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)
def group_stats (datadir, groupname, groupsize, outdir=''):
lst = os.listdir(datadir)
n = au.count_match(lst, groupname + 's_' + au.slice_regex() + au.ext_str())
if not outdir:
outdir = datadir
au.log.info ('Calculating stats from group ' + groupname + ' in ' + outdir)
for i in range(n):
slino = au.zeropad(i)
dataf = datadir + os.path.sep + groupname + 's_' + au.slice_str() + '_' + slino + au.ext_str()
volstats (dataf, groupname, groupsize, outdir)
def group_stats(datadir, groupname, groupsize, outdir=''):
lst = os.listdir(datadir)
n = au.count_match(lst, groupname + 's_' + au.slice_regex() + au.ext_str())
if not outdir:
outdir = datadir
au.log.info('Calculating stats from group ' + groupname + ' in ' + outdir)
for i in range(n):
slino = au.zeropad(i)
dataf = datadir + os.path.sep + groupname + 's_' + au.slice_str(
) + '_' + slino + au.ext_str()
volstats(dataf, groupname, groupsize, outdir)
def merge_stats_slices (datadir, group):
slice_str = au.slice_str()
groupfregex = group + 's_' + slice_str + '_????' + '.'
#This is a 4D volume with all subjects, it can be a big file, so I'm not creating it
#merge_slices (datadir, groupfregex, group + 's')
au.imrm(datadir + os.path.sep + groupfregex)
[statfnames, mnames] = get_stats_fnames (group, outdir='')
statfnames = statfnames[0]
out = []
for i in np.arange(len(statfnames)):
fname = statfnames[i]
m = mnames[i]
regex = group + 's_' + slice_str + '_????' + '_' + m
o = merge_slices (datadir, regex , fname, datadir, cleanup=False)
au.imrm(datadir + os.path.sep + regex)
out.append(o)
return out
def merge_stats_slices(datadir, group):
slice_str = au.slice_str()
groupfregex = group + 's_' + slice_str + '_????' + '.'
#This is a 4D volume with all subjects, it can be a big file, so I'm not creating it
#merge_slices (datadir, groupfregex, group + 's')
au.imrm(datadir + os.path.sep + groupfregex)
[statfnames, mnames] = get_stats_fnames(group, outdir='')
statfnames = statfnames[0]
out = []
for i in np.arange(len(statfnames)):
fname = statfnames[i]
m = mnames[i]
regex = group + 's_' + slice_str + '_????' + '_' + m
o = merge_slices(datadir, regex, fname, datadir, cleanup=False)
au.imrm(datadir + os.path.sep + regex)
out.append(o)
return out
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):
#saving exclude files in mdir
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):