def new_voxels_filename(filename, current_vorder, desired_vorder):
dirname, filename, ext = splitpath(output_path)
filename = filename + "." + ext
if output_path is None:
output_name = filename.replace('.trk', '_' + desired_vorder + '.trk')
output_path = os.path.join(dirname, output_name)
if not output_path.find('.'):
mkcdir(output_path)
output_name = filename.replace('.nii', '_' + desired_vorder + '.nii')
output_path = os.path.join(output_path, output_name)
if symmetric:
symmetric_str = '_symmetric'
else:
symmetric_str = '_non_symmetric'
trkpaths = glob.glob(os.path.join(TRK_folder, '*trk'))
pickle_folder = os.path.join(
mainpath, f'Pickle_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
centroid_folder = os.path.join(
mainpath, f'Centroids_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
stats_folder = os.path.join(
mainpath,
f'Statistics_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
excel_folder = os.path.join(
mainpath, f'Excels_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
mkcdir([pickle_folder, centroid_folder, stats_folder, excel_folder])
if not os.path.exists(TRK_folder):
raise Exception(f'cannot find TRK folder at {TRK_folder}')
#Initializing dictionaries to be filled
stream_point = {}
stream = {}
groupstreamlines = {}
groupstreamlines_orig = {}
groupLines = {}
groupPoints = {}
group_qb = {}
group_clusters = {}
groups_subjects = {}
if project == 'AD_Decode':
import os, glob
from file_tools import mkcdir
from transform_handler import recenter_nii_save_test, affine_superpose, get_affine_transform_test
mainpath = '/Volumes/Data/Badea/Lab/human/AMD/'
ref_subj_folder = os.path.join(mainpath, 'ref_subj')
ref_MDT_folder = os.path.join(mainpath, 'ref_MDT')
#DWI_folder = os.path.join(mainpath,'MDT_to_subj_testzone')
path_transforms = os.path.join(mainpath, 'Transforms')
MDT_to_subj = os.path.join(mainpath, 'MDT_to_subj_testzone')
subj_to_MDT = os.path.join(mainpath, 'subj_to_MDT_testzone')
mkcdir([MDT_to_subj, subj_to_MDT])
ext = '.nii.gz'
files = glob.glob(os.path.join(ref_subj_folder, '*'))
files = [
'/Volumes/Data/Badea/Lab/human/AMD/ref_subj/H26578_subjspace_fa.nii.gz'
]
recenter = True
overwrite = True
for ref_file in files:
subj = os.path.basename(ref_file)[:6]
ref = os.path.basename(ref_file)[17:19]
SAMBA_init = ref_file
print(ref_file)
preprocess = os.path.join(subj_to_MDT, f'{subj}_prepro.nii.gz')
if recenter and (not os.path.exists(preprocess) or overwrite):
recenter_nii_save_test(SAMBA_init, preprocess)
SAMBA_init = preprocess
bval_path = '/Users/alex/jacques/APOE/bvec_testing/N58794_bvals_fix.txt'
bvec_path = '/Users/alex/jacques/APOE/bvec_testing/N58794_bvecs_fix.txt'
bvals, bvecs = read_bval_bvec(bval_path, bvec_path)
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))
dtis = '#! /bin/bash \n'
for bvec_orient in bvec_orient_list:
strproperty = orient_to_str(bvec_orient)
results_folder = os.path.join(mainpath, strproperty.replace('_', ''))
mkcdir(results_folder)
bvecs_reoriented = reorient_bvecs(bvecs, bvec_orient)
new_bvec_file = os.path.join(results_folder,
strproperty.replace('_', '') + '_bvecs.txt')
writebvec(bvecs_reoriented,
new_bvec_file,
'',
writeformat="dsi",
overwrite=False)
output_path = os.path.join(results_folder, f'dtifit{strproperty}.nii.gz')
dti_cmd = f'dtifit -k {input_diifile} -o {output_path} -m {mask_path} -r {new_bvec_file} -b {bval_path}\n'
dtis = dtis + dti_cmd
bash_file_path = os.path.join(mainpath, 'dti_bash.sh')
with open(bash_file_path, 'w') as rsh:
rsh.write(dtis)
def launch_preprocessing(subj, raw_nii, outpath, cleanup=False, nominal_bval=4000, bonusshortcutfolder=None,
gunniespath="~/gunnies/", processes=1, masking="bet", ref=None, transpose=None,
overwrite=False, denoise='None', recenter=0, verbose=False):
proc_name ="diffusion_prep_" # Not gonna call it diffusion_calc so we don't assume it does the same thing as the civm pipeline
work_dir=os.path.join(outpath,proc_name+subj)
if verbose:
print(f"Processing diffusion data with runno/subj: {subj}")
print(f"Work directory is {work_dir}")
mkcdir(work_dir)
sbatch_folder =os.path.join(work_dir,"sbatch")
mkcdir(sbatch_folder)
nii_name=os.path.split(raw_nii)[1]
niifolder = os.path.dirname(raw_nii)
ext = ".nii.gz"
nii_ext=getext(nii_name)
bxheader = nii_name.replace(nii_ext,".bxh")
bxheader = os.path.join(niifolder, bxheader)
bvecs = os.path.join(work_dir, subj+"_bvecs.txt")
bvals =bvecs.replace("bvecs","bvals")
if verbose:
print(f"Original nifti is at {nii_name}\nbvecs are at {bvecs}\nbvals are at {bvals}\n")
if not os.path.exists(bvecs):
if verbose:
print("Extracting diff directions")
#print("Bvals and bvecs not found, using extractdiffdirs, however it it NOT RELIABLE, beware!")
bvec_cmd = (f"extractdiffdirs --colvectors --writebvals --fieldsep='\t' --space=RAI {bxheader} {bvecs} {bvals}")
os.system(bvec_cmd)
# Make dwi for mask generation purposes.
orient_string = os.path.join(work_dir,'relative_orientation.txt')
tmp_mask = os.path.join(work_dir,f"{subj}_tmp_dwi_mask{ext}")
raw_dwi = os.path.join(work_dir,f"{subj}_raw_dwi.nii.gz")
orient_string = os.path.join(work_dir,"relative_orientation.txt")
if bonusshortcutfolder is not None:
raw_nii_link = os.path.join(bonusshortcutfolder, f"{subj}_rawnii{ext}")
if not os.path.exists(raw_nii_link) or overwrite:
buildlink(raw_nii, raw_nii_link)
bvecs_new = os.path.join(bonusshortcutfolder,subj+"_bvecs.txt")
bvals_new = os.path.join(bonusshortcutfolder,subj+"_bvals.txt")
if not os.path.exists(bvecs_new) or not os.path.exists(bvals_new) or overwrite:
shutil.copyfile(bvecs,bvecs_new)
shutil.copyfile(bvals,bvals_new)
final_mask = os.path.join(work_dir, f'{subj}_dwi_mask{ext}')
if (not os.path.exists(final_mask) and not os.path.exists(tmp_mask)) or overwrite:
if not os.path.exists(raw_dwi) or overwrite:
select_cmd = f"select_dwi_vols {raw_nii} {bvals} {raw_dwi} {nominal_bval} -m"
os.system(select_cmd)
if not os.path.exists(tmp_mask) or overwrite:
if masking=="median":
tmp = tmp_mask.replace("_mask", "")
median_mask_make(raw_dwi, tmp, outpathmask=tmp_mask)
elif masking=="bet":
tmp=tmp_mask.replace("_mask", "")
bet_cmd = f"bet {raw_dwi} {tmp} -m -n -R"
os.system(bet_cmd)
else:
raise Exception("Unrecognized masking type")
mask_subjspace =
if bonusshortcutfolder is not None:
mask_subj_link = os.path.join(bonusshortcutfolder,f'{subj}_mask_subjspace{ext}')
if not os.path.exists(mask_subj_link) or overwrite:
buildlink(tmp_mask, mask_subj_link)
#if cleanup and (os.path.exists(tmp_mask) and os.path.exists(raw_dwi)):
# os.remove(raw_dwi)
#overwrite=False
# Run Local PCA Denoising algorithm on 4D nifti:
masked_nii = os.path.join(work_dir, nii_name)
if not "nii.gz" in masked_nii:
masked_nii = masked_nii.replace(".nii", ".nii.gz")
masked_nii = masked_nii.replace(ext, "_masked" + ext)
if denoise.lower()=='lpca':
D_subj=f'LPCA_{subj}';
elif denoise.lower()=='mpca':
D_subj=f'MPCA_{subj}';
elif denoise=="None" or denoise is None:
D_subj = f'{subj}'
if denoise=="None" or denoise is None:
denoised_nii = masked_nii
if not os.path.exists(masked_nii) or overwrite:
fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
os.system(fsl_cmd)
else:
denoised_nii = os.path.join(work_dir,f"{D_subj}_nii4D.nii.gz")
if not os.path.exists(denoised_nii) or overwrite:
if not os.path.exists(masked_nii) or overwrite:
fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
os.system(fsl_cmd)
basic_LPCA_denoise_func(subj,masked_nii,bvecs,denoised_nii, processes=processes,
denoise=denoise, verbose=False) #to improve and make multiprocessing
#if cleanup and os.path.exists(denoised_nii) and os.path.exists(masked_nii) and denoised_nii!=masked_nii:
