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DTC_launcher_whitson.py
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DTC_launcher_whitson.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, tract_connectome_analysis, diff_preprocessing
from bvec_handler import extractbvec_fromheader
from BIAC_tools import send_mail
from Daemonprocess import MyPool
from argument_tools import parse_arguments
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
from BIAC_tools import isempty
import sys, getopt
l = ["H21593", "H21729"]
l = ["H21729"]
l = ['H29056', 'H26578', 'H29060', 'H26637', 'H29264', 'H26765', 'H29225', 'H26660', 'H29304', 'H26890', 'H29556',
'H26862', 'H29410', 'H26966', 'H29403', 'H26841', 'H21593', 'H27126', 'H29618', 'H27111', 'H29627', 'H27164',
'H29502', 'H27100', 'H27381', 'H21836', 'H27391', 'H21850', 'H27495', 'H21729', 'H27488', 'H21915', 'H27682',
'H21956', 'H27686', 'H22331', 'H28208', 'H21990', 'H28955', 'H29878', 'H27719', 'H22102', 'H27841', 'H22101',
'H27842', 'H22228', 'H28029', 'H22140', 'H27852', 'H22276', 'H27999', 'H22369', 'H28115', 'H22644', 'H28308',
'H22574', 'H28377', 'H22368', 'H28325', 'H22320', 'H28182', 'H22898', 'H28748', 'H22683', 'H28373', 'H22536',
'H28433', 'H22825', 'H28662', 'H22864', 'H28698', 'H23143', 'H28861', 'H23157', 'H28820', 'H23028', 'H29002',
'H23210', 'H29020', 'H23309', 'H29161', 'H26949', 'H27163', 'H27246', 'H27869', 'H28068', 'H28262', 'H28856',
'H28869', 'H29044', 'H29089', 'H29127', 'H29242', 'H29254', 'H26745', 'H26850', 'H26880', 'H26958', 'H26974',
'H27017', 'H27610', 'H27640', 'H27680', 'H27778', 'H27982', 'H28338', 'H28437', 'H28463', 'H28532', 'H28809',
'H28857', 'H29013', 'H29025']
l = ["H26966", "H26637"]
l = ["H29410", "H29060"]
l = ["H26966", "H26637","H29410", "H29060"]
l = ["H29060"]
#l = ["H29410", "H29060"]
"""
l = ["H21593", "H21729"]
l = ["H21729"]
l = ['H22102', 'H27841', 'H22101',
'H27842', 'H22228', 'H28029', 'H22140', 'H27852', 'H22276', 'H27999', 'H22369', 'H28115', 'H22644', 'H28308',
'H22574', 'H28377', 'H22368', 'H28325', 'H22320', 'H28182', 'H22898', 'H28748', 'H22683', 'H28373', 'H22536',
'H28433', 'H22825', 'H28662', 'H22864', 'H28698', 'H23143', 'H28861', 'H23157', 'H28820', 'H23028', 'H29002',
'H23210', 'H29020', 'H23309', 'H29161', 'H26949', 'H27163', 'H27246', 'H27869', 'H28068', 'H28262', 'H28856',
'H28869', 'H29044', 'H29089', 'H29127', 'H29242', 'H29254', 'H26745', 'H26850', 'H26880', 'H26958', 'H26974',
'H27017', 'H27610', 'H27640', 'H27680', 'H27778', 'H27982', 'H28338', 'H28437', 'H28463', 'H28532', 'H28809',
'H28857', 'H29013', 'H29025']
"""
print("Will go from subject "+ l[0] + " to subject "+l[-1])
subject_processes, function_processes = parse_arguments(sys.argv, l)
BIGGUS_DISKUS = "/Volumes/Data/Badea/Lab/mouse"
dwipath = BIGGUS_DISKUS + "/VBM_19BrainChAMD01_IITmean_RPI_with_2yr-results/connectomics/"
dwipath_preprocessed = BIGGUS_DISKUS + "/C57_JS/diff_whiston_preprocessed/"
outtrkpath = BIGGUS_DISKUS + '/C57_JS/VBM_whiston_QA_new/'
figspath = BIGGUS_DISKUS + '/C57_JS/VBM_whiston_Figs_inclusive_new/'
outpathpickle = figspath
outpath = "/Volumes/Data/Badea/ADdecode.01/Analysis/"
atlas_legends = BIGGUS_DISKUS + "/../atlases/IITmean_RPI/IITmean_RPI_lookup.xlsx"
atlas_legends = BIGGUS_DISKUS + "/../atlases/IITmean_RPI/IITmean_RPI_index.xlsx"
atlas_legends = outpath + "/atlases/IITmean_RPI/IITmean_RPI_index.xlsx"
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 = 100
if ratio == 1:
saved_streamlines = "_all_"
else:
saved_streamlines = "_ratio_" + str(ratio)
savefa="yes"
verbose=True
denoise='none'
#denoise=None
savedenoise=True
display=False
savefig=False
doprune = True
inclusive=True
allsave=True
classifiertype = "FA"
classifiertype = "binary"
brainmask = "dwi"
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
labelslist=[]
if targetrois and (targetrois[0]!="wholebrain" or len(targetrois) > 1):
df = pd.read_excel(atlas_legends, sheet_name='Sheet1')
df['Structure'] = df['Structure'].str.lower()
for roi in targetrois:
rslt_df = df.loc[df['Structure'] == roi.lower()]
if roi.lower() == "wholebrain" or roi.lower() == "brain":
labelslist=None
else:
labelslist=np.concatenate((labelslist, np.array(rslt_df.index)))
print(labelslist)
if isempty(labelslist) and roi.lower() != "wholebrain" and roi.lower() != "brain":
txt = "Warning: Unrecognized roi, will take whole brain as ROI. The roi specified was: " + roi
print(txt)
bvec_orient=[1,2,-3]
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
forcestart = True
if forcestart:
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)
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:
#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]