Update docstrings

mne/time_frequency/multitaper.py

@@ -505,7 +505,8 @@ def tfr_array_multitaper(
           coherence across trials.
 
     return_weights : bool, default False
-        If True, return the taper weights. Only applies if ``output="complex"``.
+        If True, return the taper weights. Only applies if ``output='complex'`` or
+        ``'phase'``.
 
         .. versionadded:: 1.9.0
 
@@ -528,7 +529,7 @@ def tfr_array_multitaper(
         contain the average power and the imaginary values contain the
         inter-trial coherence: :math:`out = power_{avg} + i * ITC`.
     weights : array of shape (n_tapers, n_freqs)
-        The taper weights. Only returned if ``output="complex"`` and
+        The taper weights. Only returned if ``output='complex'`` or ``'phase'`` and
         ``return_weights=True``.
 
     See Also

mne/time_frequency/tfr.py

@@ -1215,9 +1215,6 @@ def __init__(
                 f'{classname} got unsupported parameter value{_pl(problem)} '
                 f'{" and ".join(problem)}.'
             )
-        # shim for tfr_array_morlet deprecation warning (TODO: remove after 1.7 release)
-        if method == "morlet":
-            method_kw.setdefault("zero_mean", True)
         # check method
         valid_methods = ["morlet", "multitaper"]
         if isinstance(inst, BaseEpochs):
@@ -2697,42 +2694,55 @@ def to_data_frame(
         """
         # check pandas once here, instead of in each private utils function
         pd = _check_pandas_installed()  # noqa
+        # triage for Epoch-derived or unaggregated spectra
+        from_epo = isinstance(self, EpochsTFR)
+        unagg_mt = "taper" in self._dims
         # arg checking
         valid_index_args = ["time", "freq"]
-        if isinstance(self, EpochsTFR):
+        if from_epo:
             valid_index_args.extend(["epoch", "condition"])
         valid_time_formats = ["ms", "timedelta"]
         index = _check_pandas_index_arguments(index, valid_index_args)
         time_format = _check_time_format(time_format, valid_time_formats)
         # get data
         picks = _picks_to_idx(self.info, picks, "all", exclude=())
         data, times, freqs = self.get_data(picks, return_times=True, return_freqs=True)
-        axis = self._dims.index("channel")
-        if not isinstance(self, EpochsTFR):
+        ch_axis = self._dims.index("channel")
+        if not from_epo:
             data = data[np.newaxis]  # add singleton "epochs" axis
-            axis += 1
-        n_epochs, n_picks, n_freqs, n_times = data.shape
-        # reshape to (epochs*freqs*times) x signals
-        data = np.moveaxis(data, axis, -1)
-        data = data.reshape(n_epochs * n_freqs * n_times, n_picks)
+            ch_axis += 1
+        if not unagg_mt:
+            data = np.expand_dims(data, -3)  # add singleton "tapers" axis
+        n_epochs, n_picks, n_tapers, n_freqs, n_times = data.shape
+        # reshape to (epochs*tapers*freqs*times) x signals
+        data = np.moveaxis(data, ch_axis, -1)
+        data = data.reshape(n_epochs * n_tapers * n_freqs * n_times, n_picks)
         # prepare extra columns / multiindex
         mindex = list()
+        default_index = list()
         times = _convert_times(times, time_format, self.info["meas_date"])
-        times = np.tile(times, n_epochs * n_freqs)
-        freqs = np.tile(np.repeat(freqs, n_times), n_epochs)
+        times = np.tile(times, n_epochs * n_freqs * n_tapers)
+        freqs = np.tile(np.repeat(freqs, n_times * n_tapers), n_epochs)
         mindex.append(("time", times))
         mindex.append(("freq", freqs))
-        if isinstance(self, EpochsTFR):
-            mindex.append(("epoch", np.repeat(self.selection, n_times * n_freqs)))
+        if from_epo:
+            mindex.append(
+                ("epoch", np.repeat(self.selection, n_times * n_freqs * n_tapers))
+            )
             rev_event_id = {v: k for k, v in self.event_id.items()}
             conditions = [rev_event_id[k] for k in self.events[:, 2]]
-            mindex.append(("condition", np.repeat(conditions, n_times * n_freqs)))
+            mindex.append(
+                ("condition", np.repeat(conditions, n_times * n_freqs * n_tapers))
+            )
+            default_index.extend(["condition", "epoch"])
+        default_index.extend(["freq", "time"])
+        if unagg_mt:
+            name = "taper"
+            taper_nums = np.tile(np.arange(n_tapers), n_epochs * n_freqs * n_times)
+            mindex.append((name, taper_nums))
+            default_index.append(name)
         assert all(len(mdx) == len(mindex[0]) for mdx in mindex[1:])
         # build DataFrame
-        if isinstance(self, EpochsTFR):
-            default_index = ["condition", "epoch", "freq", "time"]
-        else:
-            default_index = ["freq", "time"]
         df = _build_data_frame(
             self, data, picks, long_format, mindex, index, default_index=default_index
         )