# os.remove(masked_nii)
# Run coregistration/eddy current correction:
coreg_nii_old = f'{outpath}/co_reg_{D_subj}_m00-results/Reg_{D_subj}_nii4D{ext}';
coreg_nii = os.path.join(work_dir,f'Reg_{D_subj}_nii4D{ext}')
if not cleanup:
coreg_nii=coreg_nii_old
if not os.path.exists(coreg_nii) or overwrite:
if not os.path.exists(coreg_nii_old) or overwrite:
temp_cmd = os.path.join(gunniespath,'co_reg_4d_stack_tmpnew.bash')+f' {denoised_nii} {D_subj} 0 {outpath} 0';
os.system(temp_cmd)
if cleanup:
shutil.move(coreg_nii_old,coreg_nii)
if bonusshortcutfolder is not None:
coreg_link = os.path.join(bonusshortcutfolder,f'{subj}_coreg{ext}')
if not os.path.exists(coreg_link) or overwrite:
buildlink(coreg_nii, coreg_link)
toeddy=False
if toeddy:
#fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
#os.system(fsl_cmd)
eddy_cmd = f"eddy --imain={coreg_nii} --mask={tmp_mask} --acqp=acq_params.txt --index={os.path.join(work_dir,'index.txt')} --bvecs={bvecs} --bvals={bvals} --topup=topup_results --repol --out = {os.path.join(work_dir,f'Reg_{D_subj}_nii4D_eddy{ext}')}"
os.system(eddy_cmd)
coreg_inputs=os.path.join(outpath,f'co_reg_{D_subj}_m00-inputs')
coreg_work=coreg_inputs.replace('-inputs','-work')
coreg_results=coreg_inputs.replace('-inputs','-results')
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_inputs):
shutil.rmtree(coreg_inputs)
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_work):
shutil.rmtree(coreg_work)
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_results):
shutil.rmtree(coreg_results)
# Generate tmp DWI:
tmp_dwi_out=os.path.join(work_dir, f'{subj}_tmp_dwi{ext}')
dwi_out=os.path.join(work_dir,f'{subj}_dwi{ext}')
if not os.path.exists(dwi_out) or overwrite:
if not os.path.exists(tmp_dwi_out) or overwrite:
cmd=f'select_dwi_vols {coreg_nii} {bvals} {tmp_dwi_out} {nominal_bval} -m'
os.system(cmd)
if bonusshortcutfolder is not None:
tmp_dwi_out_link = os.path.join(bonusshortcutfolder,f'{subj}_mask_subjspace{ext}')
if not os.path.exists(tmp_dwi_out_link) or overwrite:
buildlink(tmp_dwi_out, tmp_dwi_out_link)
# Generate tmp B0:
tmp_b0_out=os.path.join(work_dir,f'{subj}_tmp_b0{ext}')
b0_out = os.path.join(work_dir, f'{subj}_b0{ext}')
if (not os.path.exists(b0_out) and not os.path.exists(tmp_b0_out)) or overwrite:
cmd=f'select_dwi_vols {coreg_nii} {bvals} {tmp_b0_out} 0 -m;'
os.system(cmd)
#overwrite=False
#elif cleanup and os.path.exists(tmp_b0_out):
# os.remove(tmp_b0_out)
# Generate DTI contrasts and perform some tracking QA:
if cleanup:
c_string=' --cleanup '
else:
c_string=''
#Important note: this is what first creates the fa, md, etc
overwrite = True
if len(glob.glob(os.path.join(work_dir,f'*.fib.gz.md{ext}'))) == 0 or overwrite:
if overwrite:
os.remove(os.path.join(work_dir, f'*.fib.gz.*{ext}'))
cmd = os.path.join(gunniespath,'dti_qa_with_dsi_studio.bash')+f' {coreg_nii} {bvecs} {tmp_mask} {work_dir} {c_string}';
os.system(cmd)
overwrite = False
#Save the subject space dti results
#Generate tmp MD:
for contrast in ['md']:
real_file=largerfile(os.path.join(work_dir,f'*.fib.gz.{contrast}{ext}')) #Catch the 'real file' for each contrast
tmp_file = f'{work_dir}/{subj}_tmp_{contrast}{ext}';
#contrast=contrast.replace('0','') #some little nonsense mostly to deal with the 'fa0' name to become 'fa', probably sohould change it
if not os.path.exists(tmp_file):
shutil.copy(real_file,tmp_file)
tmp_md = f'{work_dir}/{subj}_tmp_md{ext}';
if ref=="md" or ref is None:
reference=tmp_md
elif ref=="coreg":
reference=coreg_nii
elif os.path.exists(ref):
reference=ref
reference_file = os.path.join(work_dir, f'{subj}_reference{ext}')
if not os.path.exists(reference_file):
shutil.copy(reference, reference_file)
if bonusshortcutfolder is not None:
bonus_ref_link = os.path.join(bonusshortcutfolder, f'{subj}_reference{ext}')
if not os.path.exists(bonus_ref_link) or overwrite:
buildlink(reference_file,bonus_ref_link)
"""
for contrast in ['md']:
real_file=largerfile(os.path.join(work_dir,f'*.fib.gz.{contrast}{ext}')) #Catch the 'real file' for each contrast
tmp_file = f'{work_dir}/{subj}_tmp_{contrast}{ext}';
#contrast=contrast.replace('0','') #some little nonsense mclassifierostly to deal with the 'fa0' name to become 'fa', probably sohould change it
if not os.path.exists(tmp_md):
shutil.copy(real_file,tmp_file)
"""
#give real header to the temp files using md as reference
overwrite=True
for contrast in ['dwi', 'b0', 'dwi_mask']:
tmp_file=os.path.join(work_dir,f'{subj}_tmp_{contrast}{ext}')
tmp2_file=os.path.join(work_dir,f'{subj}_tmp2_{contrast}{ext}')
final_file=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
if ((not os.path.exists(tmp2_file) and not os.path.exists(final_file)) or overwrite):
if not os.path.exists(tmp_file):
raise Exception("Tmp file was not created, need to rerun previous processes")
else:
cmd = os.path.join(gunniespath, 'nifti_header_splicer.bash') + f' {reference_file} {tmp_file} {tmp2_file}'
os.system(cmd)
tmp_test_file = os.path.join(work_dir,f'{subj}_tmp2_test_{contrast}{ext}')
header_superpose(reference, tmp_file, outpath=tmp_test_file)
#write the relative orientation file here
if not os.path.isfile(orient_string) or overwrite:
if os.path.isfile(orient_string):
os.remove(orient_string)
file = os.path.join(work_dir,subj+'_tmp_dwi_mask'+ext);
cmd = 'bash ' + os.path.join(gunniespath,'find_relative_orientation_by_CoM.bash') + f' {reference_file} {file}'
orient_relative = subprocess.getoutput(cmd)
with open(orient_string, 'w') as f:
f.write(orient_relative)
else:
orient_relative = open(orient_string, mode='r').read()
#check extracted values from relative orientation vals
orientation_out = orient_relative.split(',')[0]
orientation_out = orientation_out.split(':')[1]
orientation_in = orient_relative.split(',')[1]
orientation_in = orientation_in.split(':')[1]
if verbose:
print(f'flexible orientation: {orientation_in}');
print(f'reference orientation: {orientation_out}');
#apply the orientation modification to specified contrasts
for contrast in ['dwi', 'b0', 'dwi_mask']:
img_in=os.path.join(work_dir,f'{subj}_tmp2_{contrast}{ext}')
img_out=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
if not os.path.isfile(img_out) or overwrite:
if orientation_out != orientation_in:
print('TRYING TO REORIENT...b0 and dwi and mask')
if os.path.exists(img_in) and (not os.path.exists(img_out) or overwrite):
img_transform_exec(img_in, orientation_in, orientation_out, img_out, recenter=recenter)
if os.path.exists(img_out):
os.remove(img_in)
elif os.path.exists(img_out) and cleanup:
os.remove(img_in)
else:
shutil.move(img_in,img_out)
if bonusshortcutfolder is not None:
bonus_link = os.path.join(bonusshortcutfolder, f'{subj}_{contrast}{ext}')
if not os.path.exists(bonus_link) or overwrite:
buildlink(img_out, bonus_link)
mask = os.path.join(work_dir,f'{subj}_mask{ext}')
b0 = os.path.join(work_dir,f'{subj}_b0{ext}')
#if cleanup and os.path.exists(dwi_out) and os.path.exists(tmp_dwi_out):
# os.remove(tmp_dwi_out)
#refine mask (to check!!!!)
bonusmask=False
if bonusmask:
mask_new = mask.replace('_mask', '_new')
bet_cmd = f'bet {b0} {mask_new} -m -f 0.3'
os.system(bet_cmd)
mask_new = mask_new.replace('_new','_new_mask')
outpath=dwi_out.replace(".nii","_newmask.nii")
applymask_samespace(dwi_out, mask_new, outpath) #(add outpath if you want to save to different location rather than overwrite)
dwi_out=outpath
outpath=b0_out.replace(".nii","_newmask.nii")
applymask_samespace(b0_out, mask_new,outpath=outpath)
for contrast in ['fa0', 'rd', 'ad', 'md']:
real_file=largerfile(os.path.join(work_dir,f'*.fib.gz.{contrast}{ext}')) # It will be fun times if we ever have more than one match to this pattern...
