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WH_MNE_Resolution_config.py
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WH_MNE_Resolution_config.py
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"""
=======================================
Config file for Wakeman&Henson data set
Based on config file for ScriptsPreprocessing
=======================================
place it in same folder as py-files
"""
# OH July 2018
##########################################################
## SYSTEM variables
##########################################################
import sys
sys.path = [
'/home/olaf/MEG/WakemanHensonEMEG/ScriptsResolution', # following list created by trial and error
'/imaging/local/software/mne_python/latest_v0.15',
'/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 os
import os.path as op
import numpy as np
##########################################################
## GENERAL
##########################################################
# path to unmaxfiltered raw data
raw_path_in = '/imaging/rh01/Methods/DanData/RawFIF/'
# output path for maxfiltered raw data
raw_path_sss = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/RAW'
# path to MRI/MEG transformations
trans_path = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/TRANS'
# where MRIs are
subjects_dir = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/MRI/'
# raw_path_sss = '/imaging/rh01/Methods/DanData/Raw'
# subject numbers (from Roni's Matlab script)
subjs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
# sessions per subject
sess = [1, 2, 3, 4, 5, 6]
# sess = [1, 2]
# reference session for trans option
ref_sess = 4
# path to results from Resolution metrics
resolution_path = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/Resolution'
##########################################################
## MAXFILTER
##########################################################
# maxfilter parameters
# problems with SVD converge in MF when doing more than 2 options at once
# (probably due to memory problems)
MF = {# 'NM_cmd': '/imaging/local/software/neuromag/bin/util/x86_64-pc-linux-gnu/maxfilter-2.2',
'st_duration': [10.], # list
'origin': [(0., 0., 0.045)], # list
'ref_sess': 4}
st_string_stem = 'ST'
ori_string_stem = 'O'
# only for use of post-MF processing
# different py-scripts for MNE/NM maxfilter options
MF_method = '_MN' # '_NM'
##########################################################
### FILTERING AND BAD CHANNELS
##########################################################
freq_l = 0.1 # Hz, high-pass filter
freq_h = 40. # Hz, low-pass filter
##########################################################
### EPOCHING
##########################################################
# path to evoked responses
evo_path = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/AVG/'
# epoch start/end latencies (s)
epo_t1, epo_t2 = -0.2, 0.5
##########################################################
### COVARIANCE
##########################################################
cov_path = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/COV/'
cov_latwins = [
[-0.4, 0.],
[-0.3, 0.],
[-0.2, 0.],
[-0.1, 0.],
[-0.2, 0.1],
[-0.2, 0.25],
[-0.2, 0.5],
[0.05, 0.5],
[0.05, 0.25]
]
# cov_methods = ['auto'] # list of lists if best covmat to be chosen
# cov_methods = ['empirical', 'shrunk', 'ledoit_wolf'] # list
cov_methods = ['empirical']
##########################################################
### SOURCE SPACE
##########################################################
src_spacing = 'oct6'
##########################################################
### BEM
##########################################################
# where BEM figures will be written to
bem_path = subjects_dir # will be in subject/bem
bem_log_file = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/MRI/BEMs/BEM_Model_and_SourceSpace.log'
bem_fig_dir = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/Figures/BEM_plot'
coor_fig_dir = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/Figures/CoorTrans_plot'
bem_ico = 4
bem_conductivity_1 = (0.3,) # for single layer
bem_conductivity_3 = (0.3, 0.006, 0.3) # for three layers
##########################################################
### FORWARD AND INVERSE OPERATORS
##########################################################
# for use in forward solution
fwd_filt_suf = '_01_400Hz_raw.fif' # filter, suffix for filenames
fwd_st = 10.
fwd_origin = (0.,0.,0.045)
# for inverse operator
inv_filt_suf = '_01_400Hz_raw.fif' # filter, suffix for filenames
# inv_lat_str = '-200_400ms'
inv_cov_latwin = [-0.2, 0.]
inv_method = 'empirical' # used for file filter, number of method name
# regularisation parameter for 'empirical' covariance matrices
inv_lambda_empirical = {'eeg': 0.1, 'mag': 0.1, 'grad': 0.1}
##########################################################
### INVERSE OPERATOR
##########################################################
stc_path = '/group/erp/data/olaf.hauk/MEG/WakemanHensonEMEG/data/STC/'
stc_method = 'MNE'
# regularisation SNR
stc_snr = 3.
# inv_modalities = ['EEGMEG', 'MEG', 'EEG'] # which invop files to apply
inv_modalities = ['EEGMEG', 'MEG']
# orientation constraint
inv_loose = [None, 0.2, 0.6]
# depth weighting
inv_depth = [None, 0.4, 0.8]
# brain to morph individual STCs to
stc_morph = 'fsaverage'
##########################################################
### LCMV BEAMFORMER
##########################################################
lcmv_datcov_latwin = [0.05, 0.25]
lcmv_noisecov_latwin = [-0.3, 0.]
