labelslist, bvec_orient, get_params, verbose)
for subject in l]).get()
# tract_results = pool.starmap_async(evaluate_tracts, [(dwipath, outtrkpath, subject, stepsize, saved_streamlines,
# figspath, function_processes, doprune, display, verbose)
# for subject in l]).get()
pool.close()
else:
for subject in l:
txtfile = dwipath + subject + "/params.txt"
for bvec_orient in bvec_orient_list:
tract_results = []
print(bvec_orient)
strproperty = orient_to_str(bvec_orient)
tract_results.append(
create_tracts(dwipath, outtrkpath, subject, figspath, stepsize,
function_processes, strproperty, ratio, masktype,
classifier, labelslist, bvec_orient, doprune,
overwrite, get_params, denoise, verbose))
print(tract_results)
with open(txtfile, 'a') as f:
for item in tract_results:
f.write("Subject %s with %s %s %s \n" %
(item[0], str(bvec_orient[0]), str(
bvec_orient[1]), str(bvec_orient[2])))
f.write("Num tracts: %s \n" % item[2][0])
f.write("Min tract length: %s \n" % item[2][1])
f.write("Max tract length: %s \n" % item[2][2])
f.write("Average tract length: %s \n" % item[2][3])
f.write("Standard deviancy tract length: %s \n" %
item[2][4])
#dwip_results = pool.starmap_async(dwi_preprocessing[(dwipath,outpath,subject,denoise,savefa,function_processes, verbose) for subject in l]).get()
vol_b0 = [0,1,2,3]
if subject_processes>1:
if function_processes>1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
#dwi_results = pool.starmap_async(diff_preprocessing, [(dwipath, dwipath_preprocessed, subject, bvec_orient, denoise, savefa, function_processes,
# createmask, vol_b0, verbose) for subject in l]).get()
tract_results = pool.starmap_async(create_tracts, [(dwipath_preprocessed, outtrkpath, subject, figspath, stepsize, function_processes,
str_identifier, ratio, classifiertype, labelslist, bvec_orient, doprune,
overwrite, get_params, verbose) for subject in l]).get()
tract_results = pool.starmap_async(tract_connectome_analysis, [(dwipath_preprocessed, outtrkpath, str_identifier, figspath,
subject, atlas_legends, bvec_orient, brainmask,
inclusive,function_processes, forcestart,
picklesave, verbose) for subject in l]).get()
pool.close()
else:
for subject in l:
#dwi_results.append(diff_preprocessing(dwipath, dwipath_preprocessed, subject, bvec_orient, denoise, savefa,
# function_processes, createmask, vol_b0, verbose))
tract_results.append(create_tracts(dwipath_preprocessed, outtrkpath, subject, figspath, stepsize, function_processes, str_identifier,
ratio, classifiertype, labelslist, bvec_orient, doprune, overwrite, get_params,
verbose))
tract_results.append(tract_connectome_analysis(dwipath, outtrkpath, str_identifier, figspath, subject,
atlas_legends, bvec_orient, brainmask, inclusive, function_processes,
forcestart, picklesave, verbose))
subject=l[0]
labelslist, bvec_orient, doprune, overwrite, get_params, denoise,
verbose) for subject in subjects]).get()
if make_connectomes:
tract_results = pool.starmap_async(
tract_connectome_analysis,
[(diff_preprocessed, trkpath, str_identifier, figspath, subject,
atlas_legends, bvec_orient, brainmask, inclusive,
function_processes, overwrite, picklesave, labeltype, symmetric,
reference_weighting, volume_weighting, verbose)
for subject in subjects]).get()
pool.close()
else:
for subject in subjects:
if make_tracts:
tract_results.append(
create_tracts(diff_preprocessed, trkpath, subject, figspath,
stepsize, function_processes, str_identifier,
ratio, brainmask, classifier, labelslist,
bvec_orient, doprune, overwrite, get_params,
denoise, verbose))
#get_diffusionattributes(diff_preprocessed, diff_preprocessed, subject, str_identifier, vol_b0, ratio, bvec_orient,
# masktype, overwrite, verbose)
if make_connectomes:
tract_results.append(
tract_connectome_analysis(
diff_preprocessed, trkpath, str_identifier, figspath,
subject, atlas_legends, bvec_orient, brainmask, inclusive,
function_processes, overwrite, picklesave, labeltype,
symmetric, reference_weighting, volume_weighting, verbose))
print(tract_results)