of-old-style-numba-captured-errors  # noqa: E501
+    if "NUMBA_CAPTURED_ERRORS" not in os.environ:
+        os.environ["NUMBA_CAPTURED_ERRORS"] = "old_style"
+
     # Warnings
     # - Once SciPy updates not to have non-integer and non-tuple errors (1.2.0)
     #   we should remove them from here.

mne/fixes.py

@@ -797,7 +797,6 @@ def _jit(func):
 
     prange = range
     bincount = np.bincount
-    mean = np.mean
 
 else:
 
@@ -823,10 +822,6 @@ def _np_apply_along_axis(func1d, axis, arr):
                 result[i] = func1d(arr[i, :])
         return result
 
-    @jit()
-    def mean(array, axis):  # noqa: D103
-        return _np_apply_along_axis(np.mean, axis, array)
-
 
 ###############################################################################
 # Matplotlib

mne/transforms.py

@@ -15,7 +15,7 @@
 from scipy.spatial.distance import cdist
 from scipy.special import sph_harm
 
-from .fixes import jit, mean, _get_img_fdata
+from .fixes import jit, _get_img_fdata
 from ._fiff.constants import FIFF
 from ._fiff.open import fiff_open
 from ._fiff.tag import read_tag
@@ -1466,16 +1466,12 @@ def _fit_matched_points(p, x, weights=None, scale=False):
     assert p.ndim == 2
     assert p.shape[1] == 3
     # (weighted) centroids
-    if weights is None:
-        mu_p = mean(p, axis=0)  # eq 23
-        mu_x = mean(x, axis=0)
-        dots = np.dot(p.T, x)
-        dots /= p.shape[0]
-    else:
-        weights_ = np.reshape(weights / weights.sum(), (weights.size, 1))
-        mu_p = np.dot(weights_.T, p)[0]
-        mu_x = np.dot(weights_.T, x)[0]
-        dots = np.dot(p.T, weights_ * x)
+    weights_ = np.full((p.shape[0], 1), 1.0 / max(p.shape[0], 1))
+    if weights is not None:
+        weights_[:] = np.reshape(weights / weights.sum(), (weights.size, 1))
+    mu_p = np.dot(weights_.T, p)[0]
+    mu_x = np.dot(weights_.T, x)[0]
+    dots = np.dot(p.T, weights_ * x)
     Sigma_px = dots - np.outer(mu_p, mu_x)  # eq 24
     # x and p should no longer be used
     A_ij = Sigma_px - Sigma_px.T

mne/viz/_3d.py

@@ -3464,6 +3464,7 @@ def plot_sparse_source_estimates(
     plt_show(show)
 
     renderer.show()
+    renderer.set_camera(distance="auto", focalpoint="auto")
     return renderer.scene()
 
 

tutorials/io/60_ctf_bst_auditory.py

@@ -21,7 +21,6 @@
 `FieldTrip bug tracker
 <http://bugzilla.fieldtriptoolbox.org/show_bug.cgi?id=2300>`__.
 """
-
 # Authors: Mainak Jas <[email protected]>
 #          Eric Larson <[email protected]>
 #          Jaakko Leppakangas <[email protected]>
@@ -39,8 +38,6 @@
 from mne.datasets.brainstorm import bst_auditory
 from mne.io import read_raw_ctf
 
-print(__doc__)
-
 # %%
 # To reduce memory consumption and running time, some of the steps are
 # precomputed. To run everything from scratch change ``use_precomputed`` to

tutorials/machine-learning/30_strf.py

@@ -93,7 +93,6 @@
 plt.autoscale(tight=True)
 mne.viz.tight_layout()
 
-
 # %%
 # Simulate a neural response
 # --------------------------
@@ -186,7 +185,7 @@
     rf.fit(X_train, y_train)
 
     # Now make predictions about the model output, given input stimuli.
-    scores[ii] = rf.score(X_test, y_test)
+    scores[ii] = rf.score(X_test, y_test).item()
     models.append(rf)
 
 times = rf.delays_ / float(rf.sfreq)
@@ -294,7 +293,7 @@
     rf.fit(X_train, y_train)
 
     # Now make predictions about the model output, given input stimuli.
-    scores_lap[ii] = rf.score(X_test, y_test)
+    scores_lap[ii] = rf.score(X_test, y_test).item()
     models_lap.append(rf)
 
 ix_best_alpha_lap = np.argmax(scores_lap)

tutorials/stats-sensor-space/10_background_stats.py

@@ -8,7 +8,6 @@
 Here we will briefly cover multiple concepts of inferential statistics in an
 introductory manner, and demonstrate how to use some MNE statistical functions.
 """
-
 # Authors: Eric Larson <[email protected]>
 #
 # License: BSD-3-Clause
@@ -250,7 +249,8 @@ def plot_t_p(t, p, title, mcc, axes=None):
 ps.append(np.zeros(width * width))
 mccs.append(False)
 for ii in range(n_src):
-    ts[-1][ii], ps[-1][ii] = permutation_t_test(X[:, [ii]], verbose=False)[:2]
+    t, p = permutation_t_test(X[:, [ii]], verbose=False)[:2]
+    ts[-1][ii], ps[-1][ii] = t[0], p[0]
 plot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])
 
 # %%