Merge branch 'main' into interpolate_to

doc/changes/devel/13107.newfeature.rst

@@ -0,0 +1 @@
+The :meth:`mne.Info.save` method now has an ``overwrite`` and a ``verbose`` parameter, by `Stefan Appelhoff`_.

mne/_fiff/meas_info.py

@@ -1935,15 +1935,24 @@ def _repr_html_(self):
         info_template = _get_html_template("repr", "info.html.jinja")
         return info_template.render(info=self)
 
-    def save(self, fname):
+    @verbose
+    def save(self, fname, *, overwrite=False, verbose=None):
         """Write measurement info in fif file.
 
         Parameters
         ----------
         fname : path-like
             The name of the file. Should end by ``'-info.fif'``.
+        %(overwrite)s
+
+            .. versionadded:: 1.10
+        %(verbose)s
+
+        See Also
+        --------
+        mne.io.write_info
         """
-        write_info(fname, self)
+        write_info(fname, self, overwrite=overwrite)
 
 
 def _simplify_info(info, *, keep=()):

mne/_fiff/tests/test_meas_info.py

@@ -975,7 +975,7 @@ def test_field_round_trip(tmp_path):
             meas_date=_stamp_to_dt((1, 2)),
         )
     fname = tmp_path / "temp-info.fif"
-    write_info(fname, info)
+    info.save(fname)
     info_read = read_info(fname)
     assert_object_equal(info, info_read)
     with pytest.raises(TypeError, match="datetime"):

mne/time_frequency/tfr.py

@@ -4291,19 +4291,20 @@ def _tfr_from_mt(x_mt, weights):
 
     Parameters
     ----------
-    x_mt : array, shape (n_channels, n_tapers, n_freqs, n_times)
+    x_mt : array, shape (..., n_tapers, n_freqs, n_times)
         The complex-valued multitaper coefficients.
     weights : array, shape (n_tapers, n_freqs)
         The weights to use to combine the tapered estimates.
 
     Returns
     -------
-    tfr : array, shape (n_channels, n_freqs, n_times)
+    tfr : array, shape (..., n_freqs, n_times)
         The time-frequency power estimates.
     """
-    weights = weights[np.newaxis, :, :, np.newaxis]  # add singleton channel & time dims
+    # add singleton dim for time and any dims preceding the tapers
+    weights = weights[..., np.newaxis]
     tfr = weights * x_mt
     tfr *= tfr.conj()
-    tfr = tfr.real.sum(axis=1)
-    tfr *= 2 / (weights * weights.conj()).real.sum(axis=1)
+    tfr = tfr.real.sum(axis=-3)
+    tfr *= 2 / (weights * weights.conj()).real.sum(axis=-3)
     return tfr