if bonusmask:
real_file_new=real_file.replace("D.nii.gz","D_newmask.nii.gz")
applymask_samespace(real_file, mask_new,outpath=real_file_new)
real_file=real_file_new
contrast=contrast.replace('0','')
#linked_file=os.path.join(shortcut_dir,f'{subj}_{contrast}{ext}')
linked_file_w=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
header_fix=True
if header_fix:
affine_superpose(dwi_out, real_file, transpose=transpose)
if not os.path.isfile(linked_file_w) or overwrite:
buildlink(real_file, linked_file_w)
if bonusshortcutfolder is not None:
blinked_file = os.path.join(bonusshortcutfolder, f'{subj}_{contrast}{ext}')
if not os.path.exists(blinked_file) or overwrite:
buildlink(real_file, blinked_file)
elif 'blade' in computer_name:
mainpath = '/mnt/munin6/Badea/Lab/human/'
atlas_legends = "/mnt/munin6/Badea/Lab/atlases/IITmean_RPI/IITmean_RPI_index.xlsx"
else:
raise Exception('No other computer name yet')
project = "AMD"
if project == 'AD_Decode':
mainpath = os.path.join(mainpath, 'AD_Decode', 'Analysis')
else:
mainpath = os.path.join(mainpath, project)
diff_preprocessed = os.path.join(mainpath, "DWI")
mkcdir([mainpath, diff_preprocessed])
subjects = [
"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", "H26841", "H26862", "H26949",
"H26966", "H27100", "H27126", "H27163", "H27246", "H27488", "H27682",
from file_tools import mkcdir, largerfile
import shutil
from argument_tools import parse_arguments
from bvec_handler import orient_to_str
gunniespath = "~/gunnies/"
mainpath = "/Volumes/Data/Badea/Lab/"
diffpath = "/Volumes/Data/Badea/Lab/RaulChavezValdez/"
#outpath = "/Users/alex/jacques/APOE_temp"
outpath = "/mnt/munin6/Badea/Lab/mouse/Chavez_series/diffusion_prep_locale/"
SAMBA_inputs_folder = "/mnt/munin6/Badea/Lab/Chavez_prep/"
shortcuts_all_folder = "/mnt/munin6/Badea/Lab/mouse/Chavez_symlink_pool_allfiles/"
if SAMBA_inputs_folder is not None:
mkcdir(SAMBA_inputs_folder)
if shortcuts_all_folder is not None:
mkcdir(shortcuts_all_folder)
mkcdir(outpath)
subjects = [
'C_20220124_001', 'C_20220124_002', 'C_20220124_003', 'C_20220124_004',
'C_20220124_005', 'C_20220124_006', 'C_20220124_007'
]
"""
subjects_folders = glob.glob(os.path.join(diffpath,'diffusion*/'))
subjects = []
for subject_folder in subjects_folders:
subjects.append(subject_folder.split('diffusion')[1][:6])
"""
removed_list = []
for remove in removed_list:
import shutil
from diffusion_preprocessing import launch_preprocessing
from file_tools import mkcdir, largerfile
from transform_handler import get_transpose
import shutil
from bvec_handler import orient_to_str
gunniespath = "~/gunnies/"
mainpath = "/mnt/munin6/Badea/ADdecode.01/"
#outpath = "/Users/alex/jacques/APOE_temp"
outpath = "/mnt/munin6/Badea/Lab/human/AD_Decode/diffusion_prep_locale/"
SAMBA_inputs_folder = "/mnt/munin6/Badea/Lab/mouse/ADDeccode_symlink_pool/"
shortcuts_all_folder = "/mnt/munin6/Badea/Lab/human/ADDeccode_symlink_pool_allfiles/"
diffpath = mainpath + "Data/Anat"
mkcdir([SAMBA_inputs_folder, shortcuts_all_folder])
subjects = [
'01912', '02110', '02224', '02227', '02230', '02231', '02266', '02289',
'02320', '02361', '02363', '02373', '02386', '02390', '02402', '02410',
'02421', '02424', '02446', '02451', '02469', '02473', '02485', '02491',
'02490', '02506', '02523', '02524', '02535', '02654', '02666', '02670',
'02686', '02690', '02695', '02715', '02720', '02737', '02745', '02753',
'02765', '02771', '02781', '02802', '02804', '02813', '02812', '02817',
'02840', '02842', '02871', '02877', '02898', '02926', '02938', '02939',
'02954', '02967', '02987', '03010', '03017', '03028', '03033', '03034',
'03045', '03048', '03069', '03225', '03265', '03293', '03308', '03321',
'03343', '03350', '03378', '03391', '03394'
]
subjects = [
from bvec_handler import orient_to_str
#gunniespath = "/mnt/clustertmp/common/rja20_dev/gunnies/"
#gunniespath = "/Users/alex/bass/gitfolder/wuconnectomes/gunnies/"
#diffpath = "/Volumes/dusom_civm-atlas/20.abb.15/research/"
#outpath = "/Volumes/Data/Badea/Lab/mouse/APOE_series/diffusion_prep_locale/"
gunniespath = ""
outpath = "/mnt/munin6/Badea/Lab/mouse/APOE_series/diffusion_prep_locale/"
#bonusshortcutfolder = "/Volumes/Data/Badea/Lab/jacques/APOE_series/19abb14/"
#bonusshortcutfolder = "/mnt/munin6/Badea/Lab/APOE_symlink_pool/"
#bonusshortcutfolder = None
SAMBA_inputs_folder = "/mnt/munin6/Badea/Lab/19abb14/"
shortcuts_all_folder = "/mnt/munin6/Badea/Lab/mouse/APOE_symlink_pool_allfiles/"
mkcdir([SAMBA_inputs_folder, shortcuts_all_folder])
subjects = [
"N58214", "N58215", "N58216", "N58217", "N58218", "N58219", "N58221",
"N58222", "N58223", "N58224", "N58225", "N58226", "N58228", "N58229",
"N58230", "N58231", "N58232", "N58633", "N58634", "N58635", "N58636",
"N58649", "N58650", "N58651", "N58653", "N58654", 'N58408', 'N58398',
'N58714', 'N58740', 'N58477', 'N58734', 'N58309', 'N58792', 'N58302',
'N58784', 'N58706', 'N58361', 'N58355', 'N58712', 'N58790', 'N58606',
'N58350', 'N58608', 'N58779', 'N58500', 'N58604', 'N58749', 'N58510',
'N58394', 'N58346', 'N58344', 'N58788', 'N58305', 'N58514', 'N58794',
'N58733', 'N58655', 'N58735', 'N58310', 'N58400', 'N58708', 'N58780',
'N58512', 'N58747', 'N58303', 'N58404', 'N58751', 'N58611', 'N58745',
'N58406', 'N58359', 'N58742', 'N58396', 'N58613', 'N58732', 'N58516',
'N58813', 'N58402'
]
if project == 'AD_Decode':
outpath = os.path.join(outpath, project, 'Analysis')
inpath = os.path.join(inpath, project, 'Analysis')
else:
outpath = os.path.join(outpath, project)
inpath = os.path.join(inpath, project)
TRK_folder = os.path.join(inpath, f'TRK_MPCA_MDT{fixed_str}{folder_ratio_str}')
TRK_folder = os.path.join(inpath, f'TRK_MDT{fixed_str}{folder_ratio_str}')
label_folder = os.path.join(outpath, 'DWI')
#trkpaths = glob.glob(os.path.join(TRK_folder, '*trk'))
excel_folder = os.path.join(
outpath, f'Excels_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
print(excel_folder)
mkcdir(excel_folder)
if not remote and os.path.exists(TRK_folder):
raise Exception(f'cannot find TRK folder at {TRK_folder}')
# Initializing dictionaries to be filled
stream_point = {}
stream = {}
groupstreamlines = {}
groupLines = {}
groupPoints = {}
group_qb = {}
group_clusters = {}
groups_subjects = {}
if project == 'AD_Decode':
ratio = 100
ratio_str = ratio_to_str(ratio)
if ratio_str == 'all':
ratio_str = ''
else:
ratio_str = ratio_str.replace('_ratio', '')
TRK_folder = os.path.join(mainpath, 'TRK_MPCA_MDT_fixed' + ratio_str)
label_folder = os.path.join(mainpath, 'DWI')
trkpaths = glob.glob(os.path.join(TRK_folder, '*trk'))
figures_folder = os.path.join(mainpath, 'Figures_MDT' + ratio_str)
pickle_folder = os.path.join(mainpath, 'Pickle_MDT' + ratio_str)
centroid_folder = os.path.join(mainpath, 'Centroids_MDT' + ratio_str)
excel_folder = os.path.join(mainpath, 'Excels_MDT' + ratio_str)
mkcdir([figures_folder, pickle_folder, centroid_folder, excel_folder])
if not os.path.exists(TRK_folder):
raise Exception(f'cannot find TRK folder at {TRK_folder}')
#reference_img refers to statistical values that we want to compare to the streamlines, say fa, rd, etc
references = ['fa', 'md', 'rd', 'ad', 'b0']
references = []
verbose = True
#Initializing dictionaries to be filled
stream_point = {}
stream = {}
groupstreamlines = {}
groupLines = {}
groupPoints = {}
import os
import shutil
from tract_handler import trk_fixer
from file_tools import mkcdir
from tract_handler import reducetractnumber, ratio_to_str, get_ratio
TRK_folder = '/Users/alex/jacques/AMD_TRK_testing/TRK_MDT'
TRK_output = '/Users/alex/jacques/AMD_TRK_testing/TRK_MDT_fixed'
TRK_folder = '/Volumes/Data/Badea/Lab/human/AMD/TRK_MDT'
TRK_output = '/Volumes/Data/Badea/Lab/human/AMD/TRK_MDT_fixed'
TRK_folder = '/mnt/paros_MRI/jacques/AD_Decode/Analysis/TRK_MPCA_neworient/'
TRK_output = '/mnt/paros_MRI/jacques/AD_Decode/Analysis/TRK_MPCA_neworient_fixed/'
mkcdir(TRK_output)
for trk_name in os.listdir(TRK_folder):
if trk_name.endswith('.trk'):
trk_path = os.path.join(TRK_folder,trk_name)
trk_newpath = os.path.join(TRK_output, trk_name)
if not os.path.exists(trk_newpath):
print(f'Beginning the fix of {trk_path}')
trk_fixer(trk_path, trk_newpath, verbose=True)
else:
print(f'Already fixed {trk_name}')
trk_folder = TRK_output
new_trk_folder = '/mnt/paros_MRI/jacques/AD_Decode/Analysis/TRK_MPCA_neworient_fixed_100/'
mkcdir(new_trk_folder)
ratio = 100
filelist = os.listdir(trk_folder)
project = "AD_Decode"
if project == "AD_Decode":
path_TRK = os.path.join(main_path, 'AD_Decode', 'Analysis', 'TRK_MPCA')
path_TRK_output = os.path.join(main_path, 'AD_Decode', 'Analysis',
'TRK_MPCA_MDT')
path_DWI = os.path.join(main_path, 'AD_Decode', 'Analysis', 'DWI')
path_DWI_MDT = os.path.join(main_path, 'AD_Decode', 'Analysis', 'DWI_MDT')
path_transforms = os.path.join(main_path, 'AD_Decode', 'Analysis',
'Transforms')
ref = "md"
path_trk_tempdir = os.path.join(main_path, 'AD_Decode', 'Analysis',
'TRK_save_alt2')
DWI_save = os.path.join(main_path, 'AD_Decode', 'Analysis',
'NII_tempsave_alt2')
mkcdir([path_trk_tempdir, path_TRK_output, DWI_save])
