partially handle ITC; comments

mne-tools · Jan 5, 2023 · 15276be · 15276be
1 parent dbe564d
commit 15276be
Showing 1 changed file with 11 additions and 6 deletions.
diff --git a/mne/time_frequency/spectrogram.py b/mne/time_frequency/spectrogram.py
@@ -59,7 +59,6 @@ def __init__(self, inst, method, freqs, tmin, tmax, picks,
         del freqs
         # make sure we get taper weights if needed.
         default = signature(tfr_array_multitaper).parameters['output'].default
-        # Default for tfr_array_multitaper
         self._is_complex_mt = (method == 'multitaper' and
                                method_kw.get('output', default) == 'complex')
         if self._is_complex_mt:
@@ -335,7 +334,7 @@ def __init__(self, inst, method, freqs, *, tmin=None, tmax=None,
         rba = 'NaN' if reject_by_annotation else None
         data = self.inst.get_data(self._picks, start, stop + 1,
                                   reject_by_annotation=rba)
-        # compute the spectra
+        # compute the TFR
         self._compute_tfr(data, n_jobs, method_kw, verbose)
         # check for correct shape and bad values
         self._check_values()
@@ -345,16 +344,22 @@ def __init__(self, inst, method, freqs, *, tmin=None, tmax=None,
 
     def _compute_tfr(self, data, n_jobs, method_kw, verbose):
         # kwargs are already incorporated (self._tfr_func is a partial)
-        result = self._tfr_func([data], self.sfreq, decim=self._decim,
-                                n_jobs=n_jobs, verbose=verbose)
+        result = self._tfr_func(
+            data[np.newaxis],  # prepend a singleton "epochs" axis
+            self.sfreq,
+            decim=self._decim,
+            n_jobs=n_jobs,
+            verbose=verbose)
         # assign ._data (handling unaggregated multitaper output)
-        if self._is_complex_mt:
+        if method_kw.get('return_itc', False):
+            self._data, self._itc, freqs = result  # TODO actually return ITC
+        elif self._is_complex_mt:
             self._data, self._mt_weights = result
         else:
             self._data = result
         # remove fake "epoch" dimension
         self._data = self._data[0]
-        # TODO triaging `result` is needed to split out ITC (for epochs)
+
         #
         # output shape of TFR funcs is:
         # - morlet, average          → (       n_ch,          n_freq, n_time)