Copy all figures into a separate folder

scripts/results/_plot_cov.py

@@ -43,4 +43,4 @@
 # axes[1].legend_.remove()
 fig.set_size_inches(8, 6, forward=True)
 plt.tight_layout()
-fig.savefig('plot_white.pdf', bbox_to_inches='tight')
+fig.savefig('figures/plot_white.pdf', bbox_to_inches='tight')

scripts/results/plot_fanning.py

@@ -49,7 +49,7 @@
                         ts_args=dict(ylim=ylim, spatial_colors=True),
                         topomap_args=dict(vmin=-300, vmax=300))
 fig.set_size_inches(12, 6, forward=True)
-fig.savefig('FanningA.pdf', bbox_to_inches='tight')
+fig.savefig('figures/FanningA.pdf', bbox_to_inches='tight')
 
 ###############################################################################
 # Next, we highpass filter (but no lowpass filter as we have already done it)
@@ -62,7 +62,7 @@
                         ts_args=dict(ylim=ylim, spatial_colors=True),
                         topomap_args=dict(vmin=-300, vmax=300))
 fig.set_size_inches(12, 6, forward=True)
-fig.savefig('FanningB.pdf', bbox_to_inches='tight')
+fig.savefig('figures/FanningB.pdf', bbox_to_inches='tight')
 
 ###############################################################################
 # Finally, we can also use the tSSS data which has a highpass
@@ -82,4 +82,4 @@
                         ts_args=dict(ylim=ylim, spatial_colors=True),
                         topomap_args=dict(vmin=-300, vmax=300))
 fig.set_size_inches(12, 6, forward=True)
-fig.savefig('FanningC.pdf', bbox_to_inches='tight')
+fig.savefig('figures/FanningC.pdf', bbox_to_inches='tight')

scripts/results/plot_filter.py

@@ -127,4 +127,4 @@ def box_off(ax):
 
 plt.tight_layout()
 plt.show()
-plt.savefig('filters.pdf', bbox_to_inches='tight')
+plt.savefig('figures/filters.pdf', bbox_to_inches='tight')

scripts/results/plot_group.py

@@ -61,7 +61,7 @@
 plt.legend()
 plt.tight_layout()
 plt.show()
-plt.savefig('grand_average_highpass-%sHz.pdf' % l_freq)
+plt.savefig('figures/grand_average_highpass-%sHz.pdf' % l_freq)
 
 ###############################################################################
 # Source-space. See :ref:`sphx_glr_auto_scripts_14-group_average_source.py`

scripts/results/plot_maxfilter.py

@@ -72,4 +72,4 @@
                                       ylim=ylim,
                                       titles={'mag': 'After Maxfilter (TM)'})
 axes[2].set_title('C')
-fig.savefig('Maxfilter.pdf', bbox_to_inches='tight')
+fig.savefig('figures/Maxfilter.pdf', bbox_to_inches='tight')

scripts/results/plot_psd.py

@@ -90,7 +90,7 @@
 
 fig.tight_layout()
 plt.show()
-fig.savefig('psdA.pdf', bbox_to_inches='tight')
+fig.savefig('figures/psdA.pdf', bbox_to_inches='tight')
 
 ###############################################################################
 # Next, the linear scale to check power line frequency
@@ -107,4 +107,4 @@
     plt.axvline(freq, linestyle='--', alpha=0.25, linewidth=2)
 
 plt.tight_layout()
-fig.savefig('psdB.pdf', bbox_to_inches='tight')
+fig.savefig('figures/psdB.pdf', bbox_to_inches='tight')

scripts/results/plot_sensor_cluster_stats_eeg_channel.py

@@ -105,4 +105,4 @@
 plt.xlim([-200, 800])
 plt.tight_layout()
 plt.show()
-plt.savefig('sensorstat.pdf', bbox_to_inches='tight')
+plt.savefig('figures/sensorstat.pdf', bbox_to_inches='tight')

scripts/results/plot_sensor_spatio_temporal_cluster_stats.py

@@ -158,4 +158,4 @@
     mne.viz.tight_layout(fig=fig)
     fig.subplots_adjust(bottom=.05)
     plt.show()
-    plt.savefig('spatiotemporal_stats_cluster-%02d.pdf' % i_clu)
+    plt.savefig('figures/spatiotemporal_stats_cluster-%02d.pdf' % i_clu)

scripts/results/plot_source_stats.py

@@ -0,0 +1,55 @@
+"""
+=====================
+Source space clusters
+=====================
+
+Clustering in source space.
+"""
+import os.path as op
+import numpy as np
+from scipy import stats
+from functools import partial
+
+import mne
+from mne import spatial_tris_connectivity, grade_to_tris
+from mne.stats import (spatio_temporal_cluster_1samp_test,
+                       summarize_clusters_stc, ttest_1samp_no_p)
+
+from library.config import meg_dir, subjects_dir
+
+exclude = [1, 5, 16]  # Excluded subjects
+contrasts = list()
+for subject_id in range(1, 20):
+    if subject_id in exclude:
+        continue
+    subject = "sub%03d" % subject_id
+    print("processing subject: %s" % subject)
+    data_path = op.join(meg_dir, subject)
+    contrast = mne.read_source_estimate(op.join(data_path, 'contrast-morphed'))
+    contrast.resample(100)
+    contrast.crop(0., None)
+    contrasts.append(contrast.data.T)
+
+X = np.abs(np.array(contrasts))
+
+connectivity = spatial_tris_connectivity(grade_to_tris(4))
+p_threshold = 0.001
+t_threshold = -stats.distributions.t.ppf(p_threshold / 2., 15)
+
+stat_fun = partial(ttest_1samp_no_p, sigma=0.5)
+T_obs, clusters, cluster_p_values, H0 = clu = \
+    spatio_temporal_cluster_1samp_test(X, connectivity=connectivity, n_jobs=1,
+                                       threshold=t_threshold,
+                                       stat_fun=stat_fun, verbose=True)
+
+good_cluster_inds = np.where(cluster_p_values < p_threshold)[0]
+tstep = contrast.tstep
+fsave_vertices = [np.arange(2562), np.arange(2562)]
+stc_all_cluster_vis = summarize_clusters_stc(clu, tstep=tstep,
+                                             vertices=fsave_vertices,
+                                             subject='fsaverage')
+
+brain = stc_all_cluster_vis.plot(hemi='both', subjects_dir=subjects_dir,
+                                 time_label='Duration significant (ms)',
+                                 views='ven',
+                                 clim=dict(lims=[0, 15, 30], kind='value'))

scripts/results/plot_time_decoding.py

@@ -63,7 +63,7 @@
 plt.legend()
 plt.tight_layout()
 plt.show()
-plt.savefig('time_decoding.pdf', bbox_to_inches='tight')
+plt.savefig('figures/time_decoding.pdf', bbox_to_inches='tight')
 
 ###############################################################################
 # It seems that `'famous'` vs `'unfamiliar'` gives much noisier time course of