copy(outf_subjs, mdir)
copy(outf_labels, mdir)
#read the measure argument and start processing
if measure == 'pea':
if not stepdone:
step = measureperslice_str() + ' ' + measure_fname + ' ' + mdir
if not is_done(chkf, step):
pear.aizkolari_data_pearson(outdir, mdir, usemask, excluf)
checklist_add(chkf, step)
step = postmerging_str() + ' ' + measure_fname + ' ' + mdir
if not is_done(chkf, step):
pearegex = pearson_str() + '_' + slice_str() + '*'
post.merge_slices(mdir, pearegex, pearson_str(), mdir)
checklist_add(chkf, step)
elif measure == 'bat' or measure == 'ttest':
if not stepdone:
gsize = np.zeros([len(classnames), 2], dtype=int)
for c in range(len(classnames)):
gname = classnames[c]
glabel = labels[c]
godir = mdir + os.path.sep + gname
print('Processing group ' + gname)
gselect = np.zeros(len(subjs))
gsubjs = list()
glabels = list()
outf_exclude = ''
if (excluf):
outf_exclude = au.exclude_str()
if expname:
outf_exclude += '_' + expname
if foldnumber:
outf_exclude += '_' + foldnumber
np.savetxt(outdir + os.path.sep + outf_exclude , excluded, fmt='%i')
np.savetxt(mdir + os.path.sep + au.exclude_str(), excluded, fmt='%i')
excluf = mdir + os.path.sep + au.exclude_str()
step = au.maskmerging_str() + ' ' + measure_fname + ' ' + mdir
if usemask and not au.is_done(chkf, step):
maskregex = au.mask_str() + '_' + au.slice_str() + '*'
post.merge_slices (slidir, maskregex, au.mask_str(), mdir, False)
au.checklist_add(chkf, step)
#CORRELATION
#read the measure argument and start processing
if measure == 'pea':
#measure pearson correlation for each population slice
step = au.measureperslice_str() + step_params
if not au.is_done(chkf, step):
pear.pearson_correlation (outdir, mdir, usemask, excluf, leave)
au.checklist_add(chkf, step)
#merge all correlation slice measures
step = au.postmerging_str() + step_params
if not au.is_done(chkf, step):
pearegex = au.pearson_str() + '_' + au.slice_str() + '*'
outf_exclude = au.exclude_str()
if expname:
outf_exclude += '_' + expname
if foldnumber:
outf_exclude += '_' + foldnumber
np.savetxt(outdir + os.path.sep + outf_exclude, excluded, fmt='%i')
np.savetxt(mdir + os.path.sep + au.exclude_str(),
excluded,
fmt='%i')
excluf = mdir + os.path.sep + au.exclude_str()
step = au.maskmerging_str() + ' ' + measure_fname + ' ' + mdir
if usemask and not au.is_done(chkf, step):
maskregex = au.mask_str() + '_' + au.slice_str() + '*'
post.merge_slices(slidir, maskregex, au.mask_str(), mdir, False)
au.checklist_add(chkf, step)
#CORRELATION
#read the measure argument and start processing
if measure == 'pea':
#measure pearson correlation for each population slice
step = au.measureperslice_str() + step_params
if not au.is_done(chkf, step):
pear.pearson_correlation(outdir, mdir, usemask, excluf, leave)
au.checklist_add(chkf, step)
#merge all correlation slice measures
step = au.postmerging_str() + step_params
if not au.is_done(chkf, step):
pearegex = au.pearson_str() + '_' + au.slice_str() + '*'
copy(outf_subjs, mdir)
copy(outf_labels, mdir)
#read the measure argument and start processing
if measure == 'pea':
if not stepdone:
step = measureperslice_str() + ' ' + measure_fname + ' ' + mdir
if not is_done(chkf, step):
pear.aizkolari_data_pearson (outdir, mdir, usemask, excluf)
checklist_add(chkf, step)
step = postmerging_str() + ' '+ measure_fname + ' ' + mdir
if not is_done(chkf, step):
pearegex = pearson_str() + '_' + slice_str() + '*'
post.merge_slices (mdir, pearegex, pearson_str(), mdir)
checklist_add(chkf, step)
elif measure == 'bat' or measure == 'ttest':
if not stepdone:
gsize = np.zeros([len(classnames),2], dtype=int)
for c in range(len(classnames)):
gname = classnames[c]
glabel = labels [c]
godir = mdir + os.path.sep + gname
print ('Processing group ' + gname)
gselect = np.zeros(len(subjs))
gsubjs = list()
glabels = list()
