# ---------------------------------------------------------
tall = time()
tract_results = []
if verbose:
txt = (
"Process running with % d max processes available on % d subjects with % d subjects in parallel each using % d processes"
% (mp.cpu_count(), np.size(l), subject_processes, function_processes))
print(txt)
send_mail(txt, subject="Main process start msg ")
duration1 = time()
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
# tract_results = pool.starmap_async(create_tracts, [(dwipath, outtrkpath, subject, stepsize, function_processes, strproperty,
# saved_streamlines, savefa, labelslist, bvec_orient, verbose) for subject in
# l]).get()
tract_results = pool.starmap_async(
evaluate_tracts,
[(dwipath, outtrkpath, subject, stepsize, saved_streamlines,
labelslist, outpathpickle, figspath, function_processes, allsave,
display, strproperty, ratio, verbose) for subject in l]).get()
pool.close()
else:
for subject in l:
# tract_results.append(create_tracts(dwipath, outtrkpath, subject, stepsize, function_processes, strproperty,
picklepath_connect = figspath + subject + str_identifier + '_connectomes.p'
excel_path = figspath + subject + str_identifier + "_connectomes.xlsx"
if os.path.exists(picklepath_connect) and os.path.exists(excel_path):
print("The writing of pickle and excel of " + str(subject) + " is already done")
donelist.append(subject)
else:
notdonelist.append(subject)
createmask = True
dwi_results = []
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:
excel_path = figspath + subject + str_identifier + "_connectomes.xlsx"
if os.path.exists(picklepath_connect) and os.path.exists(excel_path):
print("The writing of pickle and excel of " + str(subject) +
" is already done")
donelist.append(subject)
else:
notdonelist.append(subject)
dwi_results = []
tract_results = []
print(f'Overwrite is {overwrite}')
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
if make_tracts:
tract_results = pool.starmap_async(
create_tracts,
[(diff_preprocessed, trkpath, subject, figspath, stepsize,
function_processes, str_identifier, ratio, brainmask, classifier,
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,
if verbose:
txt = (
"Process running with % d max processes available on % d subjects with % d subjects in parallel each using % d processes"
% (mp.cpu_count(), np.size(l), subject_processes, function_processes))
print(txt)
send_mail(txt, subject="Main process start msg ")
duration1 = time()
txtfile = "/Users/alex/bass/testdata/"
get_params = True
print(bvec_orient_list)
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
tract_results = pool.starmap_async(
create_tracts, [(dwipath, outtrkpath, subject, stepsize,
function_processes, strproperty, ratio, savefa,
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"
if mp.cpu_count() < max_processors:
max_processors = mp.cpu_count()
subject_processes = np.size(subjects)
if max_processors < subject_processes:
subject_processes = max_processors
# accepted values are "small" for one in ten streamlines, "all or "large" for all streamlines,
# "none" or None variable for neither and "both" for both of them
nominal_bval = 1000
masking = 'bet'
denoise = 'mpca'
verbose = True
function_processes = np.int(max_processors / subject_processes)
results = []
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
results = pool.starmap_async(
launch_preprocessing,
[(proc_subjn + subject,
largerfile(
glob.glob(
os.path.join(os.path.join(
diffpath, "*" + subject + "*")))[0]), outpath, cleanup,
nominal_bval, SAMBA_inputs_folder, shortcuts_all_folder, gunniespath,
function_processes, masking, ref, transpose, overwrite, denoise,
recenter, verbose) for subject in subjects]).get()
else:
for subject in subjects:
for subject in subjects:
picklepath_connect = figspath + subject + str_identifier + '_connectomes.p'
excel_path = figspath + subject + str_identifier + "_connectomes.xlsx"
if os.path.exists(picklepath_connect) and os.path.exists(excel_path):
print("The writing of pickle and excel of " + str(subject) + " is already done")
donelist.append(subject)
else:
notdonelist.append(subject)
dwi_results = []
tract_results = []
if subject_processes>1:
if function_processes>1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
dwi_results = pool.starmap_async(dwi_preprocessing, [(datapath, dwi_preprocessed, subject, bvec_orient, denoise, savefa, function_processes,
createmask, vol_b0, verbose) for subject in subjects]).get()
tract_results = pool.starmap_async(create_tracts, [(dwi_preprocessed, trkpath, subject, figspath, stepsize, function_processes,
str_identifier, ratio, masktype, classifier, labelslist, bvec_orient, doprune,
