diff --git a/README b/README
deleted file mode 100644
index cff636962d6a01cca7287f26920bcc4b558d51cd..0000000000000000000000000000000000000000
--- a/README
+++ /dev/null
@@ -1,12 +0,0 @@
--------------------------------------------
-BRAIN IMAGING PIPELINE
-Fidel Alfaro Almagro, WIN-FMRIB
-June, 2022
--------------------------------------------
-Automated tool
-
-Installation
-------------
-If you want to install everythong that is needed from scratch, run:
-```cd /path-to-lib/install_dir/
-python setup.py install```
diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/bb_pipeline/.gitignore b/bb_pipeline/.gitignore
deleted file mode 100644
index e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf..0000000000000000000000000000000000000000
--- a/bb_pipeline/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-.DS_Store
diff --git a/bb_pipeline/__init__.py b/bb_pipeline/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/bb_pipeline/bb_pipeline.py b/bb_pipeline/bb_pipeline.py
deleted file mode 100755
index 8607956853374037a4bc2a664c2b3507218fd35e..0000000000000000000000000000000000000000
--- a/bb_pipeline/bb_pipeline.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-import nibabel as nib
-from fsl import data
-from fsl import utils
-from fsl import scripts
-from fsl import wrappers
-from shutil import copyfile
-from file_tree import FileTree
-from pipe_tree import Pipeline, In, Out, Ref, Var, update_closure
-#from bb_struct.bb_struct import bb_struct
-from my_wrappers.standard_space_roi import standard_space_roi
-from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
-
-pipe = Pipeline()
-
-BB_BIN_DIR = os.environ['BB_BIN_DIR']
-FSLDIR = os.environ['FSLDIR']
-coeff = BB_BIN_DIR + '/data/bb_GDC_coeff.grad'
-MNI = FSLDIR + '/data/standard/MNI152_T1_1mm'
-
-with open(BB_BIN_DIR + '/data/subj.txt', 'r') as f:
- subjects = [x.replace('\n','') for x in f.readlines()]
-
-tree = FileTree.read(BB_BIN_DIR + '/data/FileTree').update(subject=subjects)
-
-log_conf = {'cmd': sys.stdout}
-#Alternative (needs to be moved inside bb_struct, as logs_dir is not accessible here):
-#script_name = os.path.splitext(os.path.basename(__file__))[0]
-#log_conf = {'cmd': logs_dir + "/" + script_name + "_cmd.txt"}
-
-@pipe(submit=dict(logdir=Ref("logs_dir"), jobtime=200))
-def bb_struct( T1_orig: In,
- T1_brain: Out,
- logs_dir: Out,
- T1: Out,
- T1_orig_ud: Out,
- T1_orig_ud_warp: Out,
- T1_brain_mask: Out,
- T1_orig_defaced: Out,
- T1_defacing_mask: Out,
- T1_unbiased: Out,
- T1_unbiased_brain: Out,
- T1_brain_to_MNI: Out,
- T1_QC_face_mask_inside_brain_mask: Out,
- transform_outputs: Out("T1*_to_*"),
- fast_outputs: Out("T1_fast_*"),
- first_outputs: Out("T1_first_*"),
- T1_GDC: Ref,
- temp_outputs: Ref("T1_tmp_*")):
-
- with update_closure(temp_outputs, transform_outputs, fast_outputs,
- first_outputs):
-
- if [ coeff != '' ] :
- #Calculate and apply the Gradient Distortion Unwarp
- gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
- owarp=T1_orig_ud_warp,gradcoeff=coeff,
- vendor='siemens', nojac=True)
- else:
- pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
-
- #Calculate where does the brain start in the z dimension and extract the roi
- head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].split()[7])))
- wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).run(T1_tmp_1,
- log=log_conf)
-
- #Run a (Recursive) brain extraction on the roi
- wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True, log=log_conf)
-
- #Reduce the FOV of T1_orig_ud by calculating a registration
- #from T1_tmp_brain to ssref and apply it to T1_orig_ud
- # TODO: Once this wrapper is ready, merge it with FSL wrappers
- standard_space_roi(T1_tmp_1_brain, T1, maskNONE=True, ssref = MNI + '_brain',
- altinput=T1_orig_ud, d=True, log=log_conf)
-
- #Generate the actual affine from the orig_ud volume to the cut version
- #we haveand combine it to have an affine matrix from orig_ud to MNI
- wrappers.flirt(src=T1, ref=T1_orig_ud, omat=T1_to_T1_orig_ud_mat,
- schedule = FSLDIR + '/etc/flirtsch/xyztrans.sch', log=log_conf)
- wrappers.invxfm(inmat=T1_to_T1_orig_ud_mat, omat=T1_orig_ud_to_T1_mat,
- log=log_conf)
- wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat, btoc=T1_orig_ud_to_std_mat,
- atoc=T1_to_MNI_linear_mat, log=log_conf)
-
