add get_data() method

mne/time_frequency/spectrogram.py

@@ -233,7 +233,37 @@ def get_data(self, picks=None, exclude='bads', fmin=0, fmax=np.inf,
             The frequency values for the requested data range. Only returned if
             ``return_freqs`` is ``True``.
         """
-        pass
+        tmin = self.times[0] if tmin is None else tmin
+        tmax = self.times[-1] if tmax is None else tmax
+        picks = _picks_to_idx(self.info, picks, 'data_or_ica', exclude=exclude,
+                              with_ref_meg=False)
+        fmin_idx = np.searchsorted(self.freqs, fmin)
+        fmax_idx = np.searchsorted(self.freqs, fmax, side='right')
+        tmin_idx = np.searchsorted(self.times, tmin)
+        tmax_idx = np.searchsorted(self.times, tmax, side='right')
+        freq_picks = np.arange(fmin_idx, fmax_idx)
+        time_picks = np.arange(tmin_idx, tmax_idx)
+        freq_axis = self._dims.index('freq')
+        time_axis = self._dims.index('time')
+        chan_axis = self._dims.index('channel')
+        # normally there's a risk of np.take reducing array dimension if there
+        # were only one channel or frequency selected, but `_picks_to_idx`
+        # and np.arange both always return arrays, so we're safe; the result
+        # will always be 3D.
+        data = (self._data.take(picks, chan_axis)
+                          .take(freq_picks, freq_axis)
+                          .take(time_picks, time_axis)
+                )
+        out = (data,)
+        if return_times:
+            times = self._times[tmin_idx:tmax_idx]
+            out.append(times)
+        if return_freqs:
+            freqs = self._freqs[fmin_idx:fmax_idx]
+            out.append(freqs)
+        if not return_times and not return_freqs:
+            return out[0]
+        return out
 
 
 class RawTFR(BaseTFR):