overwrite, get_params, verbose) for subject in subjects]).get()
if make_connectomes:
tract_results = pool.starmap_async(tract_connectome_analysis, [(dwi_preprocessed, trkpath, str_identifier, figspath,
subject, atlas_legends, bvec_orient, inclusive,
function_processes, forcestart, picklesave, verbose)
for subject in subjects]).get()
pool.close()
else:
elif masktype == "T1":
#bet bia6_02491_40006.nii.gz 02491.nii.gz -m -o -f 0.4
#mv 02491_mask.nii.gz 02491_T1_binary_mask.nii.gz
mask, affinemask = getmask(outpath,subject,"T1",verbose)
import itertools
bvec_orient1 = (np.array(list(itertools.permutations([1, 2, 3]))))
bvec_orient2 = [elm*[-1, 1, 1] for elm in bvec_orient1]
bvec_orient3 = [elm*[1, -1, 1] for elm in bvec_orient1]
bvec_orient4 = [elm*[1, 1, -1] for elm in bvec_orient1]
bvec_orient_list = np.concatenate((bvec_orient1, bvec_orient2, bvec_orient3, bvec_orient4))
if subject_processes>1:
if function_processes>1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
tract_results = pool.starmap_async(create_tracts, [(dwipath, outpath, subject, figspath, stepsize, function_processes,
orient_to_str(bvec_orient), ratio, masktype, labelslist, bvec_orient, doprune,
overwrite, get_params, verbose) for bvec_orient in bvec_orient_list]).get()
pool.close()
else:
txtfile = os.path.join(outpath, subject + "_params.txt")
for bvec_orient in bvec_orient_list:
tract_results = []
print(bvec_orient)
str_identifier = orient_to_str(bvec_orient)
str_identifier = strproperty + str_identifier
tract_results.append(create_tracts(dwipath, outpath, subject, figspath, stepsize, function_processes,
if not os.path.exists(new_bvec_file):
shutil.copyfile(bvec_file, new_bvec_file)
max_processors = 20
if mp.cpu_count() < max_processors:
max_processors = mp.cpu_count()
# accepted values are "small" for one in ten streamlines, "all or "large" for all streamlines,
# "none" or None variable for neither and "both" for both of them
nominal_bval = 4000
verbose = True
function_processes = np.int(max_processors / subject_processes)
results = []
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
results = pool.starmap_async(launch_preprocessing, [
launch_preprocessing(
proc_subjn + subject, max_file, outpath, cleanup, nominal_bval,
SAMBA_inputs_folder, shortcuts_all_folder, gunniespath,
function_processes, masking, ref, transpose, overwrite, denoise,
recenter, recenter, verbose) for subject in subjects
]).get()
else:
for subject in subjects:
max_size = 0
subjectpath = glob.glob(
os.path.join(os.path.join(diffpath,
"diffusion*" + subject + "*")))[0]
if os.path.exists(picklepath_connect) and os.path.exists(excel_path):
print("The writing of pickle and excel of " + str(subject) +
" is already done")
donelist.append(subject)
else:
notdonelist.append(subject)
str_identifier = '_wholebrain_small_stepsize_2'
createmask = True
dwi_results = []
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(dwi_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_pickle,
[(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:
if verbose:
txt = (
"Process running with % d max processes available on % d subjects with % d subjects in parallel each using % d processes"
% (mp.cpu_count(), np.size(l), subject_processes, function_processes))
print(txt)
send_mail(txt, subject="Main process start msg ")
duration1 = time()
txtfile = "/Users/alex/bass/testdata/"
get_params = True
print(bvec_orient_list)
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
tract_results = pool.starmap_async(
create_tracts,
[(dwipath, outtrkpath, subject, figspath, stepsize, function_processes,
str_identifier, ratio, brainmask, classifiertype, labelslist,
bvec_orient, doprune, overwrite, get_params, denoise, 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:
max_processors = 50
if mp.cpu_count() < max_processors:
max_processors = mp.cpu_count()
subject_processes = np.size(subjects)
subject_processes = 1
if max_processors < subject_processes:
subject_processes = max_processors
# accepted values are "small" for one in ten streamlines, "all or "large" for all streamlines,
# "none" or None variable for neither and "both" for both of them
function_processes = np.int(max_processors / subject_processes)
results = []
if subject_processes > 1:
if function_processes > 1:
pool = MyPool(subject_processes)
else:
pool = mp.Pool(subject_processes)
results = pool.starmap_async(launch_preprocessing, [
(subject,
largerfile(
glob.glob(os.path.join(os.path.join(
dwipath, "*" + subject + "*")))[0]), outpath)
for subject in subjects
]).get()
else:
for subject in subjects:
max_size = 0
subjectpath = glob.glob(
os.path.join(os.path.join(dwipath, "*" + subject + "*")))[0]