- #Non-linear registration to MNI using the previously calculated alignment
- wrappers.fnirt(src = T1, ref = MNI, aff = T1_to_MNI_linear_mat,
- config = BB_BIN_DIR + '/data/bb_fnirt.cnf',
- refmask = BB_BIN_DIR + '/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz',
- logout = logs_dir + '/bb_T1_to_MNI_fnirt.log',
- cout = T1_to_MNI_warp_coef, fout = T1_to_MNI_warp,
- jout = T1_to_MNI_warp_jac, iout = T1_tmp_3,
- interp = 'spline',log=log_conf)
-
- #Combine all transforms (Gradient Distortion Unwarp and T1 to MNI) into one
- if coeff != '' :
- wrappers.convertwarp(ref=MNI, warp1=T1_orig_ud_warp,
- midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
- out=T1_orig_to_MNI_warp,log=log_conf)
- else:
- wrappers.convertwarp(ref=MNI, premat=T1_orig_ud_to_T1_mat,
- warp1=T1_orig_ud_warp, out=T1_orig_to_MNI_warp,
- log=log_conf)
-
- # Apply the previously generated transformation
- wrappers.applywarp(src=T1_orig, ref=MNI, w=T1_orig_to_MNI_warp,
- out=T1_brain_to_MNI, rel=True, interp='spline',
- log=log_conf)
-
- #Create brain mask
- wrappers.invwarp(ref=T1, warp=T1_to_MNI_warp_coef,
- out=T1_to_MNI_warp_coef_inv,
- log=log_conf)
- wrappers.applywarp(src=MNI + '_brain_mask', ref=T1 ,
- w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
- rel=True, interp='trilinear', log=log_conf)
- wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain,log=log_conf)
- wrappers.fslmaths(T1_brain_to_MNI).mul(MNI + '_brain_mask').run(T1_brain_to_MNI,
- log=log_conf)
-
- #TODO: Replace this part with proper call to fsl_deface
- #Defacing T1_orig
- wrappers.concatxfm(atob=T1_orig_ud_to_T1_mat, btoc=T1_to_MNI_linear_mat,
- atoc=T1_tmp_mat, log=log_conf)
- wrappers.concatxfm(atob=T1_tmp_mat,
- btoc=BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
- atoc=T1_tmp_mat, log=log_conf)
- wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
- wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
- ref=T1_orig, mat=T1_tmp_mat, out=T1_defacing_mask,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).binv().mul(T1_orig).run(T1_orig_defaced,
- log=log_conf)
-
- #Defacing T1
- wrappers.concatxfm(atob=T1_to_MNI_linear_mat,
- btoc = BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
- atoc = T1_tmp_mat, log=log_conf)
- wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
- wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
- ref=T1, mat=T1_tmp_mat, out=T1_defacing_mask,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).binv().mul(T1).run(T1, log=log_conf)
-
- #Generation of QC value: Number of voxels in which
- #the defacing mask goes into the brain mask
- wrappers.fslmaths(T1_brain_mask).thr(0.5).bin().run(T1_tmp_4,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).thr(0.5).bin().add(T1_tmp_4).run(T1_tmp_4,
- log=log_conf)
- vals = wrappers.fslstats(T1_tmp_4).V.run(log=log_conf)[0]
- with open(T1_QC_face_mask_inside_brain_mask, 'w') as f:
- f.write(str(vals))
-
- #Run fast
- wrappers.fast(T1_brain, out = T1_fast_dir + '/T1_brain', b=True,
- log=log_conf)
-
- #Binarize PVE masks
- wrappers.fslmaths(T1_fast_pve_0).thr(0.5).bin().run(T1_fast_CSF_mask,
- log=log_conf)
- wrappers.fslmaths(T1_fast_pve_1).thr(0.5).bin().run(T1_fast_WM_mask,
- log=log_conf)
- wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(T1_fast_GM_mask,
- log=log_conf)
-
- #Apply bias field correction to T1
- wrappers.fslmaths(T1).div(T1_fast_brain_bias).run(T1_unbiased,
- log=log_conf)
- wrappers.fslmaths(T1_brain).div(T1_fast_brain_bias).run(T1_unbiased_brain,
- log=log_conf)
-
- #Creates a link inside T1_first to T1_unbiased_brain.nii.gz
- os.symlink(src="../T1_unbiased_brain.nii.gz", dst=T1_first_unbiased_brain)
-
- #pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
-
-if __name__ == "__main__":
- pipe.cli(tree)
diff --git a/bb_struct/.gitignore b/bb_struct/.gitignore
deleted file mode 100644
index e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf..0000000000000000000000000000000000000000
--- a/bb_struct/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-.DS_Store
diff --git a/bb_struct/__init__.py b/bb_struct/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/bb_struct/bb_struct.py b/bb_struct/bb_struct.py
deleted file mode 100755
index 7fd3162a5d2e910ed4da7b50631da21c14167fc5..0000000000000000000000000000000000000000
