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WH_Res_AvgSTCs_bioRxiv0.py
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WH_Res_AvgSTCs_bioRxiv0.py
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"""
=========================================================
Grand-average of morphed STCs for resolution metrics
for WH data set.
Doesn't run in parallel mode.
e.g.: run WH_Res_AvgSTC.py WH_Res_config SensitivityMaps SensMap RMS
or: run WH_Res_AvgSTC.py WH_Res_config ResolutionMetrics locerr_peak
=========================================================
"""
# OH, July 2018
print __doc__
import os
import os.path as op
import sys
sys.path = [
'/home/olaf/MEG/WakemanHensonEMEG/ScriptsResolution', # following list created by trial and error
'/imaging/local/software/mne_python/latest_v0.16',
'/imaging/local/software/anaconda/2.4.1/2/bin',
'/imaging/local/software/anaconda/2.4.1/2/lib/python2.7/',
'/imaging/local/software/anaconda/2.4.1/2/envs/mayavi_env/lib/python2.7/site-packages',
'/imaging/local/software/anaconda/2.4.1/2/envs/mayavi_env/lib/python2.7/site-packages/pysurfer-0.8.dev0-py2.7.egg',
'/imaging/local/software/anaconda/2.4.1/2/lib/python2.7/site-packages/h5io-0.1.dev0-py2.7.egg',
'/imaging/local/software/anaconda/2.4.1/2/lib/python2.7/lib-dynload',
'/imaging/local/software/anaconda/2.4.1/2/lib/python2.7/site-packages'
]
import importlib
import glob
import numpy as np
import mne
print('MNE Version: %s\n\n' % mne.__version__) # just in case
## get analysis parameters from config file
module_name = sys.argv[1]
C = importlib.import_module(module_name)
reload(C)
# get functions for metrics etc.
R = importlib.import_module('WH_Resolution_Functions')
reload(R)
# Type of inverse method, 'norm' | 'lcmv'
inv_types = ['norm', 'lcmv']
# hack to have variables via qsub
stc_path, stc_type, metric = '', '', ''
# read variables specified via qsub
if len(sys.argv)>2: # if additional variable specified
stc_path = sys.argv[2] # pathname (e.g. 'SensitivityMap')
if len(sys.argv) > 3:
stc_type = sys.argv[3] # beginning of filename (e.g. 'LocErrPeak')
if len(sys.argv) > 4:
metric = sys.argv[4] # end of filename (e.g. 'RMS')
print('###\nChosen variables: %s, %s, %s.\n###' % (stc_path, stc_type, metric))
# Maxfilter parameters for filenames
st_duration = C.res_st_duration
origin = C.res_origin
# create dir for average if necessary
fname_avg = C.fname_STC(C, stc_path, C.stc_morph, '')
if not op.exists(fname_avg):
os.mkdir(fname_avg)
for modality in C.modalities + [x+'-'+y for (x,y) in C.modal_contr]: #['EEGMEG', 'MEG', 'EEG', 'EEGMEG-MEG', 'EEGMEG-EEG']: # EEG/MEG/EEGMEG
# contrasts for inverse methods only computed for EEGMEG
if modality == 'EEGMEG':
res_inv_types = C.res_inv_types + \
[x+'-'+y for (x,y) in C.meth_contr] + \
['dep'+str(int(100*x))+'-'+str(int(100*y)) for (x,y) in C.meth_contr_dep]
# res_inv_types = C.res_inv_types + ['MNE-dSPM', 'MNE-sLORETA', 'dSPM-sLORETA',
# 'MNE-LCMV', 'dSPM-LCMV', 'sLORETA-LCMV', 'dep0-40', 'dep0-80']
else:
res_inv_types = C.res_inv_types
for inv_type in res_inv_types: # 'MNE', 'LCMV' etc.
for lambda2 in C.res_lambda2s: # regularisation parameters
lamb2_str = str(lambda2).replace('.', '')
if len(lamb2_str) > 3:
lamb2_str = lamb2_str[:3]
# for CTFs and PSFs
for functype in ['CTF', 'PSF']:
# iterate over inverse operator types
for loose in C.inv_loose: # orientation constraint
for depth in C.inv_depth: # depth weighting
stcs = [] # Will contain STCs per subject for averaging
if loose == None: loose = 0
loo_str = 'loo%s' % str(int(100*loose))
if depth == None: depth = 0
dep_str = 'dep%s' % str(int(100*depth))
if inv_type[:3] == 'dep': # exception for depth-weighted MNE
mytext = '%s_%s_%s_%s_%s_%s' % (functype, inv_type, lamb2_str, stc_type, modality, loo_str)
else:
mytext = '%s_%s_%s_%s_%s_%s_%s' % (functype, inv_type, lamb2_str, stc_type, modality, loo_str, dep_str)
if metric != '':
mytext = mytext + '_' + metric
for sbj in C.subjs:
subject = 'Sub%02d' % sbj
fname_morph = C.fname_STC(C, stc_path, subject, mytext + '_mph')
# READ EXISTING SOURCE ESTIMATE
print('Reading: %s.' % fname_morph)
stc = mne.read_source_estimate(fname_morph, subject)
stcs.append(stc)
# average STCs across subjects
print('Averaging %d STC files.' % len(stcs))
avg = np.average([s.data for s in stcs], axis=0)
# turn average into source estimate object
avg_stc = mne.SourceEstimate(avg, stcs[0].vertices, stcs[0].tmin, stcs[0].tstep)
fname_avg = C.fname_STC(C, stc_path, C.stc_morph, mytext)
print('###\nWriting grand-average STC file %s.\n###' % fname_avg)
avg_stc.save(fname_avg)
if inv_type[:3] == 'dep': # depth-weighted MNE only for one depth
break
if inv_type[:3] == 'dep': # depth-weighted MNE only for one loose
break
# Done