# Get the values from DTC_launcher_ADDecode. Should probalby create a single parameter file for each project one day
stepsize = 2
ratio = 1
trkroi = ["wholebrain"]
str_identifier = get_str_identifier(stepsize, ratio, trkroi)
subjects_all = glob.glob(os.path.join(path_DWI, '*subjspace_dwi*.nii.gz'))
subjects = []
for subject in subjects_all:
subject_name = os.path.basename(subject)
subjects.append(subject_name[:6])
#removed_list = ['S02804', 'S02817', 'S02898', 'S02871', 'S02877','S03045', 'S02939', 'S02840']
subjects = [
mainpath = os.path.join(mainpath, project, 'Analysis')
anat_path = '/Volumes/Data/Badea/Lab/mouse/VBM_21ADDecode03_IITmean_RPI_fullrun-work/dwi/SyN_0p5_3_0p5_fa/faMDT_NoNameYet_n37_i6/median_images/MDT_b0.nii.gz'
# figures_path = '/Volumes/Data/Badea/Lab/human/AMD/Figures_MDT_non_inclusive/'
# centroid_folder = '/Volumes/Data/Badea/Lab/human/AMD/Centroids_MDT_non_inclusive/'
figures_path = os.path.join(
mainpath, f'Figures_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
centroid_folder = os.path.join(
mainpath, f'Centroids_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
trk_folder = os.path.join(
mainpath, f'Centroids_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
stats_folder = os.path.join(
mainpath,
f'Statistics_MDT{inclusive_str}{symmetric_str}{folder_ratio_str}')
mkcdir([figures_path, centroid_folder, stats_folder])
# groups = ['Initial AMD', 'Paired 2-YR AMD', 'Initial Control', 'Paired 2-YR Control', 'Paired Initial Control',
# 'Paired Initial AMD']
# anat_path = '/Volumes/Data/Badea/Lab/mouse/VBM_19BrainChAMD01_IITmean_RPI_with_2yr-work/dwi/SyN_0p5_3_0p5_dwi/dwiMDT_Control_n72_i6/median_images/MDT_dwi.nii.gz'
# superior frontal right to cerebellum right
#set parameter
num_points1 = 50
distance1 = 1
feature1 = ResampleFeature(nb_points=num_points1)
metric1 = AveragePointwiseEuclideanMetric(feature=feature1)
#group cluster parameter
def convert_images_templatespace(subject, mainpath, project_name, subject_img_path, orient_string, preppath = None, gunniespath="/Users/alex/bass/gitfolder/wuconnectomes/gunnies/", recenter=0, verbose=True):
#mainpath = "/Volumes/Data/Badea/Lab/mouse/"
#gunniespath = "/Users/alex/bass/gitfolder/wuconnectomes/gunnies/"
#project_name = "VBM_21ADDecode03_IITmean_RPI_fullrun"
#atlas_labels = "/Volumes/Data/Badea/Lab/atlas/IITmean_RPI/IITmean_RPI_labels.nii.gz"
out_dir = os.path.join(mainpath, f"{project_name}-results","connectomics",subject)
work_dir = os.path.join(mainpath, f"{project_name}-work")
dirty_dir = os.path.join(mainpath,"burn_after_reading")
mkcdir(dirty_dir)
template_type_prefix = os.path.basename(os.path.dirname(glob.glob(os.path.join(work_dir,"dwi","SyN*/"))[0]))
template_runs = glob.glob((os.path.join(work_dir,"dwi",template_type_prefix,"*/")))
mymax=-1
for template_run in template_runs:
if "NoNameYet" in template_run and template_run[-4:-2]=="_i":
if int(template_run[-2])>mymax:
mymax=int(template_run[-2])
final_template_run=template_run
if mymax==-1:
raise Exception(f"Could not find template runs in {os.path.join(mainpath, f'{project_name}-work','dwi',template_type_prefix)}")
#template_type_prefix = "faMDT_NoNameYet_n37_i6"
#final_template_run = "SyN_0p5_3_0p5_fa"
#final_ref = f"/mnt/munin6/Badea/Lab/human/AD_Decode/diffusion_prep_locale/diffusion_prep_{subject}/Reg_LPCA_{subject}_nii4D.nii.gz";
if os.path.exists(orient_string):
orient_relative = open(orient_string, mode='r').read()
orientation_out = orient_relative.split(',')[0]
orientation_out = orientation_out.split(':')[1]
orientation_in = orient_relative.split(',')[1]
orientation_in = orientation_in.split(':')[1]
else:
orientation_out = "*"
warnings.warn("Could not find orientation file, may cause errors later")
orientation_in = "LPS"
orientation_out = "RAS"
trans = os.path.join(work_dir,"preprocess","base_images","translation_xforms",f"{subject}_0DerivedInitialMovingTranslation.mat")
rigid =os.path.join(work_dir,"dwi",f"{subject}_rigid.mat")
affine = os.path.join(work_dir,"dwi",f"{subject}_affine.mat")
#MDT_to_subject = os.path.join(final_template_run,"reg_diffeo",f"MDT_to_{subject}_warp.nii.gz")
#MDT_to_atlas_affine = os.path.join(final_template_run,"stats_by_region","labels","transforms",f"MDT_*_to_{label_name}_affine.mat")
#atlas_to_MDT = os.path.join(final_template_run,"stats_by_region","labels","transforms",f"{label_name}_to_MDT_warp.nii.gz")
[trans, rigid, affine], _ = check_files([trans,rigid,affine])
preprocess_ref = os.path.join(work_dir,"preprocess",f"{subject}_fa_masked.nii.gz")
preprocess_labels = os.path.join(dirty_dir,f"{subject}_preprocess_labels.nii.gz")
fixed_preprocess_labels = os.path.join(dirty_dir,f"{subject}_fixed_preprocess_labels.nii.gz")
coreg_labels = os.path.join(dirty_dir,f"{subject}_{orientation_in}_labels.nii.gz")
# final_ref="/mnt/munin6/Badea/Lab/mouse/co_reg_LPCA_${subject:1:5}_m00-results/Reg_LPCA_${subject:1:5}_nii4D.nii.gz";
if preppath is None:
inputsfolder = os.path.join(mainpath, f"{project_name}-inputs")
final_ref = os.path.join(inputsfolder, f"{subject}_coreg.nii.gz")
if os.path.exists(final_ref):
if Path(final_ref).is_symlink():
final_ref=str(Path(final_ref).resolve())
else:
raise Exception("Could not find final registered subject file")
mytype="fa"
symbolic_ref = os.path.join(out_dir,f"{subject}_Reg_LPCA_nii4D.nii.gz")
final_image_test = os.path.join(out_dir,f"{subject}_{mytype}_to_MDT_test.nii.gz")
overwrite = False
if not os.path.exists(final_image_test) or overwrite:
if verbose:
print(f"Porting subject to MDT for subject: {subject} to {final_image_test}")
img_transposed_test = os.path.join(dirty_dir,f"{subject}_{type}_transposed_test.nii.gz")
img_transposed_test_2 = os.path.join(dirty_dir,f"{subject}_{type}_transposed_test_2.nii.gz")
if os.path.exists(subject_img_path) and preprocess_ref:
header_superpose(preprocess_ref, subject_img_path, outpath=img_transposed_test)
cmd = f"antsApplyTransforms -v 1 -d 3 -i {subject_img_path} -o {img_transposed_test_2} -r {preprocess_ref} -n MultiLabel -t [{trans}] [{rigid},1] [{affine},1]"
if verbose:
print(f"Runnings the Ants apply transforms to {subject_img_path}")
print(cmd)
os.system(cmd)
if not os.path.exists(preprocess_labels) or overwrite:
#Note, it used to be MultiLabel[1.0x1.0x1.0,2] but it seems like the default parameters tend to be based on the image themselves and work fine,
#so be careful but I removed it for now so it would work on different image sets
cmd = f"antsApplyTransforms -v 1 -d 3 -i {atlas_labels} -o {preprocess_labels} -r {preprocess_ref} -n MultiLabel -t [{trans},1] [{rigid},1] [{affine},1] {MDT_to_subject} [{MDT_to_atlas_affine},1] {atlas_to_MDT}"
if verbose:
print(f"Runnings the Ants apply transforms to {atlas_labels}")
print(cmd)
os.system(cmd)
if os.path.exists(preprocess_labels) and not ((os.path.exists(fixed_preprocess_labels)) or overwrite):
header_superpose(final_ref, preprocess_labels, outpath=fixed_preprocess_labels)
if os.path.exists(fixed_preprocess_labels) and not (os.path.exists(coreg_labels) or overwrite):
if orientation_out != orientation_in:
if verbose:
print(f"Reorientation from {orientation_out} back to {orientation_in}")
img_transform_exec(fixed_preprocess_labels, orientation_out, orientation_in, coreg_labels, recenter=recenter)
else:
if verbose:
print(f"Orientations are the same, skipping reorientation")
shutil.copy(fixed_preprocess_labels, coreg_labels)
if os.path.exists(coreg_labels):
header_superpose(final_ref, coreg_labels, outpath=final_labels)
#cmd = f"{os.path.join(gunniespath, 'nifti_header_splicer.bash')} {final_ref} {coreg_labels} {final_labels}"
#os.system(cmd)
if os.path.exists(final_labels):
if verbose:
print(f"Applying fsl maths to {final_labels}")
cmd = f"fslmaths {final_labels} -add 0 {final_labels} -odt short"
os.system(cmd)
else:
print(f"Already calculated the label file for subject {subject}")
skip_making_data_package_for_tractography = True
if not skip_making_data_package_for_tractography:
print('if you see this, make the bval fixes')
if not os.path.exists9(symbolic_ref):
cmd=f"ln - s {final_ref} {symbolic_ref}"
bvals = os.path.join(mainpath, f"diffusion_prep_{subject}", f"{subject}_bvals.txt")
bvecs = os.path.join(mainpath, f"diffusion_prep_{subject}", f"{subject}_bvecs.txt")
bval_copy = os.path.join(out_dir, f"{subject}_bvals.txt")
bvec_copy = os.path.join(out_dir, f"{subject}_bvecs.txt")
if not os.path.exists(bval_copy):
shutil.copy(bvals, bval_copy)
if not os.path.exists(bvec_copy):
shutil.copy(bvecs, bvec_copy)
munin=False
if munin:
gunniespath = "~/wuconnectomes/gunnies"
mainpath = "/mnt/munin/Whitson/BrainChAMD.01/"
outpath = "/mnt/munin6/Badea/Lab/human/AMD/diffusion_prep_locale/"
bonusshortcutfolder = "/mnt/munin6/Badea/Lab/mouse/whiston_symlink_pool_test/"
else:
gunniespath = "/Users/alex/bass/gitfolder/wuconnectomes/gunnies/"
mainpath="/Volumes/Data/Whitson/BrainChAMD.01/"
outpath = "/Volumes/Data/Badea/Lab/human/AMD/diffusion_prep_locale/"
bonusshortcutfolder = "/Volumes/Data/Badea/Lab/mouse/whiston_symlink_pool_test/"
bonusshortcutfolder = None