--- a/bb_struct/bb_struct.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python
-
-@pipe(submit=dict(logdir=Ref("logs_dir"), jobtime=200))
-def bb_struct( T1_orig: In,
- T1_brain: Out,
- logs_dir: Out,
- T1: Out,
- T1_orig_ud: Out,
- T1_orig_ud_warp: Out,
- T1_brain_mask: Out,
- T1_orig_defaced: Out,
- T1_defacing_mask: Out,
- T1_unbiased: Out,
- T1_unbiased_brain: Out,
- T1_brain_to_MNI: Out,
- T1_QC_face_mask_inside_brain_mask: Out,
- transform_outputs: Out("T1*_to_*"),
- fast_outputs: Out("T1_fast_*"),
- first_outputs: Out("T1_first_*"),
- T1_GDC: Ref,
- temp_outputs: Ref("T1_tmp_*")):
-
- with update_closure(temp_outputs, transform_outputs, fast_outputs,
- first_outputs):
-
- if [ coeff != '' ] :
- #Calculate and apply the Gradient Distortion Unwarp
- gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
- owarp=T1_orig_ud_warp,gradcoeff=coeff,
- vendor='siemens', nojac=True)
- else:
- pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
-
- #Calculate where does the brain start in the z dimension and extract the roi
- head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].split()[7])))
- wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).run(T1_tmp_1,
- log=log_conf)
-
- #Run a (Recursive) brain extraction on the roi
- wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True, log=log_conf)
-
- #Reduce the FOV of T1_orig_ud by calculating a registration
- #from T1_tmp_brain to ssref and apply it to T1_orig_ud
- # TODO: Once this wrapper is ready, merge it with FSL wrappers
- standard_space_roi(T1_tmp_1_brain, T1, maskNONE=True, ssref = MNI + '_brain',
- altinput=T1_orig_ud, d=True, log=log_conf)
-
- #Generate the actual affine from the orig_ud volume to the cut version
- #we haveand combine it to have an affine matrix from orig_ud to MNI
- wrappers.flirt(src=T1, ref=T1_orig_ud, omat=T1_to_T1_orig_ud_mat,
- schedule = FSLDIR + '/etc/flirtsch/xyztrans.sch', log=log_conf)
- wrappers.invxfm(inmat=T1_to_T1_orig_ud_mat, omat=T1_orig_ud_to_T1_mat,
- log=log_conf)
- wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat, btoc=T1_orig_ud_to_std_mat,
- atoc=T1_to_MNI_linear_mat, log=log_conf)
-
- #Non-linear registration to MNI using the previously calculated alignment
- wrappers.fnirt(src = T1, ref = MNI, aff = T1_to_MNI_linear_mat,
- config = BB_BIN_DIR + '/data/bb_fnirt.cnf',
- refmask = BB_BIN_DIR + '/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz',
- logout = logs_dir + '/bb_T1_to_MNI_fnirt.log',
- cout = T1_to_MNI_warp_coef, fout = T1_to_MNI_warp,
- jout = T1_to_MNI_warp_jac, iout = T1_tmp_3,
- interp = 'spline',log=log_conf)
-
- #Combine all transforms (Gradient Distortion Unwarp and T1 to MNI) into one
- if coeff != '' :
- wrappers.convertwarp(ref=MNI, warp1=T1_orig_ud_warp,
- midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
- out=T1_orig_to_MNI_warp,log=log_conf)
- else:
- wrappers.convertwarp(ref=MNI, premat=T1_orig_ud_to_T1_mat,
- warp1=T1_orig_ud_warp, out=T1_orig_to_MNI_warp,
- log=log_conf)
-
- # Apply the previously generated transformation
- wrappers.applywarp(src=T1_orig, ref=MNI, w=T1_orig_to_MNI_warp,
- out=T1_brain_to_MNI, rel=True, interp='spline',
- log=log_conf)
-
- #Create brain mask
- wrappers.invwarp(ref=T1, warp=T1_to_MNI_warp_coef,
- out=T1_to_MNI_warp_coef_inv,
- log=log_conf)
- wrappers.applywarp(src=MNI + '_brain_mask', ref=T1 ,
- w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
- rel=True, interp='trilinear', log=log_conf)
- wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain,log=log_conf)
- wrappers.fslmaths(T1_brain_to_MNI).mul(MNI + '_brain_mask').run(T1_brain_to_MNI,
- log=log_conf)
-
- #TODO: Replace this part with proper call to fsl_deface
- #Defacing T1_orig
- wrappers.concatxfm(atob=T1_orig_ud_to_T1_mat, btoc=T1_to_MNI_linear_mat,
- atoc=T1_tmp_mat, log=log_conf)
- wrappers.concatxfm(atob=T1_tmp_mat,
- btoc=BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
- atoc=T1_tmp_mat, log=log_conf)
- wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
- wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
- ref=T1_orig, mat=T1_tmp_mat, out=T1_defacing_mask,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).binv().mul(T1_orig).run(T1_orig_defaced,
- log=log_conf)
-
- #Defacing T1
- wrappers.concatxfm(atob=T1_to_MNI_linear_mat,
- btoc = BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
- atoc = T1_tmp_mat, log=log_conf)
- wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
- wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
- ref=T1, mat=T1_tmp_mat, out=T1_defacing_mask,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).binv().mul(T1).run(T1, log=log_conf)
-
- #Generation of QC value: Number of voxels in which
- #the defacing mask goes into the brain mask
- wrappers.fslmaths(T1_brain_mask).thr(0.5).bin().run(T1_tmp_4,
- log=log_conf)
- wrappers.fslmaths(T1_defacing_mask).thr(0.5).bin().add(T1_tmp_4).run(T1_tmp_4,
- log=log_conf)
- vals = wrappers.fslstats(T1_tmp_4).V.run(log=log_conf)[0]
- with open(T1_QC_face_mask_inside_brain_mask, 'w') as f:
- f.write(str(vals))
-
- #Run fast
- wrappers.fast(T1_brain, out = T1_fast_dir + '/T1_brain', b=True,
- log=log_conf)
-
- #Binarize PVE masks
- wrappers.fslmaths(T1_fast_pve_0).thr(0.5).bin().run(T1_fast_CSF_mask,
- log=log_conf)
- wrappers.fslmaths(T1_fast_pve_1).thr(0.5).bin().run(T1_fast_WM_mask,
- log=log_conf)
- wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(T1_fast_GM_mask,
- log=log_conf)
-
- #Apply bias field correction to T1
- wrappers.fslmaths(T1).div(T1_fast_brain_bias).run(T1_unbiased,
- log=log_conf)
- wrappers.fslmaths(T1_brain).div(T1_fast_brain_bias).run(T1_unbiased_brain,
- log=log_conf)
-
- #Creates a link inside T1_first to T1_unbiased_brain.nii.gz
- os.symlink(src="../T1_unbiased_brain.nii.gz", dst=T1_first_unbiased_brain)
-
- #pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
diff --git a/bb_tools/.gitignore b/bb_tools/.gitignore
deleted file mode 100644
index e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf..0000000000000000000000000000000000000000
--- a/bb_tools/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-.DS_Store
diff --git a/bb_tools/__init__.py b/bb_tools/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/bb_tools/correct_file_org.sh b/bb_tools/correct_file_org.sh
deleted file mode 100644
index c3817fefef1f2c2034223af9e33c2691ed4f46ee..0000000000000000000000000000000000000000
--- a/bb_tools/correct_file_org.sh
+++ /dev/null
@@ -1,105 +0,0 @@
-for elem in `cat subj.txt ` ; do
- cd $elem/DICOM ;
- mv T2.dcm T2_FLAIR.dcm ;
- mv T2.txt T2_FLAIR.txt ;
- mv T2_strings.txt T2_FLAIR_strings.txt ;
- cd ../.. ;
-done
-
-for elem in `cat subj.txt ` ; do
- cd $elem/raw ;
- if [ -f SWI_TOTAL_MAG_notNorm.nii.gz ] ; then
- mv SWI_TOTAL_MAG_notNorm.nii.gz SWI_TOTAL_MAG_notNorm_TE1.nii.gz
- fi
- if [ -f SWI_TOTAL_MAG_notNorm.json ] ; then
- mv SWI_TOTAL_MAG_notNorm.json SWI_TOTAL_MAG_notNorm_TE1.json
- fi
- cd ../.. ;
-done
-
-for elem in `cat subj.txt ` ; do
- cd $elem/
- mv fMRI rfMRI;
- mkdir tfMRI;
- if [ -f rfMRI/tfMRI.nii.gz ] ; then
- mv rfMRI/tfMRI.nii.gz tfMRI/
- fi
- if [ -f rfMRI/tfMRI.json ] ; then
- mv rfMRI/tfMRI.json tfMRI/
- fi
- if [ -f rfMRI/tfMRI_SBREF.nii.gz ] ; then
- mv rfMRI/tfMRI_SBREF.nii.gz tfMRI/
- fi
- if [ -f rfMRI/tfMRI_SBREF.json ] ; then
- mv rfMRI/tfMRI_SBREF.json tfMRI/
- fi
- cd ../
-done
-
-for elem in `cat subj.txt ` ; do
- cd $elem/SWI ;
- if [ -f SWI_TOTAL_MAG.nii.gz ] ; then
- mv SWI_TOTAL_MAG.nii.gz SWI_TOTAL_MAG_TE1.nii.gz
- fi
- if [ -f SWI_TOTAL_MAG.json ] ; then
- mv SWI_TOTAL_MAG.json SWI_TOTAL_MAG_TE1.json
- fi
- if [ -f SWI_TOTAL_PHA.nii.gz ] ; then
- mv SWI_TOTAL_PHA.nii.gz SWI_TOTAL_PHA_TE1.nii.gz
- fi
- if [ -f SWI_TOTAL_PHA.json ] ; then
- mv SWI_TOTAL_PHA.json SWI_TOTAL_PHA_TE1.json
- fi
- cd ../.. ;
-done
-
-for elem in `cat subj.txt` ; do
- cd $elem/T1 ;
- if [ -f T1.nii.gz ] ; then
- mv T1.nii.gz T1_orig.nii.gz
- fi
- if [ -f T1.json ] ; then
- mv T1.json T1_orig.json
- fi
- cd ../..;
-done
-
-for elem in `cat subj.txt` ; do
- cd $elem/T2_FLAIR ;
- if [ -f T2_FLAIR.nii.gz ] ; then
- mv T2_FLAIR.nii.gz T2_FLAIR_orig.nii.gz
- fi
- if [ -f T2_FLAIR.json ] ; then
- mv T2_FLAIR.json T2_FLAIR_orig.json
- fi
- cd ../..;
-done
-
-for elem in `cat subj.txt ` ; do
- cd $elem/SWI ;
- if [ -f SWI_TOTAL_MAG_TE1.nii.gz ] ; then
- mv SWI_TOTAL_MAG_TE1.nii.gz SWI_TOTAL_MAG_TE1_orig.nii.gz
- fi
- if [ -f SWI_TOTAL_MAG_TE1.json ] ; then
- mv SWI_TOTAL_MAG_TE1.json SWI_TOTAL_MAG_TE1_orig.json
- fi
- if [ -f SWI_TOTAL_MAG_TE2.nii.gz ] ; then
- mv SWI_TOTAL_MAG_TE2.nii.gz SWI_TOTAL_MAG_TE2_orig.nii.gz
- fi
- if [ -f SWI_TOTAL_MAG_TE2.json ] ; then
- mv SWI_TOTAL_MAG_TE2.json SWI_TOTAL_MAG_TE2_orig.json
- fi
- if [ -f SWI_TOTAL_PHA_TE1.nii.gz ] ; then
- mv SWI_TOTAL_PHA_TE1.nii.gz SWI_TOTAL_PHA_TE1_orig.nii.gz
- fi
- if [ -f SWI_TOTAL_PHA_TE1.json ] ; then
- mv SWI_TOTAL_PHA_TE1.json SWI_TOTAL_PHA_TE1_orig.json
- fi
- if [ -f SWI_TOTAL_PHA_TE2.nii.gz ] ; then
- mv SWI_TOTAL_PHA_TE2.nii.gz SWI_TOTAL_PHA_TE2_orig.nii.gz
- fi
- if [ -f SWI_TOTAL_PHA_TE2.json ] ; then
- mv SWI_TOTAL_PHA_TE2.json SWI_TOTAL_PHA_TE2_orig.json
- fi
- cd ../.. ;
-done
diff --git a/data/.gitignore b/data/.gitignore
deleted file mode 100644
index 13494ec472379c0fb984df51fe7b5e2a97607214..0000000000000000000000000000000000000000
--- a/data/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-.DS_Store
-bb_GDC_*.grad
-subj.txt
diff --git a/data/FileTree.tree b/data/FileTree.tree
deleted file mode 100644
index 32891e9c7b448891477f196159eea845365f9dc4..0000000000000000000000000000000000000000
--- a/data/FileTree.tree
+++ /dev/null
@@ -1,109 +0,0 @@
-subject = dummy
-tissue_idx = 0,1,2
-
-{subject}
- logs (logs_dir)
- T1 (T1_dir)
- T1_orig.nii.gz (T1_orig)
- T1_GDC (T1_GDC)
- T1.nii.gz (T1)
- T1_orig_ud.nii.gz (T1_orig_ud)
- T1_orig_ud_warp.nii.gz (T1_orig_ud_warp)
- T1_brain.nii.gz (T1_brain)
- T1_brain_mask.nii.gz (T1_brain_mask)
- T1_orig_defaced.nii.gz (T1_orig_defaced)
- T1_defacing_mask.nii.gz (T1_defacing_mask)
- T1_unbiased.nii.gz (T1_unbiased)
- T1_unbiased_brain.nii.gz (T1_unbiased_brain)
- T1_unbiased_mask.nii.gz (T1_unbiased_mask)
- T1_brain_to_MNI.nii.gz (T1_brain_to_MNI)
- T1_QC_face_mask_inside_brain_mask.txt (T1_QC_face_mask_inside_brain_mask)
- T1_tmp_1.nii.gz (T1_tmp_1)
- T1_tmp_2.nii.gz (T1_tmp_2)
- T1_tmp_3.nii.gz (T1_tmp_3)
- T1_tmp_4.nii.gz (T1_tmp_4)
- T1_tmp.mat (T1_tmp_mat)
- T1_tmp_1_brain.nii.gz (T1_tmp_1_brain)
- T1_tmp_to_std.mat (T1_tmp_to_std_mat)
- T1_tmp_to_std.mat (T1_orig_ud_to_std_mat)
- transforms (T1_transforms_dir)
- T1_to_T1_orig_ud.mat (T1_to_T1_orig_ud_mat)
- T1_orig_ud_to_T1.mat (T1_orig_ud_to_T1_mat)
- T1_to_MNI_linear.mat (T1_to_MNI_linear_mat)
- T1_to_MNI_warp_coef.nii.gz (T1_to_MNI_warp_coef)
- T1_to_MNI_warp.nii.gz (T1_to_MNI_warp)
- T1_to_MNI_warp_jac.nii.gz (T1_to_MNI_warp_jac)
- T1_orig_to_MNI_warp.nii.gz (T1_orig_to_MNI_warp)
- T1_to_MNI_warp_coef_inv.nii.gz (T1_to_MNI_warp_coef_inv)
- T1_fast (T1_fast_dir)
- T1_brain_pveseg.nii.gz (T1_fast_pveseg)
- T1_brain_pve_0.nii.gz (T1_fast_pve_0)
- T1_brain_pve_1.nii.gz (T1_fast_pve_1)
- T1_brain_pve_2.nii.gz (T1_fast_pve_2)
- T1_brain_CSF_mask.nii.gz (T1_fast_CSF_mask)
- T1_brain_WM_mask.nii.gz (T1_fast_WM_mask)
- T1_brain_GM_mask.nii.gz (T1_fast_GM_mask)
- T1_brain_bias.nii.gz (T1_fast_brain_bias)
- T1_first (T1_first_dir)
- T1_unbiased_brain.nii.gz (T1_first_unbiased_brain)
- T2_FLAIR (T2_FLAIR_dir)
- T2_FLAIR_orig.nii.gz (T2_FLAIR_orig)
- dMRI (dMRI_dir)
- raw (dMRI_raw_dir)
- AP.nii.gz (AP)
- PA.nii.gz (PA)
- rfMRI (rfMRI_dir)
- rfMRI.nii.gz (rfMRI)
- rfMRI_SBREF.nii.gz (rfMRI_SBREF)
- tfMRI (tfMRI_dir)
- tfMRI.nii.gz (tfMRI)
- tfMRI_SBREF.nii.gz (tfMRI_SBREF)
- SWI (SWI_dir)
- SWI_TOTAL_MAG_TE1_orig.nii.gz (SWI_MAG_TE1_orig)
- SWI_TOTAL_MAG_TE2_orig.nii.gz (SWI_MAG_TE2_orig)
- SWI_TOTAL_PHA_TE1_orig.nii.gz (SWI_PHA_TE1_orig)
- SWI_TOTAL_PHA_TE2_orig.nii.gz (SWI_PHA_TE2_orig)
- MAG_TE1
- SWI_3MM_UPDATED_V1.1_COILH9_ECHO1_{COIL}.nii.gz (SWI_MAG_TE1_COILS)
- MAG_TE2
- SWI_3MM_UPDATED_V1.1_COILH9_ECHO2_{COIL}.nii.gz (SWI_MAG_TE2_COILS)
- PHA_TE1
- SWI_3MM_UPDATED_V1.1_COILH10_ECHO1_{COIL}_PH.nii.gz (SWI_PHA_TE1_COILS)
- PHA_TE2
- SWI_3MM_UPDATED_V1.1_COILH10_ECHO2_{COIL}_PH.nii.gz (SWI_PHA_TE2_COILS)
- ASL (ASL_dir)
- raw (ASL_raw_dir)
- ASL_M0.nii.gz (ASL_M0)
- ASL_M0_NORM.nii.gz (ASL_M0_NORM)
- ASL_PLD_0400_control.nii.gz (ASL_PLD_0400_control)
- ASL_PLD_0400_label.nii.gz (ASL_PLD_0400_label)
- ASL_PLD_0800_control.nii.gz (ASL_PLD_0800_control)
- ASL_PLD_0800_label.nii.gz (ASL_PLD_0800_label)
- ASL_PLD_1200_control.nii.gz (ASL_PLD_1200_control)
- ASL_PLD_1200_label.nii.gz (ASL_PLD_1200_label)
- ASL_PLD_1600_control.nii.gz (ASL_PLD_1600_control)
- ASL_PLD_1600_label.nii.gz (ASL_PLD_1600_label)
- ASL_PLD_2000_control.nii.gz (ASL_PLD_2000_control)
- ASL_PLD_2000_label.nii.gz (ASL_PLD_2000_label)
- DICOM (DICOM_dir)
- T1.txt (T1_DICOM_HEADER)
- T2_FLAIR.txt (T2_FLAIR_DICOM_HEADER)
- dMRI.txt (dMRI_DICOM_HEADER)
