def pearson_correlation(datadir,
outdir,
usemask=True,
excludef='',
exclude_idx=-1):
slidir = datadir + os.path.sep + au.slices_str()
subjsfile = datadir + os.path.sep + au.subjects_str()
labelsfile = datadir + os.path.sep + au.labels_str()
lst = os.listdir(slidir)
n = au.count_match(lst, au.data_str() + '_' + au.slice_regex())
exclude_log = ''
if exclude_idx > -1:
exclude_log = ' excluding subject ' + str(exclude_idx)
au.log.info('Calculating correlation of ' + slidir + os.path.sep +
au.data_str() + '_' + au.slice_regex() + exclude_log)
for i in range(n):
slino = au.zeropad(i)
dataf = slidir + os.path.sep + au.data_str() + '_' + au.slice_str(
) + '_' + slino + au.ext_str()
maskf = slidir + os.path.sep + au.mask_str() + '_' + au.slice_str(
) + '_' + slino + au.ext_str()
outf = outdir + os.path.sep + au.pearson_str() + '_' + au.slice_str(
) + '_' + slino
if exclude_idx > -1:
outf += '_' + au.excluded_str() + str(exclude_idx) + au.ext_str()
else:
outf += au.ext_str()
if not os.path.isfile(dataf):
au.log.error('Could not find ' + dataf)
continue
if not usemask:
maskf = ''
try:
measure_pearson(dataf, labelsfile, outf, maskf, excludef,
exclude_idx)
except:
au.log.error(
'pearson_correlation: Error measuring correlation on ' + dataf)
au.log.error("Unexpected error: ", sys.exc_info()[0])
exit(1)
def pearson_correlation (datadir, outdir, usemask=True, excludef='', exclude_idx=-1):
slidir = datadir + os.path.sep + au.slices_str()
subjsfile = datadir + os.path.sep + au.subjects_str()
labelsfile = datadir + os.path.sep + au.labels_str()
lst = os.listdir(slidir)
n = au.count_match(lst, au.data_str() + '_' + au.slice_regex())
exclude_log = ''
if exclude_idx > -1:
exclude_log = ' excluding subject ' + str(exclude_idx)
au.log.info ('Calculating correlation of ' + slidir + os.path.sep + au.data_str() + '_' + au.slice_regex() + exclude_log)
for i in range(n):
slino = au.zeropad(i)
dataf = slidir + os.path.sep + au.data_str() + '_' + au.slice_str() + '_' + slino + au.ext_str()
maskf = slidir + os.path.sep + au.mask_str() + '_' + au.slice_str() + '_' + slino + au.ext_str()
outf = outdir + os.path.sep + au.pearson_str() + '_' + au.slice_str() + '_' + slino
if exclude_idx > -1:
outf += '_' + au.excluded_str() + str(exclude_idx) + au.ext_str()
else:
outf += au.ext_str()
if not os.path.isfile(dataf):
au.log.error('Could not find ' + dataf)
continue
if not usemask:
maskf = ''
try:
measure_pearson(dataf, labelsfile, outf, maskf, excludef, exclude_idx)
except:
au.log.error('pearson_correlation: Error measuring correlation on ' + dataf)
au.log.error("Unexpected error: ", sys.exc_info()[0] )
exit(1)
[subjlabels, subjs] = parse_subjects_list(subjsf, datadir)
#if output dir does not exist, create
if not (os.path.exists(outdir)):
os.mkdir(outdir)
#checklist_fname
if chklst:
chkf = outdir + os.path.sep + au.checklist_str()
if not (os.path.exists(chkf)):
au.touch(chkf)
else:
chkf = ''
#saving data in files where further processes can find them
outf_subjs = outdir + os.path.sep + au.subjects_str()
outf_labels = outdir + os.path.sep + au.labels_str()
np.savetxt(outf_subjs, subjs, fmt='%s')
np.savetxt(outf_labels, subjlabels, fmt='%i')
#creating folder for slices
slidir = outdir + os.path.sep + au.slices_str()
if not (os.path.exists(slidir)):
os.mkdir(slidir)
#slice the volumes
#creating group and mask slices
pre.slice_and_merge(outf_subjs, outf_labels, chkf, outdir, maskf)
#creating measure output folder
if measure == 'pea':
[subjlabels, subjs] = parse_subjects_list (subjsf, datadir)
#if output dir does not exist, create
if not(os.path.exists(outdir)):
os.mkdir(outdir)
#checklist_fname
if chklst:
chkf = outdir + os.path.sep + au.checklist_str()
if not(os.path.exists(chkf)):
au.touch(chkf)
else:
chkf = ''
#saving data in files where further processes can find them
outf_subjs = outdir + os.path.sep + au.subjects_str()
outf_labels = outdir + os.path.sep + au.labels_str()
np.savetxt(outf_subjs, subjs, fmt='%s')
np.savetxt(outf_labels, subjlabels, fmt='%i')
#creating folder for slices
slidir = outdir + os.path.sep + au.slices_str()
if not(os.path.exists(slidir)):
os.mkdir(slidir)
#slice the volumes
#creating group and mask slices
pre.slice_and_merge(outf_subjs, outf_labels, chkf, outdir, maskf)
#creating measure output folder
if measure == 'pea':