simple test of return generator code for apply_inverse_epochs.py

Test_returnGenerators.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)