"GFP" trace in STC time viewer is actually RMS

I've also rearranged the formula such that it becomes
immediately obvious that we're dealing with RMS values.

See mne-tools#8775 for our lengthy discussion on this for sensor-level
data.

doc/changes/latest.inc

@@ -158,6 +158,8 @@ Bugs
 
 - `~mne.io.read_raw_bti` erroneously treated response channels as respiratory channels (:gh:`8855` by `Richard Höchenberger`_)
 
+- The RMS trace shown in the time viewer of `~mne.SourceEstimate` plots is now correctly labeled as ``RMS`` (was ``GFP`` before) (:gh:`8965` by `Richard Höchenberger`_)
+
 API changes
 ~~~~~~~~~~~
 - ``mne.read_selection`` has been deprecated in favor of `mne.read_vectorview_selection`. ``mne.read_selection`` will be removed in MNE-Python 0.24 (:gh:`8870` by `Richard Höchenberger`_)

mne/viz/_brain/_brain.py

@@ -602,7 +602,7 @@ def setup_time_viewer(self, time_viewer=True, show_traces=True):
         self.color_list.remove("#7f7f7f")
         self.color_cycle = _ReuseCycle(self.color_list)
         self.mpl_canvas = None
-        self.gfp = None
+        self.rms = None
         self.picked_patches = {key: list() for key in all_keys}
         self.picked_points = {key: list() for key in all_keys}
         self.pick_table = dict()
@@ -1130,13 +1130,15 @@ def _configure_vertex_time_course(self):
         else:
             self.clear_glyphs()
 
-        # plot the GFP
+        # plot RMS of the activation
         y = np.concatenate(list(v[0] for v in self.act_data_smooth.values()
                                 if v[0] is not None))
-        y = np.linalg.norm(y, axis=0) / np.sqrt(len(y))
-        self.gfp, = self.mpl_canvas.axes.plot(
-            self._data['time'], y,
-            lw=3, label='GFP', zorder=3, color=self._fg_color,
+        rms = np.sqrt((y ** 2).mean(axis=0))
+        del y
+
+        self.rms = self.mpl_canvas.axes.plot(
+            self._data['time'], rms,
+            lw=3, label='RMS', zorder=3, color=self._fg_color,
             alpha=0.5, ls=':')
 
         # now plot the time line
@@ -1695,9 +1697,9 @@ def clear_glyphs(self):
             for label_id in list(self.picked_patches[hemi]):
                 self._remove_label_glyph(hemi, label_id)
         assert sum(len(v) for v in self.picked_patches.values()) == 0
-        if self.gfp is not None:
-            self.gfp.remove()
-            self.gfp = None
+        if self.rms is not None:
+            self.rms.remove()
+            self.rms = None
         self._update()
 
     def plot_time_course(self, hemi, vertex_id, color):

tutorials/source-modeling/plot_mne_dspm_source_localization.py

@@ -8,6 +8,8 @@
 minimum-norm inverse method on evoked/raw/epochs data.
 """
 
+import os.path as op
+
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -19,7 +21,8 @@
 # Process MEG data
 
 data_path = sample.data_path()
-raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
+raw_fname = op.join(data_path, 'MEG', 'sample',
+                    'sample_audvis_filt-0-40_raw.fif')
 
 raw = mne.io.read_raw_fif(raw_fname)  # already has an average reference
 events = mne.find_events(raw, stim_channel='STI 014')