FIX: Fix for dep

Author: larsoner

examples/inverse/dics_source_power.py

@@ -78,7 +78,7 @@
 # timecourse.
 fwd = mne.read_forward_solution(fname_fwd)
 filters = make_dics(info, fwd, csd, noise_csd=csd_baseline,
-                    pick_ori='max-power', reduce_rank=True)
+                    pick_ori='max-power', reduce_rank=True, real_filter=True)
 del fwd
 
 ###############################################################################

examples/inverse/evoked_ers_source_power.py

@@ -88,7 +88,7 @@ def _gen_dics(active_win, baseline_win, epochs):
     csd_ers = csd_morlet(epochs, freqs, tmin=active_win[0], tmax=active_win[1],
                          decim=20)
     filters = make_dics(epochs.info, fwd, csd.mean(), pick_ori='max-power',
-                        reduce_rank=True)
+                        reduce_rank=True, real_filter=True)
     stc_base, freqs = apply_dics_csd(csd_baseline.mean(), filters)
     stc_act, freqs = apply_dics_csd(csd_ers.mean(), filters)
     stc_act /= stc_base

tutorials/simulation/80_dics.py

@@ -251,12 +251,12 @@ def coh_signal_gen():
 # Compute the spatial filters for each vertex, using two approaches.
 filters_approach1 = make_dics(
     info, fwd, csd_signal, reg=0.05, pick_ori='max-power', depth=1.,
-    inversion='single', weight_norm=None)
+    inversion='single', weight_norm=None, real_filter=True)
 print(filters_approach1)
 
 filters_approach2 = make_dics(
     info, fwd, csd_signal, reg=0.05, pick_ori='max-power', depth=None,
-    inversion='matrix', weight_norm='unit-noise-gain')
+    inversion='matrix', weight_norm='unit-noise-gain', real_filter=True)
 print(filters_approach2)
 
 # You can save these to disk with: