From 6d10dc83d3f3a2ca699a261d8f4b2c3ccf552a02 Mon Sep 17 00:00:00 2001
From: Fidel Alfaro Almagro <falmagro@fmrib.ox.ac.uk>
Date: Thu, 9 Feb 2023 10:00:07 +0000
Subject: [PATCH] Re-created bip repository
---
.gitattributes | 5 +
.gitignore | 6 +
MANIFEST.in | 0
README.md | 12 +
bip/commands/__init__.py | 13 +
bip/commands/bb_get_b0s.py | 103 ++
bip/commands/bb_read_json_field.py | 77 +
bip/commands/get_dwell_time.py | 28 +
bip/data/FileTree.tree | 3 +
bip/data/GDC/UKB.txt | 3 +
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bip/data/MNI/MNI152_T1_1mm_brain_mask.nii.gz | 3 +
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.../MNI/MNI152_T1_1mm_right_eye_dil.nii.gz | 3 +
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bip/data/MNI/MNI_to_MNI_BigFoV_facemask.mat | 3 +
bip/data/asl/asl_format.txt | 3 +
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.../MNI_seg_max_prob_masked_RandL.txt | 3 +
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.../asl/ukb_rois/VascularTerritories_ero.txt | 3 +
.../asl/ukb_rois/create_biobank_ASL_ROIs.sh | 3 +
bip/data/config/ideal_config_sizes.json | 3 +
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.../config/ideal_config_sizes_CNS_LIV.json | 3 +
bip/data/config/ideal_config_sizes_UKBB.json | 3 +
bip/data/config/naming_pattern_CNS_KCL.json | 3 +
bip/data/config/naming_pattern_CNS_LIV.json | 3 +
bip/data/config/naming_pattern_UKBB.json | 3 +
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.../autoptx/protocols/unc_l/._seed.nii.gz | 3 +
.../autoptx/protocols/unc_l/._target.nii.gz | 3 +
.../autoptx/protocols/unc_l/exclude.nii.gz | 3 +
.../dMRI/autoptx/protocols/unc_l/seed.nii.gz | 3 +
.../autoptx/protocols/unc_l/target.nii.gz | 3 +
.../autoptx/protocols/unc_r/._exclude.nii.gz | 3 +
.../autoptx/protocols/unc_r/._seed.nii.gz | 3 +
.../autoptx/protocols/unc_r/._target.nii.gz | 3 +
.../autoptx/protocols/unc_r/exclude.nii.gz | 3 +
.../dMRI/autoptx/protocols/unc_r/seed.nii.gz | 3 +
.../autoptx/protocols/unc_r/target.nii.gz | 3 +
bip/data/dMRI/autoptx/struct_list.json | 3 +
bip/data/dMRI/b0_threshold.txt | 3 +
.../dMRI_reg_optimal_parameters/exampleReg | 3 +
.../dMRI_reg_optimal_parameters/exampleReg_2 | 3 +
.../dMRI_reg_optimal_parameters/exampleReg_3 | 3 +
.../dMRI_reg_optimal_parameters/oxford_s1.cnf | 3 +
.../dMRI_reg_optimal_parameters/oxford_s2.cnf | 3 +
.../dMRI_reg_optimal_parameters/oxford_s3.cnf | 3 +
.../rotterdam_s1.cnf | 3 +
.../rotterdam_s2.cnf | 3 +
.../rotterdam_s3.cnf | 3 +
bip/data/fMRI_fsf/designF.txt | 3 +
bip/data/fMRI_fsf/designS.txt | 3 +
bip/data/fMRI_fsf/design_ICA_nonSmoothed.fsf | 3 +
bip/data/fMRI_fsf/design_TASK.fsf | 3 +
bip/data/fix/UKBB_model.pyfix_model | 3 +
bip/data/fix/UKBB_model_2.pyfix_model | 3 +
bip/data/fnirt/bb_fnirt.cnf | 3 +
bip/data/netmats/groupMask1.nii.gz | 3 +
bip/data/netmats/groupMask2.nii.gz | 3 +
bip/data/netmats/groupMask5.nii.gz | 3 +
bip/data/netmats/groupMask5a.nii.gz | 3 +
bip/data/netmats/melodic_IC_100.nii.gz | 3 +
bip/data/netmats/melodic_IC_25.nii.gz | 3 +
bip/data/swMRI/hann_filter.txt | 3 +
bip/ext_wrappers/__init__.py | 14 +
bip/ext_wrappers/extwrapperutils.py | 1297 +++++++++++++++++
bip/ext_wrappers/freesurfer.py | 31 +
bip/main.py | 356 +++++
bip/pipelines/__init__.py | 0
bip/pipelines/dMRI_diff/__init__.py | 0
bip/pipelines/dMRI_diff/dMRI_diff.py | 52 +
bip/pipelines/dMRI_diff/diff_autoptx.py | 122 ++
bip/pipelines/dMRI_diff/diff_bedpostx.py | 85 ++
bip/pipelines/dMRI_diff/diff_dtifit.py | 87 ++
bip/pipelines/dMRI_diff/diff_eddy.py | 91 ++
bip/pipelines/dMRI_diff/diff_noddi.py | 87 ++
bip/pipelines/dMRI_diff/diff_tbss.py | 161 ++
bip/pipelines/dMRI_fieldmap/__init__.py | 0
bip/pipelines/dMRI_fieldmap/dMRI_fieldmap.py | 35 +
.../dMRI_fieldmap/fieldmap_post_topup.py | 148 ++
.../dMRI_fieldmap/fieldmap_pre_topup.py | 202 +++
bip/pipelines/dMRI_fieldmap/fieldmap_topup.py | 35 +
bip/pipelines/fMRI_rest/__init__.py | 0
bip/pipelines/fMRI_rest/fMRI_rest.py | 35 +
bip/pipelines/fMRI_rest/rfMRI_fix.py | 98 ++
bip/pipelines/fMRI_rest/rfMRI_melodic.py | 48 +
bip/pipelines/fMRI_rest/rfMRI_netmats.py | 52 +
bip/pipelines/fMRI_rest/rfMRI_netmats_fnc.py | 139 ++
bip/pipelines/fMRI_rest/rfMRI_prepare.py | 115 ++
bip/pipelines/fMRI_task/__init__.py | 0
bip/pipelines/fMRI_task/fMRI_task.py | 30 +
bip/pipelines/fMRI_task/tfMRI_feat.py | 26 +
bip/pipelines/fMRI_task/tfMRI_prepare.py | 115 ++
bip/pipelines/struct_T1/T1_QC_CNR_corners.py | 89 ++
bip/pipelines/struct_T1/T1_QC_CNR_eyes.py | 109 ++
bip/pipelines/struct_T1/T1_QC_COG.py | 50 +
bip/pipelines/struct_T1/T1_brain_extract.py | 103 ++
bip/pipelines/struct_T1/T1_defacing.py | 66 +
bip/pipelines/struct_T1/T1_fast.py | 44 +
bip/pipelines/struct_T1/T1_first.py | 42 +
bip/pipelines/struct_T1/T1_gdc.py | 36 +
bip/pipelines/struct_T1/T1_sienax.py | 145 ++
bip/pipelines/struct_T1/__init__.py | 0
bip/pipelines/struct_T1/struct_T1.py | 53 +
.../struct_T2_FLAIR/T2_FLAIR_apply_bfc.py | 38 +
.../struct_T2_FLAIR/T2_FLAIR_brain_extract.py | 91 ++
.../struct_T2_FLAIR/T2_FLAIR_defacing.py | 67 +
bip/pipelines/struct_T2_FLAIR/T2_FLAIR_gdc.py | 38 +
bip/pipelines/struct_T2_FLAIR/__init__.py | 0
.../struct_T2_FLAIR/struct_T2_FLAIR.py | 37 +
bip/pipelines/struct_asl/asl_get_IDPs.py | 72 +
bip/pipelines/struct_asl/asl_proc.py | 167 +++
bip/pipelines/struct_asl/struct_asl.py | 29 +
bip/pipelines/struct_swMRI/struct_swMRI.py | 26 +
bip/pipelines/struct_swMRI/swMRI_gdc.py | 36 +
bip/pipelines/struct_swMRI/swMRI_proc.py | 121 ++
bip/pipelines/struct_swMRI/swMRI_proc_fnc.py | 121 ++
bip/utils.py | 116 ++
init_vars | 18 +
requirements.txt | 12 +
setup.py | 11 +
413 files changed, 6465 insertions(+)
create mode 100644 MANIFEST.in
create mode 100644 README.md
create mode 100644 bip/commands/__init__.py
create mode 100755 bip/commands/bb_get_b0s.py
create mode 100755 bip/commands/bb_read_json_field.py
create mode 100755 bip/commands/get_dwell_time.py
create mode 100644 bip/data/FileTree.tree
create mode 100644 bip/data/GDC/UKB.txt
create mode 100644 bip/data/MNI/MNI152_T1_1mm_BigFoV_facemask.nii.gz
create mode 100755 bip/data/MNI/MNI152_T1_1mm_brain_mask.nii.gz
create mode 100644 bip/data/MNI/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz
create mode 100755 bip/data/MNI/MNI152_T1_1mm_left_eye_dil.nii.gz
create mode 100755 bip/data/MNI/MNI152_T1_1mm_right_eye_dil.nii.gz
create mode 100755 bip/data/MNI/MNI152_T1_2mm_brain_mask_bin.nii.gz
create mode 100644 bip/data/MNI/MNI_to_MNI_BigFoV_facemask.mat
create mode 100755 bip/data/asl/asl_format.txt
create mode 100755 bip/data/asl/ukb_rois/._HO_L_Cerebral_WM_thr80.nii.gz
create mode 100755 bip/data/asl/ukb_rois/._HO_R_Cerebral_WM_thr80.nii.gz
create mode 100755 bip/data/asl/ukb_rois/._MNI_seg_max_prob_masked_RandL.nii.gz
create mode 100755 bip/data/asl/ukb_rois/._MostProbableSupplier_sm3.nii.gz
create mode 100755 bip/data/asl/ukb_rois/._VesMasks_sm3Thr80.nii.gz
create mode 100755 bip/data/asl/ukb_rois/._create_biobank_ASL_ROIs.sh
create mode 100755 bip/data/asl/ukb_rois/HO_L_Cerebral_WM_thr80.nii.gz
create mode 100755 bip/data/asl/ukb_rois/HO_L_Cerebral_WM_thr80.txt
create mode 100755 bip/data/asl/ukb_rois/HO_R_Cerebral_WM_thr80.nii.gz
create mode 100755 bip/data/asl/ukb_rois/HO_R_Cerebral_WM_thr80.txt
create mode 100755 bip/data/asl/ukb_rois/MNI_seg_max_prob_masked_RandL.nii.gz
create mode 100755 bip/data/asl/ukb_rois/MNI_seg_max_prob_masked_RandL.txt
create mode 100755 bip/data/asl/ukb_rois/VascularTerritories_ero.nii.gz
create mode 100755 bip/data/asl/ukb_rois/VascularTerritories_ero.txt
create mode 100755 bip/data/asl/ukb_rois/create_biobank_ASL_ROIs.sh
create mode 100644 bip/data/config/ideal_config_sizes.json
create mode 100755 bip/data/config/ideal_config_sizes_CNS_KCL.json
create mode 100755 bip/data/config/ideal_config_sizes_CNS_LIV.json
create mode 100755 bip/data/config/ideal_config_sizes_UKBB.json
create mode 100755 bip/data/config/naming_pattern_CNS_KCL.json
create mode 100755 bip/data/config/naming_pattern_CNS_LIV.json
create mode 100755 bip/data/config/naming_pattern_UKBB.json
create mode 100755 bip/data/dMRI/autoptx/luts/._c1.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c10.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c11.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c12.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c13.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c14.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c15.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c16.lut
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create mode 100755 bip/data/dMRI/autoptx/luts/._c2.lut
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create mode 100755 bip/data/dMRI/autoptx/luts/._c22.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c23.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c24.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c25.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c26.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c27.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c28.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c29.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c3.lut
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create mode 100755 bip/data/dMRI/autoptx/luts/._c4.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c5.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c6.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c7.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c8.lut
create mode 100755 bip/data/dMRI/autoptx/luts/._c9.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c1.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c10.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c11.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c12.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c13.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c14.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c15.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c16.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c17.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c18.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c19.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c2.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c20.lut
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create mode 100755 bip/data/dMRI/autoptx/luts/c6.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c7.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c8.lut
create mode 100755 bip/data/dMRI/autoptx/luts/c9.lut
create mode 100755 bip/data/dMRI/autoptx/protocols/._ar_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._ar_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._atr_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._atr_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._cgc_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._cgc_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._cgh_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._cgh_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._cst_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._cst_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._fma
create mode 100755 bip/data/dMRI/autoptx/protocols/._fmi
create mode 100755 bip/data/dMRI/autoptx/protocols/._ifo_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._ifo_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._ilf_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._ilf_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._mcp
create mode 100755 bip/data/dMRI/autoptx/protocols/._ml_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._ml_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._ptr_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._ptr_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._slf_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._slf_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._str_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._str_r
create mode 100755 bip/data/dMRI/autoptx/protocols/._unc_l
create mode 100755 bip/data/dMRI/autoptx/protocols/._unc_r
create mode 100755 bip/data/dMRI/autoptx/protocols/ar_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ar_l/._invert
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create mode 100755 bip/data/dMRI/autoptx/protocols/ar_r/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ar_r/._target.nii.gz
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create mode 100755 bip/data/dMRI/autoptx/protocols/ar_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/atr_l/._exclude.nii.gz
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create mode 100755 bip/data/dMRI/autoptx/protocols/atr_r/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/atr_r/._target.nii.gz
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create mode 100755 bip/data/dMRI/autoptx/protocols/atr_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/atr_r/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/atr_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/cgc_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/cgc_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/cgc_l/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/cgc_l/._target.nii.gz
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create mode 100755 bip/data/dMRI/autoptx/protocols/fmi/exclude.nii.gz
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create mode 100755 bip/data/dMRI/autoptx/protocols/fmi/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/fmi/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ifo_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ilf_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/._invert
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/invert
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/mcp/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ml_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/ptr_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/._invert
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/invert
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/._invert
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/invert
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/slf_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/._stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/stop.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/str_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_l/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/._exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/._seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/._target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/exclude.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/seed.nii.gz
create mode 100755 bip/data/dMRI/autoptx/protocols/unc_r/target.nii.gz
create mode 100755 bip/data/dMRI/autoptx/struct_list.json
create mode 100755 bip/data/dMRI/b0_threshold.txt
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/exampleReg
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/exampleReg_2
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/exampleReg_3
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/oxford_s1.cnf
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/oxford_s2.cnf
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/oxford_s3.cnf
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/rotterdam_s1.cnf
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/rotterdam_s2.cnf
create mode 100755 bip/data/dMRI/dMRI_reg_optimal_parameters/rotterdam_s3.cnf
create mode 100755 bip/data/fMRI_fsf/designF.txt
create mode 100755 bip/data/fMRI_fsf/designS.txt
create mode 100755 bip/data/fMRI_fsf/design_ICA_nonSmoothed.fsf
create mode 100755 bip/data/fMRI_fsf/design_TASK.fsf
create mode 100644 bip/data/fix/UKBB_model.pyfix_model
create mode 100644 bip/data/fix/UKBB_model_2.pyfix_model
create mode 100755 bip/data/fnirt/bb_fnirt.cnf
create mode 100644 bip/data/netmats/groupMask1.nii.gz
create mode 100644 bip/data/netmats/groupMask2.nii.gz
create mode 100644 bip/data/netmats/groupMask5.nii.gz
create mode 100644 bip/data/netmats/groupMask5a.nii.gz
create mode 100644 bip/data/netmats/melodic_IC_100.nii.gz
create mode 100644 bip/data/netmats/melodic_IC_25.nii.gz
create mode 100644 bip/data/swMRI/hann_filter.txt
create mode 100755 bip/ext_wrappers/__init__.py
create mode 100644 bip/ext_wrappers/extwrapperutils.py
create mode 100644 bip/ext_wrappers/freesurfer.py
create mode 100755 bip/main.py
create mode 100644 bip/pipelines/__init__.py
create mode 100644 bip/pipelines/dMRI_diff/__init__.py
create mode 100755 bip/pipelines/dMRI_diff/dMRI_diff.py
create mode 100755 bip/pipelines/dMRI_diff/diff_autoptx.py
create mode 100755 bip/pipelines/dMRI_diff/diff_bedpostx.py
create mode 100755 bip/pipelines/dMRI_diff/diff_dtifit.py
create mode 100755 bip/pipelines/dMRI_diff/diff_eddy.py
create mode 100755 bip/pipelines/dMRI_diff/diff_noddi.py
create mode 100755 bip/pipelines/dMRI_diff/diff_tbss.py
create mode 100644 bip/pipelines/dMRI_fieldmap/__init__.py
create mode 100644 bip/pipelines/dMRI_fieldmap/dMRI_fieldmap.py
create mode 100755 bip/pipelines/dMRI_fieldmap/fieldmap_post_topup.py
create mode 100755 bip/pipelines/dMRI_fieldmap/fieldmap_pre_topup.py
create mode 100755 bip/pipelines/dMRI_fieldmap/fieldmap_topup.py
create mode 100755 bip/pipelines/fMRI_rest/__init__.py
create mode 100755 bip/pipelines/fMRI_rest/fMRI_rest.py
create mode 100755 bip/pipelines/fMRI_rest/rfMRI_fix.py
create mode 100755 bip/pipelines/fMRI_rest/rfMRI_melodic.py
create mode 100755 bip/pipelines/fMRI_rest/rfMRI_netmats.py
create mode 100644 bip/pipelines/fMRI_rest/rfMRI_netmats_fnc.py
create mode 100755 bip/pipelines/fMRI_rest/rfMRI_prepare.py
create mode 100755 bip/pipelines/fMRI_task/__init__.py
create mode 100755 bip/pipelines/fMRI_task/fMRI_task.py
create mode 100755 bip/pipelines/fMRI_task/tfMRI_feat.py
create mode 100755 bip/pipelines/fMRI_task/tfMRI_prepare.py
create mode 100755 bip/pipelines/struct_T1/T1_QC_CNR_corners.py
create mode 100755 bip/pipelines/struct_T1/T1_QC_CNR_eyes.py
create mode 100755 bip/pipelines/struct_T1/T1_QC_COG.py
create mode 100755 bip/pipelines/struct_T1/T1_brain_extract.py
create mode 100755 bip/pipelines/struct_T1/T1_defacing.py
create mode 100755 bip/pipelines/struct_T1/T1_fast.py
create mode 100755 bip/pipelines/struct_T1/T1_first.py
create mode 100755 bip/pipelines/struct_T1/T1_gdc.py
create mode 100755 bip/pipelines/struct_T1/T1_sienax.py
create mode 100755 bip/pipelines/struct_T1/__init__.py
create mode 100755 bip/pipelines/struct_T1/struct_T1.py
create mode 100755 bip/pipelines/struct_T2_FLAIR/T2_FLAIR_apply_bfc.py
create mode 100755 bip/pipelines/struct_T2_FLAIR/T2_FLAIR_brain_extract.py
create mode 100755 bip/pipelines/struct_T2_FLAIR/T2_FLAIR_defacing.py
create mode 100755 bip/pipelines/struct_T2_FLAIR/T2_FLAIR_gdc.py
create mode 100644 bip/pipelines/struct_T2_FLAIR/__init__.py
create mode 100755 bip/pipelines/struct_T2_FLAIR/struct_T2_FLAIR.py
create mode 100755 bip/pipelines/struct_asl/asl_get_IDPs.py
create mode 100755 bip/pipelines/struct_asl/asl_proc.py
create mode 100755 bip/pipelines/struct_asl/struct_asl.py
create mode 100755 bip/pipelines/struct_swMRI/struct_swMRI.py
create mode 100755 bip/pipelines/struct_swMRI/swMRI_gdc.py
create mode 100755 bip/pipelines/struct_swMRI/swMRI_proc.py
create mode 100755 bip/pipelines/struct_swMRI/swMRI_proc_fnc.py
create mode 100755 bip/utils.py
create mode 100755 init_vars
create mode 100644 requirements.txt
create mode 100644 setup.py
diff --git a/.gitattributes b/.gitattributes
index e793abd..ca63599 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1,6 @@
+*.nii.gz filter=lfs diff=lfs merge=lfs -text
+*.pyfix_model filter=lfs diff=lfs merge=lfs -text
+*.lut filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+bip/data/* filter=lfs diff=lfs merge=lfs -text
bip/data/** filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
index 63e6c93..3fe5323 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,8 @@ __pycache__/
# Distribution / packaging
.Python
build/
+obsolete/
+structure
develop-eggs/
dist/
downloads/
@@ -22,10 +24,14 @@ parts/
sdist/
var/
wheels/
+external/
+bip/data/GDC/bb_GDC_*
+bip/data/dMRI/kernels.zip*
*.egg-info/
.installed.cfg
*.egg
.*.py
+fsl_sub.yml
# PyInstaller
# Usually these files are written by a python script from a template
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..e69de29
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..cff6369
--- /dev/null
+++ b/README.md
@@ -0,0 +1,12 @@
+-------------------------------------------
+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/bip/commands/__init__.py b/bip/commands/__init__.py
new file mode 100644
index 0000000..4cbd17a
--- /dev/null
+++ b/bip/commands/__init__.py
@@ -0,0 +1,13 @@
+import shutil
+from fsl.wrappers.wrapperutils import cmdwrapper
+
+installed_sienax = shutil.which('bb_sienax')
+
+if installed_sienax:
+ @cmdwrapper
+ def bb_sienax(input, output=None, **kwargs):
+ return ['bb_sienax', input, output]
+else:
+ def bb_sienax(input, output=None, **kwargs):
+ from . import sienax
+ sienax.bb_sienax(input, output)
diff --git a/bip/commands/bb_get_b0s.py b/bip/commands/bb_get_b0s.py
new file mode 100755
index 0000000..0d52e45
--- /dev/null
+++ b/bip/commands/bb_get_b0s.py
@@ -0,0 +1,103 @@
+#!/bin/env python
+'''
+ Authors: Fidel Alfaro Almagro
+ FMRIB, Oxford University
+ $01-May-2014 11:44:20$
+ Version $1.0
+ ProjectDir =
+ '''
+import sys
+import argparse
+import os.path
+from fsl import wrappers
+
+class MyParser(argparse.ArgumentParser):
+ def error(self, message):
+ sys.stderr.write('error: %s\n' % message)
+ self.print_help()
+ sys.exit(2)
+
+class Usage(Exception):
+ def __init__(self, msg):
+ self.msg = msg
+
+def bb_get_b0s(inputFile, bvalFilename, outputFile, outputIndFile,
+ desiredNumber, B0limit=100):
+ with open(bvalFilename, 'r', encoding="utf-8") as f:
+ line = f.readlines()
+
+ line = line[0].split()
+ indices = [i for i, x in enumerate(line) if int(x) < B0limit]
+
+ with open (outputIndFile ,'wt', encoding="utf-8") as f:
+ f.write(" ".join([str(x) for x in indices]))
+
+ indices = indices[0:desiredNumber]
+
+ wrappers.fslselectvols(src=inputFile, out=outputFile, vols=indices)
+
+
+def main():
+ parser = MyParser(description='UKB tool to get a B0 of a set of B0 images')
+ parser.add_argument('-i', dest="inputFile", type=str, nargs=1,
+ help='Input File')
+ parser.add_argument('-o', dest="outputFile", type=str, nargs=1,
+ help='Output File')
+ parser.add_argument('-n', dest='desiredNumber',type=int, nargs=1,
+ help='Desired number of B0s from file. If none specified, all will be selected')
+ parser.add_argument('-l', dest='B0limit',type=int, default=[100], nargs=1,
+ help='Limit B0 value. (Default 100)')
+ parser.add_argument('-a', dest='bvalFilename',type=str, default='', nargs=1,
+ help='bval file. (Default: Same basename as the input file)')
+
+ argsa = parser.parse_args()
+
+ if argsa.inputFile is None:
+ parser.print_help()
+ sys.exit()
+
+ if argsa.outputFile is None:
+ parser.print_help()
+ sys.exit()
+
+ inputFile = argsa.inputFile[0]
+ outputFile = argsa.outputFile[0]
+
+ baseDir = os.path.dirname(inputFile)
+ outDir = os.path.dirname(outputFile)
+ baseN = os.path.basename(inputFile).split('.')[0]
+ outN = os.path.basename(outputFile).split('.')[0]
+
+ if argsa.bvalFilename == '':
+ bvalFilename = baseDir + "/" + baseN+".bval"
+ else:
+ bvalFilename = argsa.bvalFilename[0]
+
+
+ with open(bvalFilename, 'r', encoding="utf-8") as f:
+ line = f.readlines()
+
+ line = line[0].split()
+ B0limit = int(argsa.B0limit[0])
+ indices = [i for i, x in enumerate(line) if int(x) < B0limit]
+
+ if argsa.desiredNumber is None:
+ desiredNumber = len(indices)
+ else:
+ desiredNumber = argsa.desiredNumber[0]
+
+ if desiredNumber > len(indices):
+ print(("There are only %i B0. It is not possible to have %i" % (len(indices), desiredNumber)))
+ sys.exit()
+
+ if desiredNumber <= 0:
+ print("The number of B0 must be positive")
+ sys.exit()
+
+ outputIndFile = outDir + '/' + outN +'_indices.txt'
+
+ bb_get_b0s(inputFile, bvalFilename, outputFile, outputIndFile,
+ desiredNumber, B0limit)
+
+if __name__ == "__main__":
+ main()
diff --git a/bip/commands/bb_read_json_field.py b/bip/commands/bb_read_json_field.py
new file mode 100755
index 0000000..161b458
--- /dev/null
+++ b/bip/commands/bb_read_json_field.py
@@ -0,0 +1,77 @@
+#!/bin/env python
+'''
+ Authors: Fidel Alfaro Almagro
+ FMRIB, Oxford University
+ $15-Dec-2014 10:41:10$
+ Version $1.0
+ ProjectDir =
+ '''
+import sys
+import json
+import numbers
+import argparse
+
+
+class MyParser(argparse.ArgumentParser):
+ def error(self, message):
+ sys.stderr.write('error: %s\n' % message)
+ self.print_help()
+ sys.exit(2)
+
+class Usage(Exception):
+ def __init__(self, msg):
+ self.msg = msg
+
+def bb_read_json_field(fileName, fieldName, rounding=0, multFactor=1):
+
+ result=[]
+ with open(fileName, 'r', encoding="utf-8") as data_file:
+ data=json.load(data_file)
+
+ if fieldName in data.keys():
+ value=data[fieldName]
+ if isinstance(value, numbers.Number):
+ if rounding !=0:
+ result=round(data[fieldName]*multFactor,rounding)
+ else:
+ result=data[fieldName]*multFactor
+ else:
+ result=str(data[fieldName])
+
+ return result
+
+
+def main():
+
+ parser = MyParser(description='BioBank Dicom Header Reader')
+ parser.add_argument('-F', dest="file", type=str, nargs=1,
+ help='Read json file')
+ parser.add_argument('-f', dest="field", type=str, nargs=1, default="NONE",
+ help='Read field')
+ parser.add_argument('-r', dest="rounding", type=int, default=0,
+ help='Round the value the selected number of decimals (Default: No rounding')
+ parser.add_argument('-m', dest="multFactor", type=float, default=1,
+ help='Multiplication factor for the selected value (Default 1)')
+
+ argsa = parser.parse_args()
+
+ if argsa.file is None:
+ parser.print_help()
+ sys.exit()
+
+ if argsa.field is None:
+ parser.print_help()
+ sys.exit()
+
+ rounding=argsa.rounding
+ multFactor=argsa.multFactor
+
+ fileName = argsa.file[0]
+ fieldName = argsa.field[0]
+
+ res=bb_read_json_field(fileName, fieldName, rounding, multFactor)
+
+ print(str(res))
+
+if __name__ == "__main__":
+ main()
diff --git a/bip/commands/get_dwell_time.py b/bip/commands/get_dwell_time.py
new file mode 100755
index 0000000..9b5edcd
--- /dev/null
+++ b/bip/commands/get_dwell_time.py
@@ -0,0 +1,28 @@
+import os
+import nibabel as nib
+from bip.commands.bb_read_json_field import bb_read_json_field
+
+def get_dt(img, json):
+ if os.path.exists(json):
+ t = bb_read_json_field(fileName=json, fieldName="EffectiveEchoSpacing",
+ rounding=4, multFactor=1000)
+ if t in ["[]", ""]:
+ t = bb_read_json_field(fileName=json,
+ fieldName="EstimatedEffectiveEchoSpacing",
+ rounding=4, multFactor=1000)
+ return t
+
+ im = nib.load(img)
+ descrip = str(im.header['descrip'])
+
+ fields = descrip.split(";")
+
+ for field in fields:
+ t = field.split("=")
+
+ fieldName = t[0]
+ fieldValue = t[1]
+
+ if fieldName == "dwell":
+ return fieldValue
+ return 0
diff --git a/bip/data/FileTree.tree b/bip/data/FileTree.tree
new file mode 100644
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--- /dev/null
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new file mode 100644
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--- /dev/null
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new file mode 100644
index 0000000..7e5d30c
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new file mode 100755
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new file mode 100755
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index 0000000..5d0d7a2
--- /dev/null
+++ b/bip/data/fix/UKBB_model_2.pyfix_model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a582ac9ef9709d4c7e1d9930ec1cea38e62e786022cf59268ccaf51b26efd0e
+size 479929
diff --git a/bip/data/fnirt/bb_fnirt.cnf b/bip/data/fnirt/bb_fnirt.cnf
new file mode 100755
index 0000000..ca3b548
--- /dev/null
+++ b/bip/data/fnirt/bb_fnirt.cnf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41e2f1b068e4841c8f6d7934375651fbe2cf2e66ac40da3d21f3fbb9b9576e37
+size 2095
diff --git a/bip/data/netmats/groupMask1.nii.gz b/bip/data/netmats/groupMask1.nii.gz
new file mode 100644
index 0000000..09a1f59
--- /dev/null
+++ b/bip/data/netmats/groupMask1.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae191189dfb9dd61c615695524b609712b891091e0bb9268627b14334bf09191
+size 12596
diff --git a/bip/data/netmats/groupMask2.nii.gz b/bip/data/netmats/groupMask2.nii.gz
new file mode 100644
index 0000000..763c578
--- /dev/null
+++ b/bip/data/netmats/groupMask2.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67aad7cba913142158726cc90fcffda64bf5b4d50f0e87e3362a1f537ae9d6db
+size 12315
diff --git a/bip/data/netmats/groupMask5.nii.gz b/bip/data/netmats/groupMask5.nii.gz
new file mode 100644
index 0000000..dabbafd
--- /dev/null
+++ b/bip/data/netmats/groupMask5.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bad580a85019111b481be06bde3704ee8866d75dabe89ff390a65cae95ba0532
+size 5642
diff --git a/bip/data/netmats/groupMask5a.nii.gz b/bip/data/netmats/groupMask5a.nii.gz
new file mode 100644
index 0000000..610f4b8
--- /dev/null
+++ b/bip/data/netmats/groupMask5a.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eea197dc41c09f8f907b0743a6a2c13dd0e72f40d0e3a4d4a3f4ceb7ba23ba07
+size 4082
diff --git a/bip/data/netmats/melodic_IC_100.nii.gz b/bip/data/netmats/melodic_IC_100.nii.gz
new file mode 100644
index 0000000..cc13126
--- /dev/null
+++ b/bip/data/netmats/melodic_IC_100.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 87745042
diff --git a/bip/data/netmats/melodic_IC_25.nii.gz b/bip/data/netmats/melodic_IC_25.nii.gz
new file mode 100644
index 0000000..0fd07cb
--- /dev/null
+++ b/bip/data/netmats/melodic_IC_25.nii.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 21972882
diff --git a/bip/data/swMRI/hann_filter.txt b/bip/data/swMRI/hann_filter.txt
new file mode 100644
index 0000000..a85bf86
--- /dev/null
+++ b/bip/data/swMRI/hann_filter.txt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:056ad1b95d3b20e68e9d81aa57a6365399c316db9dbb875941178339a8a43e15
+size 77858
diff --git a/bip/ext_wrappers/__init__.py b/bip/ext_wrappers/__init__.py
new file mode 100755
index 0000000..6f78978
--- /dev/null
+++ b/bip/ext_wrappers/__init__.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env python
+#
+# pylint: disable=unused-import
+# flake8: noqa: F401
+#
+# __init__.py - Wrappers for external command-line tools.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+#
+"""This package contains wrappers for various command line tools, allowing
+them to be called from Python.
+"""
+
+from bip.ext_wrappers.freesurfer import (recon_all)
diff --git a/bip/ext_wrappers/extwrapperutils.py b/bip/ext_wrappers/extwrapperutils.py
new file mode 100644
index 0000000..31fe79b
--- /dev/null
+++ b/bip/ext_wrappers/extwrapperutils.py
@@ -0,0 +1,1297 @@
+#!/usr/bin/env python
+#
+# wrapperutils.py - Functions and decorators used by the FSL wrapper
+# functions.
+#
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Martin Craig <martin.craig@eng.ox.ac.uk>
+#
+"""This module contains functions and decorators used by the FSL wrapper
+functions.
+
+
+The :func:`cmdwrapper` and :func:`fslwrapper` functions are convenience
+decorators which allow you to write your wrapper function such that it simply
+generates the command-line needed to respectively run a standard shell
+command or a FSL command. For example::
+
+
+ @fslwrapper
+ def fslreorient2std(input, output):
+ return ['fslreorient2std', input, output]
+
+
+When this ``fslreorient2std`` function is called, the ``fslwrapper`` decorator
+will take care of invoking the command in a standardised way.
+
+
+The :func:`applyArgStyle` function can be used to automatically convert
+keyword arguments into command-line arguments, based on a set of standard
+patterns. For example::
+
+
+ @fslwrapper
+ def flirt(src, ref, **kwargs):
+ cmd = ['flirt', '-in', src, '-ref', ref]
+ return cmd + applyArgStyle('-=', **kwargs)
+
+
+The :func:`fileOrImage` and :func:`fileOrArray` functions can be used to
+decorate a wrapper function such that in-memory ``nibabel`` images or Numpy
+arrays can be passed in as arguments - they will be automatically saved out to
+files, and then the file names passed into the wrapper function. For example::
+
+
+ @fileOrImage('src', 'ref')
+ @fslwrapper
+ def flirt(src, ref, **kwargs):
+ cmd = ['flirt', '-in', src, '-ref', ref]
+ return cmd + applyArgStyle('-=', **kwargs)
+
+
+Now this ``flirt`` function can be called either with file names, or
+``nibabel`` images.
+
+
+.. note:: Because the :func:`fileOrImage` and :func:`fileOrArray` decorators
+ manipulate the return value of the decorated function, they should
+ be applied *after* any other decorators. Furthermore, if you need to
+ apply both a ``fileOrImage`` and ``fileOrArray`` decorator to a
+ function, they should be grouped together, e.g.::
+
+ @fileOrImage('a', 'b')
+ @fileOrArray('c', 'd')
+ @fslwrapper
+ def func(**kwargs):
+ ...
+
+
+Command outputs can also be loaded back into memory by using the special
+:data:`LOAD` value when calling a wrapper function. For example::
+
+
+ @fileOrImage('src', 'ref', 'out')
+ @fslwrapper
+ def flirt(src, ref, **kwargs):
+ cmd = ['flirt', '-in', src, '-ref', ref]
+ return cmd + applyArgStyle('-=', **kwargs)
+
+
+If we set the ``out`` argument to ``LOAD``, the output image will be loaded
+and returned::
+
+ src = nib.load('src.nii')
+ ref = nib.load('ref.nii')
+ aligned = flirt(src, ref, out=LOAD)['out']
+"""
+
+
+import itertools as it
+import os.path as op
+import collections.abc as abc
+import os
+import re
+import sys
+import glob
+import random
+import string
+import pathlib
+import fnmatch
+import inspect
+import logging
+import tempfile
+import warnings
+import functools
+import contextlib
+
+import nibabel as nib
+import numpy as np
+
+import fsl.utils.run as run
+import fsl.utils.assertions as asrt
+import fsl.utils.path as fslpath
+import fsl.utils.tempdir as tempdir
+import fsl.data.image as fslimage
+
+
+log = logging.getLogger(__name__)
+
+
+def _update_wrapper(wrapper, wrapped, *args, **kwargs):
+ """Replacement for the built-in ``functools.update_wrapper``. This
+ implementation ensures that the wrapper function has an attribute
+ called ``__wrapped__``, which refers to the ``wrapped`` function.
+
+ This custom function is only needed in Python versions < 3.4.
+ """
+
+ wrapper = functools.update_wrapper(wrapper, wrapped, *args, **kwargs)
+
+ # Python >= 3.4 does things right
+ if (sys.version_info[0] * 10 + sys.version_info[1]) < 34:
+ wrapper.__wrapped__ = wrapped
+ return wrapper
+
+
+def _unwrap(func):
+ """Replacement for the built-in ``inspect.unwrap`` function, which
+ is not present in Python versions prior to 3.4.
+ """
+
+ # Python >= 3.4 has an inspect.unwrap function
+ if (sys.version_info[0] * 10 + sys.version_info[1]) >= 34:
+ return inspect.unwrap(func)
+
+ # Otherwise we follow the __wrapped__ chain ourselves
+ if hasattr(func, '__wrapped__'):
+ return _unwrap(func.__wrapped__)
+
+ return func
+
+
+def genxwrapper(func, runner):
+ """This function is used by :func:`cmdwrapper` and :func:`fslwrapper`.
+ It is not intended to be used in any other circumstances.
+
+ This function generates a wrapper function which calls ``func`` to
+ generate a command-line call, and then uses ``runner`` to invoke that
+ command.
+
+ ``func`` is assumed to be a wrapper function which generates a command-
+ line. ``runner`` is assumed to be Either :func:`.run.run` or
+ :func:`.run.runfsl`.
+
+ The generated wrapper function will pass all of its arguments to ``func``,
+ and will then pass the generated command-line to ``runner``, returning
+ whatever is returned.
+
+ The following keyword arguments will be intercepted by the wrapper
+ function, and will *not* be passed to ``func``:
+
+ - ``stdout``: Passed to ``runner``. Defaults to ``True``.
+ - ``stderr``: Passed to ``runner``. Defaults to ``True``.
+ - ``exitcode``: Passed to ``runner``. Defaults to ``False``.
+ - ``submit``: Passed to ``runner``. Defaults to ``None``.
+ - ``log``: Passed to ``runner``. Defaults to ``{'tee':True}``.
+ - ``cmdonly``: Passed to ``runner``. Defaults to ``False``.
+
+ The default values for these arguments are stored in the
+ ``genxwrapper.run_options`` dictionary. This dictionary should not be
+ changed directly, but rather can be temporarily modified via the
+ :func:`wrapperconfig` context manager function.
+
+ :arg func: A function which generates a command line.
+ :arg runner: Either :func:`.run.run` or :func:`.run.runfsl`.
+ """
+
+ def wrapper(*args, **kwargs):
+ opts = genxwrapper.run_options
+ stdout = kwargs.pop('stdout', opts['stdout'])
+ stderr = kwargs.pop('stderr', opts['stderr'])
+ exitcode = kwargs.pop('exitcode', opts['exitcode'])
+ submit = kwargs.pop('submit', opts['submit'])
+ cmdonly = kwargs.pop('cmdonly', opts['cmdonly'])
+ logg = kwargs.pop('log', opts['log'])
+
+ # many wrapper functions use fsl.utils.assertions
+ # statements to check that input arguments are
+ # valid. Disable these if the cmdonly argument is
+ # being used to generate a command without running
+ # it.
+ with asrt.disabled(cmdonly):
+ cmd = func(*args, **kwargs)
+
+ return runner(cmd,
+ stderr=stderr,
+ log=logg,
+ submit=submit,
+ cmdonly=cmdonly,
+ stdout=stdout,
+ exitcode=exitcode)
+
+ return _update_wrapper(wrapper, func)
+
+
+genxwrapper.run_options = {
+ 'stdout' : True,
+ 'stderr' : True,
+ 'exitcode' : False,
+ 'submit' : None,
+ 'log' : {'tee' : True},
+ 'cmdonly' : False
+}
+
+
+def cmdwrapper(func):
+ """This decorator can be used on functions which generate a command line.
+ It will pass the return value of the function to the
+ :func:`fsl.utils.run.run` function in a standardised manner.
+
+ See the :func:`genxwrapper` function for details.
+ """
+ return genxwrapper(func, run.run)
+
+
+def fslwrapper(func):
+ """This decorator can be used on functions which generate a FSL command
+ line. It will pass the return value of the function to the
+ :func:`fsl.utils.run.runfsl` function in a standardised manner.
+
+ See the :func:`genxwrapper` function for details.
+ """
+ return genxwrapper(func, run.runfsl)
+
+
+@contextlib.contextmanager
+def wrapperconfig(**kwargs):
+ """Context manager to be used when calling wrapper functions. Can modify
+ the options/arguments that are passed to :func:`fsl.utils.run.run` when
+ calling a command from a wrapper function. For example::
+
+ with wrapperconfig(stdout=False):
+ bet('struct', 'struct_brain')
+ """
+ opts = dict(genxwrapper.run_options)
+ genxwrapper.run_options.update(kwargs)
+ try:
+ yield
+ finally:
+ genxwrapper.run_options = opts
+
+
+SHOW_IF_TRUE = object()
+"""Constant to be used in the ``valmap`` passed to the :func:`applyArgStyle`
+function.
+
+When a ``SHOW_IF_TRUE`` argument is ``True``, it is added to the generated
+command line arguments.
+"""
+
+
+HIDE_IF_TRUE = object()
+"""Constant to be used in the ``valmap`` passed to the :func:`applyArgStyle`
+function.
+
+When a ``HIDE_IF_TRUE`` argument is ``True``, it is suppressed from the
+generated command line arguments.
+"""
+
+
+def applyArgStyle(style=None,
+ valsep=None,
+ argmap=None,
+ valmap=None,
+ singlechar_args=False,
+ charstyle=None,
+ charsep=None,
+ **kwargs):
+ """Turns the given ``kwargs`` into command line options. This function
+ is intended to be used to automatically generate command line options
+ from arguments passed into a Python function.
+
+ The default settings will generate arguments that match typical UNIX
+ conventions, e.g. ``-a val``, ``--arg=val``, ``-a val1 val2``,
+ ``--arg=val1,val2``.
+
+ The ``style`` and ``valsep`` options (and ``charstyle`` and ``charsep``,
+ for single-character/short arguments) control how key-value pairs are
+ converted into command-line options:
+
+
+ ========= ========== ===========================
+ ``style`` ``valsep`` Result
+ ========= ========== ===========================
+ ``'-'`` ``' '`` ``-name val1 val2 val3``
+ ``'-'`` ``'"'`` ``-name "val1 val2 val3"``
+ ``'-'`` ``','`` ``-name val1,val2,val3``
+ ``'--'`` ``' '`` ``--name val1 val2 val3``
+ ``'--'`` ``'"'`` ``--name "val1 val2 val3"``
+ ``'--'`` ``','`` ``--name val1,val2,val3``
+ ``'-='`` ``' '`` Not supported
+ ``'-='`` ``'"'`` ``-name="val1 val2 val3"``
+ ``'-='`` ``','`` ``-name=val1,val2,val3``
+ ``'--='`` ``' '`` Not supported
+ ``'--='`` ``'"'`` ``--name="val1 val2 val3"``
+ ``'--='`` ``','`` ``--name=val1,val2,val3``
+ ========= ========== ===========================
+
+
+ :arg style: Controls how the ``kwargs`` are converted into command-line
+ options - must be one of ``'-'``, ``'--'``, ``'-='``, or
+ ``'--='`` (the default).
+
+ :arg valsep: Controls how the values passed to command-line options
+ which expect multiple arguments are delimited - must be
+ one of ``' '``, ``','`` or ``'"'``. Defaults to ``' '``
+ if ``'=' not in style``, ``','`` otherwise.
+
+ :arg argmap: Dictionary of ``{kwarg-name : cli-name}`` mappings. This be
+ used if you want to use different argument names in your
+ Python function for the command-line options.
+
+ :arg valmap: Dictionary of ``{cli-name : value}`` mappings. This can be
+ used to define specific semantics for some command-line
+ options. Acceptable values for ``value`` are as follows
+
+ - :data:`SHOW_IF_TRUE` - if the argument is present, and
+ ``True`` in ``kwargs``, the command line option
+ will be added (without any arguments).
+
+ - :data:`HIDE_IF_TRUE` - if the argument is present, and
+ ``False`` in ``kwargs``, the command line option
+ will be added (without any arguments).
+
+ - Any other constant value. If the argument is present
+ in ``kwargs``, its command-line option will be
+ added, with the constant value as its argument.
+
+ The argument for any options not specified in the
+ ``valmap`` will be converted into strings.
+
+ :arg charstyle: Separate style specification for single-character
+ arguments. If ``style == '--='``, defaults to ``'-'``,
+ matching UNIX conventions. Otherwise defaults to the
+ value of ``style``.
+
+ :arg charsep: Controls how the values passed to command-line options
+ which expect multiple arguments are delimited - must be
+ one of ``' '``, ``','`` or ``'"'``. Defaults to ``' '``
+ if ``'=' not in style``, ``','`` otherwise.
+
+ :arg singlechar_args: If ``True``, equivalent to ``charstyle='-'``. This
+ argument remains for compatibility, but may be
+ removed in a future version.
+
+ :arg kwargs: Arguments to be converted into command-line options.
+
+ :returns: A sequence containing the generated command-line options, the
+ same as what ``shlex.split`` would generate for a properly
+ quoted string.
+ """
+
+ if style is None:
+ style = '--='
+
+ if charstyle is None:
+ if singlechar_args: charstyle = '-'
+ elif style == '--=': charstyle = '-'
+ else: charstyle = style
+
+ if valsep is None:
+ if '=' in style: valsep = ','
+ else: valsep = ' '
+
+ if charsep is None:
+ if '=' in charstyle: charsep = ','
+ else: charsep = ' '
+
+ if style not in ('-', '--', '-=', '--='):
+ raise ValueError(f'Invalid style: {style}')
+ if charstyle not in ('-', '--', '-=', '--='):
+ raise ValueError(f'Invalid charstyle: {charstyle}')
+ if valsep not in (' ', ',', '"'):
+ raise ValueError(f'Invalid valsep: {valsep}')
+ if charsep not in (' ', ',', '"'):
+ raise ValueError(f'Invalid charsep: {charsep}')
+
+ # It makes no sense to combine argument+value
+ # with an equals sign, but not have the value
+ # quoted (e.g "--arg=val1 val2 val3").
+ if '=' in style and valsep == ' ':
+ raise ValueError(f'Incompatible style {style} '
+ 'and valsep ({valsep})')
+ if '=' in charstyle and charsep == ' ':
+ raise ValueError(f'Incompatible style {charstyle} '
+ 'and valsep ({charsep})')
+
+ if argmap is None: argmap = {}
+ if valmap is None: valmap = {}
+
+ # Format the argument.
+ def fmtarg(arg, style):
+ if style in ('--', '--='): return f'--{arg}'
+ else: return f'-{arg}'
+
+ # Formt the argument value.
+ def fmtval(val, sep):
+ if isinstance(val, abc.Sequence) and (not isinstance(val, str)):
+ val = [str(v) for v in val]
+ if sep == ' ': return val
+ elif sep == '"': return [' '.join(val)]
+ else: return [sep.join(val)]
+ else:
+ return [str(val)]
+
+ # Combine the argument and value together.
+ # val is assumed to be a sequence.
+ def fmtargval(arg, val, style):
+ # if '=' in style, val will
+ # always be a single string
+ if '=' in style: return ['{}={}'.format(arg, val[0])]
+ else: return [arg] + val
+
+ args = []
+
+ for k, v in kwargs.items():
+
+ if v is None: continue
+
+ if len(k) == 1: sty, sep = charstyle, charsep
+ else: sty, sep = style, valsep
+
+ k = argmap.get(k, k)
+ mapv = valmap.get(k, fmtval(v, sep))
+ k = fmtarg(k, sty)
+
+ if mapv in (SHOW_IF_TRUE, HIDE_IF_TRUE):
+ if (mapv is SHOW_IF_TRUE and v) or \
+ (mapv is HIDE_IF_TRUE and not v):
+ args.append(k)
+ else:
+ args.extend(fmtargval(k, mapv, sty))
+
+ return args
+
+
+def namedPositionals(func, args):
+ """Given a function, and a sequence of positional arguments destined
+ for that function, identifies the name for each positional argument.
+ Variable positional arguments are given an automatic name.
+
+ :arg func: Function which will accept ``args`` as positionals.
+ :arg args: Tuple of positional arguments to be passed to ``func``.
+ """
+
+ # Remove any decorators
+ # from the function
+ func = _unwrap(func)
+
+ # getargspec is the only way to
+ # get the names of positional
+ # arguments in Python 2.x.
+ if sys.version_info[0] < 3:
+ spec = inspect.getargspec(func)
+ argnames = spec.args
+ varargs = spec.varargs
+
+ # But getargspec is deprecated
+ # in python 3.x
+ else:
+
+ # getfullargspec is deprecated in
+ # python 3.5, but not in python 3.6.
+ with warnings.catch_warnings():
+ warnings.filterwarnings('ignore', category=DeprecationWarning)
+ spec = inspect.getfullargspec(func)
+ argnames = spec.args
+ varargs = spec.varargs
+
+ # we only care about the arguments
+ # that are being passed in
+ argnames = argnames[:len(args)]
+
+ # generate names for varargs
+ nvarargs = len(args) - len(argnames)
+ suffix = namedPositionals.varargsSuffix
+ if varargs is not None and nvarargs > 0:
+ argnames += [f'{varargs}{i}{suffix}' for i in range(nvarargs)]
+
+ return argnames
+namedPositionals.varargsSuffix = '_VARARG'
+
+
+LOAD = object()
+"""Constant used by the :class:`FileOrThing` class to indicate that an output
+file should be loaded into memory and returned as a Python object.
+"""
+
+
+class FileOrThing:
+ """Decorator which ensures that certain arguments which are passed into the
+ decorated function are always passed as file names. Both positional and
+ keyword arguments can be specified.
+
+
+ The ``FileOrThing`` class is not intended to be used directly - see the
+ :func:`fileOrImage` and :func:`fileOrArray` decorator functions for more
+ details.
+
+
+ These decorators are intended for functions which wrap a command-line tool,
+ i.e. where some inputs/outputs need to be specified as file names.
+
+
+ **Inputs**
+
+
+ Any arguments which are not of type ``Thing`` are passed through to the
+ decorated function unmodified. Arguments which are of type ``Thing`` are
+ saved to a temporary file, and the name of that file is passed to the
+ function.
+
+
+ **Outputs**
+
+
+ If an argument is given the special :data:`LOAD` value, it is assumed
+ to be an output argument. In this case, it is replaced with a temporary
+ file name then, after the function has completed, that file is loaded
+ into memory, and the value returned (along with the function's output,
+ and any other arguments with a value of ``LOAD``).
+
+
+ **Return value**
+
+
+ Functions decorated with a ``FileOrThing`` decorator will always return a
+ ``dict``-like object, where the function's actual return value is
+ accessible via an attribute called ``stdout``. All output arguments with a
+ value of ``LOAD`` will be present as dictionary entries, with the keyword
+ argument names used as keys; these values will also be accessible as
+ attributes of the results dict, when possible. Any ``LOAD`` output
+ arguments which were not generated by the function will not be present in
+ the dictionary.
+
+
+ **Exceptions**
+
+
+ The above description holds in all situations, except when arguments called
+ ``submit`` and/or ``cmdonly`` are passed, and are set to values which
+ evaluate to ``True``. In this case, the ``FileOrThing`` decorator will pass
+ all arguments straight through to the decorated function, and will return
+ its return value unchanged.
+
+ This is because most functions that are decorated with the
+ :func:`fileOrImage` or :func:`fileOrArray` decorators will invoke a call
+ to :func:`.run.run` or :func:`.runfsl`, where:
+
+ - a value of ``submit=True`` will cause the command to be executed
+ asynchronously on a cluster platform.
+ - a value of ``cmdonly=True`` will cause the command to *not* be executed,
+ but instead the command that would have been executed is returned.
+
+ A :exc:`ValueError` will be raised if the decorated function is called
+ with ``submit=True`` and/or ``cmdonly=True``, and with any in-memory
+ objects or ``LOAD`` symbols.
+
+
+ **Example**
+
+
+ As an example of using the ``fileOrArray`` decorator on a function
+ which concatenates two files containing affine transformations, and
+ saves the output to a file::
+
+ # if atob, btoc, or output are passed
+ # in as arrays, they are converted to
+ # file names.
+ @fileOrArray('atob', 'btoc', 'output')
+ def concat(atob, btoc, output=None):
+
+ # inputs are guaranteed to be files
+ atob = np.loadtxt(atob)
+ btoc = np.loadtxt(atoc)
+
+ atoc = np.dot(btoc, atob)
+
+ if output is not None:
+ np.savetxt(output, atoc)
+
+ return 'Done'
+
+
+ Because we have decorated the ``concat`` function with :func:`fileToArray`,
+ it can be called with either file names, or Numpy arrays::
+
+
+ # All arguments are passed through
+ # unmodified - the output will be
+ # saved to a file called atoc.mat.
+ concat('atob.txt', 'btoc.txt', 'atoc.mat')
+
+ # The function's return value
+ # is accessed via an attribute called
+ # "stdout" on the dict
+ assert concat('atob.txt', 'btoc.txt', 'atoc.mat').stdout == 'Done'
+
+ # Outputs to be loaded into memory
+ # are returned in a dictionary,
+ # with argument names as keys. Values
+ # can be accessed as dict items, or
+ # as attributes.
+ atoc = concat('atob.txt', 'btoc.txt', LOAD)['atoc']
+ atoc = concat('atob.txt', 'btoc.txt', LOAD).atoc
+
+ # In-memory inputs are saved to
+ # temporary files, and those file
+ # names are passed to the concat
+ # function.
+ atoc = concat(np.diag([2, 2, 2, 0]),
+ np.diag([3, 3, 3, 3]), LOAD).atoc
+
+
+ **Using with other decorators**
+
+
+ ``FileOrThing`` decorators can be chained with other ``FileOrThing``
+ decorators, and other decorators. When multiple ``FileOrThing``
+ decorators are used on a single function, the outputs from each decorator
+ are merged together into a single dict-like object.
+
+
+ ``FileOrThing`` decorators can be used with any other decorators
+ **as long as** they do not manipulate the return value, and as long as
+ the ``FileOrThing`` decorators are adjacent to each other.
+ """
+
+
+ class Results(dict):
+ """A custom ``dict`` type used to return outputs from a function
+ decorated with ``FileOrThing``. All outputs are stored as dictionary
+ items, with the argument name as key, and the output object (the
+ "thing") as value.
+
+ Where possible (i.e. for outputs named with a valid Python
+ identifier), the outputs are also made accessible as attributes of
+ the ``Results`` object.
+
+ The decorated function's actual return value is accessible via the
+ :meth:`stdout` property.
+ """
+
+
+ def __init__(self, stdout):
+ """Create a ``Results`` dict.
+
+ :arg stdout: Return value of the decorated function (typically a
+ tuple containing the standard output and error of the
+ underlying command).
+ """
+ super().__init__()
+ self.__stdout = stdout
+
+
+ def __setitem__(self, key, val):
+ """Add an item to the dict. The item is also added as an attribute.
+ """
+ super().__setitem__(key, val)
+ setattr(self, key, val)
+
+
+ @property
+ def stdout(self):
+ """Access the return value of the decorated function. """
+ return self.__stdout
+
+
+ def __init__(self,
+ func,
+ prepIn,
+ prepOut,
+ load,
+ removeExt,
+ *args,
+ **kwargs):
+ """Initialise a ``FileOrThing`` decorator.
+
+ :arg func: The function to be decorated.
+
+ :arg prepIn: Function which returns a file name to be used in
+ place of an input argument.
+
+ :arg prepOut: Function which generates a file name to use for
+ arguments that were set to :data:`LOAD`.
+
+ :arg load: Function which is called to load items for arguments
+ that were set to :data:`LOAD`. Must accept the
+ following arguments:
+
+ - the name of the argument
+ - path to the file to be loaded
+
+ :arg removeExt: Function which can remove a file extension from a file
+ path.
+
+ :arg outprefix: Must be passed as a keyword argument. The name of a
+ positional or keyword argument to the function, which
+ specifies an output file name prefix. All other
+ arguments with names that begin with this prefix may
+ be interpreted as things to ``LOAD``.
+
+ All other positional arguments are interpreted as the names of the
+ arguments to the function which will be handled by this
+ ``FileOrThing`` decorator. If not provided, *all* arguments passed to
+ the function will be handled.
+
+
+ The ``prepIn`` and ``prepOut`` functions must accept the following
+ positional arguments:
+
+ - A directory in which all temporary input/output files should be
+ stored
+
+ - The name of the keyword argument to be processed
+
+ - The argument value that was passed in
+ """
+
+ self.__func = func
+ self.__prepIn = prepIn
+ self.__prepOut = prepOut
+ self.__load = load
+ self.__removeExt = removeExt
+ self.__things = args
+ self.__outprefix = kwargs.get('outprefix', None)
+
+
+ def __call__(self, *args, **kwargs):
+ """Function which calls ``func``, ensuring that any arguments of
+ type ``Thing`` are saved to temporary files, and any arguments
+ with the value :data:`LOAD` are loaded and returned.
+
+ All other arguments are passed through to ``func``.
+ """
+
+ func = self.__func
+ argnames = namedPositionals(func, args)
+
+ # Special case - if fsl.utils.run[fsl] is
+ # being decorated (e.g. via cmdwrapper/
+ # fslwrapper), and submit=True or
+ # cmdonly=True, this call will ultimately
+ # submit the job to the cluster, or will
+ # return the command that would have been
+ # executed, and will return immediately.
+ #
+ # We error if we are given any in-memory
+ # things, or LOAD symbols.
+ #
+ # n.b. testing values to be strings could
+ # interfere with the fileOrText decorator.
+ # Possible solution is to use pathlib?
+ if kwargs.get('submit', False) or \
+ kwargs.get('cmdonly', False):
+ allargs = {**dict(zip(argnames, args)), **kwargs}
+ for name, val in allargs.items():
+ if (name in self.__things) and (not isinstance(val, str)):
+ raise ValueError('Cannot use in-memory objects '
+ 'or LOAD with submit=True!')
+ return func(*args, **kwargs)
+
+ # If this FileOrThing is being called
+ # by another FileOrThing don't create
+ # another working directory. We do this
+ # sneakily, by setting an attribute on
+ # the wrapped function which stores the
+ # current working directory.
+ wrapped = _unwrap(func)
+ fot_workdir = getattr(wrapped, '_fot_workdir', None)
+ parent = fot_workdir is None
+
+ # Create a tempdir to store any temporary
+ # input/output things, but don't change
+ # into it, as file paths passed to the
+ # function may be relative.
+ with tempdir.tempdir(changeto=False, override=fot_workdir) as td:
+
+ log.debug('Redirecting LOADed outputs to %s', td)
+
+ # Replace any things with file names.
+ # Also get a list of LOAD outputs
+ args = self.__prepareArgs(parent, td, argnames, args, kwargs)
+ args, kwargs, outprefix, outfiles, prefixes = args
+
+ # The prefix/patterns may be
+ # overridden by a parent FoT
+ outprefix = getattr(wrapped, '_fot_outprefix', outprefix)
+ prefixes = getattr(wrapped, '_fot_prefixes', prefixes)
+
+ # if there are any other FileOrThings
+ # in the decorator chain, get them to
+ # use our working directory, and
+ # prefixes, instead of creating their
+ # own.
+ if parent:
+ setattr(wrapped, '_fot_workdir', td)
+ setattr(wrapped, '_fot_outprefix', outprefix)
+ setattr(wrapped, '_fot_prefixes', prefixes)
+
+ # Call the function
+ try:
+ result = func(*args, **kwargs)
+
+ finally:
+ # if we're the top-level FileOrThing
+ # decorator, remove the attributes we
+ # added above.
+ if parent:
+ delattr(wrapped, '_fot_workdir')
+ delattr(wrapped, '_fot_outprefix')
+ delattr(wrapped, '_fot_prefixes')
+
+ return self.__generateResult(
+ td, result, outprefix, outfiles, prefixes)
+
+
+ def __prepareArgs(self, parent, workdir, argnames, args, kwargs):
+ """Prepares all input and output arguments to be passed to the
+ decorated function. Any arguments with a value of :data:`LOAD` are
+ passed to the ``prepOut`` function specified at :meth:`__init__`.
+ All other arguments are passed through the ``prepIn`` function.
+
+ :arg parent: ``True`` if this ``FileOrThing`` is the first in a
+ chain of ``FileOrThing`` decorators.
+
+ :arg workdir: Directory in which all temporary files should be stored.
+
+ :arg argnames: Names for each positional argument.
+
+ :arg args: Positional arguments to be passed to the decorated
+ function.
+
+ :arg kwargs: Keyword arguments to be passed to the decorated
+ function.
+
+ :returns: A tuple containing:
+
+ - An updated copy of ``args``.
+
+ - An updated copy of ``kwargs``.
+
+ - The output file prefix that was actually passed in
+ (it is subsequently modified so that prefixed outputs
+ are redirected to a temporary location). All prefixed
+ outputs that are not ``LOAD``ed should be moved into
+ this directory. ``None`` if there is no output
+ prefix.
+
+ - A dictionary of ``{ name : filename }`` mappings,
+ for all arguments with a value of ``LOAD``.
+
+ - A dictionary of ``{ filepat : replstr }`` paths, for
+ all output-prefix arguments with a value of ``LOAD``.
+ """
+
+ # These containers keep track
+ # of output files which are to
+ # be loaded into memory
+ outfiles = dict()
+ prefixedFiles = dict()
+
+ allargs = {k : v for k, v in zip(argnames, args)}
+ allargs.update(kwargs)
+
+ # Has an output prefix been specified?
+ prefix = allargs.get(self.__outprefix, None)
+ realPrefix = None
+
+ # Prefixed outputs are only
+ # managed by the parent
+ # FileOrthing in a chain of
+ # FoT decorators.
+ if not parent:
+ prefix = None
+
+ # If so, replace it with a new output
+ # prefix which will redirect all output
+ # to the temp dir.
+ #
+ # Importantly, here we assume that the
+ # underlying function (and hence the
+ # underlying command-line tool) will
+ # accept an output prefix which contains
+ # a directory path.
+ if prefix is not None:
+ if isinstance(prefix, pathlib.Path):
+ prefix = op.abspath(prefix)
+
+ # If prefix is set to LOAD,
+ # all generated output files
+ # should be loaded - we use a
+ # randomly generated prefix,
+ # and add it to prefixedFiles,
+ # so that every file which
+ # starts with it will be
+ # loaded.
+ if prefix is LOAD:
+ prefix = random.sample(string.ascii_letters, 10)
+ prefix = ''.join(prefix)
+ prefixedFiles[prefix] = self.__outprefix
+
+ realPrefix = prefix
+ fakePrefix = op.join(workdir, prefix)
+ allargs[self.__outprefix] = fakePrefix
+
+ log.debug('Replacing output prefix: %s -> %s',
+ realPrefix, fakePrefix)
+
+ # If the prefix specifies a
+ # directory, make sure it
+ # exists (remember that we're
+ # in a temporary directory)
+ pdir = op.dirname(fakePrefix)
+ if pdir != '' and not op.exists(pdir):
+ os.makedirs(pdir)
+
+ if len(self.__things) > 0: things = self.__things
+ else: things = allargs.keys()
+
+ for name, val in list(allargs.items()):
+
+ # don't process the
+ # outprefix argument
+ if name == self.__outprefix:
+ continue
+
+ # is this argument referring
+ # to a prefixed output?
+ isprefixed = (prefix is not None and
+ name.startswith(prefix))
+
+ if not any((isprefixed,
+ name in things,
+ name.endswith(namedPositionals.varargsSuffix))):
+ continue
+
+ # Prefixed output files may only
+ # be given a value of LOAD
+ if isprefixed and val is not LOAD:
+ raise ValueError('Cannot specify name of prefixed file - the '
+ 'name is defined by the output prefix: '
+ '{}'.format(name))
+
+ if val is LOAD:
+
+ # this argument refers to an output
+ # that is generated from the output
+ # prefix argument, and doesn't map
+ # directly to an argument of the
+ # function. So we don't pass it
+ # through.
+ if isprefixed:
+ prefixedFiles[name] = name
+ allargs.pop(name)
+
+ # regular output-file argument
+ else:
+ outfile = self.__prepOut(workdir, name, val)
+ outfiles[name] = outfile
+ allargs[ name] = outfile
+
+ # Assumed to be an input file
+ else:
+ # sequences may be
+ # accepted for inputs
+ if isinstance(val, (list, tuple)):
+ infile = list(val)
+ for i, v in enumerate(val):
+ v = self.__prepIn(workdir, name, v)
+ if v is not None:
+ infile[i] = v
+
+ else:
+ infile = self.__prepIn(workdir, name, val)
+
+ if infile is not None:
+ allargs[name] = infile
+
+ if realPrefix is not None and len(prefixedFiles) == 0:
+ allargs[self.__outprefix] = realPrefix
+
+ # Turn any Path objects into absolute path
+ # strings. Don't use Path.resolve(), as it
+ # returns a relative path for non-existent
+ # files/dirs on Windows/certain Python
+ # versions.
+ for k, v in allargs.items():
+ if isinstance(v, pathlib.Path):
+ allargs[k] = op.abspath(v)
+
+ args = [allargs.pop(k) for k in argnames]
+ kwargs = allargs
+
+ return args, kwargs, realPrefix, outfiles, prefixedFiles
+
+
+ def __generateResult(
+ self, workdir, result, outprefix, outfiles, prefixes):
+ """Loads function outputs and returns a :class:`Results` object.
+
+ Called by :meth:`__call__` after the decorated function has been
+ called. Figures out what files should be loaded, and loads them into
+ a ``Results`` object.
+
+ :arg workdir: Directory which contains the function outputs.
+ :arg result: Function return value.
+ :arg outprefix: Original output prefix that was passed into the
+ function (or ``None`` if one wasn't passed)
+ :arg outfiles: Dictionary containing output files to be loaded (see
+ :meth:`__prepareArgs`).
+ :arg prefixes: Dictionary containing output-prefix patterns to be
+ loaded (see :meth:`__prepareArgs`).
+
+ :returns: A ``Results`` object containing all loaded outputs.
+ """
+
+ # make a Results object to store
+ # the output. If we are decorating
+ # another FileOrThing, the
+ # results will get merged together
+ # into a single Results dict.
+ if not isinstance(result, FileOrThing.Results):
+ result = FileOrThing.Results(result)
+
+ # Load the LOADed outputs
+ for oname, ofile in outfiles.items():
+
+ log.debug('Loading output %s: %s', oname, ofile)
+
+ if op.exists(ofile): oval = self.__load(oname, ofile)
+ else: oval = None
+
+ result[oname] = oval
+
+ # No output prefix - we're done
+ if outprefix is None or len(prefixes) == 0:
+ return result
+
+ # Load or move output-prefixed files.
+ # Find all files with a name that
+ # matches the prefix that was passed
+ # in (recursing into matching sub-
+ # directories too).
+ allPrefixed = glob.glob(op.join(workdir, '{}*'.format(outprefix)))
+ allPrefixed = [fslpath.allFiles(f) if op.isdir(f) else [f]
+ for f in allPrefixed]
+
+ for prefixed in it.chain(*allPrefixed):
+ fullpath = prefixed
+ prefixed = op.relpath(prefixed, workdir)
+ for prefPat, prefName in prefixes.items():
+ if not fnmatch.fnmatch(prefixed, '{}*'.format(prefPat)):
+ continue
+
+ log.debug('Loading prefixed output %s [%s]: %s',
+ prefPat, prefName, prefixed)
+
+ noext = self.__removeExt(prefixed)
+ prefPat = prefPat.replace('\\', '\\\\')
+ noext = re.sub('^' + prefPat, prefName, noext)
+ withext = re.sub('^' + prefPat, prefName, prefixed)
+
+ # if the load function returns
+ # None, this file is probably
+ # not of the correct type.
+ fval = self.__load(noext, fullpath)
+ if fval is not None:
+
+ # If there is already an item in result with the
+ # name (stripped of prefix), then instead store
+ # the result with the full prefixed name
+ if noext not in result:
+ result[noext] = fval
+ else:
+ result[withext] = fval
+ break
+
+ return result
+
+
+def fileOrImage(*args, **kwargs):
+ """Decorator which can be used to ensure that any NIfTI images are saved
+ to file, and output images can be loaded and returned as ``nibabel``
+ image objects or :class:`.Image` objects.
+ """
+
+ # keep track of the input argument
+ # types on each call, so we know
+ # whether to return a fsl.Image or
+ # a nibabel image
+ intypes = []
+
+ def prepIn(workdir, name, val):
+
+ infile = None
+
+ if isinstance(val, fslimage.Image):
+ intypes.append(fslimage.Image)
+
+ elif isinstance(val, nib.nifti1.Nifti1Image):
+ intypes.append(nib.nifti1.Nifti1Image)
+
+ if isinstance(val, fslimage.Image):
+ val = val.nibImage
+
+ if isinstance(val, nib.nifti1.Nifti1Image):
+ infile = val.get_filename()
+
+ # in-memory image - we have
+ # to save it out to a file
+ if infile is None or not op.exists(infile):
+ hd, infile = tempfile.mkstemp(fslimage.defaultExt(),
+ dir=workdir)
+ os.close(hd)
+
+ # Create a copy of the input image and
+ # save that, so the original doesn't
+ # get associated with the temp file
+ val = nib.nifti1.Nifti1Image(
+ np.asanyarray(val.dataobj), None, val.header)
+ val.to_filename(infile)
+
+ return infile
+
+ def prepOut(workdir, name, val):
+ return op.join(workdir, '{}.nii.gz'.format(name))
+
+ def load(name, path):
+
+ if not fslimage.looksLikeImage(path):
+ return None
+
+ # create an independent in-memory
+ # copy of the image file
+ img = nib.load(path, mmap=False)
+ data = np.asanyarray(img.dataobj)
+
+ # if any arguments were fsl images,
+ # that takes precedence.
+ if fslimage.Image in intypes:
+ return fslimage.Image(data, header=img.header, name=name)
+
+ # but if all inputs were file names,
+ # nibabel takes precedence
+ elif nib.nifti1.Nifti1Image in intypes or len(intypes) == 0:
+ return nib.nifti1.Nifti1Image(data, None, img.header)
+
+ # this function should not be called
+ # under any other circumstances
+ else:
+ raise RuntimeError('Cannot handle type: {}'.format(intypes))
+
+ def decorator(func):
+ fot = FileOrThing(func,
+ prepIn,
+ prepOut,
+ load,
+ fslimage.removeExt,
+ *args,
+ **kwargs)
+
+ def wrapper(*args, **kwargs):
+ result = fot(*args, **kwargs)
+ intypes[:] = []
+ return result
+
+ return _update_wrapper(wrapper, func)
+
+ return decorator
+
+
+def fileOrArray(*args, **kwargs):
+ """Decorator which can be used to ensure that any Numpy arrays are saved
+ to text files, and output files can be loaded and returned as Numpy arrays.
+ """
+
+ def prepIn(workdir, name, val):
+
+ infile = None
+
+ if isinstance(val, np.ndarray):
+ hd, infile = tempfile.mkstemp('.txt', dir=workdir)
+ os.close(hd)
+ np.savetxt(infile, val, fmt='%0.18f')
+
+ return infile
+
+ def prepOut(workdir, name, val):
+ return op.join(workdir, '{}.txt'.format(name))
+
+ def load(_, path):
+ try: return np.loadtxt(path)
+ except Exception: return None
+
+ def decorator(func):
+ fot = FileOrThing(func,
+ prepIn,
+ prepOut,
+ load,
+ fslpath.removeExt,
+ *args,
+ **kwargs)
+
+ def wrapper(*args, **kwargs):
+ return fot(*args, **kwargs)
+
+ return _update_wrapper(wrapper, func)
+
+ return decorator
+
+
+def fileOrText(*args, **kwargs):
+ """Decorator which can be used to ensure that any text output (e.g. log
+ file) are saved to text files, and output files can be loaded and returned
+ as strings.
+
+ To be able to distinguish between input values and input file paths, the
+ ``fileOrText`` decorator requires that input and output file paths are
+ passed in as ``pathlib.Path`` objects. For example, given a function
+ like this::
+
+ @fileOrText()
+ def myfunc(infile, outfile):
+ ...
+
+ if we want to pass file paths for both ``infile`` and ``outfile``, we would
+ do this::
+
+ from pathlib import Path
+ myfunc(Path('input.txt'), Path('output.txt'))
+
+ Input values may be passed in as normal strings, e.g.::
+
+ myfunc('input data', Path('output.txt'))
+
+ Output values can be loaded as normal via the :attr:`LOAD` symbol, e.g.::
+
+ myfunc(Path('input.txt'), LOAD)
+ """
+
+ def prepIn(workdir, name, val):
+
+ infile = None
+
+ if not isinstance(val, pathlib.Path):
+ with tempfile.NamedTemporaryFile(mode='w',
+ suffix='.txt',
+ dir=workdir,
+ delete=False) as f:
+ f.write(val)
+ infile = f.name
+ return infile
+
+ def prepOut(workdir, name, val):
+ return op.join(workdir, '{}.txt'.format(name))
+
+ def load(_, path):
+ try:
+ with open(path, "r") as f:
+ return f.read()
+ except Exception: return None
+
+ def decorator(func):
+ fot = FileOrThing(func,
+ prepIn,
+ prepOut,
+ load,
+ fslpath.removeExt,
+ *args,
+ **kwargs)
+
+ def wrapper(*args, **kwargs):
+ return fot(*args, **kwargs)
+
+ return _update_wrapper(wrapper, func)
+
+ return decorator
diff --git a/bip/ext_wrappers/freesurfer.py b/bip/ext_wrappers/freesurfer.py
new file mode 100644
index 0000000..6aa8dab
--- /dev/null
+++ b/bip/ext_wrappers/freesurfer.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+#
+# freesurfer.py - Wrappers for FreeSurfer and its sub-functions
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+#
+"""This module provides wrapper functions for FreeSurfer
+
+"""
+
+
+import fsl.utils.assertions as asrt
+from fsl.utils.deprecated import deprecated
+from . import wrapperutils as wutils
+
+
+@wutils.cmdwrapper
+def recon_all(subjid, directive, **kwargs):
+ """Wrapper for the ``xfibres_gpu`` command.
+
+"""
+
+ valmap = {
+ 'FLAIRpial' : wutils.SHOW_IF_TRUE,
+ }
+
+ cmd = ['recon-all', "-"+directive]
+
+ cmd += wutils.applyArgStyle('-', valmap=valmap, **kwargs)
+
+ return cmd
diff --git a/bip/main.py b/bip/main.py
new file mode 100755
index 0000000..70da130
--- /dev/null
+++ b/bip/main.py
@@ -0,0 +1,356 @@
+#!/usr/bin/env python
+'''
+ WIN-FMRIB, Oxford University
+ $28-Nov-2022 16:00:00$
+ Version $1.0
+ Authors: Fidel Alfaro-Almagro, Stephen M. Smith, Paul McCarthy, Michiel Cottaar
+ '''
+
+import os
+import os.path
+import sys
+import logging
+import argparse
+import json
+from dataclasses import dataclass, field
+from file_tree import FileTree
+from pipe_tree import Pipeline, Ref
+import bip
+from bip.utils import setup_logging
+from bip.pipelines.struct_T1 import struct_T1
+from bip.pipelines.struct_T2_FLAIR import struct_T2_FLAIR
+from bip.pipelines.struct_swMRI import struct_swMRI
+from bip.pipelines.struct_asl import struct_asl
+from bip.pipelines.dMRI_fieldmap import dMRI_fieldmap
+from bip.pipelines.fMRI_task import fMRI_task
+from bip.pipelines.fMRI_rest import fMRI_rest
+from bip.pipelines.dMRI_diff import dMRI_diff
+
+log = logging.getLogger('bip.main')
+
+
+class MyParser(argparse.ArgumentParser):
+ def error(self, message):
+ sys.stderr.write(f'error: {message}\n')
+ self.print_help()
+ sys.exit(2)
+
+class Usage(Exception):
+ def __init__(self, msg):
+ self.msg = msg
+
+@dataclass
+class Context:
+ BB_BIN_DIR: str = bip.__path__[0]
+ FSLDIR: str = os.environ['FSLDIR']
+ gdc: str = field(init=False)
+
+ with open(BB_BIN_DIR + '/data/dMRI/autoptx/struct_list.json', 'r') as f:
+ tract_struct = json.load(f)
+
+ def __post_init__(self):
+ self.gdc = self.get_data('GDC/UKB.txt')
+
+ @property
+ def MNI(self):
+ return self.FSLDIR + '/data/standard/MNI152_T1_1mm'
+
+ @property
+ def MNI_brain_mask(self):
+ return self.FSLDIR + '/data/standard/MNI152_T1_1mm_brain_mask'
+
+ def get_standard(self, fileName):
+ return self.FSLDIR + '/data/standard/' + fileName
+
+ def get_atlas(self, fileName):
+ return self.FSLDIR + '/data/atlases/' + fileName
+
+ def get_data(self, fileName):
+ return self.BB_BIN_DIR + '/data/' + fileName
+
+ #def save_context(self, file_name):
+ # with open(file_name, "wt") as config_file:
+ # config_file.write(self.to_json())
+
+
+def parseArguments(ctx):
+
+ #########################
+ # ARGUMENT PARSING CODE #
+ #########################
+ parser = MyParser(description='BioBank Pipeline Manager V. 2.0')
+ parser.add_argument("subjectFolder", help='Subject Folder', action="store")
+ parser.add_argument("-q", "--queue", help='Queue modifier (default: normal)',
+ action="store", nargs="?", dest="queue", default="normal")
+ parser.add_argument("-n", "--normcheck", action="store_false", default=True,
+ help='Do NOT check Normalisation in structural image (default if flag not used: Check normalisation)',
+ dest="norm_check")
+ parser.add_argument("-P", "--namingPatterns", action="store", nargs="?",
+ default=ctx.get_data("config/naming_pattern_UKBB.json"),
+ help='File with the naming patterns coming from dcm2niix (default: UKB naming patterns)',
+ dest="naming_patterns")
+ parser.add_argument("-c", "--coeff", action="store", nargs="?",
+ default=ctx.get_data("GDC/UKB.txt"),
+ help='Coefficient file for the GDC (Gradient Distiortion Correction). \n' +
+ ' Options: \n' +
+ ' none--> No GDC is performed' +
+ ' <path to file> --> Uses the user-pecified gradients file' +
+ ' --> If this option is not used, the default is the UKB',
+ dest="coeff")
+ parser.add_argument("-C", "--coils_SWI", action="store", nargs="?", default=32,
+ help='Number of coils for SWI data. Default: 32. 0 means "no separate coil data"',
+ dest="coils_SWI")
+ parser.add_argument("-E", "--echoes_SWI", action="store", nargs="?", default=2,
+ help='Number of echo times for SWI data (default: 2)',
+ dest="echoes_SWI")
+ parser.add_argument("-Q", "--basic_QC_file", action="store", nargs="?",
+ default=ctx.get_data("config/ideal_config_sizes_UKBB.json"),
+ help='File with the ideal configuration of the files (default: UKB)',
+ dest="basic_QC_file")
+ parser.add_argument("-p", "--complex_phase", action="store_true", default=False,
+ help='SWI phase is complex (default: False)',
+ dest="complex_phase")
+ parser.add_argument("-i", "--inverted_PED", action="store_true", default=False,
+ help='Use if the dMRI Phase Encoding Direction is inverted (default if flag not used: False)',
+ dest="inverted_PED")
+ parser.add_argument("-b", "--b_value_shell", action="store", nargs="?",
+ default=1000,
+ help='B value for the single shell data to be used (default: 1000)',
+ dest="b_value_shell")
+ parser.add_argument("-B", "--B_files", action="store", nargs="?",
+ default="",
+ help='Name of the directory with the bval and bvec files for dMRI data.\n' +
+ 'If specified, the directory needs to have these 4 files: \n' +
+ ' - AP.bval\n ' +
+ ' - AP.bvec\n ' +
+ ' - PA.bval\n ' +
+ ' - PA.bvec\n ' +
+ 'If not set, the pipeline will use the dcm2niix generated files',
+ dest="B_files")
+ parser.add_argument("-S", "--num_shells", action="store", nargs="?", default=2,
+ help='Number of different shells (B-values) for dMRI data.',
+ dest="num_shells")
+ parser.add_argument("-X", "--exec_block", action="store", nargs="?", default=0,
+ help='Pipeline block(s) to execute: Option (default 0): \n' +
+ ' - 0: Run the whole pipeline with default parameters \n' +
+ ' - 1: Run (CPU) 1st block (up until eddy) \n' +
+ ' - 2: Run (GPU) 2nd block (eddy) \n' +
+ ' - 3: Run (CPU) 3rd block (after eddy, before bedpostX + probtrackX) \n' +
+ ' - 4: Run (GPU) 4th block (bedpostX + probtrackX) \n' +
+ ' - 5: Run (CPU) 5th block (everything after bedpostX + probtrackX) \n' +
+ ' - 20: Run (CPU) 2nd block (eddy) \n '+
+ ' - 40: Run (CPU) 4th block (bedpostX + probtrackX) \n ' +
+ ' - 123: Run (CPU) 1st, 2nd, & 3rd block (up until bedpostX) \n ',
+ dest="exec_block")
+
+ argsa = parser.parse_args()
+
+ # Subject argument
+ subject = argsa.subjectFolder
+ subject = subject.strip()
+ if subject[-1] =='/':
+ subject = subject[0:len(subject)-1]
+
+ ctx.subject = subject
+ ctx.argsa = argsa
+
+ # Setting up logging
+ # Parsing coefficient file argument
+ if not argsa.coeff:
+ coeff = "skyra"
+ else:
+ coeff = argsa.coeff
+ if coeff == "":
+ coeff = "skyra"
+ if coeff not in ["skyra", "prisma", "none"]:
+ if not os.path.exists(coeff):
+ log.error("ERROR: Subject cannot be run. Incorrect GDC file specified: " +
+ coeff)
+ sys.exit(1)
+
+ # Deciding Grafient coefficients file
+ if coeff == "skyra":
+ coeff = ctx.get_data("GDC/bb_GDC_coeff_skyra.grad")
+ elif coeff == "prisma":
+ coeff = ctx.get_data("GDC/bb_GDC_coeff_prisma.grad")
+ if not os.path.exists(coeff):
+ coeff = "none"
+
+ # Parsing number of SWI coils argument
+ if not argsa.coils_SWI:
+ coils_SWI = 32
+ elif argsa.coils_SWI == "":
+ coils_SWI = 32
+ else:
+ try:
+ coils_SWI = int(argsa.coils_SWI)
+ except ValueError:
+ log.warning("ERROR in the specified number of SWI coils: " +
+ coils_SWI + '. Will use default: 32')
+ coils_SWI = 32
+
+ # Parsing number of SWI echo times argument
+ if not argsa.echoes_SWI:
+ echoes_SWI = 2
+ elif argsa.echoes_SWI == "":
+ echoes_SWI = 2
+ else:
+ try:
+ echoes_SWI = int(argsa.echoes_SWI)
+ except ValueError:
+ log.warning("ERROR in the specified number of shells: " +
+ echoes_SWI + '. Will use default: 2')
+ echoes_SWI = 2
+
+ # Parsing number of dMRI shells argument
+ if not argsa.num_shells:
+ num_shells = 2
+ elif argsa.num_shells == "":
+ num_shells = 2
+ else:
+ try:
+ num_shells = int(argsa.num_shells)
+ except ValueError:
+ log.warning("ERROR in the specified number of SWI echo times: " +
+ num_shells + '. Will use default: 2')
+ num_shells = 2
+
+
+ # Parsing single-shell dMRI B-value argument
+ if not argsa.b_value_shell:
+ b_value_shell = 1000
+ elif argsa.b_value_shell == "":
+ b_value_shell = 1000
+ else:
+ try:
+ b_value_shell = int(argsa.b_value_shell)
+ except ValueError:
+ log.warning("ERROR in the specified b value for single-shell dMRI: " +
+ b_value_shell + '. Will use default: 1000')
+ b_value_shell = 1000
+
+ # Parsing queue argument
+ if argsa.queue == []:
+ queue = "normal"
+ elif argsa.queue == 'normal':
+ queue = "normal"
+ else:
+ queue = argsa.queue.split(",")
+
+ # Check normalisation argument
+ norm_check = argsa.norm_check
+
+ # Check complex phase
+ complex_phase = argsa.complex_phase
+
+ # Check inverted Phase Encoding Direction for dMRI
+ inverted_PED = argsa.inverted_PED
+
+ # Parsing naming pattern argument
+ naming_patterns = argsa.naming_patterns
+ naming_patterns = naming_patterns.strip()
+
+ if not os.path.exists(naming_patterns):
+ log.error("ERROR: Subject cannot be run. Incorrect naming pattern file specified: " +
+ naming_patterns)
+ sys.exit(1)
+ ctx.naming_patterns = naming_patterns
+
+ # Parsing B files argument
+ B_files = argsa.B_files
+ B_files = B_files.strip()
+
+ if B_files != "":
+ if not os.path.exists(B_files):
+ log.error("ERROR: Subject cannot be run. Incorrect B-files directory specified: " +
+ B_files)
+ sys.exit(1)
+ else:
+ for fil in ["AP.bval", "AP.bvec" , "PA.bval", "PA.bvec"]:
+ if not os.path.exists(B_files + '/' + fil ):
+ log.error("ERROR: Subject cannot be run. Non-existent B-file: " +
+ B_files + '/' + fil )
+ sys.exit(1)
+ if not os.path.isabs(B_files):
+ B_files = os.path.abspath(B_files)
+
+ # Parsing basic QC file argument
+ basic_QC_file = argsa.basic_QC_file
+ basic_QC_file = basic_QC_file.strip()
+ if not os.path.exists(basic_QC_file):
+ log.error("ERROR: Subject cannot be run. Incorrect basic QC file specified: " +
+ basic_QC_file)
+ sys.exit(1)
+
+ # Parsing execute block argument
+ exec_block = argsa.exec_block
+ if not argsa.exec_block:
+ exec_block = 0
+ elif argsa.exec_block == "":
+ exec_block = 0
+ else:
+ try:
+ exec_block = int(argsa.exec_block)
+ allowed_values = [0, 1, 2, 3, 4, 5, 20, 40, 123]
+
+ if exec_block not in allowed_values:
+ log.warning("ERROR in the eXecute block: " + exec_block +
+ ': Value not allowed. \nAllowed values: ' +
+ str(allowed_values) + '\nWill use default: 0')
+ exec_block = 0
+
+ except ValueError:
+ log.warning("ERROR in the eXecute block: " +
+ exec_block + ': Will use default: 0')
+ exec_block = 0
+
+ ################################
+ # END OF ARGUMENT PARSING CODE #
+ ################################
+
+def main():
+
+ #ctx = Context(gdc=)
+ ctx = Context()
+
+ parseArguments(ctx)
+
+ setup_logging(ctx.subject + '/logs/main.log')
+
+ tree = FileTree.read(ctx.get_data('FileTree'),
+ subject=ctx.subject,
+ autoptx_tract=list(ctx.tract_struct.keys()),
+ netmats_dim=["25", "100"])
+
+ # add submit parameters here that should
+ # be applied to all jobs in the pipeline
+ pipe = Pipeline(default_submit=dict(logdir=Ref("logs_dir"),
+ export_vars=list(os.environ.keys())))
+
+ #pipe = Pipeline(default_submit=dict(logdir=Ref("logs_dir")))
+ #ctx.save_context(tree.get('config_file'))
+
+ # This list will be filled with all desired outputs
+ targets = []
+
+ pipe, targets = struct_T1.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = struct_T2_FLAIR.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = struct_swMRI.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = struct_asl.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = dMRI_fieldmap.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = fMRI_task.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = fMRI_rest.add_to_pipeline(ctx, pipe, tree, targets)
+ pipe, targets = dMRI_diff.add_to_pipeline(ctx, pipe, tree, targets)
+
+ # The skip-missing flag deals with cases where the subject is missing
+ # the data of some modalities. For more details, check:
+ # https://open.win.ox.ac.uk/pages/ndcn0236/pipe-tree/tutorial.html#dealing-with-missing-data
+ jobs = pipe.generate_jobs(tree).filter(targets, skip_missing=True)
+
+ jobs.report()
+
+ jobs.run("submit")
+ #jobs.run("local")
+
+if __name__ == "__main__":
+ main()
diff --git a/bip/pipelines/__init__.py b/bip/pipelines/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bip/pipelines/dMRI_diff/__init__.py b/bip/pipelines/dMRI_diff/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bip/pipelines/dMRI_diff/dMRI_diff.py b/bip/pipelines/dMRI_diff/dMRI_diff.py
new file mode 100755
index 0000000..4863366
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/dMRI_diff.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+#
+# dMRI_diff.py - Pipeline with the dfMRI processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.dMRI_diff import diff_eddy
+from bip.pipelines.dMRI_diff import diff_dtifit
+from bip.pipelines.dMRI_diff import diff_noddi
+from bip.pipelines.dMRI_diff import diff_tbss
+from bip.pipelines.dMRI_diff import diff_bedpostx
+from bip.pipelines.dMRI_diff import diff_autoptx
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_dMRI_fieldmap', outdir=logs_dir):
+
+ # TODO: Make this configurable
+ cuda_dict_1 = dict(queue="short.qg@@win-uk-biobank-gpu",
+ coprocessor="cuda",
+ coprocessor_class="P",
+ coprocessor_toolkit="8.0")
+ cuda_dict_2 = dict(queue="short.qg@@win-uk-biobank-gpu",
+ coprocessor="cuda",
+ coprocessor_class="P",
+ coprocessor_toolkit="6.5")
+ #pipe(diff_eddy.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ pipe(diff_eddy.run, submit=cuda_dict_1, kwargs={'ctx' : ctx})
+ targets.append('eddy_data')
+ pipe(diff_dtifit.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('FA')
+ pipe(diff_noddi.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('ISOVF')
+ pipe(diff_tbss.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('JHUrois_FA')
+ pipe(diff_bedpostx.run, submit=cuda_dict_2, kwargs={'ctx' : ctx})
+ targets.append('bedpostx_eye')
+ pipe(diff_autoptx.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('tractsNorm')
+
+ return pipe, targets
diff --git a/bip/pipelines/dMRI_diff/diff_autoptx.py b/bip/pipelines/dMRI_diff/diff_autoptx.py
new file mode 100755
index 0000000..1255756
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_autoptx.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+#
+# diff_autoptx.py - Sub-pipeline with FSL's autoPtx processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import random
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref, Var
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ autoptx_tract: Var,
+ bedpostx_eye: In,
+ TBSS_FA_to_MNI_warp: In,
+ TBSS_MNI_to_dti_FA_warp: In,
+ bedpostx_nodif_brain_mask: In,
+ logs_dir: Ref,
+ aptx_stop: Ref,
+ bedpostx_merged: Ref,
+ aptx_tracts_sub_dir: Ref,
+ aptx_tractsinv_sub_dir: Ref,
+ density_inv: Ref,
+ waytotal_inv: Ref,
+ fwdensity: Ref,
+ aptx_seed: Out,
+ aptx_target: Out,
+ aptx_exclude: Out,
+ density: Out,
+ waytotal: Out,
+ tractsNorm: Out):
+
+ with redirect_logging('diff_autoptx_' + autoptx_tract.value,
+ outdir=logs_dir):
+
+ t_name = autoptx_tract.value
+ #TODO: Allow for customization for this multiplication factor
+ t_seeds = int(ctx.tract_struct[t_name]["num_seeds"] * 300)
+
+ orig_tract_dir = ctx.get_data('dMRI/autoptx/protocols/' + t_name + '/')
+
+ # Does the protocol defines a second
+ # run with inverted seed / target masks?
+ if os.path.exists(orig_tract_dir + 'invert'):
+ symtrack=True
+ if os.path.exists(waytotal):
+ os.remove(waytotal)
+ else:
+ symtrack=False
+
+ # Copy the seed, target and exclude files for the tract
+ copyfile(orig_tract_dir + 'seed.nii.gz', aptx_seed)
+ copyfile(orig_tract_dir + 'target.nii.gz', aptx_target)
+ copyfile(orig_tract_dir + 'exclude.nii.gz', aptx_exclude)
+
+ if os.path.exists(waytotal):
+ os.remove(waytotal)
+
+ # Building arguments dictionary for probtrackx
+ kwargs = {
+ 'samples' : bedpostx_merged,
+ 'mask' : bedpostx_nodif_brain_mask,
+ 'out' : 'density',
+ 'nsamples' : t_seeds,
+ 'avoid' : aptx_exclude,
+ 'opd' : True,
+ 'loopcheck' : True,
+ 'forcedir' : True,
+ 'sampvox' : True,
+ 'xfm' : TBSS_MNI_to_dti_FA_warp,
+ 'invxfm' : TBSS_FA_to_MNI_warp,
+ 'rseed' : int(random.randrange(32767))
+ }
+
+ # Is there a stop criterion defined
+ # in the protocol for this struct?
+ if os.path.exists(orig_tract_dir + 'stop.nii.gz'):
+ kwargs['stop'] = aptx_stop
+ copyfile(orig_tract_dir + 'stop.nii.gz', aptx_stop)
+
+ # Process structure
+ wrappers.probtrackx(seed=aptx_seed,
+ waypoints=aptx_target,
+ dir=aptx_tracts_sub_dir,
+ **kwargs)
+
+ with open(waytotal, 'r', encoding="utf-8") as f:
+ way = int(f.read())
+
+ # Calculate inverse tractography for this tract and
+ # merge runs for forward and inverted tractographies
+ if symtrack:
+ wrappers.probtrackx(seed=aptx_target,
+ waypoints=aptx_seed,
+ dir=aptx_tractsinv_sub_dir,
+ **kwargs)
+
+ copyfile(density, fwdensity)
+ wrappers.fslmaths(density).add(density_inv).run(density)
+
+ with open(waytotal, 'r', encoding="utf-8") as f:
+ way1 = int(f.read())
+ with open(waytotal_inv, 'r', encoding="utf-8") as f:
+ way2 = int(f.read())
+ if os.path.exists(waytotal):
+ os.remove(waytotal)
+ with open(waytotal, 'wt', encoding="utf-8") as f:
+ way = way1 + way2
+ f.write(f'{way}\n')
+
+ wrappers.fslmaths(density).div(way).range().run(tractsNorm)
diff --git a/bip/pipelines/dMRI_diff/diff_bedpostx.py b/bip/pipelines/dMRI_diff/diff_bedpostx.py
new file mode 100755
index 0000000..d8e5acd
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_bedpostx.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python
+#
+# diff_bedpostx.py - Sub-pipeline with FSL's bedpostx processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ AP_bval: In,
+ AP_bvec: In,
+ eddy_data_ud: In,
+ eddy_nodif: In,
+ eddy_nodif_brain_mask: In,
+ logs_dir: Ref,
+ eddy_dir: Ref,
+ bedpostx_dir: Ref,
+ bedpostx_log_part: Ref,
+ bedpostx_data_0: Ref,
+ bedpostx_bvals: Out,
+ bedpostx_bvecs: Out,
+ bedpostx_nodif_brain: Out,
+ bedpostx_nodif_brain_mask: Out,
+ bedpostx_nvox: Out,
+ bedpostx_eye: Out,
+ bp_logs_gpu_dir: Out):
+
+ with redirect_logging('diff_bedpostx', outdir=logs_dir):
+
+ w = os.getcwd() + "/"
+
+ # Copy of bvals and bvecs
+ copyfile(src=AP_bval, dst=bedpostx_bvals)
+ copyfile(src=AP_bvec, dst=bedpostx_bvecs)
+ copyfile(src=eddy_nodif_brain_mask, dst=bedpostx_nodif_brain_mask)
+
+ wrappers.fslmaths(eddy_nodif).mas(eddy_nodif_brain_mask).run(bedpostx_nodif_brain)
+
+ nvox=wrappers.fslstats(bedpostx_nodif_brain_mask).V.run()[0]
+ with open(bedpostx_nvox, 'wt', encoding="utf-8") as f:
+ f.write(str(nvox))
+
+ wrappers.split_parts_gpu(Datafile=w + eddy_data_ud,
+ Maskfile=w + eddy_nodif_brain_mask,
+ Bvalsfile=w + bedpostx_bvals,
+ Bvecsfile=w + bedpostx_bvecs,
+ TotalNumParts="1",
+ OutputDirectory=w + bedpostx_dir)
+
+ wrappers.xfibres_gpu(data=w + bedpostx_data_0,
+ mask=w + bedpostx_nodif_brain_mask,
+ bvals=w + bedpostx_bvals, bvecs=w + bedpostx_bvecs,
+ forcedir=True, logdir=w + bedpostx_log_part,
+ nfibres="3", fudge="1", burnin="3000",
+ njumps="1250", sampleevery="25", model="2",
+ cnonlinear=True, subjdir=w + eddy_dir,
+ idpart="0", nparts="1",
+ numvoxels=str(nvox))
+
+ wrappers.bedpostx_postproc_gpu(data=w + bedpostx_data_0,
+ mask=w + bedpostx_nodif_brain_mask,
+ bvals=w + bedpostx_bvals,
+ bvecs=w + bedpostx_bvecs, forcedir=True,
+ logdir=w + bedpostx_log_part, nf="3",
+ fudge="1", bi="3000", nj="1250", se="25",
+ model="2", cnonlinear=True,
+ TotalNumVoxels=str(nvox),
+ SubjectDir=w + eddy_dir,
+ TotalNumParts="1", bindir=ctx.FSLDIR)
+
+ if os.path.exists(bedpostx_data_0):
+ os.remove(bedpostx_data_0)
diff --git a/bip/pipelines/dMRI_diff/diff_dtifit.py b/bip/pipelines/dMRI_diff/diff_dtifit.py
new file mode 100755
index 0000000..1b7b8c6
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_dtifit.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+#
+# diff_dtifit.py - Sub-pipeline with FSL's dtifit processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import logging
+from shutil import copyfile
+import numpy as np
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ eddy_data: In,
+ eddy_bvals: In,
+ eddy_bvecs: In,
+ eddy_nodif_brain_mask_ud: In,
+ logs_dir: Ref,
+ eddy_data_GDC: Ref,
+ dtifit_output_prefix: Ref,
+ eddy_data_ud: Out,
+ eddy_data_ud_warp: Out,
+ eddy_data_ud_1_shell: Out,
+ eddy_data_ud_1_shell_bval: Out,
+ eddy_data_ud_1_shell_bvec: Out,
+ FA: Out):
+
+ with redirect_logging('diff_dtifit', outdir=logs_dir):
+
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=eddy_data_GDC,
+ infile=eddy_data,
+ outfile=eddy_data_ud,
+ owarp=eddy_data_ud_warp,
+ gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=True)
+ else:
+ copyfile(src=eddy_data, dst=eddy_data_ud)
+
+
+ #Correct input for dtifit using one shell
+ list_vals = []
+ b_vals = np.loadtxt(eddy_bvals)
+ b_vecs = np.loadtxt(eddy_bvecs)
+ lim_file = ctx.get_data('dMRI/b0_threshold.txt')
+
+ lim = int(np.loadtxt(lim_file))
+
+ # TODO: This can be modifiable by the user
+ b_value_shell_to_keep = 1000
+
+ for count, b_val in enumerate(b_vals):
+ # These are the b0 images: They are automatically included
+ if b_val < lim:
+ list_vals.append(count)
+ else:
+ if abs(b_val - b_value_shell_to_keep) < lim:
+ list_vals.append(count)
+
+ new_b_vals = b_vals[list_vals]
+ new_b_vecs = b_vecs[:,list_vals]
+
+ np.savetxt(eddy_data_ud_1_shell_bval, new_b_vals, newline=" ", fmt='%.1f')
+ np.savetxt(eddy_data_ud_1_shell_bvec, new_b_vecs, fmt='%.6f')
+
+ wrappers.fslselectvols(src=eddy_data_ud, out=eddy_data_ud_1_shell,
+ vols=list_vals)
+
+ wrappers.dtifit(data = eddy_data_ud_1_shell,
+ bvecs = eddy_data_ud_1_shell_bvec,
+ bvals = eddy_data_ud_1_shell_bval,
+ mask = eddy_nodif_brain_mask_ud,
+ out = dtifit_output_prefix,
+ save_tensor = True)
diff --git a/bip/pipelines/dMRI_diff/diff_eddy.py b/bip/pipelines/dMRI_diff/diff_eddy.py
new file mode 100755
index 0000000..9c710b2
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_eddy.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+#
+# diff_eddy.py - Sub-pipeline with FSL's eddy processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import logging
+from shutil import copyfile
+import nibabel as nib
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ AP: In,
+ AP_bval: In,
+ AP_bvec: In,
+ AP_best_index: In,
+ fieldmap_iout_mean: In,
+ fieldmap_mask: In,
+ fieldmap_mask_ud: In,
+ acqparams: In,
+ logs_dir: Ref,
+ eddy_data_prefix: Ref,
+ fieldmap_out_prefix: Ref,
+ eddy_AP: Out,
+ eddy_nodif: Out,
+ eddy_nodif_brain_mask: Out,
+ eddy_nodif_brain_mask_ud: Out,
+ eddy_bvals: Out,
+ eddy_bvecs: Out,
+ eddy_index: Out,
+ eddy_data: Out):
+
+ with redirect_logging('diff_eddy', outdir=logs_dir):
+
+ # Creates links
+ # TODO: These links are NOT relative. This may cause future problems.
+ if not os.path.exists(eddy_AP):
+ os.symlink(src="../../../" + AP, dst=eddy_AP)
+ if not os.path.exists(eddy_nodif):
+ os.symlink(src="../../../" + fieldmap_iout_mean, dst=eddy_nodif)
+ if not os.path.exists(eddy_nodif_brain_mask):
+ os.symlink(src="../../../" + fieldmap_mask, dst=eddy_nodif_brain_mask)
+ if not os.path.exists(eddy_nodif_brain_mask_ud):
+ os.symlink(src="../../../" + fieldmap_mask_ud, dst=eddy_nodif_brain_mask_ud)
+
+ # Generation of FSF file
+ copyfile(src=AP_bval, dst=eddy_bvals)
+ copyfile(src=AP_bvec, dst=eddy_bvecs)
+
+ AP_img = nib.load(eddy_AP)
+ AP_numvol = AP_img.header['dim'][4]
+
+ indices = ("1 " * AP_numvol).strip() + "\n"
+
+ with open(AP_best_index, 'r', encoding="utf-8") as f:
+ AP_index = int(f.read())
+
+ with open(eddy_index, 'wt', encoding="utf-8") as f:
+ f.write(indices)
+
+ wrappers.eddy(imain=eddy_AP,
+ mask=eddy_nodif_brain_mask,
+ topup=fieldmap_out_prefix,
+ acqp=acqparams,
+ index=eddy_index,
+ bvecs=eddy_bvecs,
+ bvals=eddy_bvals,
+ out=eddy_data_prefix,
+ ref_scan_no=AP_index,
+ flm="quadratic",
+ resamp="jac",
+ slm="linear",
+ niter=8,
+ fwhm=[10,8,4,2,0,0,0,0],
+ ff=10,
+ sep_offs_move=True,
+ nvoxhp=1000,
+ very_verbose=True,
+ repol=True,
+ rms=True)
diff --git a/bip/pipelines/dMRI_diff/diff_noddi.py b/bip/pipelines/dMRI_diff/diff_noddi.py
new file mode 100755
index 0000000..a7252ec
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_noddi.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+#
+# diff_noddi.py - Sub-pipeline with AMICO processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import os
+import logging
+from shutil import move, rmtree
+import zipfile
+import gzip
+import shutil
+import amico
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref
+
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ eddy_data_ud: In,
+ eddy_nodif_brain_mask_ud: In,
+ eddy_bvals: In,
+ eddy_bvecs: In,
+ logs_dir: Ref,
+ tmp_dir: Ref,
+ NODDI_dir: Ref,
+ AMICO_dir: Ref,
+ NODDI_kernels_dir: Ref,
+ NODDI_scheme: Out,
+ ICVF: Out,
+ ISOVF: Out,
+ OD: Out,
+ NODDI_dir_file: Out):
+
+ with redirect_logging('diff_noddi', outdir=logs_dir),\
+ tempdir(tmp_dir):
+ amtmp = tmp_dir + '/amtmp.nii'
+ amtmp_mask = tmp_dir + '/amtmp_mask.nii'
+
+ # In case the kernels were already calculated for this study:
+ # TODO: Verify that this is regular UKB data.
+ # Otherwise, this should not be done
+ if not os.path.exists('./kernels/'):
+ if os.path.exists(ctx.get_data('dMRI/kernels.zip')):
+ with zipfile.ZipFile(ctx.get_data('dMRI/kernels.zip'), 'r') as zip_ref:
+ zip_ref.extractall("./")
+
+ with gzip.open(eddy_data_ud, 'rb') as f_in:
+ with open(amtmp, 'wb') as f_out:
+ shutil.copyfileobj(f_in, f_out)
+
+ with gzip.open(eddy_nodif_brain_mask_ud, 'rb') as f_in:
+ with open(amtmp_mask , 'wb') as f_out:
+ shutil.copyfileobj(f_in, f_out)
+
+ amico.setup()
+ amico.util.fsl2scheme(bvalsFilename=eddy_bvals,
+ bvecsFilename=eddy_bvecs,
+ schemeFilename=NODDI_scheme,
+ bStep=50)
+
+ ae = amico.Evaluation("./", "./", output_path=NODDI_dir)
+ ae.load_data(amtmp, NODDI_scheme, mask_filename=amtmp_mask,
+ b0_min_signal=1e-4)
+ ae.set_model("NODDI")
+ ae.generate_kernels(ndirs=500,regenerate=True)
+ ae.load_kernels()
+ ae.fit()
+ ae.save_results()
+
+ files = [ICVF, ISOVF, OD, NODDI_dir_file]
+
+ # Move files to proper place
+ for file_n in files:
+ old_name = file_n.replace("/NODDI_","/FIT_")
+ move(old_name, file_n)
+
+ # Delete created folders
+ for dir_to_delete in [AMICO_dir, NODDI_kernels_dir]:
+ if os.path.exists(dir_to_delete):
+ rmtree(dir_to_delete)
diff --git a/bip/pipelines/dMRI_diff/diff_tbss.py b/bip/pipelines/dMRI_diff/diff_tbss.py
new file mode 100755
index 0000000..f490e92
--- /dev/null
+++ b/bip/pipelines/dMRI_diff/diff_tbss.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+#
+# diff_tbss.py - Sub-pipeline with FSL's TBSS processing of the dMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613,C0206
+#
+
+import os
+import logging
+from shutil import copyfile
+import nibabel as nib
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ FA: In,
+ ISOVF: In(optional=True),
+ logs_dir: Ref,
+ tmp_dir: Ref,
+ TBSS_MNI_to_dti_FA_warp_msf: Ref,
+ dtifit_prefix: Ref,
+ NODDI_prefix: Ref,
+ TBSS_prefix: Ref,
+ JHUrois_prefix: Ref,
+ TBSS_FA_to_MNI_int: Ref,
+ TBSS_FA_dir_FA: Out,
+ TBSS_FA_dir_FA_mask: Out,
+ TBSS_FA: Out,
+ TBSS_MNI: Out,
+ TBSS_FA_to_MNI_affine_mat: Out,
+ TBSS_FA_to_MNI_warp: Out,
+ TBSS_FA_to_MNI_int_txt: Out,
+ TBSS_log_1: Out,
+ TBSS_log_2: Out,
+ TBSS_log_3: Out,
+ TBSS_dti_FA_to_MNI: Out,
+ TBSS_MNI_to_dti_FA_warp: Out,
+ TBSS_all_FA: Out,
+ TBSS_mean_FA_mask: Out,
+ TBSS_mean_FA: Out,
+ TBSS_mean_FA_skeleton: Out,
+ TBSS_mean_FA_skeleton_mask: Out,
+ TBSS_all_FA_skeletonised: Out,
+ JHUrois_FA: Out):
+
+ with redirect_logging('diff_tbss', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ TBSS_FA_tmp = tmp_dir + '/TBSS_FA_tmp.nii.gz'
+ TBSS_FA_to_MNI_warp_s1 = tmp_dir + '/FA_to_MNI_warp_s1.nii.gz'
+ TBSS_FA_to_MNI_warp_s2 = tmp_dir + '/FA_to_MNI_warp_s2.nii.gz'
+
+ # Creates links
+ # TODO: These links are NOT relative. This may cause future problems.
