[Mne_analysis] Fwd: epochs baseline problem (pandas dataframe indexing?)

[Nakagawa Tristan T]

Hi,

is the baselining limited to certain kinds of channels?
I can't get it to work right for physiology (emg,ecg), the means aren't
0 for the baseline period.

Here the details:
I have this raw file:
https://www.dropbox.com/s/6t4z52tno5tl228/raw.fif?dl=0   (1GB!)
and I create epochs with the below code, baseline correcting it.

It may also be related to a possible bug or something I'm doing wrong in
pandas dataframe indexing, see here:
http://stackoverflow.com/questions/36589702/pandas-dataframe-row-multiindex-skip-one/36590006#36590006

Thanks for any input.
best,
Tristan

-----------------------------------

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mne
import scipy.signal as signal

# load data
raw = Raw('raw.fif', preload = True) #load Data
raw._first_samps[0]=0 # From my older question, I still need this..
mne.channels.rename_channels(raw.info,{'BIO003':'emg1','BIO004':'emg2',
                                       'BIO005':'emg3','BIO006':'emg4',
                                       'BIO007':'emg5','BIO008':'ecg'})
raw.set_channel_types({'emg1':'emg','emg2':'emg','emg3':'emg','emg4':'emg','emg5':'emg','ecg':'ecg'})
picks_emg = mne.pick_types(raw.info,emg=True)
# preprocess emg signals: hilbert envelope!
raw.filter(10, 150, picks=picks_emg)

# For viewability, I put the events below, load them first!

# Create & save Epochs
event_id = dict(CS=1, OS=2, NS=3) 
tmin = -1.1  # start of each epoch
tmax = 6 # end of each epoch
baseline = (-1, -.05) # in seconds
epochs = mne.Epochs(raw,events, event_id, tmin=tmin, tmax=tmax,
proj=False, picks=[0,1,2,3,4,5],
                    baseline=(-.5,.05), preload=True,reject=None)

# Confirm that mean for baseline period is 0:
test=epochs['CS'].to_data_frame()
# mean=test.loc[idx['CS',:, -1000:-50], :].mean() # doesn't work, see
stackoverflow link!
mean=test.loc[idx['CS',test.index.levels[1], -1000:-50], :].mean()
print(mean)

# events
events=np.array([[ 11527,      0,      1],
       [ 22344,      0,      2],
       [ 31541,      0,      3],
       [ 42542,      0,      2],
       [ 52757,      0,      3],
       [ 61972,      0,      1],
       [ 71586,      0,      3],
       [ 81785,      0,      3],
       [ 92786,      0,      3],
       [103803,      0,      3],
       [113000,      0,      3],
       [124001,      0,      2],
       [135018,      0,      2],
       [145016,      0,      2],
       [155616,      0,      2],
       [164830,      0,      3],
       [174629,      0,      2],
       [185629,      0,      1],
       [196245,      0,      3],
       [207246,      0,      2],
       [217645,      0,      1],
       [226659,      0,      1],
       [237459,      0,      2],
       [248476,      0,      1],
       [258875,      0,      1],
       [269475,      0,      1],
       [280091,      0,      1],
       [289890,      0,      3],
       [300289,      0,      1],
       [309503,      0,      2],
       [319903,      0,      1],
       [328900,      0,      1],
       [338515,      0,      3],
       [347512,      0,      2],
       [356726,      0,      2],
       [367526,      0,      3],
       [377926,      0,      2],
       [387540,      0,      2],
       [398557,      0,      3],
       [407571,      0,      3],
       [417370,      0,      1],
       [427168,      0,      2],
       [437784,      0,      1],
       [448384,      0,      3],
       [457782,      0,      1],
       [467797,      0,      1],
       [477596,      0,      1],
       [488012,      0,      2],
       [498411,      0,      3],
       [509011,      0,      3],
       [518626,      0,      2],
       [529025,      0,      3],
       [539425,      0,      3],
       [548639,      0,      2],
       [557836,      0,      2],
       [567835,      0,      3],
       [578852,      0,      1],
       [588450,      0,      1],
       [598666,      0,      1],
       [608064,      0,      2]])