- rfMRI.txt (rfMRI_DICOM_HEADER)
- tfMRI.txt (tfMRI_DICOM_HEADER)
- SWI.txt (SWI_DICOM_HEADER)
- ASL.txt (ASL_DICOM_HEADER)
- T1_strings.txt (T1_DICOM_STRINGS)
- T2_FLAIR_strings.txt (T2_FLAIR_DICOM_STRINGS)
- dMRI_strings.txt (dMRI_DICOM_STRINGS)
- rfMRI_strings.txt (rfMRI_DICOM_STRINGS)
- tfMRI_strings.txt (tfMRI_DICOM_STRINGS)
- SWI_strings.txt (SWI_DICOM_STRINGS)
- ASL_strings.txt (ASL_DICOM_STRINGS)
- raw (raw_dir)
- T1_notNorm.nii.gz (T1_NOTNORM)
- T2_FLAIR_notNorm.nii.gz (T2_FLAIR_NOTNORM)
- AP_SBREF.nii.gz (AP_SBREF)
- PA_SBREF.nii.gz (PA_SBREF)
- SWI_TOTAL_MAG_notNorm_TE1.nii.gz (SWI_MAG_TE1_NOTNORM)
- SWI_TOTAL_MAG_notNorm_TE2.nii.gz (SWI_MAG_TE2_NOTNORM)
diff --git a/data/MNI152_T1_1mm_BigFoV_facemask.nii.gz b/data/MNI152_T1_1mm_BigFoV_facemask.nii.gz
deleted file mode 100644
index bb9db35e6608ac03902587e2023ba29c391f3855..0000000000000000000000000000000000000000
Binary files a/data/MNI152_T1_1mm_BigFoV_facemask.nii.gz and /dev/null differ
diff --git a/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz b/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz
deleted file mode 100644
index 9d11bde2aa02bc90230b12a87d99850d33a61b67..0000000000000000000000000000000000000000
Binary files a/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz and /dev/null differ
diff --git a/data/MNI_to_MNI_BigFoV_facemask.mat b/data/MNI_to_MNI_BigFoV_facemask.mat
deleted file mode 100644
index 063ab124ad4e2bc38530327103b6ac1bc92b266f..0000000000000000000000000000000000000000
--- a/data/MNI_to_MNI_BigFoV_facemask.mat
+++ /dev/null
@@ -1,4 +0,0 @@
-1 0 0 50
-0 1 0 50
-0 0 1 100
-0 0 0 1
diff --git a/data/bb_fnirt.cnf b/data/bb_fnirt.cnf
deleted file mode 100644
index 940d9a199be7a40381def1720edf225230386c6e..0000000000000000000000000000000000000000
--- a/data/bb_fnirt.cnf
+++ /dev/null
@@ -1,46 +0,0 @@
-# name of reference image
-# --ref=/usr/local/fsl/data/standard/MNI152_T1_2mm.nii.gz
---ref=MNI152_T1_2mm
-# name of file with mask in reference space
-# --refmask=/usr/local/fsl/data/standard/MNI152_T1_2mm_brain_mask_dil.nii.gz
---refmask=MNI152_T1_2mm_brain_mask_dil
-# If =1, use implicit masking based on value in --ref image. Default =1
---imprefm=1
-# If =1, use implicit masking based on value in --in image, Default =1
---impinm=1
-# Value to mask out in --ref image. Default =0.0
---imprefval=0
-# Value to mask out in --in image. Default =0.0
---impinval=0
-# sub-sampling scheme, default 4,2,1,1
---subsamp=4,4,2,2,1,1
-# Max # of non-linear iterations, default 5,5,5,5
---miter=5,5,5,5,5,10
-# FWHM (in mm) of gaussian smoothing kernel for input volume, default 6,4,2,2
---infwhm=8,6,5,4.5,3,2
-# FWHM (in mm) of gaussian smoothing kernel for ref volume, default 4,2,0,0
---reffwhm=8,6,5,4,2,0
-# Weigth of membrane energy regularisation, default depending on --ssqlambda and --regmod switches. See user documetation.
---lambda=300,150,100,50,40,30
-# Estimate intensity-mapping if set, deafult 1 (true)
---estint=1,1,1,1,1,0
-# Apply the mask if set, default 1 (true)
---applyrefmask=1,1,1,1,1,1
-# Apply the mask if set, default 1 (true)
---applyinmask=1
-# (approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10,10,10
---warpres=10,10,10
-# If set (=1), lambda is weighted by current ssq, default 1
---ssqlambda=1
-# Model for regularisation of warp-field [membrane_energy bending_energy], default bending_energy
---regmod=bending_energy
-# Model for intensity-mapping [none global_linear global_non_linear local_linear global_non_linear_with_bias local_non_linear]
---intmod=global_non_linear_with_bias
-# Order of poynomial for mapping intensities, default 5
---intorder=5
-# Resolution (in mm) of bias-field modelling local intensities, default 50,50,50
---biasres=50,50,50
-# Weight of regularisation for bias-field, default 10000
---biaslambda=10000
-# If =1, ref image is used to calculate derivatives. Default =0
---refderiv=0
diff --git a/data/orig_FileTree.txt b/data/orig_FileTree.txt
deleted file mode 100644
index a1dcb6ec6232fcc02b4549797c23e00c3e0002fd..0000000000000000000000000000000000000000
--- a/data/orig_FileTree.txt
+++ /dev/null
@@ -1,98 +0,0 @@
-{subject}
- T1
- T1.nii.gz (T1)
- T1.json
- T2_FLAIR
- T2_FLAIR.nii.gz (T2)
- T2_FLAIR.json
- dMRI
- raw
- AP.nii.gz (AP)
- AP.bval
- AP.bvec
- AP.json
- PA.nii.gz (PA)