+ if not os.path.exists(TBSS_FA):
+ os.symlink(src="../../../" + FA, dst=TBSS_FA)
+ if not os.path.exists(TBSS_MNI):
+ os.symlink(src=ctx.get_standard('FMRIB58_FA_1mm.nii.gz'), dst=TBSS_MNI)
+ FA_img = nib.load(TBSS_FA)
+ x, y, z = FA_img.header['dim'][1:4]
+
+ # erode a little and zero end slices
+ wrappers.fslmaths(FA).min(1).ero().roi(1,x,1,y,1,z,0,1).run(TBSS_FA_dir_FA)
+
+ # create mask (for use in FLIRT & FNIRT)
+ wrappers.fslmaths(TBSS_FA_dir_FA).bin().run(TBSS_FA_dir_FA_mask)
+ #TODO: investigate how to add the -odt=char option
+ wrappers.fslmaths(TBSS_FA_dir_FA_mask).dilD(2).sub(1).abs().add(TBSS_FA_dir_FA_mask).run(TBSS_FA_dir_FA_mask)
+
+ if not os.path.exists(TBSS_MNI_to_dti_FA_warp_msf):
+ #New Optimal Registration
+ wrappers.flirt(ref=TBSS_MNI,
+ src=TBSS_FA_dir_FA,
+ inweight=TBSS_FA_dir_FA_mask,
+ omat=TBSS_FA_to_MNI_affine_mat)
+
+ # perform FNIRT cascade of registrations
+ wc1 = ctx.get_data('dMRI/dMRI_reg_optimal_parameters/oxford_s1.cnf')
+ wc2 = ctx.get_data('dMRI/dMRI_reg_optimal_parameters/oxford_s2.cnf')
+ wc3 = ctx.get_data('dMRI/dMRI_reg_optimal_parameters/oxford_s3.cnf')
+
+ wrappers.fnirt(ref=TBSS_MNI, src=TBSS_FA_dir_FA,
+ cout=TBSS_FA_to_MNI_warp_s1, config=wc1,
+ aff=TBSS_FA_to_MNI_affine_mat,
+ intout=TBSS_FA_to_MNI_int, logout=TBSS_log_1)
+ wrappers.fnirt(ref=TBSS_MNI, src=TBSS_FA_dir_FA,
+ cout=TBSS_FA_to_MNI_warp_s2, config=wc2,
+ inwarp=TBSS_FA_to_MNI_warp_s1,
+ intin=TBSS_FA_to_MNI_int_txt, logout=TBSS_log_2)
+ wrappers.fnirt(ref=TBSS_MNI, src=TBSS_FA_dir_FA,
+ cout=TBSS_FA_to_MNI_warp, config=wc3,
+ iout=TBSS_dti_FA_to_MNI,
+ inwarp=TBSS_FA_to_MNI_warp_s2,
+ intin=TBSS_FA_to_MNI_int_txt, logout=TBSS_log_3)
+ wrappers.invwarp(ref=TBSS_FA_dir_FA, warp=TBSS_FA_to_MNI_warp,
+ out=TBSS_MNI_to_dti_FA_warp)
+
+
+ # now estimate the mean deformation
+ wrappers.fslmaths(TBSS_FA_to_MNI_warp).sqr().Tmean().run(TBSS_FA_tmp)
+ mean = wrappers.fslstats(TBSS_FA_tmp).M.run()
+ median = wrappers.fslstats(TBSS_FA_tmp).P(50).run()
+
+ with open(TBSS_MNI_to_dti_FA_warp_msf, 'wt', encoding="utf-8") as f:
+ f.write(f'{mean} {median}')
+
+ wrappers.applywarp(src=TBSS_FA_dir_FA, out=TBSS_dti_FA_to_MNI,
+ ref=TBSS_MNI, warp=TBSS_FA_to_MNI_warp,
+ rel=True, interp='trilinear')
+
+ copyfile(src=TBSS_dti_FA_to_MNI, dst=TBSS_all_FA)
+
+ # Create mean FA
+ skel = ctx.get_standard('FMRIB58_FA-skeleton_1mm.nii.gz')
+ wrappers.fslmaths(TBSS_all_FA).bin().mul(TBSS_MNI).bin().run(TBSS_mean_FA_mask)
+ wrappers.fslmaths(TBSS_all_FA).mas(TBSS_mean_FA_mask).run(TBSS_all_FA)
+ wrappers.fslmaths(TBSS_MNI).mas(TBSS_mean_FA_mask).run(TBSS_mean_FA)
+ wrappers.fslmaths(skel).mas(TBSS_mean_FA_mask).run(TBSS_mean_FA_skeleton)
+
+ # Create Skeleton mask
+ # TODO: This threshold could be configurable by user
+ thresh = 2000
+ wrappers.fslmaths(TBSS_mean_FA_skeleton).thr(thresh).bin().run(TBSS_mean_FA_skeleton_mask)
+ wrappers.fslmaths(TBSS_all_FA).mas(TBSS_mean_FA_skeleton_mask).run(TBSS_all_FA_skeletonised)
+
+ atlas = ctx.get_atlas('JHU/JHU-ICBM-labels-1mm.nii.gz')
+ mean = wrappers.fslstats(TBSS_all_FA_skeletonised, K=atlas).M.run()
+
+ with open(JHUrois_FA, 'wt', encoding="utf-8") as f:
+ f.write(f'{mean}')
+
+ # Applying to the outputs of both dtifit and noddi
+ dict_suffix={dtifit_prefix: ['L1', 'L2', 'L3', 'MO', 'MD'],
+ NODDI_prefix: ['ICVF', 'OD', 'ISOVF']}
+
+ for prefix in dict_suffix:
+
+ for suffix in dict_suffix[prefix]:
+
+ d_input = prefix + suffix + ".nii.gz"
+
+ if os.path.exists(d_input):
+ d_output = TBSS_prefix + suffix +".nii.gz"
+ d_output_skel = TBSS_prefix + suffix +"_skeletonised.nii.gz"
+ d_output_txt = JHUrois_prefix + suffix + '.txt'
+ wrappers.applywarp(src=d_input, out=d_output, ref=TBSS_MNI,
+ warp=TBSS_FA_to_MNI_warp, rel=True,
+ interp='trilinear')
+ wrappers.fslmaths(d_output).mas(TBSS_mean_FA_skeleton_mask).run(d_output_skel)
+ mean = wrappers.fslstats( d_output_skel, K=atlas).M.run()
+
+ with open(d_output_txt, 'wt', encoding="utf-8") as f:
+ f.write(f'{mean}')
diff --git a/bip/pipelines/dMRI_fieldmap/__init__.py b/bip/pipelines/dMRI_fieldmap/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bip/pipelines/dMRI_fieldmap/dMRI_fieldmap.py b/bip/pipelines/dMRI_fieldmap/dMRI_fieldmap.py
new file mode 100644
index 0000000..d320f01
--- /dev/null
+++ b/bip/pipelines/dMRI_fieldmap/dMRI_fieldmap.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+#
+# dMRI_fieldmap.py - Pipeline with the fieldmap processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.dMRI_fieldmap import fieldmap_pre_topup
+from bip.pipelines.dMRI_fieldmap import fieldmap_topup
+from bip.pipelines.dMRI_fieldmap import fieldmap_post_topup
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_dMRI_fieldmap', outdir=logs_dir):
+ pipe(fieldmap_pre_topup.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('total_B0_PA')
+ pipe(fieldmap_topup.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('fieldmap_fout')
+ pipe(fieldmap_post_topup.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('fieldmap_mask')
+
+ return pipe, targets
diff --git a/bip/pipelines/dMRI_fieldmap/fieldmap_post_topup.py b/bip/pipelines/dMRI_fieldmap/fieldmap_post_topup.py
new file mode 100755
index 0000000..4e1bfdb
--- /dev/null
+++ b/bip/pipelines/dMRI_fieldmap/fieldmap_post_topup.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+#
+# fieldmap_post_topup.py - Sub-pipeline with the fieldmap processing after topup
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from shutil import copyfile
+import numpy as np
+import nibabel as nib
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ T1_brain_mask: In,
+ T1_fast_WM_mask: In,
+ T1_brain_mask_dil: Out,
+ B0_AP: In,
+ B0_PA: In,
+ acqparams: In,
+ logs_dir: Ref,
+ tmp_dir: Ref,
+ fieldmap_out_prefix: Ref,
+ fieldmap_GDC: Ref,
+ fieldmap_fout: Ref,
+ T1_to_fieldmap_iout_mat: Out,
+ fieldmap_iout: Out,
+ fieldmap_iout_mean: Out,
+ fieldmap_iout_mean_ud: Out,
+ fieldmap_iout_mean_ud_warp: Out,
+ fieldmap_iout_to_T1: Out,
+ fieldmap_fout_to_T1: Out,
+ fieldmap_fout_to_T1_brain: Out,
+ fieldmap_fout_to_T1_brain_rad: Out,
+ fieldmap_iout_to_T1_mat: Out,
+ fieldmap_mask: Out,
+ fieldmap_mask_ud: Out,
+ fieldmap_iout_mean_ud_inv_warp: Out):
+
+ with redirect_logging('fieldmap_post_topup', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ B0_AP_corr_tmp = tmp_dir + '/B0_AP_corr_tmp.nii.gz'
+ B0_PA_corr_tmp = tmp_dir + '/B0_PA_corr_tmp.nii.gz'
+ B0_AP_fixed_tmp = tmp_dir + '/B0_AP_fixed_tmp.nii.gz'
+ B0_PA_fixed_tmp = tmp_dir + '/B0_PA_fixed_tmp.nii.gz'
+ fieldmap_tmp = tmp_dir + '/fieldmap_tmp.nii.gz'
+
+ #Get corrected B=0 for AP and PA and average them avoiding zero values
+ wrappers.applytopup(imain=B0_AP, datain=acqparams, index=1,
+ topup=fieldmap_out_prefix,
+ out=B0_AP_corr_tmp, method="jac")
+ wrappers.applytopup(imain=B0_PA, datain=acqparams, index=2,
+ topup=fieldmap_out_prefix,
+ out=B0_PA_corr_tmp, method="jac")
+
+ B0_AP_corr_tmp_img = nib.load(B0_AP_corr_tmp)
+ B0_PA_corr_tmp_img = nib.load(B0_PA_corr_tmp)
+
+ B0_AP_corr_tmp_imgf = B0_AP_corr_tmp_img.get_fdata()
+ B0_PA_corr_tmp_imgf = B0_PA_corr_tmp_img.get_fdata()
+
+ #Get a list of indices the zero-valued voxels for AP and PA
+ x1,y1,z1 = np.where(B0_AP_corr_tmp_imgf==0)
+ x2,y2,z2 = np.where(B0_PA_corr_tmp_imgf==0)
+
+ #For AP voxels with 0 value,, get the values in PA (And viceversa)
+ B0_AP_fixed_tmp_imgf = B0_AP_corr_tmp_imgf
+ B0_PA_fixed_tmp_imgf = B0_PA_corr_tmp_imgf
+ B0_AP_fixed_tmp_imgf[x1,y1,z1] = B0_PA_corr_tmp_imgf[x1,y1,z1]
+ B0_PA_fixed_tmp_imgf[x2,y2,z2] = B0_AP_corr_tmp_imgf[x2,y2,z2]
+
+ B0_AP_fixed_tmp_img = nib.Nifti1Image(B0_AP_fixed_tmp_imgf,
+ B0_AP_corr_tmp_img.affine,
+ B0_AP_corr_tmp_img.header)
+
+ B0_PA_fixed_tmp_img = nib.Nifti1Image(B0_PA_fixed_tmp_imgf,
+ B0_PA_corr_tmp_img.affine,
+ B0_PA_corr_tmp_img.header)
+
+ nib.save(B0_AP_fixed_tmp_img, B0_AP_fixed_tmp)
+ nib.save(B0_PA_fixed_tmp_img, B0_PA_fixed_tmp)
+
+ #Merge and average them
+ wrappers.fslmerge("t", fieldmap_iout, B0_AP_fixed_tmp, B0_PA_fixed_tmp)
+ wrappers.fslmaths(fieldmap_iout).Tmean().run(fieldmap_iout_mean)
+
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=fieldmap_GDC, infile=fieldmap_iout_mean,
+ outfile=fieldmap_iout_mean_ud,
+ owarp=fieldmap_iout_mean_ud_warp,
+ gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=True)
+ else:
+ copyfile(src=fieldmap_iout_mean, dst=fieldmap_iout_mean_ud)
+
+ wrappers.fslcpgeom(B0_AP,fieldmap_fout)
+
+ # Get the topup iout (magnitude) to struct space and
+ # apply the transformation to fout (fieldmap)
+ wrappers.epi_reg(epi=fieldmap_iout_mean_ud, t1=T1, t1brain=T1_brain,
+ out=fieldmap_iout_to_T1, wmseg=T1_fast_WM_mask)
+ wrappers.applywarp(src=fieldmap_fout, ref=T1,
+ out=fieldmap_fout_to_T1,
+ w=fieldmap_iout_mean_ud_warp,
+ postmat=fieldmap_iout_to_T1_mat,
+ rel=True, interp='spline')
+
+ # Mask the warped fout (fieldmap) using T1 brain mask
+ wrappers.fslmaths(fieldmap_fout_to_T1).mul(T1_brain_mask).run(fieldmap_fout_to_T1_brain)
+
+ # Multiply the warped & masked fout (fieldmap) by 2*Pi to have it in radians
+ wrappers.fslmaths(fieldmap_fout_to_T1_brain).mul(2*np.pi).run(fieldmap_fout_to_T1_brain_rad)
+
+ # Generate a mask for topup output by inverting the
+ # previous registration and applying it to T1 brain mask
+ wrappers.invxfm(inmat=fieldmap_iout_to_T1_mat, omat=T1_to_fieldmap_iout_mat)
+ wrappers.fslmaths(T1_brain_mask).thr(0.1).run(fieldmap_tmp)
+ wrappers.fslmaths(fieldmap_tmp).kernel("sphere",1.1).dilF().run(fieldmap_tmp)
+ wrappers.fslmaths(fieldmap_tmp).bin().fillh().run(T1_brain_mask_dil)
+ wrappers.applyxfm(src=T1_brain_mask_dil, ref=fieldmap_iout_mean,
+ mat=T1_to_fieldmap_iout_mat, out=fieldmap_mask_ud,
+ interp="trilinear")
+ wrappers.fslmaths(fieldmap_mask_ud).thr(0.25).bin().run(fieldmap_mask_ud)
+
+ #Warp the dilated T1 brain mask to the Gradient Distorted space by inverting
+ wrappers.invwarp(ref=fieldmap_iout_mean, warp=fieldmap_iout_mean_ud_warp,
+ out=fieldmap_iout_mean_ud_inv_warp)
+ wrappers.applywarp(src=T1_brain_mask_dil, ref=fieldmap_iout_mean,
+ out=fieldmap_mask,
+ premat=T1_to_fieldmap_iout_mat,
+ w=fieldmap_iout_mean_ud_inv_warp,
+ rel=True, interp='trilinear')
+ wrappers.fslmaths(fieldmap_mask).thr(0.25).bin().fillh().run(fieldmap_mask)
diff --git a/bip/pipelines/dMRI_fieldmap/fieldmap_pre_topup.py b/bip/pipelines/dMRI_fieldmap/fieldmap_pre_topup.py
new file mode 100755
index 0000000..b75956c
--- /dev/null
+++ b/bip/pipelines/dMRI_fieldmap/fieldmap_pre_topup.py
@@ -0,0 +1,202 @@
+#!/usr/bin/env python
+#
+# fieldmap_pre_topup.py - Sub-pipeline with the fieldmap processing before topup
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613,R0912
+#
+
+import glob
+import logging
+from shutil import copyfile
+import numpy as np
+import nibabel as nib
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from bip.commands import bb_get_b0s, get_dwell_time
+from bip.commands.bb_read_json_field import bb_read_json_field
+from pipe_tree import In, Out, Ref
+
+
+log = logging.getLogger(__name__)
+
+def choose_best_B0(ctx, img, bval, total_B0, indices, tmp, best_index, B0,
+ fieldmap_flag, tmp_dir):
+
+ blah = tmp_dir + '/blah.nii.gz'
+ blah_mat = tmp_dir + '/blah.mat'
+
+ b0_threshold=int(np.loadtxt(ctx.get_data('dMRI/b0_threshold.txt')))
+
+ bvals = np.loadtxt(bval)
+
+ if type(bvals) is not np.ndarray:
+ num_vols = 1
+ else:
+ num_vols = len(np.where(bvals <= b0_threshold)[0])
+
+ bb_get_b0s.bb_get_b0s(img, bval, total_B0, indices, num_vols, b0_threshold)
+ ind_values = [int(x) for x in np.loadtxt(indices)]
+
+ wrappers.fslsplit(total_B0, tmp)
+
+ files = glob.glob(tmp + '*')
+ files.sort()
+
+ N = len(files)
+
+ if N == 1:
+ with open(best_index, 'wt', encoding="utf-8") as f:
+ f.write('0\n')
+ copyfile(src=total_B0, dst=B0)
+ else:
+ scores= np.zeros([N,N])
+ for i in range(N):
+ for j in range(N):
+ if j > i:
+ wrappers.flirt(src=files[i], ref=files[j], nosearch=True,
+ dof=6, out=blah, omat=blah_mat)
+ im1 = nib.load(blah)
+ im2 = nib.load(files[j])
+ im1f = im1.get_fdata().flatten()
+ im2f = im2.get_fdata().flatten()
+ corr = np.corrcoef(im1f,im2f)[0,1]
+ scores[i,j] = corr
+ scores[j,i] = corr
+
+ final_scores = np.zeros(N)
+ for i in range(N):
+ final_scores[i]=np.sum(scores[:,i]) / (N - 1)
+
+ best_ind = final_scores.argmax()
+ best_score = final_scores[best_ind]
+
+ if final_scores[0] > 0.98:
+ best_ind = 0
+ best_score = final_scores[best_ind]
+ if best_score < 0.95:
+ with open(fieldmap_flag, 'wt', encoding="utf-8") as f:
+ f.write(f'The best score was {best_score}.' +
+ 'The subject will probably have problems with this DWI data\n')
+ with open(best_index, 'wt', encoding="utf-8") as f:
+ f.write(f'{ind_values[best_ind]}\n')
+ copyfile(src=files[best_ind], dst=B0)
+
+
+def generate_acqparams(AP, PA, AP_json, PA_json, numAP, numPA, acqparams):
+
+ enc_dir_AP = bb_read_json_field(fileName=AP_json,
+ fieldName="PhaseEncodingDirection")
+ enc_dir_PA = bb_read_json_field(fileName=PA_json,
+ fieldName="PhaseEncodingDirection")
+
+ # TODO: Write a way to evaluate that they are indeed opposite directions
+
+ # Generating the specific strings for the acqparams file for AP
+ # according to the Phase Encoding Direction in the AP json file
+ if enc_dir_AP == "j-":
+ dim_AP = 2
+ values_AP = "0 -1 0"
+ elif enc_dir_AP == "j":
+ dim_AP = 2
+ values_AP = "0 1 0"
+ elif enc_dir_AP == "i-":
+ dim_AP = 1
+ values_AP = "-1 0 0"
+ elif enc_dir_AP == "i":
+ dim_AP = 1
+ values_AP = "1 0 0"
+ elif enc_dir_AP == "k-":
+ dim_AP = 3
+ values_AP = "0 0 -1"
+ else:
+ dim_AP = 3
+ values_AP = "0 0 1"
+
+ # Generating the specific strings for the acqparams file for PA
+ # according to the Phase Encoding Direction in the PA json file
+ if enc_dir_PA == "j-":
+ #dim_PA = 2
+ values_PA = "0 -1 0"
+ elif enc_dir_PA == "j":
+ #dim_PA = 2
+ values_PA = "0 1 0"
+ elif enc_dir_PA == "i-":
+ #dim_PA = 1
+ values_PA = "-1 0 0"
+ elif enc_dir_PA == "i":
+ #dim_PA = 1
+ values_PA = "1 0 0"
+ elif enc_dir_PA == "k-":
+ #dim_PA = 3
+ values_PA = "0 0 -1"
+ else:
+ #dim_PA = 3
+ values_PA = "0 0 1"
+
+
+ imAP = nib.load(AP)
+ numlines = imAP.header['dim'][dim_AP]
+
+ dtiDwell = get_dwell_time.get_dt(AP, AP_json)
+
+ topupValue = (dtiDwell * (numlines -1)) / 1000.0
+
+ values_AP = values_AP + " " + str(topupValue) + "\n"
+ values_PA = values_PA + " " + str(topupValue) + "\n"
+
+ with open(acqparams, 'wt', encoding="utf-8") as f:
+ for i in range(numAP):
+ f.write(values_AP)
+ for i in range(numPA):
+ f.write(values_PA)
+
+
+def run(ctx,
+ AP: In,
+ PA: In,
+ AP_bval: In,
+ PA_bval: In,
+ AP_json: In,
+ PA_json: In,
+ AP_indices: Out,
+ PA_indices: Out,
+ total_B0_AP: Out,
+ total_B0_PA: Out,
+ B0_AP: Out,
+ B0_PA: Out,
+ AP_best_index: Out,
+ PA_best_index: Out,
+ B0_AP_PA: Out,
+ acqparams: Out,
+ AP_fieldmap_flag: Ref,
+ PA_fieldmap_flag: Ref,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('fieldmap_pre_topup', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ AP_tmp = tmp_dir + '/AP_tmp_'
+ PA_tmp = tmp_dir + '/PA_tmp_'
+
+ choose_best_B0(ctx, AP, AP_bval, total_B0_AP, AP_indices, AP_tmp,
+ AP_best_index, B0_AP, AP_fieldmap_flag, tmp_dir)
+ choose_best_B0(ctx, PA, PA_bval, total_B0_PA, PA_indices, PA_tmp,
+ PA_best_index, B0_PA, PA_fieldmap_flag, tmp_dir)
+
+ wrappers.fslmerge("t", B0_AP_PA, B0_AP, B0_PA)
+
+ generate_acqparams(AP, PA, AP_json, PA_json, 1, 1, acqparams)
+
+ # Improvement by Kevin Anderson on 20/12/2017
+ # This guarantees that the number of slices is even
+ im_AP_PA = nib.load(B0_AP_PA)
+ Zslices=im_AP_PA.header['dim'][3]
+
+ if (Zslices % 2) != 0:
+ wrappers.fslroi(B0_AP_PA, B0_AP_PA, 0, -1, 0, -1, 0, Zslices -1)
diff --git a/bip/pipelines/dMRI_fieldmap/fieldmap_topup.py b/bip/pipelines/dMRI_fieldmap/fieldmap_topup.py
new file mode 100755
index 0000000..15bf030
--- /dev/null
+++ b/bip/pipelines/dMRI_fieldmap/fieldmap_topup.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+#
+# fieldmap_topup.py - Sub-pipeline with the fieldmap generation with topup
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import logging
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ B0_AP_PA: In,
+ acqparams: In,
+ logs_dir: Ref,
+ fieldmap_out_prefix: Ref,
+ fieldmap_jacout: Ref,
+ fieldmap_fout: Out):
+
+ with redirect_logging('fieldmap_topup', outdir=logs_dir):
+ wrappers.topup(imain=B0_AP_PA,
+ datain=acqparams,
+ config="b02b0.cnf",
+ out=fieldmap_out_prefix,
+ fout=fieldmap_fout,
+ jacout=fieldmap_jacout,
+ verbose=True)
diff --git a/bip/pipelines/fMRI_rest/__init__.py b/bip/pipelines/fMRI_rest/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/bip/pipelines/fMRI_rest/fMRI_rest.py b/bip/pipelines/fMRI_rest/fMRI_rest.py
new file mode 100755
index 0000000..3c1238e
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/fMRI_rest.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+#
+# fMRI_rest.py - Pipeline with the resting state fMRI processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.fMRI_rest import rfMRI_prepare
+from bip.pipelines.fMRI_rest import rfMRI_melodic
+from bip.pipelines.fMRI_rest import rfMRI_fix
+from bip.pipelines.fMRI_rest import rfMRI_netmats
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_fMRI_task', outdir=logs_dir):
+ pipe(rfMRI_prepare.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('rfMRI_fsf')
+ pipe(rfMRI_melodic.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('rfMRI_ica')
+ pipe(rfMRI_fix.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('filtered_func_data_clean_stdmask')
+ pipe(rfMRI_netmats.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('node_amplitudes')
+
+ return pipe, targets
diff --git a/bip/pipelines/fMRI_rest/rfMRI_fix.py b/bip/pipelines/fMRI_rest/rfMRI_fix.py
new file mode 100755
index 0000000..9899d50
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/rfMRI_fix.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+#
+# rfMRI_fix.py - Sub-pipeline with the FIX processing of the rest fMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import os
+import logging
+from shutil import copyfile
+from pipe_tree import In, Out, Ref
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pyfix import extract, legacy, io, clean, classify
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_fast_pveseg: In,
+ rfMRI_ica: In,
+ rfMRI_example_func2standard_warp: In,
+ rfMRI_example_func: In,
+ rfMRI_standard: In,
+ filtered_func_data: In,
+ melodic_mix: In,
+ rfMRI_mc_parameters: In,
+ logs_dir: Ref,
+ rfMRI_reg_standard_dir: Ref,
+ rfMRI_highres_pveseg: Out,
+ rfMRI_standard2example_func_warp: Out,
+ fix_dir: Out,
+ fix_features: Out,
+ filtered_func_data_clean: Out,
+ filtered_func_data_clean_std: Out,
+ filtered_func_data_clean_stdmask: Out,
+ fix4melview: Out):
+
+ with redirect_logging('rfMRI_fix', outdir=logs_dir):
+
+ # Generate files needed to extract features
+ if not os.path.exists(rfMRI_highres_pveseg):
+ copyfile(T1_fast_pveseg, rfMRI_highres_pveseg)
+ if not os.path.exists(rfMRI_standard2example_func_warp):
+ wrappers.invwarp(ref=rfMRI_example_func,
+ warp=rfMRI_example_func2standard_warp,
+ out=rfMRI_standard2example_func_warp)
+
+ # FIX: Extract features for subject
+ if not os.path.exists(fix_features):
+ d = extract.FixData.from_melodic_dir(rfMRI_ica, fixdir = fix_dir)
+
+ f = extract.extract_features(data=d,
+ features=legacy.LEGACY_FEATURE_EXTRACTORS)
+ io.save_features(os.path.join(fix_dir, 'features'), f)
+ else:
+ f = io.read_features(os.path.join(fix_dir, 'features'))
+
+ # FIX: Classify components for subject
+ clf = io.load_model(ctx.get_data('/fix/UKBB_model_2.pyfix_model'))
+ y_pred_pb = classify.classify(f, clf)[1]
+ y_pred = legacy.prob_threshold(y_pred_pb, threshold=0.2)
+
+ fin_list=[]
+ with open(fix4melview, 'wt', encoding="utf-8") as f:
+ for ind, pred in enumerate(y_pred):
+ # Opposite of the convention in MelView
+ if not pred:
+ f.write(str(ind + 1) + ", Unclassified Noise, True\n")
+ fin_list.append(ind + 1)
+ else:
+ f.write(str(ind + 1) + ", Signal, False\n")
+ f.write(f'{fin_list}\n')
+
+ # FIX: Clean
+ clean.apply(func_fname=filtered_func_data,
+ icamix=melodic_mix,
+ noise_idx=y_pred,
+ func_clean_fname=filtered_func_data_clean,
+ do_motion_regression=True,
+ motion_parameters=rfMRI_mc_parameters)
+
+ # Create this directory in case it does not exist. This may
+ # happen due to the overwrite function issue in Melodic
+ if not os.path.exists(rfMRI_reg_standard_dir):
+ os.mkdir(rfMRI_reg_standard_dir)
+
+ # Generate the data in standard space
+ wrappers.applywarp(ref=rfMRI_standard, src=filtered_func_data_clean,
+ out=filtered_func_data_clean_std,
+ warp=rfMRI_example_func2standard_warp,
+ interp="spline")
+ mask = ctx.get_data('MNI/MNI152_T1_2mm_brain_mask_bin.nii.gz')
+ wrappers.fslmaths(filtered_func_data_clean_std).mas(mask).run(filtered_func_data_clean_std)
+ wrappers.fslmaths(filtered_func_data_clean_std).Tstd().bin().run(filtered_func_data_clean_stdmask)
diff --git a/bip/pipelines/fMRI_rest/rfMRI_melodic.py b/bip/pipelines/fMRI_rest/rfMRI_melodic.py
new file mode 100755
index 0000000..b38b69d
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/rfMRI_melodic.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+#
+# rfMRI_melodic.py - Sub-pipeline with FSL's MELODIC tool
+# applied to the rest fMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import shutil
+import logging
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ rfMRI_fsf: In,
+ logs_dir: Ref,
+ rfMRI_ica: Out,
+ rfMRI_example_func2standard_warp: Out,
+ rfMRI_example_func: Out,
+ rfMRI_standard: Out,
+ filtered_func_data: Out,
+ melodic_mix: Out,
+ rfMRI_mc_parameters: Out):
+
+ with redirect_logging('rfMRI_melodic', outdir=logs_dir):
+ wrappers.feat(fsf=rfMRI_fsf)
+
+ # The ending of this script is needed because the overwrite output
+ # function in MELODIC is not working, and therefore, the output is
+ # generated in a folder called "rfMI+.ica". Once this function works,
+ # we can remove these last 8 lines
+ name_dir_alt = rfMRI_ica.split(".")