diffpath = os.path.join(mainpath, "Data","Anat")
mkcdir(outpath)
subjects = ["29056", "26578", "29060", "26637", "29264", "26765", "29225", "26660", "29304", "26890", "29556", "26862", "29410", "26966", "29403", "26841", "21593", "27126", "29618", "27111", "29627", "27164", "29502", "27100", "27381", "21836", "27391", "21850", "27495", "21729", "27488", "21915", "27682", "21956", "27686", "22331", "28208", "21990", "28955", "29878", "27719", "22102", "27841", "22101", "27842", "22228", "28029", "22140", "27852", "22276", "27999", "22369", "28115", "22644", "28308", "22574", "28377", "22368", "28325", "22320", "28182", "22898", "28748", "22683", "28373", "22536", "28433", "22825", "28662", "22864", "28698", "23143", "28861", "23157", "28820", "23028", "29002", "23210", "29020", "23309", "29161", "26841", "26862", "26949", "26966", "27100", "27126", "27163", "27246", "27488", "27682", "27686", "27719", "27841", "27842", "27852", "27869", "27999", "28029", "28068", "28208", "28262", "28325", "28820", "28856", "28869", "28955", "29002", "29044", "29089", "29127", "29161", "29242", "29254", "26578", "26637", "26660", "26745", "26765", "26850", "26880", "26890", "26958", "26974", "27017", "27111", "27164", "27381", "27391", "27495", "27610", "27640", "27680", "27778", "27982", "28115", "28308", "28338", "28373", "28377", "28433", "28437", "28463", "28532", "28662", "28698", "28748", "28809", "28857", "28861", "29013", "29020", "29025"]
subjects = ["27999"]
subject_processes, function_processes = parse_arguments(sys.argv,subjects)
proc_subjn="H"
proc_name ="diffusion_prep_"+proc_subjn
denoise = "mpca"
masking = "bet"
overwrite=True
cleanup = True
atlas = None
gettranspose=False
verbose=True
nominal_bval=1000
def create_backport_labels(subject, mainpath, project_name, prep_folder, atlas_labels, headfile=None, overwrite=False, verbose=True):
#mainpath = "/Volumes/Data/Badea/Lab/mouse/"
#gunniespath = "/Users/alex/bass/gitfolder/wuconnectomes/gunnies/"
#project_name = "VBM_21ADDecode03_IITmean_RPI_fullrun"
#atlas_labels = "/Volumes/Data/Badea/Lab/atlas/IITmean_RPI/IITmean_RPI_labels.nii.gz"
out_dir = os.path.join(mainpath, f"{project_name}-results","connectomics",subject)
mkcdir(out_dir)
work_dir = os.path.join(mainpath, f"{project_name}-work")
dirty_dir = os.path.join(mainpath,"burn_after_reading")
mkcdir(dirty_dir)
template_type_prefix = os.path.basename(os.path.dirname(glob.glob(os.path.join(work_dir,"dwi","SyN*/"))[0]))
template_runs = glob.glob((os.path.join(work_dir,"dwi",template_type_prefix,"*/")))
mymax=-1
if mymax==-1:
for template_run in template_runs:
if "NoNameYet" in template_run and template_run[-4:-2] == "_i":
if int(template_run[-2]) > mymax:
mymax = int(template_run[-2])
final_template_run = template_run
if mymax==-1:
for template_run in template_runs:
if "dwiMDT_Control_n72" in template_run and template_run[-4:-2]=="_i":
if int(template_run[-2])>mymax:
mymax=int(template_run[-2])
final_template_run=template_run
if mymax == -1:
raise Exception(f"Could not find template runs in {os.path.join(mainpath, f'{project_name}-work','dwi',template_type_prefix)}")
#template_type_prefix = "faMDT_NoNameYet_n37_i6"
#final_template_run = "SyN_0p5_3_0p5_fa"
#final_ref = f"/mnt/munin6/Badea/Lab/human/AD_Decode/diffusion_prep_locale/diffusion_prep_{subject}/Reg_LPCA_{subject}_nii4D.nii.gz";
orient_string = os.path.join(prep_folder, f'{subject}_relative_orientation.txt')
if not os.path.exists(orient_string):
orient_strings = glob.glob(os.path.join(prep_folder, f'*_relative_orientation.txt'))
if np.size(orient_strings)>0:
orient_string = orient_strings[0]
if os.path.exists(orient_string):
orient_relative = open(orient_string, mode='r').read()
orientation_out = orient_relative.split(',')[0]
orientation_out = orientation_out.split(':')[1]
orientation_in = orient_relative.split(',')[1]
orientation_in = orientation_in.split(':')[1]
else:
orientation_out = "*"
warnings.warn("Could not find orientation file, may cause errors later")
orientation_in = "RAS"
orientation_out = "RAS"
if headfile is not None:
SAMBA_orientation_in, SAMBA_orientation_out = get_info_SAMBA_headfile(headfile)
else:
SAMBA_orientation_in, SAMBA_orientation_out = 'RAS', 'RAS'
trans = os.path.join(work_dir,"preprocess","base_images","translation_xforms",f"{subject}_0DerivedInitialMovingTranslation.mat")
rigid =os.path.join(work_dir,"dwi",f"{subject}_rigid.mat")
affine = os.path.join(work_dir,"dwi",f"{subject}_affine.mat")
MDT_to_subject = os.path.join(final_template_run,"reg_diffeo",f"MDT_to_{subject}_warp.nii.gz")
label_name = os.path.basename(atlas_labels)
label_name = label_name.split("_labels")[0]
MDT_to_atlas_affine = os.path.join(final_template_run,"stats_by_region","labels","transforms",f"MDT_*_to_{label_name}_affine.mat")
atlas_to_MDT = os.path.join(final_template_run,"stats_by_region","labels","transforms",f"{label_name}_to_MDT_warp.nii.gz")
[trans, rigid, affine, MDT_to_subject, MDT_to_atlas_affine, atlas_to_MDT], exists = check_files([trans,rigid,affine,MDT_to_subject,MDT_to_atlas_affine,atlas_to_MDT])
preprocess_ref = os.path.join(work_dir,"preprocess",f"{subject}_fa_masked.nii.gz")
preprocess_labels = os.path.join(dirty_dir,f"{subject}_preprocess_labels.nii.gz")
fixed_preprocess_labels = os.path.join(dirty_dir,f"{subject}_fixed_preprocess_labels.nii.gz")
coreg_labels = os.path.join(dirty_dir,f"{subject}_{orientation_out}_labels.nii.gz")
coreg_reorient_labels = os.path.join(dirty_dir,f"{subject}_{SAMBA_orientation_in}_labels.nii.gz")
# final_ref=f"/mnt/munin6/Badea/Lab/mouse/co_reg_LPCA_${subject:1:5}_m00-results/Reg_LPCA_${subject:1:5}_nii4D.nii.gz";
subjspace_coreg = os.path.join(prep_folder, f"{subject}_subjspace_coreg.nii.gz")
final_refs = [subjspace_coreg]
abb20s = glob.glob(f'/Volumes/dusom_civm-atlas/20.abb.15/research/diffusion{subject}*/*.nii')
if np.size(abb20s)>0:
final_refs.append(abb20s[0])
abb19s = glob.glob(f'/Volumes/dusom_civm-atlas/19.abb.14/research/diffusion{subject}*/*.nii')
if np.size(abb19s)>0:
final_refs.append(abb19s[0])
abb18s = glob.glob(f'/Volumes/dusom_civm-atlas/18.abb.11/research/diffusion{subject}*/*.nii')
if np.size(abb18s) > 0:
final_refs.append(abb18s[0])
final_ref = None
for pos_final_ref in final_refs:
if isinstance(pos_final_ref, str) and os.path.exists(pos_final_ref):
if Path(pos_final_ref).is_symlink():
final_ref=str(Path(pos_final_ref).resolve())
else:
final_ref = pos_final_ref
if final_ref is None:
txt = f"Could not find final registered subject file for subject {subject}"
warnings.warn(txt)
return
symbolic_ref = os.path.join(out_dir,f"{subject}_Reg_LPCA_nii4D.nii.gz")
final_labels = os.path.join(out_dir,f"{subject}_IITmean_preprocess_labels.nii.gz")
final_labels_backup = os.path.join(dirty_dir,f"{subject}_IITmean_preprocess_labels.nii.gz")
superpose=True
if not os.path.exists(final_labels) or overwrite:
if verbose:
print(f"Backporting labels to raw space for subject: {subject} to {final_labels}")
if not os.path.exists(preprocess_labels) or overwrite:
#Note, it used to be MultiLabel[1.0x1.0x1.0,2] but it seems like the default parameters tend to be based on the image themselves and work fine,
#so be careful but I removed it for now so it would work on different image sets
cmd = f"antsApplyTransforms -v 1 -d 3 -i {atlas_labels} -o {preprocess_labels} -r {preprocess_ref} -n MultiLabel -t [{trans},1] [{rigid},1] [{affine},1] {MDT_to_subject} [{MDT_to_atlas_affine},1] {atlas_to_MDT}"
if verbose:
print(f"Runnings the Ants apply transforms to {atlas_labels}")
print(cmd)
os.system(cmd)
if os.path.exists(preprocess_labels) and ((not os.path.exists(fixed_preprocess_labels)) or overwrite):
header_superpose(final_ref, preprocess_labels, outpath=fixed_preprocess_labels)
if os.path.exists(fixed_preprocess_labels) and (not os.path.exists(coreg_labels) or overwrite):
if orientation_out != orientation_in:
if verbose:
print(f"Reorientation from {orientation_out} back to {orientation_in}")
img_transform_exec(fixed_preprocess_labels, orientation_out, orientation_in, coreg_labels)
else:
if verbose:
print(f"Orientations are the same, skipping reorientation")
shutil.copy(fixed_preprocess_labels, coreg_labels)
if os.path.exists(coreg_labels) and (not os.path.exists(coreg_reorient_labels) or overwrite):
if SAMBA_orientation_out != SAMBA_orientation_in:
if verbose:
print(f"Reorientation from {SAMBA_orientation_out} back to {SAMBA_orientation_in}")
img_transform_exec(coreg_labels, SAMBA_orientation_out, SAMBA_orientation_in,
output_path=coreg_reorient_labels)
else:
if verbose:
print(f"Orientations are the same, skipping reorientation")
if coreg_labels != coreg_reorient_labels:
warnings.warn('There is a discrepancy between the stated input orientation in headfile and the '
'one found by find_relative_orientation_by_CoM.bash, beware')
coreg_reorient_labels = coreg_labels
if os.path.exists(coreg_reorient_labels):
header_superpose(final_ref, coreg_reorient_labels, outpath=final_labels)
#cmd = f"{os.path.join(gunniespath, 'nifti_header_splicer.bash')} {final_ref} {coreg_labels} {final_labels}"
