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WH_Res_MorphSTC.py
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WH_Res_MorphSTC.py
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"""
=========================================================
Morph STCs of resolution metrics for W&H data set.
e.g.: run WH_Res_MorphSTC.py WH_MNE_Resolution_config 11 SensitivityMaps SensMap RMS
or: run WH_Res_MorphSTC.py WH_MNE_Resolution_config 11 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)
# get subject ID to process
# qsub start at 0, thus +1 here
sbj_ids = [int(sys.argv[2]) + 1]
# hack to have variables via qsub
stc_path, stc_type, metric = '', '', ''
# read variables specified via qsub
if len(sys.argv)>3: # if additional variable specified
stc_path = sys.argv[3] # pathname (e.g. 'SensitivityMap')
if len(sys.argv) > 4:
stc_type = sys.argv[4] # beginning of filename (e.g. 'LocErrPeak')
if len(sys.argv) > 5:
metric = sys.argv[5] # end of filename (e.g. 'RMS')
print('###\nChosen variables: %s, %s, %s.\n###' % (stc_path, stc_type, metric))
# for filenames
st_duration = C.res_st_duration
origin = C.res_origin
# only one morph_mat per subject needed
morph_mat = []
###
for sbj in sbj_ids:
subject = 'Sub%02d' % sbj
print('###\nAbout to morph STCs for %s.\n###' % (subject))
for modality in C.modalities + [x+'-'+y for (x,y) in C.modal_contr]: # EEG/MEG/EEGMEG and subtractions
# subtraction 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]
# + ['MNE-dSPM', 'MNE-sLORETA', 'dSPM-sLORETA', 'MNE-LCMV', 'dSPM-LCMV', 'sLORETA-LCMV']
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
print('\n###\nDoing %ss for %s and %s.\n###\n' % (functype, inv_type, modality))
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))
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
fname_stc = C.fname_STC(C, stc_path, subject, mytext)
fname_morph = C.fname_STC(C, stc_path, subject, mytext + '_mph')
# read existing source estimate
print('Reading: %s.' % fname_stc)
stc = mne.read_source_estimate(fname_stc, subject)
# compute morph_mat only once per subject
if morph_mat == []:
vertices_to = mne.grade_to_vertices(subject=C.stc_morph, grade=5, subjects_dir=C.subjects_dir)
morph_mat = mne.compute_morph_matrix(subject_from=subject, subject_to=C.stc_morph,
vertices_from=stc.vertices, vertices_to=vertices_to,
subjects_dir=C.subjects_dir)
# Morphing to standard brain
morphed = mne.morph_data_precomputed(subject_from=subject, subject_to=C.stc_morph, stc_from=stc,
vertices_to=vertices_to, morph_mat=morph_mat)
print('Writing morphed to: %s.' % fname_morph)
morphed.save(fname_morph)
# Depth weighting is separate
for modality in ['EEGMEG']: # EEG/MEG/EEGMEG
# create strings for filenames corresponding to contrasts
res_inv_types = ['dep'+str(int(100*x))+'-'+str(int(100*y)) for (x,y) in C.meth_contr_dep]
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']:
loose = 0
loo_str = 'loo%s' % str(int(100*loose))
mytext = '%s_%s_%s_%s_%s_%s' % (functype, inv_type, lamb2_str, stc_type, modality, loo_str)
if metric != '':
mytext = mytext + '_' + metric
fname_stc = C.fname_STC(C, stc_path, subject, mytext)
fname_morph = C.fname_STC(C, stc_path, subject, mytext + '_mph')
# read existing source estimate
print('Reading: %s.' % fname_stc)
stc = mne.read_source_estimate(fname_stc, subject)
# compute morph_mat only once per subject
if morph_mat == []:
vertices_to = mne.grade_to_vertices(subject=C.stc_morph, grade=5, subjects_dir=C.subjects_dir)
morph_mat = mne.compute_morph_matrix(subject_from=subject, subject_to=C.stc_morph,
vertices_from=stc.vertices, vertices_to=vertices_to,
subjects_dir=C.subjects_dir)
# Morphing to standard brain
morphed = mne.morph_data_precomputed(subject_from=subject, subject_to=C.stc_morph, stc_from=stc,
vertices_to=vertices_to, morph_mat=morph_mat)
print('Writing morphed to: %s.' % fname_morph)
morphed.save(fname_morph)
# Done