# type of covariance matrix
lcmv_dat_method = 'empirical'
lcmv_noise_method = 'empirical'
# regularisation parameter for 'empirical' covariance matrices
lcmv_lambda_empirical = {'eeg': 0.1, 'mag': 0.1, 'grad': 0.1}
# which forward solutions to use
lcmv_modalities = ['EEGMEG', 'MEG']
##########################################################
### Resolution metrics
##########################################################
# Maxfilter parameters to use for filenames
res_st_duration = 10.
res_origin = [0., 0., 0.045]
res_lambda_empirical = {'eeg': 0.2, 'mag': 0.2, 'grad': 0.2}
res_cov_latwin = [-0.2, 0.]
# Type of inverse method, 'MNE' | 'sLORETA' | 'dSPM' | 'LCMV'
res_inv_types = ['MNE', 'sLORETA', 'dSPM', 'LCMV']
###########################################################
# FUNCTIONS FOR FILENAMES
###########################################################
def fname_epo(C, subject, st_duration, origin):
subject = str(subject)
st_string = _st_str(C.st_string_stem, st_duration)
# for file name only use z-component in mm
ori_string = C.ori_string_stem + str(origin[2]*1000).split('.')[0]
lat_str = _lat_str(C.epo_t1, C.epo_t2)
filt_str = _filt_str(C.freq_l, C.freq_h)
epo_fname = op.join(C.epo_path, subject, subject + '_' + st_string + '_' + ori_string + C.MF_method +
'_' + lat_str + "_" + filt_str + "-epo.fif")
return epo_fname
def fname_cov(C, subject, st_duration, origin, latwin, method, cov_i):
subject = str(subject)
st_string = _st_str(C.st_string_stem, st_duration)
# for file name only use z-component in mm
ori_string = C.ori_string_stem + str(origin[2]*1000).split('.')[0]
lat_str = _lat_str(C.epo_t1, C.epo_t2)
filt_str = _filt_str(C.freq_l, C.freq_h)
cov_str = _cov_str(latwin)
method_now = method + str(cov_i) # add the ranking of covmat
cov_fname = op.join(C.cov_path, subject, subject + '_' + st_string + '_' + ori_string +
C.MF_method + '_' + lat_str + '_' + filt_str + '_' + cov_str + '_'
+ method_now + '-cov.fif')
return cov_fname
def fname_evo(C, subject, st_duration, origin):
subject = str(subject)
st_string = _st_str(C.st_string_stem, st_duration)
# for file name only use z-component in mm
ori_string = C.ori_string_stem + str(origin[2]*1000).split('.')[0]
lat_str = _lat_str(C.epo_t1, C.epo_t2)
filt_str = _filt_str(C.freq_l, C.freq_h)
evo_fname = op.join(C.evo_path, subject, subject + '_' + st_string + '_' + ori_string +
C.MF_method + '_' + lat_str + '_' + filt_str + '-ave.fif')
return evo_fname
def fname_src_space(C, subject):
subject = str(subject)
src_fname = op.join(C.bem_path, subject, 'bem', subject + '_' + C.src_spacing + '-src.fif')
return src_fname
def fname_BEM(C, subject, modality):
# modality: e.g. EEG, MEG or EEGMEG
subject = str(subject)
bem_fname = op.join(C.bem_path, subject, 'bem', subject + '_' + modality + '-bem.fif')
return bem_fname
def fname_ForwardSolution(C, subject, modality):
# modality: e.g. EEG, MEG or EEGMEG
subject = str(subject)
fwd_fname = op.join(C.evo_path, subject, subject + '_' + C.MF_method + '_' + modality + '-fwd.fif')
return fwd_fname
def fname_InverseOperator(C, subject, st_duration, origin, latwin, modality, loose, depth):
# modality: e.g. EEG, MEG or EEGMEG
subject = str(subject)
st_string = _st_str(C.st_string_stem, st_duration)
# for file name only use z-component in mm
ori_string = C.ori_string_stem + str(origin[2]*1000).split('.')[0]
filt_str = _filt_str(C.freq_l, C.freq_h)
cov_str = _cov_str(latwin)
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))
inv_fname = op.join(C.evo_path, subject, subject + '_' + st_string + '_' + ori_string +
C.MF_method + '_' + filt_str + '_' + cov_str + '_' + C.inv_method +
'_' + modality + loo_str + dep_str + '-inv.fif')
return inv_fname
def fname_STC(C, folder, subject, mytext):
subject = str(subject)
stc_fname = op.join(C.resolution_path, folder, subject, mytext)
return stc_fname
###########################################################
# UTILITY FUNCTIONS
###########################################################
def _st_str(st_string_stem, st_duration):
if st_duration == None:
st_str = st_string_stem + '0'
else:
st_str = st_string_stem + str(st_duration).replace(".", "-")
return st_str
def _lat_str(t1, t2):
lat_tmp = str(int(1000*t1)) + '_' + str(int(1000*t2)) + "ms"
lat_str = lat_tmp.replace(".", "")
return lat_str
def _filt_str(l_freq, h_freq):
filt_tmp = str(l_freq) + '_' + str(h_freq) + "Hz"
filt_str = filt_tmp.replace(".", "")
return filt_str
def _cov_str(latwin):
if not latwin==None:
tmin_str = str(int(1000*latwin[0])) # time interval as string in ms
tmax_str = str(int(1000*latwin[1]))
cov_str = 'cov' + tmin_str + '_' + tmax_str
else:
cov_str = 'cov_id' # covariance identity matrix
return cov_str