data frame

examples/export/README.txt

@@ -0,0 +1,6 @@
+
+Export of MNE data for us in other packages
+-------------------------------------------
+
+Export as data frames in Pandas, TimeSeries in nitime etc.
+

examples/export/epochs_as_data_frame.py → examples/export/plot_epochs_as_data_frame.py

@@ -1,27 +1,41 @@
+"""
+======================================
+Export Epochs to a dataframe in Pandas
+======================================
+
+"""
+
+# Author: Denis Engemann <d.engemann@fz-juelich.de>
+#
+# License: BSD (3-clause)
+
+print __doc__
+
 import mne
+import pylab as pl
 import numpy as np
 from mne.fiff import Raw
 from mne.datasets import sample
+
 from pandas.stats.api import rolling_mean
 
-data_path = sample.data_path('examples/')
+data_path = sample.data_path('..')
 raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
 
 raw = Raw(raw_fname)
 events = mne.find_events(raw, stim_channel='STI 014')
-exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053']
-picks = mne.fiff.pick_types(raw.info, meg=True, eeg=True, eog=True, stim=False, exclude=exclude)
+raw.info['bads'] = ['MEG 2443', 'EEG 053']
+picks = mne.fiff.pick_types(raw.info, meg=True, eeg=True, eog=True, stim=False,
+                            exclude=raw.info['bads'])
 
-event_id = 1
-tmin = -0.2
-tmax = 0.5
+tmin, tmax, event_id = -0.2, 0.5, 1
 baseline = (None, 0)
 reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
 
 epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
                     baseline=baseline, preload=False, reject=reject)
 
-epochs_df = epochs.to_data_frame()
+epochs_df = epochs.as_data_frame()
 
 meg_chs = [c for c in epochs.ch_names if c.startswith("MEG")]
 
@@ -34,7 +48,7 @@
 # then create a quick average plot
 grouped_tsl.mean().plot(legend=0)
 
-# or a trellis plot on a few channels 
+# or a trellis plot on a few channels
 grouped_tsl.mean()[meg_chs[:10]].plot(subplots=1)
 
 # use median instead
@@ -43,7 +57,7 @@
 # use custom numpy function
 grouped_tsl.agg(np.std).plot(legend=0)
 
-# average and then smooth using a rolling mean and finally plot in one sinfle line!
+# average then smooth using a rolling mean and finally plot in one single line!
 grouped_tsl.apply(lambda x: rolling_mean(x.mean(), 10)).plot(legend=0)
 
 # apply different functio for channels
@@ -58,3 +72,5 @@
 grouped_epochs.std()["MEG 0113"].plot()
 
 grouped_tsl.agg(np.std).plot(legend=0)
+
+pl.show()