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DTC_launcher_bvecorientation_checker.py
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DTC_launcher_bvecorientation_checker.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Eleftherios and Serge
Wenlin make some changes to track on the whole brain
Wenlin add for loop to run all the animals 2018-20-25
"""
from time import time
import numpy as np
import os
import multiprocessing as mp
import pickle
from tract_manager import create_tracts
from BIAC_tools import send_mail
from Daemonprocess import MyPool
def orient_to_str(bvec_orient):
mystr=""
for i in np.arange(3):
if np.abs(bvec_orient[i]) == 1:
if bvec_orient[i]<0:
mystr = mystr+"mx"
else:
mystr = mystr+"px"
if np.abs(bvec_orient[i]) == 2:
if bvec_orient[i] < 0:
mystr = mystr + "my"
else:
mystr = mystr + "py"
if np.abs(bvec_orient[i])==3:
if bvec_orient[i]<0:
mystr = mystr+"mz"
else:
mystr = mystr+"pz"
return mystr
#l = ['N57433']
l = ['N54717']
l = ['H28029']
l = ['H21850']
max_processors = 16
if mp.cpu_count() < max_processors:
max_processors = mp.cpu_count()
print("Running on ", max_processors, " processors")
#pool = mp.Pool(mp.cpu_count())
# please set the parameter here
BIGGUS_DISKUS = "/Volumes/Badea/Lab/mouse"
#BIGGUS_DISKUS = "/Volumes/Data/Badea/Lab/mouse/VBM_19BrainChAMD01_IITmean_RPI_with_2yr-results/connectomics/"
BIGGUS_DISKUS = "/mnt/munin6/Badea/Lab/mouse/VBM_19BrainChAMD01_IITmean_RPI_with_2yr-results/connectomics/"
#dwipath = BIGGUS_DISKUS + "/C57_JS/DWI_RAS/"
dwipath = BIGGUS_DISKUS
outtrkpath = '/mnt/munin6/Badea/Lab/mouse/C57_JS/VBM_whistson_QA/'
outpath = "/Users/alex/jacques/AMD_TRK_testing/"
figspath = os.path.join(outpath, "Figures")
dwipath = os.path.join(outpath, "DWI")
trkpath = os.path.join(outpath, "bvec_orients")
#outtrkpath = '/Volumes/Data/Badea/Lab/mouse/C57_JS/VBM_whistson_QA/'
#figspath = BIGGUS_DISKUS + "/C57_JS/Figures_RAS/"
figspath = outtrkpath
#outpathpickle = BIGGUS_DISKUS + "/C57_JS/PicklesFig_RAS/"
stepsize = 2
subject_processes = np.size(l)
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)
"""
extractbvec_fromheader('/Users/alex/brain_data/19abb14/N57433/co_reg_N57433_m00.headfile','/Users/alex/brain_data/19abb14/4DNifti/N57433',"all")
extractbvec_fromheader('/Users/alex/brain_data/19abb14/N57434/co_reg_N57434_m00.headfile','/Users/alex/brain_data/19abb14/4DNifti/N57434',"all")
extractbvec_fromheader('/Users/alex/brain_data/19abb14/N57435/co_reg_N57435_m00.headfile','/Users/alex/brain_data/19abb14/4DNifti/N57435',"all")
extractbvec_fromheader('/Users/alex/brain_data/19abb14/N57436/co_reg_N57436_m00.headfile','/Users/alex/brain_data/19abb14/4DNifti/N57436',"all")
extractbvec_fromheader('/Users/alex/brain_data/19abb14/N57437/co_reg_N57437_m00.headfile','/Users/alex/brain_data/19abb14/4DNifti/N57437',"all")
"""
saved_streamlines = "large"
savefa="no"
verbose=True
denoise='mpca'
savedenoise=True
display=False
savefig=False
doprune=False
get_params=False
overwrite = False
masktype = "dwi"
classifier = "binary"
strproperty = "_pzmypx_fimbria"
#labelslist = [120,1120]#fimbria
labelslist = []
bvec_orient = [1, 2, 3]
ratio = 100
# ---------------------------------------------------------
tall = time()
tract_results = []
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 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"
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()
#tract_results = pool.starmap_async(create_tracts,[(dwipath, outpath, subject, stepsize, function_processes,
# saved_streamlines, denoise, savefa, verbose) for subject in l]).get()
subject=l[0]
#dwip_results = dwi_preprocessing(dwipath,dwipath,subject,denoise,savedenoise=savedenoise, savefa=savefa, processes=function_processes, verbose=verbose)
#tract_results = dwi_create_tracts(dwipath, outtrkpath, subject, stepsize, function_processes,
# saved_streamlines, denoise, savefa, verbose)
#tracteval_results = evaluate_tracts(dwipath, outtrkpath, subject, stepsize, saved_streamlines, outpathfig=figspath,
# processes=function_processes, doprune=True, display=display, verbose=verbose)
tall = time() - tall
if verbose:
text = ("Process was finished after %.3f s" % (tall))
print(text)
send_mail(text, subject="End Process information")
#picklepath = '/Users/alex/jacques/allsubjects_test_eval.p'
#pickle.dump(tracteval_results, open(picklepath,"wb"))
# for j in range(np.size(l)):
# print(j+1)
# subject = l[j]
#pool.starmap_async(create_tracts(mypath,outpath,subject,step_size,function_processes))
duration_all = time() - tall
print('All animals tracking finished, running time is {}'.format(duration_all))