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@bloyl
Created October 2, 2013 19:48
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simple test of return generator code for apply_inverse_epochs.py
import numpy as np
import pylab as pl
import mne
from mne.datasets import sample
from mne.fiff import Raw, pick_types
from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
import time
from guppy import hpy
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
event_id, tmin, tmax = 1, -0.2, 0.5
snr = 1.0 # use smaller SNR for raw data
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw = Raw(fname_raw)
events = mne.read_events(fname_event)
# Set up pick list
include = []
# Add a bad channel
raw.info['bads'] += ['EEG 053'] # bads + 1 more
# pick MEG channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,
eog=150e-6))
# Compute inverse solution and stcs for each epoch
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, None,
pick_ori="normal",return_generator=True)
#modified for a test...
for stc in stcs:
print stc.data.shape[0]
print stc.times[0]
time.sleep(.1)
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