[MRG] ENH/DOC: demo custom spectrum creation

tutorials/simulation/10_array_objs.py

@@ -195,3 +195,48 @@
                                nave=data.shape[0], comment='simulated')
 print(evoked_array)
 evoked_array.plot()
+
+
+# %%
+# In certain situations you may wish to use a custom time-frequency
+# decomposition for estimation of power spectra. Or you may wish to
+# to process pre-computed power spectra in MNE.
+# Following the same logic, it is possible to instantiate averaged power
+# spectrum using the `~mne.time_frequency.Spectrum` class. This is slightly
+# experimental at the moment but works. An API for doing this may follow.
+
+# compute power spectrum
+
+psd, freqs = mne.time_frequency.psd_array_welch(
+    data, info['sfreq'], n_fft=128, n_per_seg=32)
+
+psd_ave = psd.mean(0)
+
+# map to `~mne.time_frequency.Spectrum` class and explore API
+
+
+def spectrum_from_array(data: np.ndarray,  # spectral features
+                        freqs: np.ndarray,  # frequencies
+                        inst_info: mne.Info  # the meta data of MNE instance
+                        ) -> mne.time_frequency.Spectrum:  # Spectrum object
+    """Create MNE averaged power spectrum object from custom data"""
+    state = dict(
+        method='my_welch',
+        data=data,
+        sfreq=inst_info['sfreq'],
+        dims=('channel', 'freq'),
+        freqs=freqs,
+        inst_type_str='Raw',
+        data_type='simulated signals',
+        info=inst_info,
+    )
+    defaults = dict(
+        method=None, fmin=None, fmax=None, tmin=None, tmax=None,
+        picks=None, proj=None, reject_by_annotation=None, n_jobs=None,
+        verbose=None
+    )
+    return mne.time_frequency.Spectrum(state, **defaults)
+
+
+spectrum = spectrum_from_array(data=psd_ave, freqs=freqs, inst_info=info)
+spectrum.plot(picks=[0, 1])