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DTC_launcher_bvecorientation_checker_APOE202110.py
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DTC_launcher_bvecorientation_checker_APOE202110.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
from argument_tools import parse_arguments
from file_tools import mkcdir
import sys
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 = ['N57500']
l = ['N58651']
subject_processes, function_processes = parse_arguments(sys.argv, l)
#main_folder = "/Volumes/dusom_dibs_ad_decode/all_staff/APOE_temp/"
#dwipath = main_folder + "/VBM_19BrainChAMD01_IITmean_RPI_with_2yr-results/connectomics/"
main_folder = "/Users/alex/jacques/APOE_temp/"
atlas_legends = "/Volumes/Data/Badea/Lab/atlases/CHASSSYMM3AtlasLegends.xlsx"
samos = True
if samos:
main_folder = "/mnt/paros_MRI/jacques/APOE/"
atlas_legends = "/mnt/paros_MRI/jacques/atlases/CHASSSYMM3AtlasLegends.xlsx"
dwipath_preprocessed = os.path.join(main_folder,"diff_whiston_preprocessed")
dwipath = os.path.join(main_folder,'DWI_allsubj')
outtrkpath = os.path.join(main_folder,'TRK_bvecs')
figspath = os.path.join(main_folder,"Figures_RAS_allsubj_lr")
txtpath = os.path.join(main_folder, "Parameters")
mkcdir([outtrkpath,figspath,txtpath])
outpathpickle = figspath
stepsize = 2
# 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
targetrois = ["Cerebellum"]
ratio = 1
if ratio == 1:
saved_streamlines = "_all_"
else:
saved_streamlines = "_ratio_" + str(ratio) + '_'
savefa="yes"
verbose=True
denoise='none'
denoise='coreg'
savedenoise=True
display=False
savefig=False
doprune = True
inclusive=False
allsave=True
labelslist=None
brainmask = "dwi"
classifiertype = "FA"
classifiertype = "binary"
brainmask = "dwi"
labeltype = "lrordered"
if classifiertype == "FA":
classifiertype = "_fa"
else:
classifiertype = "_binary"
trkroi = ["wholebrain"]
if len(trkroi)==1:
roistring = "_" + trkroi[0] #+ "_"
elif len(trkroi)>1:
roistring="_"
for roi in trkroi:
roistring = roistring + roi[0:4]
roistring = roistring #+ "_"
#str_identifier = '_stepsize_' + str(stepsize) + saved_streamlines+ roistring
str_identifier = '_stepsize_' + str(stepsize) + classifiertype + roistring + saved_streamlines #to be reimplemented if full calc, disabled for now
str_identifier = roistring + saved_streamlines + 'stepsize_' + str(stepsize)
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()
overwrite = False
get_params = False
if overwrite:
print("WARNING: FORCESTART EMPLOYED. THIS WILL COPY OVER PREVIOUS DATA")
picklesave = True
donelist = []
notdonelist = []
for subject in l:
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)
#str_identifier='_wholebrain_small_stepsize_2'
createmask = True
dwi_results = []
vol_b0 = [0,1,2,3]
labeltype = 'lrordered'
labelslist = []
bvec_orient = [1, 2, 3]
# ---------------------------------------------------------
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, 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:
txtfile = os.path.join(txtpath, subject+"_" + "params.txt")
with open(txtfile, 'a') as fi:
fi.write("Parameters for subject %s \n" % subject)
for bvec_orient in bvec_orient_list:
tract_results = []
print(bvec_orient)
strproperty = orient_to_str(bvec_orient)
print(f'this is the strproperty {strproperty}')
tract_results.append(create_tracts(dwipath, outtrkpath, subject, figspath, stepsize, function_processes, strproperty,
ratio, brainmask, classifiertype, 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])