- PA.bval
- PA.bvec
- PA.json
- rfMRI
- rfMRI.nii.gz (rfMRI)
- rfMRI.json
- rfMRI_SBREF.nii.gz (rfMRI_SBREF)
- rfMRI_SBREF.json
- tfMRI
- tfMRI.nii.gz (tfMRI)
- tfMRI.json
- tfMRI_SBREF.nii.gz (tfMRI_SBREF)
- tfMRI_SBREF.json
- SWI
- SWI_TOTAL_MAG_TE1.nii.gz (SWI_MAG_TE1)
- SWI_TOTAL_MAG_TE1.json
- SWI_TOTAL_MAG_TE2.nii.gz (SWI_MAG_TE2)
- SWI_TOTAL_MAG_TE2.json
- SWI_TOTAL_PHA_TE1.nii.gz (SWI_PHA_TE1)
- SWI_TOTAL_PHA_TE1.json
- SWI_TOTAL_PHA_TE2.nii.gz (SWI_PHA_TE2)
- SWI_TOTAL_PHA_TE2.json
- MAG_TE1
- SWI_3MM_UPDATED_V1.1_COILH9_ECHO1_{COIL_MAG_TE1}.nii.gz (SWI_MAG_TE1_COILS)
- MAG_TE2
- SWI_3MM_UPDATED_V1.1_COILH9_ECHO2_{COIL_MAG_TE2}.nii.gz (SWI_MAG_TE2_COILS)
- PHA_TE1
- SWI_3MM_UPDATED_V1.1_COILH10_ECHO1_{COIL_PHA_TE1}_PH.nii.gz (SWI_PHA_TE1_COILS)
- PHA_TE2
- SWI_3MM_UPDATED_V1.1_COILH10_ECHO2_{COIL_PHA_TE2}_PH.nii.gz (SWI_PHA_TE2_COILS)
- ASL
- raw
- ASL_M0.nii.gz (ASL_M0)
- ASL_M0.json
- ASL_M0_NORM.nii.gz (ASL_M0_NORM)
- ASL_M0_NORM.json
- ASL_PLD_0400_control.nii.gz (ASL_PLD_0400_control)
- ASL_PLD_0400_control.json
- ASL_PLD_0400_label.nii.gz (ASL_PLD_0400_label)
- ASL_PLD_0400_label.json
- ASL_PLD_0800_control.nii.gz (ASL_PLD_0800_control)
- ASL_PLD_0800_control.json
- ASL_PLD_0800_label.nii.gz (ASL_PLD_0800_label)
- ASL_PLD_0800_label.json
- ASL_PLD_1200_control.nii.gz (ASL_PLD_1200_control)
- ASL_PLD_1200_control.json
- ASL_PLD_1200_label.nii.gz (ASL_PLD_1200_label)
- ASL_PLD_1200_label.json
- ASL_PLD_1600_control.nii.gz (ASL_PLD_1600_control)
- ASL_PLD_1600_control.json
- ASL_PLD_1600_label.nii.gz (ASL_PLD_1600_label)
- ASL_PLD_1600_label.json
- ASL_PLD_2000_control.nii.gz (ASL_PLD_2000_control)
- ASL_PLD_2000_control.json
- ASL_PLD_2000_label.nii.gz (ASL_PLD_2000_label)
- ASL_PLD_2000_label.json
- DICOM
- T1.txt (T1_DICOM_HEADER)
- T2_FLAIR.txt (T2_FLAIR_DICOM_HEADER)
- dMRI.txt (dMRI_DICOM_HEADER)
- rfMRI.txt (rfMRI_DICOM_HEADER)
- tfMRI.txt (tfMRI_DICOM_HEADER)
- SWI.txt (SWI_DICOM_HEADER)
- ASL.txt (ASL_DICOM_HEADER)
- T1_strings.txt (T1_DICOM_STRINGS)
- T2_strings.txt (T2_DICOM_STRINGS)
- dMRI_strings.txt (dMRI_DICOM_STRINGS)
- rfMRI_strings.txt (rfMRI_DICOM_STRINGS)
- tfMRI_strings.txt (tfMRI_DICOM_STRINGS)
- SWI_strings.txt (SWI_DICOM_STRINGS)
- ASL_strings.txt (ASL_DICOM_STRINGS)
- raw
- T1_notNorm.nii.gz (T1_NOTNORM)
- T1_notNorm.json
- T2_FLAIR_notNorm.nii.gz (T2_FLAIR_NOTNORM)
- T2_FLAIR_notNorm.json
- AP_SBREF.nii.gz (AP_SBREF)
- AP_SBREF.json
- PA_SBREF.nii.gz (PA_SBREF)
- PA_SBREF.json
- SWI_TOTAL_MAG_notNorm_TE1.nii.gz (SWI_MAG_TE1_NOTNORM)
- SWI_TOTAL_MAG_notNorm_TE1.json
- SWI_TOTAL_MAG_notNorm_TE2.nii.gz (SWI_MAG_TE2_NOTNORM)
- SWI_TOTAL_MAG_notNorm_TE2.json
diff --git a/init.sh b/init.sh
deleted file mode 100755
index 265fd031639454d66e51ec0905cc7c6d87a383c2..0000000000000000000000000000000000000000
--- a/init.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-
-export FSLDIR="$HOME/fsl" # Change according to the user's FSL installation
-export BB_BIN_DIR="$HOME/BIP/" # Change according to the present folder
-export PATH="$BB_BIN_DIR:$BB_BIN_DIR/data:$BB_BIN_DIR/bb_pipeline:$PATH"
-export PYTHONPATH="$BB_BIN_DIR:$PYTHONPATH"
diff --git a/install_dir/.gitignore b/install_dir/.gitignore
deleted file mode 100644
index 9d7cbf04f28780547e37e9e1e0d6f74e5046f82a..0000000000000000000000000000000000000000
--- a/install_dir/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-.DS_Store
-list_*.txt
diff --git a/install_dir/gradunwarp_FMRIB.tar.gz b/install_dir/gradunwarp_FMRIB.tar.gz
deleted file mode 100644
index a0882a3647c74b892d73111de16a0349e4aeba0c..0000000000000000000000000000000000000000
Binary files a/install_dir/gradunwarp_FMRIB.tar.gz and /dev/null differ
diff --git a/install_dir/manual_install.txt b/install_dir/manual_install.txt
deleted file mode 100644
index 2dfb0308aa5d7fd8efd90ef96c4f26f52a05dde1..0000000000000000000000000000000000000000
--- a/install_dir/manual_install.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-
-# Installing FSL (with its own miniconda)
-curl https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/releases/fslinstaller.py > fslinstaller.py
-python ./fslinstaller.py --no_env --dest $HOME/fsl/
-export FSLDIR="$HOME/fsl"
-export PATH="$FSLDIR/bin/:$PATH"
-pushd $FSLDIR
-git clone https://git.fmrib.ox.ac.uk/fsl/fslpy
-cd fslpy
-$FSLDIR/bin/pip install -e .