#os.system(cmd)
if os.path.exists(final_labels):
if verbose:
print(f"Applying fsl maths to {final_labels}")
cmd = f"fslmaths {final_labels} -add 0 {final_labels} -odt short"
os.system(cmd)
shutil.copy(final_labels, final_labels_backup)
else:
print(f"Already calculated the label file for subject {subject}")
skip_making_data_package_for_tractography = True
if not skip_making_data_package_for_tractography:
print('if you see this, make the bval fixes')
if not os.path.exists(symbolic_ref):
cmd=f"ln - s {final_ref} {symbolic_ref}"
bvals = os.path.join(mainpath, f"diffusion_prep_{subject}", f"{subject}_bvals.txt")
bvecs = os.path.join(mainpath, f"diffusion_prep_{subject}", f"{subject}_bvecs.txt")
bval_copy = os.path.join(out_dir, f"{subject}_bvals.txt")
bvec_copy = os.path.join(out_dir, f"{subject}_bvecs.txt")
if not os.path.exists(bval_copy):
shutil.copy(bvals, bval_copy)
if not os.path.exists(bvec_copy):
shutil.copy(bvecs, bvec_copy)
trk_folder = '/Volumes/Data/Badea/Lab/jacques/ADDecode_test_connectomes/TRK_MPCA_fixed'
new_trk_folder = '/Volumes/Data/Badea/Lab/jacques/ADDecode_test_connectomes/TRK_MPCA_fixed_100'
trk_folder = '/Users/alex/jacques/AD_decode_comparison_temp/'
new_trk_folder = '/Users/alex/jacques/AD_decode_comparison_100_temp/'
trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA'
new_trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA_100'
trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA_MDT_fixed'
new_trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA_MDT_fixed_100'
trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA_fixed'
new_trk_folder = '/Volumes/Data/Badea/Lab/human/AD_Decode/Analysis/TRK_MPCA_fixed_100'
mkcdir(new_trk_folder)
ratio = 100
filelist = os.listdir(trk_folder)
filelist = sorted(filelist)
random.shuffle(filelist)
#filelist.reverse()
for filepath in filelist:
filename, f_ext = os.path.splitext(filepath)
if f_ext == '.trk' and f'ratio_{str(ratio)}' not in filename:
newfilename = filename.replace('_all','_ratio_'+str(ratio))
newfilepath = os.path.join(new_trk_folder, newfilename +f_ext)
if not os.path.exists(newfilepath):
print(f'Downsampling from {os.path.join(trk_folder,filepath)} to {newfilepath} with ratio {str(ratio)}')
reducetractnumber(os.path.join(trk_folder,filepath), newfilepath, getdata=False, ratio=100, return_affine= False, verbose=False)
print(f'succesfully created {newfilepath}')
main_path = '/Volumes/Data/Badea/Lab/human/'
else:
raise Exception('No other computer name yet')
project = "AD_Decode"
if project == "AD_Decode":
path_TRK = os.path.join(main_path, 'AD_Decode', 'Analysis', 'TRK_MPCA_100')
path_TRK_output = os.path.join(main_path, 'AD_Decode', 'Analysis', 'TRK_MPCA_MDT_100')
path_DWI = os.path.join(main_path, 'AD_Decode', 'Analysis', 'DWI')
path_transforms = os.path.join(main_path, 'AD_Decode', 'Analysis','Transforms')
ref = "md"
path_trk_tempdir = os.path.join(main_path, 'AD_Decode', 'Analysis', 'TRK_save')
DWI_save = os.path.join(main_path, 'AD_Decode', 'Analysis', 'NII_tempsave')
mkcdir([path_trk_tempdir,path_TRK_output, DWI_save])
#Get the values from DTC_launcher_ADDecode. Should probalby create a single parameter file for each project one day
stepsize = 2
ratio = 100
trkroi = ["wholebrain"]
str_identifier = get_str_identifier(stepsize, ratio, trkroi)
overwrite = True
cleanup = False
verbose=True
save_temp_files = True
recenter=1
contrast='dwi'
native_ref=''
orient_string = os.path.join(path_DWI, 'relative_orientation.txt')
from pandas import ExcelFile
from BIAC_tools import isempty
import sys, getopt
subjects = ["00393", "00490", "00560", "00680", "00699", "00795","01952","02263"]
weird_subjects = ["02432"]
atlas_legends = "/Users/alex/jacques/connectomes_testing//atlases/CHASSSYMM3AtlasLegends.xlsx"
outpath = "/Volumes/Data/Badea/Lab/human/Sinha_epilepsy/"
datapath = os.path.join(outpath, "DWI")
figspath = os.path.join(outpath, "Figures")
dwi_preprocessed = os.path.join(outpath, "DWI_temp")
trkpath = os.path.join(outpath, "TRK")
mkcdir([outpath, figspath, dwi_preprocessed, trkpath])
masktype = "FA"
masktype = "T1"
masktype = "dwi"
max_processors = 10
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
function_processes = np.int(max_processors / subject_processes)
outpath=outpath,
writeformat=writeformat,
overwrite=overwrite) #extractbvals_research
#fbvals, fbvecs = rewrite_subject_bvalues(diffpath, subject, outpath=outpath, writeformat=writeformat, overwrite=overwrite)
elif btables == "copy":
for subject in subjects:
#outpathsubj = "/Volumes/dusom_dibs_ad_decode/all_staff/APOE_temp/diffusion_prep_58214/"
outpathsubj = os.path.join(outpath, proc_name + subject)
outpathbval = os.path.join(outpathsubj,
proc_subjn + subject + "_bvals.txt")
outpathbvec = os.path.join(outpathsubj,
proc_subjn + subject + "_bvecs.txt")
outpathrelative = os.path.join(outpath, "relative_orientation.txt")
newoutpathrelative = os.path.join(outpathsubj,
"relative_orientation.txt")
mkcdir(outpathsubj)
shutil.copy(outpathrelative, newoutpathrelative)
if not os.path.exists(outpathbval) or not os.path.exists(
outpathbvec) or overwrite:
mkcdir(outpathsubj)
writeformat = "tab"
writeformat = "dsi"
overwrite = True
bvals = glob.glob(os.path.join(outpath, "*N58408*bvals*.txt"))
bvecs = glob.glob(os.path.join(outpath, "*N58408*bvec*.txt"))
if np.size(bvals) > 0 and np.size(bvecs) > 0:
shutil.copy(bvals[0], outpathbval)
shutil.copy(bvecs[0], outpathbvec)
bval_file = "/Volumes/Data/Badea/Lab/jacques/APOE_series/diffusion_prep_locale/diffusion_prep_N58408/N58408_bvals.txt"
bvec_file = "/Volumes/Data/Badea/Lab/jacques/APOE_series/diffusion_prep_locale/diffusion_prep_N58408/N58408_bvecs.txt"
#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 = False
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_allsubj')
figspath = os.path.join(main_folder + "Figures_RAS_allsubj_lr")
mkcdir([outtrkpath,figspath])
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) + '_'
output_folder = '/Users/alex/jacques/APOE_test/DWI_allsubj_RAS/'
if 'samos' in computer_name:
DWI_folder = '/mnt/paros_MRI/jacques/APOE/DWI_allsubj/'
labels_folder = '/mnt/paros_MRI/jacques/APOE/DWI_allsubj/'
output_folder = '/mnt/paros_MRI/jacques/APOE/DWI_allsubj_RAS/'
subjects_all = glob.glob(os.path.join(DWI_folder, '*_subjspace_coreg.nii.gz'))
subjects_list = []
for subject in subjects_all:
subject_name = os.path.basename(subject)
subjects_list.append(subject_name[:6])
subjects_list.sort()
subjects_list = subjects_list[:]
print(subjects_list)
mkcdir(output_folder)
for subject in subjects_list:
coreg_file = os.path.join(DWI_folder, f'{subject}_subjspace_coreg.nii.gz')
labels_file = os.path.join(labels_folder, f'{subject}_labels.nii.gz')
labels_lr_file = os.path.join(labels_folder,
f'{subject}_labels_lr_ordered.nii.gz')
mask_file = os.path.join(DWI_folder, f'{subject}_subjspace_mask.nii.gz')
coreg_RAS_file = os.path.join(output_folder, f'{subject}_coreg_RAS.nii.gz')
labels_RAS_file = os.path.join(output_folder,
f'{subject}_labels_RAS.nii.gz')
labels_RAS_lr_file = os.path.join(
output_folder, f'{subject}_labels_lr_ordered_RAS.nii.gz')
mask_RAS_file = os.path.join(output_folder, f'{subject}_RAS_mask.nii.gz')
transferred = 0
if not os.path.exists(coreg_RAS_file):
def launch_preprocessing(subj, raw_nii, outpath, cleanup=False, nominal_bval=4000, SAMBA_inputs_folder=None,
shortcuts_all_folder = None, gunniespath="~/gunnies/", processes=1, masking="bet", ref=None,
transpose=None, overwrite=False, denoise='None', recenter=0, verbose=False):
proc_name ="diffusion_prep_" # Not gonna call it diffusion_calc so we don't assume it does the same thing as the civm pipeline
work_dir=os.path.join(outpath,proc_name+subj)
"""
for filePath in glob.glob(os.path.join(work_dir,'*')):
modTimesinceEpoc = os.path.getmtime(filePath)
modificationTime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(modTimesinceEpoc))
if modificationTime[5:7]=='09' and int(modificationTime[8:10])>9:
os.remove(filePath)
"""
if verbose:
print(f"Processing diffusion data with runno/subj: {subj}")
print(f"Work directory is {work_dir}")
mkcdir(work_dir)
sbatch_folder =os.path.join(work_dir,"sbatch")
mkcdir(sbatch_folder)
#nii_path = os.path.join(work_dir,'nii4D_'+subj + '.nii.gz')
#if not os.path.exists(nii_path):
# shutil.copy(raw_nii, nii_path)
nii_name=os.path.split(raw_nii)[1]
niifolder = os.path.dirname(raw_nii)
ext = ".nii.gz"
nii_ext=getext(nii_name)
bxheader = nii_name.replace(nii_ext,".bxh")
bxheader = os.path.join(niifolder, bxheader)
bvecs = os.path.join(work_dir, subj+"_bvecs.txt")
bvals =bvecs.replace("bvecs","bvals")
if verbose:
print(f"Original nifti is at {nii_name}\nbvecs are at {bvecs}\nbvals are at {bvals}\n")
if not os.path.exists(bvecs):
if verbose:
print("Extracting diff directions")
#print("Bvals and bvecs not found, using extractdiffdirs, however it it NOT RELIABLE, beware!")
bvec_cmd = (f"extractdiffdirs --colvectors --writebvals --fieldsep='\t' --space=RAI {bxheader} {bvecs} {bvals}")
os.system(bvec_cmd)