+ name_dir_alt[-2] = name_dir_alt[-2] + "+"
+ name_dir_alt = ".".join(name_dir_alt)
+
+ if os.path.exists(name_dir_alt):
+ if os.path.exists(rfMRI_ica):
+ shutil.rmtree(rfMRI_ica)
+ os.rename(name_dir_alt, rfMRI_ica)
diff --git a/bip/pipelines/fMRI_rest/rfMRI_netmats.py b/bip/pipelines/fMRI_rest/rfMRI_netmats.py
new file mode 100755
index 0000000..9419385
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/rfMRI_netmats.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+#
+# rfMRI_netmats.py - Sub-pipeline with the netmats processing of the rest fMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import os
+import logging
+from fsl import wrappers
+from pipe_tree import In, Out, Ref, Var
+from bip.utils import redirect_logging
+from bip.pipelines.fMRI_rest.rfMRI_netmats_fnc import ICA_dr
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ netmats_dim: Var,
+ filtered_func_data: In,
+ filtered_func_data_clean_std: In,
+ logs_dir: Ref,
+ netmats_dir: Out,
+ dr_stage1: Out,
+ full_corr: Out,
+ partial_corr: Out,
+ node_amplitudes: Out):
+
+ D = netmats_dim.value
+
+ with redirect_logging('rfMRI_netmats_d' + D, outdir=logs_dir):
+
+ MNI_2mm_mask = ctx.get_standard("MNI152_T1_2mm_brain_mask.nii.gz")
+ group_IC = ctx.get_data("/netmats/melodic_IC_" + D + ".nii.gz")
+
+
+ # Create this directory in case it does not exist. This may
+ # happen due to the overwrite function issue in Melodic
+ if not os.path.exists(netmats_dir):
+ os.mkdir(netmats_dir)
+
+
+ wrappers.fsl_glm(input=filtered_func_data_clean_std,
+ design=group_IC, out=dr_stage1, demean=True,
+ mask = MNI_2mm_mask)
+
+ # Implement in python
+ ICA_dr(filtered_func_data, dr_stage1, D, full_corr, partial_corr,
+ node_amplitudes)
diff --git a/bip/pipelines/fMRI_rest/rfMRI_netmats_fnc.py b/bip/pipelines/fMRI_rest/rfMRI_netmats_fnc.py
new file mode 100644
index 0000000..dcc4d5e
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/rfMRI_netmats_fnc.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python
+#
+# rfMRI_netmats_fnc.py - Functions for the netmats processing of the rest fMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=E1130
+#
+
+import numpy as np
+import nibabel as nib
+
+def nets_load(timeseries_name,tr,varnorm,numvols=0):
+
+ timeseries = np.loadtxt(timeseries_name)
+ gn = timeseries.shape[0]
+ gd = timeseries.shape[1]
+
+ ts={}
+
+ ts['NtimepointsPerSubject'] = numvols
+
+ if ts['NtimepointsPerSubject'] == 0:
+ ts["NtimepointsPerSubject"] = gn
+ TS=np.full(timeseries.shape, np.nan)
+
+ TS = timeseries
+ TS = TS - np.nanmean(TS)
+
+ if varnorm == 1:
+ TS = TS / np.nanstd(TS)
+
+ ts["ts"] = TS
+ ts["tr"] = tr
+ ts["Nsubjects"] = 1
+ ts["Nnodes"] = gd
+ ts["NnodesOrig"] = ts["Nnodes"]
+ ts["Ntimepoints"] = gn
+ ts["DD"] = [*range(ts["Nnodes"])]
+ ts["UNK"] = []
+
+ return ts
+
+def nets_tsclean(ts,aggressive):
+ ts["NnodesOrig"]=ts["Nnodes"]
+
+ nongood = [x for x in range(ts["Nnodes"]) if x not in ts["DD"]]
+ bad = [x for x in nongood if x not in ts["UNK"]] # Only bad components
+
+ # Good components
+ goodTS=ts["ts"][:,ts["DD"]]
+
+ # Bad components
+ badTS=ts["ts"][:,bad]
+
+ if aggressive == 1:
+ ts["ts"] = goodTS - np.dot(badTS, np.dot(np.linalg.pinv(badTS),goodTS))
+ else:
+ ts["ts"] = goodTS
+
+ ts["Nnodes"] = ts["ts"].shape[1]
+
+ return ts
+
+#netmats2 = nets_netmats(ts, -r2zPARTIAL, 'ridgep', 0.5)
+def nets_netmats(ts, do_rtoz, method, arg_method=None):
+
+ N=ts["Nnodes"]
+ netmats=[]
+ grot=ts["ts"]
+
+ # We are keeping just the amplitudes
+ if method.lower() in ['corr','correlation']:
+ grot=np.corrcoef(grot,rowvar=False)
+ np.fill_diagonal(grot, 0)
+
+ elif method.lower() in ['ridgep']:
+ grot=np.cov(grot, rowvar=False)
+ grot=grot/np.sqrt(np.nanmean(np.diag(grot) * np.diag(grot)))
+ if not arg_method:
+ rho=0.1
+ else:
+ rho=arg_method
+ if rho<0:
+ rho = -(rho)
+
+ grot=-(np.linalg.inv(grot+rho*np.eye(N)))
+ val0 = np.sqrt(np.abs(np.diag(grot)))
+ val2 = np.tile(val0, (N,1))
+ val1 = np.transpose(val2)
+ grot = (grot / val1) / val2
+
+ np.fill_diagonal(grot, 0)
+
+ netmats=grot.reshape(1,N*N, order='F').copy()
+ netmats=0.5*np.log((1+netmats)/(1-netmats))* (-do_rtoz)
+
+ return netmats
+
+def ICA_dr(func, timeseries, D, full_corr, partial_corr, node_amplitudes):
+ im_func = nib.load(func)
+ tr = im_func.header['pixdim'][4]
+ numvols = im_func.header['dim'][4]
+
+ ts = nets_load(timeseries, tr, 0, numvols)
+
+ if D == "25":
+ good_components = [3, 22,23,24]
+ ts["DD"] = [x for x in ts["DD"] if x not in good_components]
+ r2zFULL=10.6484
+ r2zPARTIAL=10.6707
+ else:
+ good_components = [0, 43, 46, 50, 53, 54, 55, 58, 60, 61]
+ good_components += [*range(64,92)] + [*range(93,100)]
+ ts["DD"] = [x for x in ts["DD"] if x not in good_components]
+ r2zFULL = 19.7177
+ r2zPARTIAL = 18.8310
+
+ ts = nets_tsclean(ts,1)
+
+ netmats1 = nets_netmats(ts, -r2zFULL, 'corr')
+ netmats2 = nets_netmats(ts, -r2zPARTIAL, 'ridgep', 0.5)
+
+ tmp = np.reshape(netmats1,(ts["Nnodes"],ts["Nnodes"]), order="F")
+ data = tmp[np.where(np.triu(np.ones([ts["Nnodes"],ts["Nnodes"]]),k=1) ==1)]
+ with open(full_corr, 'wt', encoding="utf-8") as f:
+ np.savetxt(f, data, fmt="%14.8f", newline=" ")
+
+ tmp = np.reshape(netmats2,(ts["Nnodes"],ts["Nnodes"]), order="F")
+ data = tmp[np.where(np.triu(np.ones([ts["Nnodes"],ts["Nnodes"]]),k=1) ==1)]
+ with open(partial_corr, 'wt', encoding="utf-8") as f:
+ np.savetxt(f, data, fmt="%14.8f", newline=" ")
+
+ data = np.nanstd(ts["ts"], axis=0)
+ with open(node_amplitudes, 'wt', encoding="utf-8") as f:
+ np.savetxt(f, data, fmt="%14.8f", newline=" ")
diff --git a/bip/pipelines/fMRI_rest/rfMRI_prepare.py b/bip/pipelines/fMRI_rest/rfMRI_prepare.py
new file mode 100755
index 0000000..f496829
--- /dev/null
+++ b/bip/pipelines/fMRI_rest/rfMRI_prepare.py
@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+#
+# rfMRI_prepare.py - Sub-pipeline with the processing previous to Melodic rfMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import logging
+from shutil import copyfile
+import nibabel as nib
+from pipe_tree import In, Out, Ref
+from fsl import wrappers
+from bip.commands import get_dwell_time
+from bip.utils import redirect_logging
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ T1_to_MNI_warp: In,
+ T1_to_MNI_linear_mat: In,
+ T1_fast_pve_2: In,
+ rfMRI: In,
+ rfMRI_SBREF: In,
+ rfMRI_json: In,
+ fieldmap_fout_to_T1_brain_rad: In,
+ logs_dir: Ref,
+ rfMRI_ica: Ref,
+ rfMRI_SBREF_GDC: Out,
+ rfMRI_T1: Out,
+ rfMRI_T1_brain: Out,
+ rfMRI_T1_brain2MNI152_T1_2mm_brain_warp: Out,
+ rfMRI_T1_brain2MNI152_T1_2mm_brain_mat: Out,
+ rfMRI_T1_brain_wmseg: Out,
+ rfMRI_SBREF_ud: Out,
+ rfMRI_SBREF_ud_warp: Out,
+ rfMRI_fsf: Out):
+
+ with redirect_logging('rfMRI_prepare', outdir=logs_dir):
+
+ # Creates links
+ # TODO: These links ARE hard-coded. This may cause future problems.
+ if not os.path.exists(rfMRI_T1):
+ os.symlink(src="../../T1/T1.nii.gz",
+ dst=rfMRI_T1)
+ if not os.path.exists(rfMRI_T1_brain):
+ os.symlink(src="../../T1/T1_brain.nii.gz",
+ dst=rfMRI_T1_brain)
+ if not os.path.exists(rfMRI_T1_brain2MNI152_T1_2mm_brain_warp):
+ os.symlink(src="../../T1/transforms/T1_to_MNI_warp.nii.gz",
+ dst=rfMRI_T1_brain2MNI152_T1_2mm_brain_warp)
+ if not os.path.exists(rfMRI_T1_brain2MNI152_T1_2mm_brain_mat):
+ os.symlink(src="../../T1/transforms/T1_to_MNI_linear.mat",
+ dst=rfMRI_T1_brain2MNI152_T1_2mm_brain_mat)
+
+ wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(rfMRI_T1_brain_wmseg)
+
+ # Generation of FSF file
+ copyfile(src=ctx.get_data('fMRI_fsf/design_ICA_nonSmoothed.fsf'),
+ dst=rfMRI_fsf)
+
+ fMRI_img = nib.load(rfMRI)
+ fmriNumVol = fMRI_img.header['dim'][4]
+ fmriTR = fMRI_img.header['pixdim'][4]
+ fmriDwell = get_dwell_time.get_dt(rfMRI, rfMRI_json)
+ fmriTE = 39 # Default value for task fMRI TE
+ descrip = str(fMRI_img.header['descrip'])
+
+ fields = descrip.split(";")
+
+ for field in fields:
+ t = field.split("=")
+
+ fieldName = t[0]
+ fieldValue = t[1]
+
+ if fieldName == "TE":
+ fmriTE = fieldValue
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=rfMRI_SBREF_GDC,
+ infile=rfMRI_SBREF,
+ outfile=rfMRI_SBREF_ud,
+ owarp=rfMRI_SBREF_ud_warp,
+ gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=False)
+ else:
+ copyfile(src=rfMRI_SBREF, dst=rfMRI_SBREF_ud)
+
+ with open(rfMRI_fsf, 'a', encoding="utf-8") as f:
+ f.write('set fmri(regstandard) "' + ctx.FSLDIR + \
+ '/data/standard/MNI152_T1_2mm_brain"\n')
+ f.write('set fmri(outputdir) "' + os.getcwd() + "/" + rfMRI_ica + '"\n')
+ f.write('set feat_files(1) "' + os.getcwd() + "/" + rfMRI + '"\n')
+ f.write('set alt_ex_func(1) "' + os.getcwd() + "/" + rfMRI_SBREF + '"\n')
+ f.write('set unwarp_files(1) "' + os.getcwd() + "/" + fieldmap_fout_to_T1_brain_rad + '"\n')
+ f.write('set unwarp_files_mag(1) "' + os.getcwd() + "/" + rfMRI_T1_brain + '"\n')
+ f.write('set highres_files(1) "' + os.getcwd() + "/" + rfMRI_T1_brain + '"\n')
+ if ctx.gdc != '':
+ f.write('set fmri(gdc) "' + os.getcwd() + "/" + rfMRI_SBREF_ud_warp + '"\n')
+ f.write('set fmri(tr) ' + str(fmriTR) + '\n')
+ f.write('set fmri(npts) ' + str(fmriNumVol) + '\n')
+ f.write('set fmri(dwell) ' + str(fmriDwell) + '\n')
+ f.write('set fmri(te) ' + str(fmriTE) + '\n')
+
\ No newline at end of file
diff --git a/bip/pipelines/fMRI_task/__init__.py b/bip/pipelines/fMRI_task/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/bip/pipelines/fMRI_task/fMRI_task.py b/bip/pipelines/fMRI_task/fMRI_task.py
new file mode 100755
index 0000000..f30fd94
--- /dev/null
+++ b/bip/pipelines/fMRI_task/fMRI_task.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+#
+# fMRI_task.py - Pipeline with the task fMRI processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.fMRI_task import tfMRI_prepare
+from bip.pipelines.fMRI_task import tfMRI_feat
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_fMRI_task', outdir=logs_dir):
+ pipe(tfMRI_prepare.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('tfMRI_fsf')
+ pipe(tfMRI_feat.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('tfMRI_feat')
+
+ return pipe, targets
diff --git a/bip/pipelines/fMRI_task/tfMRI_feat.py b/bip/pipelines/fMRI_task/tfMRI_feat.py
new file mode 100755
index 0000000..54706af
--- /dev/null
+++ b/bip/pipelines/fMRI_task/tfMRI_feat.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+#
+# tfMRI_feat.py - Sub-pipeline with FSL's FEAT tool applied to the task fMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import logging
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ tfMRI_fsf: In,
+ logs_dir: Ref,
+ tfMRI_feat: Out):
+
+ with redirect_logging('tfMRI_feat', outdir=logs_dir):
+ wrappers.feat(fsf=tfMRI_fsf)
diff --git a/bip/pipelines/fMRI_task/tfMRI_prepare.py b/bip/pipelines/fMRI_task/tfMRI_prepare.py
new file mode 100755
index 0000000..3eef64b
--- /dev/null
+++ b/bip/pipelines/fMRI_task/tfMRI_prepare.py
@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+#
+# tfMRI_prepare.py - Sub-pipeline with the processing previous to feat tfMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import logging
+from shutil import copyfile
+import nibabel as nib
+from pipe_tree import In, Out, Ref
+from fsl import wrappers
+from bip.commands import get_dwell_time
+from bip.utils import redirect_logging
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ T1_to_MNI_warp: In,
+ T1_to_MNI_linear_mat: In,
+ T1_fast_pve_2: In,
+ tfMRI: In,
+ tfMRI_SBREF: In,
+ tfMRI_json: In,
+ fieldmap_fout_to_T1_brain_rad: In,
+ logs_dir: Ref,
+ tfMRI_feat: Ref,
+ tfMRI_SBREF_GDC: Out,
+ tfMRI_T1: Out,
+ tfMRI_T1_brain: Out,
+ tfMRI_T1_brain2MNI152_T1_2mm_brain_warp: Out,
+ tfMRI_T1_brain2MNI152_T1_2mm_brain_mat: Out,
+ tfMRI_T1_brain_wmseg: Out,
+ tfMRI_SBREF_ud: Out,
+ tfMRI_SBREF_ud_warp: Out,
+ tfMRI_fsf: Out):
+
+ with redirect_logging('tfMRI_prepare', outdir=logs_dir):
+
+ # Creates links
+ # TODO: These links ARE hard-coded. This may cause future problems.
