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 check_data (subsfname, labelsfname, outdir, maskfname, outbase):
au.log.debug('Checking data')
nsubs = au.file_len(subsfname)
nlabs = au.file_len(labelsfname)
if (nsubs != nlabs):
err = 'Not same number of lines in input files\n'
err += labelsfname + ': ' + str(nlabs) + '\n'
err += (subsfname + ': ' + str(nsubs)) + '\n'
raise IOError(err)
if not outdir:
outdir = os.getcwd()
slicesdir = outdir + os.path.sep + au.slices_str()
if not os.path.exists (slicesdir):
os.mkdir (slicesdir)
tempdir = outdir + os.path.sep + au.temp_str()
if not os.path.exists (tempdir):
os.mkdir (tempdir)
au.log.info (slicesdir)
#check all files
subsfile = open(subsfname, 'r')
if maskfname:
for line in subsfile:
fpath = line.strip()
au.check_has_same_geometry (maskfname, fpath)
else:
path1 = ''
path2 = ''
for line in subsfile:
path1 = line.strip()
if not path2:
path2 = line.strip()
continue
else:
au.check_has_same_geometry (path1, path2)
subsfile.close()
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)
#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':
measure_fname = au.pearson_str()
elif measure == 'bat':
measure_fname = au.bhattacharyya_str()
elif measure == 'ttest':
measure_fname = au.ttest_str()
#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':
measure_fname = au.pearson_str()
elif measure == 'bat':
measure_fname = au.bhattacharyya_str()
elif measure == 'ttest':
measure_fname = au.ttest_str()
def slice_and_merge (subsfname, labelsfname, checkfname, outdir='', maskfname='', outbase=''):
check_data (subsfname, labelsfname, outdir, maskfname, outbase)
nsubs = au.file_len(subsfname)
nlabs = au.file_len(labelsfname)
if not outdir:
outdir = os.getcwd()
slicesdir = outdir + os.path.sep + au.slices_str()
tempdir = outdir + os.path.sep + au.temp_str()
tmpdirlst = os.listdir(tempdir)
fpath = ''
olddir = os.getcwd()
if not au.is_done (checkfname, au.preslicingdata_str()):
#slicing subjects
au.log.info ('Slicing all subjects: takes a while')
subsfile = open(subsfname, 'r')
for line in subsfile:
fpath = line.strip()
isfile = os.path.basename(fpath)
osfile = tempdir + os.path.sep + isfile
#test if file has been sliced
fdim3 = au.fslval(fpath, 'dim3')
regex = au.remove_ext(isfile) + '*'
nslices = au.count_match (tmpdirlst, regex)
if fdim3 != nslices:
#if not, then slice it in tempdir
au.log.debug('Slicing ' + isfile)
os.chdir (tempdir)
shutil.copy (fpath, tempdir)
au.fslslice (osfile)
os.remove(osfile)
os.chdir (olddir)
else:
au.log.debug(isfile + ' previously sliced')
subsfile.close()
au.checklist_add (checkfname, au.preslicingdata_str())
if not au.is_done (checkfname, au.premergingdata_str()):
#merging each slice
if not outbase:
outbase = au.data_str()
if not fpath:
subsfile = open(subsfname, 'r')
for line in subsfile:
fpath = line.strip()
break
au.log.info ('Merging all subject slices: takes a while')
nslices = int(au.fslval(fpath,'dim3'))
for slice in range(nslices):
slicezp = au.zeropad(slice)
mergeout = outbase + '_slice_' + slicezp
outdata = slicesdir + os.path.sep + mergeout
#check if data file exists
if not au.imtest(outdata):
#if not, then create it, merging all the corresponding slices
au.log.debug('Merging slice ' + slicezp)
imglob = ''
subsfile = open(subsfname, 'r')
for line in subsfile:
fpath = line.strip()
isfile = au.remove_ext(os.path.basename(fpath)).strip()
isfile = tempdir + os.path.sep + isfile + '_slice_' + slicezp
imglob += isfile + ' '
os.system('fslmerge -t ' + outdata + ' ' + imglob)
for f in imglob.split():
au.imrm(f)
else:
au.log.debug('Slice ' + slicezp + ' previously done')
au.checklist_add (checkfname, au.premergingdata_str())
#slicing mask
if maskfname:
if os.path.exists(maskfname) and not au.is_done (checkfname, au.preslicingmask_str()):
au.log.info('Slicing mask ' + maskfname)
au.imcp (maskfname, slicesdir + os.path.sep + au.mask_str())
os.chdir (slicesdir)
au.fslslice (au.mask_str())
au.imrm (au.mask_str())
au.checklist_add (checkfname, au.preslicingmask_str())
os.chdir (olddir)
au.log.debug('Done preprocessing')