# Make dwi for mask generation purposes.
tmp_mask = os.path.join(work_dir,f"{subj}_tmp_mask{ext}")
raw_dwi = os.path.join(work_dir,f"{subj}_raw_dwi.nii.gz")
orient_string = os.path.join(work_dir,"relative_orientation.txt")
if shortcuts_all_folder is not None:
#nii_path_link = os.path.join(shortcuts_all_folder, f"{subj}_rawnii{ext}")
#if not os.path.exists(nii_path_link) or overwrite:
# buildlink(nii_path, nii_path_link)
bvecs_new = os.path.join(shortcuts_all_folder, subj + "_bvecs.txt")
bvals_new = os.path.join(shortcuts_all_folder, subj + "_bvals.txt")
if not os.path.exists(bvecs_new) or not os.path.exists(bvals_new) or overwrite:
shutil.copyfile(bvecs, bvecs_new)
shutil.copyfile(bvals, bvals_new)
final_mask = os.path.join(work_dir, f'{subj}_mask{ext}')
#if (not os.path.exists(final_mask) and not os.path.exists(tmp_mask)) or overwrite:
if not os.path.exists(tmp_mask) or overwrite:
if not os.path.exists(raw_dwi) or overwrite:
select_cmd = f"select_dwi_vols {raw_nii} {bvals} {raw_dwi} {nominal_bval} -m"
os.system(select_cmd)
if not os.path.exists(tmp_mask) or overwrite:
if 'median' in masking:
tmp = tmp_mask.replace("_mask", "")
if np.size(masking.split('_'))>1:
median_radius = int(masking.split('_')[1])
else:
median_radius = 4
median_mask_make(raw_dwi, tmp, outpathmask=tmp_mask, median_radius = median_radius, numpass=median_radius)
elif masking=="bet":
tmp=tmp_mask.replace("_mask", "")
bet_cmd = f"bet {raw_dwi} {tmp} -m -n -R"
os.system(bet_cmd)
else:
raise Exception("Unrecognized masking type")
# I think this part is done later more properly: if create_subj_space_files: for contrast in ['dwi', 'b0', 'mask']:
#if SAMBA_inputs_folder is not None:
# mask_subj_link = os.path.join(SAMBA_inputs_folder,f'{subj}_subjspace_mask{ext}')
# if not os.path.exists(mask_subj_link) or overwrite:
# shutil.copy(tmp_mask, mask_subj_link)
#if cleanup and (os.path.exists(tmp_mask) and os.path.exists(raw_dwi)):
# os.remove(raw_dwi)
#overwrite=False
# Run Local PCA Denoising algorithm on 4D nifti:
masked_nii = os.path.join(work_dir, nii_name)
if not "nii.gz" in masked_nii:
masked_nii = masked_nii.replace(".nii", ".nii.gz")
masked_nii = masked_nii.replace(ext, "_masked" + ext)
if denoise.lower()=='lpca':
D_subj=f'LPCA_{subj}';
elif denoise.lower()=='mpca':
D_subj=f'MPCA_{subj}';
elif denoise=="None" or denoise is None:
D_subj = f'{subj}'
if denoise=="None" or denoise is None:
denoised_nii = masked_nii
if not os.path.exists(masked_nii) or overwrite:
fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
os.system(fsl_cmd)
else:
denoised_nii = os.path.join(work_dir,f"{D_subj}_nii4D.nii.gz")
if not os.path.exists(denoised_nii) or overwrite:
if not os.path.exists(masked_nii) or overwrite:
fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
os.system(fsl_cmd)
basic_LPCA_denoise_func(subj,masked_nii,bvecs,denoised_nii, processes=processes,
denoise=denoise, verbose=False) #to improve and make multiprocessing
#if cleanup and os.path.exists(denoised_nii) and os.path.exists(masked_nii) and denoised_nii!=masked_nii:
# os.remove(masked_nii)
# Run coregistration/eddy current correction:
coreg_nii_old = f'{outpath}/co_reg_{D_subj}_m00-results/Reg_{D_subj}_nii4D{ext}';
coreg_nii = os.path.join(work_dir,f'Reg_{D_subj}_nii4D{ext}')
if not cleanup:
coreg_nii=coreg_nii_old
if not os.path.exists(coreg_nii) or overwrite:
if not os.path.exists(coreg_nii_old) or overwrite:
temp_cmd = os.path.join(gunniespath,'co_reg_4d_stack_tmpnew.bash')+f' {denoised_nii} {D_subj} 0 {outpath} 0';
os.system(temp_cmd)
if cleanup:
shutil.move(coreg_nii_old,coreg_nii)
if shortcuts_all_folder is not None:
coreg_link = os.path.join(shortcuts_all_folder,f'{subj}_subjspace_coreg{ext}')
if not os.path.exists(coreg_link) or overwrite:
buildlink(coreg_nii, coreg_link)
toeddy=False
if toeddy:
#fsl_cmd = f"fslmaths {raw_nii} -mas {tmp_mask} {masked_nii} -odt 'input'";
#os.system(fsl_cmd)
eddy_cmd = f"eddy --imain={coreg_nii} --mask={tmp_mask} --acqp=acq_params.txt --index={os.path.join(work_dir,'index.txt')} --bvecs={bvecs} --bvals={bvals} --topup=topup_results --repol --out = {os.path.join(work_dir,f'Reg_{D_subj}_nii4D_eddy{ext}')}"
os.system(eddy_cmd)
coreg_inputs=os.path.join(outpath,f'co_reg_{D_subj}_m00-inputs')
coreg_work=coreg_inputs.replace('-inputs','-work')
coreg_results=coreg_inputs.replace('-inputs','-results')
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_inputs):
shutil.rmtree(coreg_inputs)
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_work):
shutil.rmtree(coreg_work)
if cleanup and os.path.exists(coreg_nii) and os.path.isdir(coreg_results):
shutil.rmtree(coreg_results)
# Generate tmp DWI:
tmp_dwi_out=os.path.join(work_dir, f'{subj}_tmp_dwi{ext}')
dwi_out=os.path.join(work_dir,f'{subj}_dwi{ext}')
if not os.path.exists(tmp_dwi_out) or overwrite:
cmd=f'select_dwi_vols {coreg_nii} {bvals} {tmp_dwi_out} {nominal_bval} -m'
os.system(cmd)
# Generate tmp B0:
tmp_b0_out=os.path.join(work_dir,f'{subj}_tmp_b0{ext}')
b0_out = os.path.join(work_dir, f'{subj}_b0{ext}')
if (not os.path.exists(b0_out) and not os.path.exists(tmp_b0_out)) or overwrite:
cmd=f'select_dwi_vols {coreg_nii} {bvals} {tmp_b0_out} 0 -m;'
os.system(cmd)
#overwrite=False
#elif cleanup and os.path.exists(tmp_b0_out):
# os.remove(tmp_b0_out)
# Generate DTI contrasts and perform some tracking QA:
if cleanup:
c_string=' --cleanup '
else:
c_string=''
#Important note: this is what first creates the fa, md, etc
if len(glob.glob(os.path.join(work_dir,f'*.fib.gz.md{ext}'))) == 0 or overwrite:
if overwrite:
oldfiles = glob.glob(os.path.join(work_dir, f'*.fib.gz*'))
for oldfile in oldfiles:
os.remove(oldfile)
cmd = 'bash ' + os.path.join(gunniespath,'dti_qa_with_dsi_studio_weirdcall.bash')+f' {coreg_nii} {bvecs} {tmp_mask} {work_dir} {c_string}';
os.system(cmd)
#Save the subject space dti results
#Generate tmp MD:
for contrast in ['md']:
real_file=largerfile(os.path.join(work_dir,f'*.fib.gz.{contrast}{ext}')) #Catch the 'real file' for each contrast
tmp_file = f'{work_dir}/{subj}_tmp_{contrast}{ext}';
if not os.path.exists(tmp_file):
shutil.copy(real_file,tmp_file)
tmp_md = f'{work_dir}/{subj}_tmp_md{ext}';
if ref=="md" or ref is None:
reference=tmp_md
elif ref=="coreg":
reference=coreg_nii
elif os.path.exists(ref):
reference=ref
reference_file = os.path.join(work_dir, f'{subj}_reference{ext}')
if not os.path.exists(reference_file):
shutil.copy(reference, reference_file)
if shortcuts_all_folder is not None:
bonus_ref_link = os.path.join(shortcuts_all_folder, f'{subj}_reference{ext}')
if not os.path.exists(bonus_ref_link) or overwrite:
buildlink(reference_file,bonus_ref_link)
#give new header to the non-dti files using md as reference
for contrast in ['dwi', 'b0', 'mask']:
tmp_file=os.path.join(work_dir,f'{subj}_tmp_{contrast}{ext}')
tmp2_file=os.path.join(work_dir,f'{subj}_tmp2_{contrast}{ext}')
final_file=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
if ((not os.path.exists(tmp2_file) and not os.path.exists(final_file)) or overwrite):
if not os.path.exists(tmp_file):
raise Exception("Tmp file was not created, need to rerun previous processes")
else:
header_superpose(reference, tmp_file, outpath=tmp2_file)
create_subj_space_files = True
if create_subj_space_files:
for contrast in ['dwi', 'b0', 'mask']:
tmp_file = os.path.join(work_dir, f'{subj}_tmp_{contrast}{ext}')
subj_file = os.path.join(work_dir, f'{subj}_subjspace_{contrast}{ext}')
if not os.path.exists(subj_file) or overwrite:
if not os.path.exists(tmp_file):
raise Exception("Tmp file was not created, need to rerun previous processes")
else:
header_superpose(raw_dwi, tmp_file, outpath=subj_file)
if shortcuts_all_folder is not None:
subj_link = os.path.join(shortcuts_all_folder, f'{subj}_subjspace_{contrast}{ext}')
if not os.path.exists(subj_link) or overwrite:
buildlink(subj_file, subj_link)
#write the relative orientation file here
if not os.path.isfile(orient_string) or overwrite:
if os.path.isfile(orient_string):
os.remove(orient_string)
file = os.path.join(work_dir,subj+'_tmp_mask'+ext);
cmd = 'bash ' + os.path.join(gunniespath,'find_relative_orientation_by_CoM.bash') + f' {reference_file} {file}'
orient_relative = subprocess.getoutput(cmd)
with open(orient_string, 'w') as f:
f.write(orient_relative)
else:
orient_relative = open(orient_string, mode='r').read()
if SAMBA_inputs_folder is not None:
subj_orient_string = os.path.join(SAMBA_inputs_folder, f'{subj}_relative_orientation.txt')
shutil.copy(orient_string, subj_orient_string)
if shortcuts_all_folder is not None:
subj_orient_string = os.path.join(shortcuts_all_folder, f'{subj}_relative_orientation.txt')
shutil.copy(orient_string, subj_orient_string)
#check extracted values from relative orientation vals
orientation_out = orient_relative.split(',')[0]
orientation_out = orientation_out.split(':')[1]
orientation_in = orient_relative.split(',')[1]
orientation_in = orientation_in.split(':')[1]
if verbose:
print(f'flexible orientation: {orientation_in}');
print(f'reference orientation: {orientation_out}');
#apply the orientation modification to specified contrasts
for contrast in ['dwi', 'b0', 'mask']:
img_in=os.path.join(work_dir,f'{subj}_tmp2_{contrast}{ext}')
img_out=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
if not os.path.isfile(img_out) or overwrite:
if orientation_out != orientation_in:
print('TRYING TO REORIENT...b0 and dwi and mask')
if os.path.exists(img_in) and (not os.path.exists(img_out) or overwrite):
img_transform_exec(img_in, orientation_in, orientation_out, img_out)
if os.path.exists(img_out):
os.remove(img_in)
elif os.path.exists(img_out) and cleanup:
os.remove(img_in)
else:
shutil.move(img_in,img_out)
if SAMBA_inputs_folder is not None:
inputs_space_link = os.path.join(SAMBA_inputs_folder, f'{subj}_{contrast}{ext}')
if not os.path.exists(inputs_space_link) or overwrite:
buildlink(img_out, inputs_space_link)
if shortcuts_all_folder is not None:
inputs_space_link = os.path.join(shortcuts_all_folder, f'{subj}_{contrast}{ext}')
if not os.path.exists(inputs_space_link) or overwrite:
buildlink(img_out, inputs_space_link)
mask = os.path.join(work_dir,f'{subj}_mask{ext}')
b0 = os.path.join(work_dir,f'{subj}_b0{ext}')
#if cleanup and os.path.exists(dwi_out) and os.path.exists(tmp_dwi_out):
# os.remove(tmp_dwi_out)
for contrast in ['fa0', 'rd', 'ad', 'md']:
real_file=largerfile(os.path.join(work_dir,f'*.fib.gz.{contrast}{ext}')) # It will be fun times if we ever have more than one match to this pattern...