-popd
-rm -f ./fslinstaller.py
-
-# Creating a new python environment
-$FSLDIR/bin/conda create -n ukb python=3.10
-source $FSLDIR/bin/activate ukb
-pip install file-tree file-tree-fsl
-pip install git+https://git.fmrib.ox.ac.uk/ndcn0236/pipe-tree.git
-pip install -r $BB_BIN_DIR/install_dir/requirements.txt
-pip list &> $BB_BIN_DIR/install_dir/list_`date +%Y_%m_%d`.txt
-
-# Install gradunwarp
-tar -zxvf gradunwarp_FMRIB.tar.gz
-cd gradunwarp_FMRIB
-python setup.py install
-cd ..
-rm -rf gradunwarp_FMRIB
diff --git a/install_dir/requirements.txt b/install_dir/requirements.txt
deleted file mode 100644
index 82aaba22bf9a4320ff1a3e462d6366dbf7756c27..0000000000000000000000000000000000000000
--- a/install_dir/requirements.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-oxasl
-oxasl-deblur
-pickle-mixin
-Babel
-Jinja2
-MarkupSafe
-Pillow
-PyWavelets
-Sphinx
-Theano
-bottom
-deprecation
-jupyter-core
-niicat
-nose
-pygam
-retrying
-simplegeneric
-graphviz
-pickleshare
-ipython
-seaborn
-jsonschema
-python-utils
-zipp
-urllib3
-scikit-image
-scikit-learn
diff --git a/install_dir/setup.py b/install_dir/setup.py
deleted file mode 100644
index 9ff1a1247e6c5cd241653bf17f1290be023ed3c5..0000000000000000000000000000000000000000
--- a/install_dir/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from setuptools import setup,find_packages
-with open('requirements.txt', 'rt') as f:
- install_requires = [l.strip() for l in f.readlines()]
-
-setup(name='BIP',
- version='1.0.0',
- description='Brain Imaging Pipeline',
- author='Fidel Alfaro Almagro',
- install_requires=install_requires,
- scripts=['BIP/general/BIP'],
- packages=find_packages(),
- include_package_data=True)
diff --git a/my_wrappers/.gitignore b/my_wrappers/.gitignore
deleted file mode 100644
index e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf..0000000000000000000000000000000000000000
--- a/my_wrappers/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-.DS_Store
diff --git a/my_wrappers/__init__.py b/my_wrappers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/my_wrappers/standard_space_roi.py b/my_wrappers/standard_space_roi.py
deleted file mode 100644
index 76622b88598bc46928c2b160858c21e2e8e7c351..0000000000000000000000000000000000000000
--- a/my_wrappers/standard_space_roi.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env python
-#
-# standard_space_roi.py - Wrapper for the standard_space_roi command.
-#
-# Author: Fidel Alfaro Almagro
-#
-"""This module provides the :func:`standard_space_roi` function, a wrapper for
-the FSL `standard_space_roi`_ command.
-"""
-
-import fsl.utils.assertions as asrt
-from fsl.wrappers import wrapperutils as wutils
-
-@wutils.fileOrImage('input', 'maskMASK', 'roiMASK', 'ssref', 'altinput',outprefix='output')
-@wutils.fslwrapper
-def standard_space_roi(input, output, **kwargs):
- """Wrapper for the ``standard_space_roi`` command.
-
- :arg maskFOV: Mask output using transformed standard space FOV (default)
- :arg maskMASK: Mask output using transformed standard space mask
- :arg maskNONE: Do not mask output
-
- :arg roiFOV: Cut down input FOV using bounding box of the transformed standard space FOV (default)
- :arg roiMASK: Cut down input FOV using nonbackground bounding box of the transformed standard space mask
- :arg roiNONE: Co not cut down input FOV
-
- :arg ssref Standard space reference image to use (default $FSLDIR/data/standard/MNI152_T1)
- :arg altinput Alternative input image to apply the ROI to (instead of the one used to register to the reference)
- :arg d Debug (don't delete intermediate files)
- :arg b Betpremask, equivalent to: -maskMASK $FSLDIR/data/standard/MNI152_T1_2mm_brain_mask_dil -roiNONE
-
- Refer to the ``standard_space_roi`` command-line help for details on all arguments.
- """
- asrt.assertIsNifti(input)
-
- valmap = {
- 'maskFOV' : wutils.SHOW_IF_TRUE,
- 'maskNONE' : wutils.SHOW_IF_TRUE,
- 'roiFOV' : wutils.SHOW_IF_TRUE,
- 'roiNONE' : wutils.SHOW_IF_TRUE,
- 'd' : wutils.SHOW_IF_TRUE,
- 'b' : wutils.SHOW_IF_TRUE,
- }
-
- cmd = ['standard_space_roi', input, output]
-
- cmd += wutils.applyArgStyle('-', valmap=valmap, **kwargs)
-
- return cmd