#inputspace = real_file
inputspace = os.path.join(work_dir, f'{subj}_inputspace_{contrast}{ext}')
contrast=contrast.replace('0','')
#linked_file=os.path.join(shortcut_dir,f'{subj}_{contrast}{ext}')
linked_file_w=os.path.join(work_dir,f'{subj}_{contrast}{ext}')
made_newfile = affine_superpose(dwi_out, real_file, outpath = inputspace, transpose=transpose)
if not made_newfile:
inputspace = real_file
if not os.path.isfile(linked_file_w) or overwrite:
buildlink(inputspace, linked_file_w)
if SAMBA_inputs_folder is not None:
#warnings.warn('should reach this!')
blinked_file = os.path.join(SAMBA_inputs_folder, f'{subj}_{contrast}{ext}')
if not os.path.exists(blinked_file) or overwrite:
buildlink(inputspace, blinked_file)
print(f'build link from {inputspace} to {blinked_file}')
if shortcuts_all_folder is not None:
#warnings.warn('should reach this!')
blinked_file = os.path.join(shortcuts_all_folder, f'{subj}_{contrast}{ext}')
if not os.path.exists(blinked_file) or overwrite:
buildlink(inputspace, blinked_file)
print(f'build link from {inputspace} to {blinked_file}')
if create_subj_space_files:
for contrast in ['fa0', 'rd', 'ad', 'md']:
real_file = largerfile(os.path.join(work_dir,
f'*.fib.gz.{contrast}{ext}')) # It will be fun times if we ever have more than one match to this pattern...
contrast = contrast.replace('0', '')
subj_file_tmp = os.path.join(work_dir, f'{subj}_subjspace_tmp_{contrast}{ext}')
subj_file = os.path.join(work_dir, f'{subj}_subjspace_{contrast}{ext}')
if not os.path.exists(subj_file) or overwrite:
if not os.path.exists(subj_file_tmp):
if orientation_out != orientation_in:
print('TRYING TO REORIENT...b0 and dwi and mask')
if os.path.exists(real_file) and (not os.path.exists(subj_file) or overwrite):
img_transform_exec(real_file, orientation_out, orientation_in, subj_file_tmp)
else:
shutil.copy(real_file,subj_file_tmp)
header_superpose(raw_dwi, subj_file_tmp, outpath=subj_file)
if shortcuts_all_folder is not None:
subj_link = os.path.join(shortcuts_all_folder, f'{subj}_subjspace_{contrast}{ext}')
buildlink(subj_file, subj_link)
#if cleanup:
# tmp_files = glob.glob(os.path.join(work_dir, '*tmp*'))
# for file in tmp_files:
# os.remove(file)
nii_path = os.path.join(work_dir,'nii4D_'+subj + ext)
if os.path.exists(nii_path):
os.remove(nii_path)
if cleanup:
reg_src = os.path.join(work_dir,'Reg_' + subj + f'_nii4D{ext}.src.gz')
if os.path.exists(reg_src):
os.remove(reg_src)
reg_src_fib = os.path.join(work_dir,'Reg_' + subj + f'_nii4D{ext}.src.gz.dti.fib.gz')
if os.path.exists(reg_src):
os.remove(reg_src)
def img_transform_exec(img,
current_vorder,
desired_vorder,
output_path=None,
write_transform=0,
verbose=False):
is_RGB = 0
is_vector = 0
is_tensor = 0
if not os.path.exists(img):
raise ('Nifti img file does not exists')
for char in current_vorder:
if char.islower():
warnings.warn("Use uppercase for current order")
current_vorder = current_vorder.upper()
for char in desired_vorder:
if char.islower():
warnings.warn("Use uppercase for desired order")
current_vorder = desired_vorder.upper()
ordervals = 'RLAPSI'
if not ordervals.find(current_vorder[0]) and not ordervals.find(
current_vorder[1]) and not ordervals.find(current_vorder[2]):
raise TypeError(
'Please use only R L A P S or I for current voxel order')
if not ordervals.find(desired_vorder[0]) and not ordervals.find(
desired_vorder[1]) and not ordervals.find(desired_vorder[2]):
raise TypeError(
'Please use only R L A P S or I for desired voxel order')
dirname, filename, ext = splitpath(img)
filename = filename + "." + ext
if output_path is None:
output_name = filename.replace('.nii', '_' + desired_vorder + '.nii')
output_path = os.path.join(dirname, output_name)
if not output_path.find('.'):
mkcdir(output_path)
output_name = filename.replace('.nii', '_' + desired_vorder + '.nii')
output_path = os.path.join(output_path, output_name)
out_dir, _, _ = splitpath(output_path)
affine_out = os.path.join(
out_dir, current_vorder + '_to_' + desired_vorder + '_affine.pickle')
overwrite = True
if os.path.isfile(output_path) and not overwrite and (
not write_transform or
(write_transform and os.path.exists(affine_out))):
warnings.warn('Existing output:%s, not regenerating', output_path)
orig_string = 'RLAPSI'
flip_string = 'LRPAIS'
orig_current_vorder = current_vorder
try:
nii = nib.load(img)
except:
raise ('Could not load img at ' + img)
nii_data = nii.get_data()
hdr = nii.header
dims = nii.shape
if np.size(dims) > 6:
raise ('Image has > 5 dimensions')
elif np.size(dims) < 3:
raise ('Image has < 3 dimensions')
new_data = nii_data
affine = nii._affine
if desired_vorder != orig_current_vorder:
if ((np.size(dims) > 4) and (dims(5) == 3)):
is_vector = 1
elif ((np.size(dims) > 5) and (dims(5) == 6)):
is_tensor = 1
#x_row = [1, 0, 0];
#y_row = [0, 1, 0];
#z_row = [0, 0, 1];
x_row = affine[0, :]
y_row = affine[1, :]
z_row = affine[2, :]
xpos = desired_vorder.find(current_vorder[0])
if xpos == -1:
if verbose:
print('Flipping first dimension')
val = 0
new_data = np.flip(new_data, 0)
orig_ind = orig_string.find(current_vorder[0])
current_vorder = current_vorder[0:val] + flip_string[
orig_ind] + current_vorder[val + 1:]
if is_vector:
new_data[:, :, :, 0, 1] = -new_data[:, :, :, 0, 1]
x_row = [-1 * val for val in x_row]
ypos = desired_vorder.find(current_vorder[1])
if ypos == -1:
if verbose:
print('Flipping second dimension')
val = 1
new_data = np.flip(new_data, 1)
orig_ind = orig_string.find(current_vorder[1])
current_vorder = current_vorder[0:val] + flip_string[
orig_ind] + current_vorder[val + 1:]
if is_vector:
new_data[:, :, :, 0, 2] = -new_data[:, :, :, 0, 2]
y_row = [-1 * val for val in y_row]
zpos = desired_vorder.find(current_vorder[2])
if zpos == -1:
if verbose:
print('Flipping third dimension')
val = 2
new_data = np.flip(new_data, 2)
orig_ind = orig_string.find(current_vorder[2])
current_vorder = current_vorder[0:val] + flip_string[
orig_ind] + current_vorder[val + 1:]
if is_vector:
new_data[:, :, :, 0, 2] = -new_data[:, :, :, 0, 2]
z_row = [-1 * val for val in z_row]
xpos = current_vorder.find(desired_vorder[0])
ypos = current_vorder.find(desired_vorder[1])
zpos = current_vorder.find(desired_vorder[2])
if verbose:
print([
'Dimension order is:' + str(xpos) + ' ' + str(ypos) + ' ' +
str(zpos)
])
if not os.path.isfile(output_path) or overwrite:
if np.size(dims) == 5:
if is_tensor:
new_data = new_data.tranpose(xpos, ypos, zpos, 3, 4)
else:
if is_vector: # =>> honestly looking at the original code, this doesnt really make sense to me, so deactivated for now. Will raise warning in case it happens
warnings.warn('is vector not properly implemented')
# new[:,:,:,1,:] = new[:,:,:,1].transpose(xpos, ypos, zpos)
#new=new(:,:,:,[xpos, ypos, zpos]);
new_data.transpose(xpos, ypos, zpos, 3, 4)
elif np.size(dims) == 4:
if is_RGB:
('is rgb not properly implemented')
#new=new(:,:,:,[xpos, ypos, zpos]);
new_data = new_data.transpose(xpos, ypos, zpos, 3)
elif np.size(dims) == 3:
new_data = new_data.transpose(xpos, ypos, zpos)
if not os.path.isfile(affine_out) and write_transform:
intermediate_affine_matrix = [x_row, y_row, z_row]
iam = intermediate_affine_matrix
affine_matrix_for_points = [
iam[xpos, :], iam[ypos, :], iam[zpos, :]
]
affine_matrix_for_images = np.inv(affine_matrix_for_points)
am4i = affine_matrix_for_images
affine_matrix_string = [
am4i[1, :] + am4i[2, :] + am4i[3, :] + '0 0 0'
]
affine_fixed_string = [
'0',
'0',
'0',
]
try:
#write_affine_xform_for_ants(affine_out,affine_matrix_string,affine_fixed_string);
#needs to be implemented if this is a desired functionality
print("nope, not implemented")
except:
print("nope, not implemented")
"""
if test is not None:
newaffine = np.array([[0.045, 0., 0., 0.42525001],[0., 0.045, 0., 0.25920002],
[0., 0.,0.045, 0.25920002], [0., 0., 0., 1.]])
new_nii = nib.Nifti1Image(new_data, newaffine, hdr)
test = str(test)
nib.save(new_nii, test)
"""
origin = affine[0:3, 3]
if desired_vorder != orig_current_vorder:
trueorigin = origin * [x_row[0], y_row[1], z_row[2]]
trueorigin[2] = trueorigin[2] * (-1)
else:
trueorigin = origin
newaffine = np.zeros([4, 4])
newaffine[0:3, 0:3] = affine[0:3, 0:3]
newaffine[3, :] = [0, 0, 0, 1]
newaffine[:3, 3] = trueorigin
#newaffine[0,:]=x_row
#newaffine[1,:]=y_row
#newaffine[2,:]=z_row
#newhdr.srow_x=[newaffine[0,0:3]]
#newhdr.srow_y=[newaffine[1,0:3]]
#newhdr.srow_z=[newaffine[2,0:3]]
#newhdr.pixdim=hdr.pixdim
new_nii = nib.Nifti1Image(new_data, newaffine, hdr)
output_path = str(output_path)
if verbose:
print(f'Saving nifti file to {output_path}')
nib.save(new_nii, output_path)
if verbose:
print(f'Saved')
"""