+ if not os.path.exists(tfMRI_T1):
+ os.symlink(src="../../T1/T1.nii.gz",
+ dst=tfMRI_T1)
+ if not os.path.exists(tfMRI_T1_brain):
+ os.symlink(src="../../T1/T1_brain.nii.gz",
+ dst=tfMRI_T1_brain)
+ if not os.path.exists(tfMRI_T1_brain2MNI152_T1_2mm_brain_warp):
+ os.symlink(src="../../T1/transforms/T1_to_MNI_warp.nii.gz",
+ dst=tfMRI_T1_brain2MNI152_T1_2mm_brain_warp)
+ if not os.path.exists(tfMRI_T1_brain2MNI152_T1_2mm_brain_mat):
+ os.symlink(src="../../T1/transforms/T1_to_MNI_linear.mat",
+ dst=tfMRI_T1_brain2MNI152_T1_2mm_brain_mat)
+
+ wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(tfMRI_T1_brain_wmseg)
+
+ # Generation of FSF file
+ copyfile(src=ctx.get_data('fMRI_fsf/design_TASK.fsf'), dst=tfMRI_fsf)
+
+ fMRI_img = nib.load(tfMRI)
+ fmriNumVol = fMRI_img.header['dim'][4]
+ fmriTR = fMRI_img.header['pixdim'][4]
+ fmriDwell = get_dwell_time.get_dt(tfMRI, tfMRI_json)
+ fmriTE = 39 # Default value for task fMRI TE
+ descrip = str(fMRI_img.header['descrip'])
+
+ fields = descrip.split(";")
+
+ for field in fields:
+ t = field.split("=")
+
+ fieldName = t[0]
+ fieldValue = t[1]
+
+ if fieldName == "TE":
+ fmriTE = fieldValue
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=tfMRI_SBREF_GDC,
+ infile=tfMRI_SBREF,
+ outfile=tfMRI_SBREF_ud,
+ owarp=tfMRI_SBREF_ud_warp,
+ gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=False)
+ else:
+ copyfile(src=tfMRI_SBREF, dst=tfMRI_SBREF_ud)
+
+ with open(tfMRI_fsf, 'a', encoding="utf-8") as f:
+ f.write('set fmri(regstandard) "' + ctx.FSLDIR + \
+ '/data/standard/MNI152_T1_2mm_brain"\n')
+ f.write('set fmri(custom1) "' + ctx.get_data('fMRI_fsf/designS.txt') + '"\n')
+ f.write('set fmri(custom2) "' + ctx.get_data('fMRI_fsf/designF.txt') + '"\n')
+ f.write('set fmri(outputdir) "' + os.getcwd() + "/" + tfMRI_feat + '"\n')
+ f.write('set feat_files(1) "' + os.getcwd() + "/" + tfMRI + '"\n')
+ f.write('set alt_ex_func(1) "' + os.getcwd() + "/" + tfMRI_SBREF + '"\n')
+ f.write('set unwarp_files(1) "' + os.getcwd() + "/" + fieldmap_fout_to_T1_brain_rad + '"\n')
+ f.write('set unwarp_files_mag(1) "' + os.getcwd() + "/" + tfMRI_T1_brain + '"\n')
+ f.write('set highres_files(1) "' + os.getcwd() + "/" + tfMRI_T1_brain + '"\n')
+ if ctx.gdc != '':
+ f.write('set fmri(gdc) "' + os.getcwd() + "/" + tfMRI_SBREF_ud_warp + '"\n')
+ f.write('set fmri(tr) ' + str(fmriTR) + '\n')
+ f.write('set fmri(npts) ' + str(fmriNumVol) + '\n')
+ f.write('set fmri(dwell) ' + str(fmriDwell) + '\n')
+ f.write('set fmri(te) ' + str(fmriTE) + '\n')
diff --git a/bip/pipelines/struct_T1/T1_QC_CNR_corners.py b/bip/pipelines/struct_T1/T1_QC_CNR_corners.py
new file mode 100755
index 0000000..fee0701
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_QC_CNR_corners.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+#
+# T1_QC_CNR_corners.py - Sub-pipeline with the QC (CNR in the corners) of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import logging
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging, tempdir
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_orig: In,
+ T1_notNorm: In,
+ T1_fast_GM_mask: In,
+ T1_fast_WM_mask: In,
+ T1_orig_QC_CNR_lower: Out,
+ T1_orig_QC_CNR_upper: Out,
+ T1_notNorm_QC_CNR_lower: Out,
+ T1_notNorm_QC_CNR_upper: Out,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T1_QC_CNR_corners', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ roi_1_1 = tmp_dir + '/roi_1_1.nii.gz'
+ roi_1_245 = tmp_dir + '/roi_1_245.nii.gz'
+ roi_197_1 = tmp_dir + '/roi_197_1.nii.gz'
+ roi_197_245 = tmp_dir + '/roi_197_245.nii.gz'
+ roi_x_1 = tmp_dir + '/roi_x_1.nii.gz'
+ roi_x_245 = tmp_dir + '/roi_x_245.nii.gz'
+ roi_lower = tmp_dir + '/roi_lower.nii.gz'
+ roi_upper = tmp_dir + '/roi_upper.nii.gz'
+
+
+ in_files = [T1_orig, T1_notNorm]
+ out_files_lo = [T1_orig_QC_CNR_lower, T1_notNorm_QC_CNR_lower]
+ out_files_up = [T1_orig_QC_CNR_upper, T1_notNorm_QC_CNR_upper]
+
+ for i in [0,1]:
+ # Lower corners
+ wrappers.fslroi(in_files[i], roi_1_1, 1, 10, 1, 10, 0, 10)
+ wrappers.fslroi(in_files[i], roi_1_245, 1, 10, 245, 10, 0, 10)
+ wrappers.fslroi(in_files[i], roi_197_1, 197, 10, 1, 10, 0, 10)
+ wrappers.fslroi(in_files[i], roi_197_245, 197, 10, 245, 10, 0, 10)
+
+ wrappers.fslmerge("x", roi_x_1, roi_1_1, roi_197_1)
+ wrappers.fslmerge("x", roi_x_245, roi_1_245, roi_197_245)
+ wrappers.fslmerge("y", roi_lower, roi_x_1, roi_x_245)
+
+ # Upper corners
+ wrappers.fslroi(in_files[i], roi_1_1, 1, 10, 1, 10, 245, 10)
+ wrappers.fslroi(in_files[i], roi_1_245, 1, 10, 245, 10, 245, 10)
+ wrappers.fslroi(in_files[i], roi_197_1, 197, 10, 1, 10, 245, 10)
+ wrappers.fslroi(in_files[i], roi_197_245, 197, 10, 245, 10, 245, 10)
+
+ wrappers.fslmerge("x", roi_x_1, roi_1_1, roi_197_1)
+ wrappers.fslmerge("x", roi_x_245, roi_1_245, roi_197_245)
+ wrappers.fslmerge("y", roi_upper, roi_x_1, roi_x_245)
+
+ grey = wrappers.fslstats(T1).k(T1_fast_GM_mask).m.run()
+ white = wrappers.fslstats(T1).k(T1_fast_WM_mask).m.run()
+ threshold = wrappers.fslstats(T1).k(T1_fast_GM_mask).P(1).run() / 2
+
+ wrappers.fslmaths(roi_lower).uthr(threshold).run(roi_lower)
+ wrappers.fslmaths(roi_upper).uthr(threshold).run(roi_upper)
+
+ noise_lower = wrappers.fslstats(roi_lower).S.run()
+ noise_upper = wrappers.fslstats(roi_upper).S.run()
+
+ brain = (grey + white) / 2
+
+ result_lower = noise_lower / brain
+ result_upper = noise_upper / brain
+
+ with open(out_files_lo[i], 'wt', encoding="utf-8") as f:
+ f.write(f'{result_lower}\n')
+ with open(out_files_up[i], 'wt', encoding="utf-8") as f:
+ f.write(f'{result_upper}\n')
diff --git a/bip/pipelines/struct_T1/T1_QC_CNR_eyes.py b/bip/pipelines/struct_T1/T1_QC_CNR_eyes.py
new file mode 100755
index 0000000..5bc8dfb
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_QC_CNR_eyes.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+#
+# T1_QC_CNR_eyes.py - Sub-pipeline with the QC (CNR in front of eyes) of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+
+import os
+import logging
+import nibabel as nib
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging, tempdir
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_brain_mask: In,
+ T1_orig: In,
+ T1_notNorm: In,
+ T1_fast_GM_mask: In,
+ T1_fast_WM_mask: In,
+ T1_orig_ud_warp: In,
+ T1_orig_ud_to_T1_mat: In,
+ T1_to_MNI_linear_mat: In,
+ T1_to_T1_orig_ud_mat: In,
+ T1_eyes_cuboid_mask: Out,
+ MNI_to_T1_orig_ud_mat: Out,
+ T1_orig_ud_to_MNI_mat: Out,
+ left_eye_flirted: Out,
+ right_eye_flirted: Out,
+ T1_QC_CNR_eyes: Out,
+ T1_orig_ud_warp_inv: Ref,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T1_QC_CNR_eyes', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ T1_brain_GM_mask_orig = tmp_dir + '/T1_brain_GM_mask_orig.nii.gz'
+ T1_brain_WM_mask_orig = tmp_dir + '/T1_brain_WM_mask_orig.nii.gz'
+ lmask = tmp_dir + '/lmask.nii.gz'
+ rmask = tmp_dir + '/rmask.nii.gz'
+ T1_tmp_7 = tmp_dir + '/T1_tmp_7.nii.gz'
+
+ if not os.path.exists(T1_orig_ud_warp_inv):
+ wrappers.invwarp(ref=T1_orig, warp=T1_orig_ud_warp,
+ out=T1_orig_ud_warp_inv)
+
+ wrappers.applywarp(src=T1_fast_GM_mask, ref=T1_orig,
+ w=T1_orig_ud_warp_inv, premat=T1_to_T1_orig_ud_mat,
+ out=T1_brain_GM_mask_orig, rel=True, interp='nn')
+ wrappers.applywarp(src=T1_fast_WM_mask, ref=T1_orig,
+ w=T1_orig_ud_warp_inv, premat=T1_to_T1_orig_ud_mat,
+ out=T1_brain_WM_mask_orig, rel=True, interp='nn')
+
+ wrappers.concatxfm(atob=T1_orig_ud_to_T1_mat, btoc=T1_to_MNI_linear_mat,
+ atoc=T1_orig_ud_to_MNI_mat)
+ wrappers.invxfm(inmat=T1_orig_ud_to_MNI_mat, omat=MNI_to_T1_orig_ud_mat)
+ wrappers.applyxfm(src=ctx.get_data('MNI/MNI152_T1_1mm_left_eye_dil.nii.gz'),
+ ref=T1_orig, mat=MNI_to_T1_orig_ud_mat,
+ out=left_eye_flirted, interp="nearestneighbour")
+ wrappers.applyxfm(src=ctx.get_data('MNI/MNI152_T1_1mm_right_eye_dil.nii.gz'),
+ ref=T1_orig, mat=MNI_to_T1_orig_ud_mat,
+ out=right_eye_flirted, interp="nearestneighbour")
+
+ lx, _, lz = wrappers.fslstats(left_eye_flirted).C.run()
+ rx, _, rz = wrappers.fslstats(right_eye_flirted).C.run()
+
+ size = 40
+ half_size = size / 2
+
+ T1_orig_img = nib.load(T1_orig)
+ dimY = int(round(T1_orig_img.header['dim'][2] - size))
+
+ lDimX = int(round(lx - half_size))
+ lDimZ = int(round(lz - half_size))
+ rDimX = int(round(rx - half_size))
+ rDimZ = int(round(rz - half_size))
+
+ wrappers.fslmaths(T1_orig).mul(0).add(1).roi(lDimX, size, dimY, size,
+ lDimZ, size, 0, 1).run(lmask)
+ wrappers.fslmaths(T1_orig).mul(0).add(1).roi(rDimX, size, dimY, size,
+ rDimZ, size, 0, 1).run(rmask)
+ wrappers.fslmaths(rmask).add(lmask).run(T1_eyes_cuboid_mask)
+ wrappers.fslmaths(T1_eyes_cuboid_mask).mul(T1_notNorm).run(T1_tmp_7)
+
+ grey = wrappers.fslstats(T1_notNorm).k(T1_brain_GM_mask_orig).m.run()
+ white = wrappers.fslstats(T1_notNorm).k(T1_brain_WM_mask_orig).m.run()
+ threshold = wrappers.fslstats(T1_notNorm).k(T1_brain_GM_mask_orig).P(3).run()
+
+ wrappers.fslmaths(T1_tmp_7).uthr(threshold).bin().run(T1_tmp_7)
+ wrappers.fslmaths(T1_tmp_7).dilM(2).ero(4).mul(T1_notNorm).run(T1_tmp_7)
+
+ noise = wrappers.fslstats(T1_tmp_7).S.run()
+ brain = int(round((grey + white) / 2))
+ #TODO: The roundings are only there to mimic the behaviour of
+ # previous version. We do not have to do it in next version
+
+ result = noise / brain
+
+ with open(T1_QC_CNR_eyes, 'wt', encoding="utf-8") as f:
+ f.write(f'{result}\n')
diff --git a/bip/pipelines/struct_T1/T1_QC_COG.py b/bip/pipelines/struct_T1/T1_QC_COG.py
new file mode 100755
index 0000000..5d4ea84
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_QC_COG.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+#
+# T1_QC_COG.py - Sub-pipeline with the QC (Calculate Center of Gravity) of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+import numpy as np
+import nibabel as nib
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_brain_mask: In,
+ T1_QC_COG: Out,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T1_QC_COG', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ T1_tmp_5 = tmp_dir + '/T1_tmp_5.nii.gz'
+ T1_tmp_6 = tmp_dir + '/T1_tmp_6.nii.gz'
+
+ refSubj = ctx.get_data('MNI/MNI152_T1_1mm_BigFoV_facemask.nii.gz')
+
+ COG_coord = wrappers.fslstats(T1_brain_mask).C.run()
+ T1_brain_mask_img = nib.load(T1_brain_mask)
+
+ qform = T1_brain_mask_img.get_qform()
+
+ new_coord = qform.dot(np.append(COG_coord,1))
+ new_x = new_coord[0]
+ new_z = new_coord[2]
+
+ wrappers.fslmaths(T1_brain_mask).thr(0.5).bin().run(T1_tmp_5)
+ wrappers.flirt(src=T1_tmp_5, ref=refSubj, out=T1_tmp_6, applyxfm=True,
+ usesqform=True, interp='nearestneighbour')
+
+ new_y = wrappers.fslstats(T1_tmp_6).w.run()[2]
+
+ np.savetxt(T1_QC_COG, [new_x, new_y, new_z], fmt='%.6f', newline=' ')
diff --git a/bip/pipelines/struct_T1/T1_brain_extract.py b/bip/pipelines/struct_T1/T1_brain_extract.py
new file mode 100755
index 0000000..221507a
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_brain_extract.py
@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+#
+# T1_brain_extract.py - Sub-pipeline with the brain extraction of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref, update_closure
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_orig: In,
+ T1_orig_ud: In,
+ T1_orig_ud_warp: In,
+ T1_brain: Out,
+ T1_brain_mask: Out,
+ T1_brain_to_MNI: Out,
+ T1_to_MNI_linear_mat: Out,
+ T1_orig_ud_to_T1_mat: Out,
+ T1_to_MNI_warp: Out,
+ T1_to_MNI_warp_coef_inv: Out,
+ T1_to_T1_orig_ud_mat: Out,
+ logs_dir: Ref,
+ T1: Ref,
+ tmp_dir: Ref,
+ tmp_T1_prefix: Ref,
+ transform_outputs: Ref("T1*_to_*")):
+
+ with redirect_logging('T1_brain_extract', outdir=logs_dir),\
+ tempdir(tmp_dir),\
+ update_closure(transform_outputs):
+
+ T1_tmp_1 = tmp_dir + '/T1_tmp_1.nii.gz'
+ T1_tmp_2 = tmp_dir + '/T1_tmp_2.nii.gz'
+ T1_tmp_1_brain = tmp_dir + '/T1_tmp_1_brain.nii.gz'
+ T1_tmp_orig_ud_to_std_mat = tmp_dir + '/T1_tmp_to_std.mat'
+ T1_tmp = tmp_dir + '/T1.nii.gz'
+
+ #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)
+
+ #Run a (Recursive) brain extraction on the roi
+ wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True)
+
+ #Reduce the FOV of T1_orig_ud by calculating a registration
+ #from T1_tmp_brain to ssref and apply it to T1_orig_ud
+ wrappers.standard_space_roi(T1_tmp_1_brain, tmp_T1_prefix, maskNONE=True,
+ ssref = ctx.MNI + '_brain',
+ altinput=T1_orig_ud, d=True)
+ copyfile(src=T1_tmp, dst=T1)
+ copyfile(src=T1_tmp_orig_ud_to_std_mat, dst=T1_orig_ud_to_std_mat)
+
+ #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 = ctx.FSLDIR + '/etc/flirtsch/xyztrans.sch')
+ wrappers.invxfm(inmat=T1_to_T1_orig_ud_mat, omat=T1_orig_ud_to_T1_mat)
+ wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat, btoc=T1_orig_ud_to_std_mat,
+ atoc=T1_to_MNI_linear_mat)
+
+ #Non-linear registration to MNI using the previously calculated alignment
+ wrappers.fnirt(src = T1, ref = ctx.MNI, aff = T1_to_MNI_linear_mat,
+ config = ctx.get_data('fnirt/bb_fnirt.cnf'),
+ refmask = ctx.get_data('MNI/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_2,
+ interp = 'spline')
+
+ #Combine all transforms (Gradient Distortion Unwarp and T1 to ctx.MNI) into one
+ if ctx.gdc != '' :
+ wrappers.convertwarp(ref=ctx.MNI, warp1=T1_orig_ud_warp,
+ midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
+ out=T1_orig_to_MNI_warp)
+ else:
+ wrappers.convertwarp(ref=ctx.MNI, premat=T1_orig_ud_to_T1_mat,
+ warp1=T1_orig_ud_warp, out=T1_orig_to_MNI_warp)
+
+ # Apply the previously generated transformation
+ wrappers.applywarp(src=T1_orig, ref=ctx.MNI, w=T1_orig_to_MNI_warp,
+ out=T1_brain_to_MNI, rel=True, interp='spline')
+
+ #Create brain mask
+ wrappers.invwarp(ref=T1, warp=T1_to_MNI_warp_coef,
+ out=T1_to_MNI_warp_coef_inv)
+ MNI_var_name = 'MNI/MNI152_T1_1mm_brain_mask.nii.gz'
+ wrappers.applywarp(src=ctx.get_data(MNI_var_name), ref=T1,
+ w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
+ rel=True, interp='trilinear')
+ wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain)
+ wrappers.fslmaths(T1_brain_to_MNI).mul(ctx.get_data(MNI_var_name)).run(T1_brain_to_MNI)
diff --git a/bip/pipelines/struct_T1/T1_defacing.py b/bip/pipelines/struct_T1/T1_defacing.py
new file mode 100755
index 0000000..ff1ff7f
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_defacing.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+#
+# T1_defacing.py - Sub-pipeline with the defacing processing of the T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging, tempdir
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_orig: In,
+ T1_brain_mask: In,
+ T1_to_MNI_linear_mat: In,
+ T1_orig_ud_to_T1_mat: In,
+ T1: Out,
+ T1_orig_defaced: Out,
+ T1_defacing_mask: Out,
+ T1_QC_face_mask_inside_brain_mask: Out,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T1_defacing', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ T1_tmp_4 = tmp_dir + '/T1_tmp_4.nii.gz'
+ T1_tmp_mat = tmp_dir + '/T1_tmp.mat'
+
+ #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)
+ wrappers.concatxfm(atob=T1_tmp_mat,
+ btoc=ctx.get_data('MNI/MNI_to_MNI_BigFoV_facemask.mat'),
+ atoc=T1_tmp_mat)
+ wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat)
+ wrappers.applyxfm(src=ctx.get_data('MNI/MNI152_T1_1mm_BigFoV_facemask'),
+ ref=T1_orig, mat=T1_tmp_mat, out=T1_defacing_mask,
+ interp="trilinear")
+ wrappers.fslmaths(T1_defacing_mask).binv().mul(T1_orig).run(T1_orig_defaced)
+
+ #Defacing T1
+ wrappers.concatxfm(atob=T1_to_MNI_linear_mat,
+ btoc = ctx.get_data('MNI/MNI_to_MNI_BigFoV_facemask.mat'),
+ atoc = T1_tmp_mat)
+ wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat)
+ wrappers.applyxfm(src=ctx.get_data('MNI/MNI152_T1_1mm_BigFoV_facemask'),
+ ref=T1, mat=T1_tmp_mat, out=T1_defacing_mask,
+ interp="trilinear")
+ wrappers.fslmaths(T1_defacing_mask).binv().mul(T1).run(T1)
+
+ #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)
+ wrappers.fslmaths(T1_defacing_mask).thr(0.5).bin().add(T1_tmp_4).run(T1_tmp_4)
+ vals = wrappers.fslstats(T1_tmp_4).V.run()[0]
+ with open(T1_QC_face_mask_inside_brain_mask, 'wt', encoding="utf-8") as f:
+ f.write(str(vals))
diff --git a/bip/pipelines/struct_T1/T1_fast.py b/bip/pipelines/struct_T1/T1_fast.py
new file mode 100755
index 0000000..c912330
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_fast.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+#
+# T1_fast.py - Sub-pipeline with FSL's FAST processing of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import logging
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref, update_closure
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ logs_dir: Ref,
+ T1_unbiased: Out,
+ T1_unbiased_brain: Out,
+ T1_fast_pve_0: Out,
+ T1_fast_pve_1: Out,
+ T1_fast_pve_2: Out,
+ fast_outputs: Out("T1_fast_*")):
+
+ with redirect_logging('T1_fast', outdir=logs_dir),\
+ update_closure(fast_outputs):
+
+ #Run fast
+ wrappers.fast(T1_brain, out = T1_fast_dir + '/T1_brain', b=True)
+
+ #Binarize PVE masks
+ wrappers.fslmaths(T1_fast_pve_0).thr(0.5).bin().run(T1_fast_CSF_mask)
+ wrappers.fslmaths(T1_fast_pve_1).thr(0.5).bin().run(T1_fast_GM_mask)
+ wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(T1_fast_WM_mask)
+
+ #Apply bias field correction to T1
+ wrappers.fslmaths(T1).div(T1_fast_brain_bias).run(T1_unbiased)
+ wrappers.fslmaths(T1_brain).div(T1_fast_brain_bias).run(T1_unbiased_brain)
diff --git a/bip/pipelines/struct_T1/T1_first.py b/bip/pipelines/struct_T1/T1_first.py
new file mode 100755
index 0000000..02729a4
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_first.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python
+#
+# T1_first.py - Sub-pipeline with FSL's FIRST processing of T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import glob
+import logging
+from pipe_tree import In, Out, Ref
+from fsl import wrappers
+from bip.utils import redirect_logging
+
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_unbiased_brain: In,
+ T1_first_unbiased_brain: Ref,
+ T1_first_prefix: Ref,
+ logs_dir: Ref,
+ T1_first_all_fast_firstseg: Out):
+
+ with redirect_logging('T1_first', outdir=logs_dir):
+
+ # Creates a link inside T1_first to T1_unbiased_brain.nii.gz
+ if not os.path.exists(T1_first_unbiased_brain):
+ os.symlink(src="../T1_unbiased_brain.nii.gz",
+ dst=T1_first_unbiased_brain)
+ wrappers.run_first_all(input=T1_first_unbiased_brain,
+ output=T1_first_prefix, b=True)
+
+ for f in glob.glob(T1_first_prefix + "-*_first*.nii.gz"):
+ os.remove(f)
+ for f in glob.glob(T1_first_prefix + "-*_corr*.nii.gz"):
+ os.remove(f)
diff --git a/bip/pipelines/struct_T1/T1_gdc.py b/bip/pipelines/struct_T1/T1_gdc.py
new file mode 100755
index 0000000..bf8f7e4
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_gdc.py
@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+#
+# T1_gdc.py - Sub-pipeline with the Gradient Distortion Correction of the T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from shutil import copyfile
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_orig: In,
+ T1_orig_ud: Out,
+ T1_orig_ud_warp: Out,
+ logs_dir: Ref,
+ T1_GDC: Ref):
+
+ with redirect_logging('T1_gdc', outdir=logs_dir):
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
+ owarp=T1_orig_ud_warp,gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=True)
+ else:
+ copyfile(src=T1_orig, dst=T1_orig_ud)
diff --git a/bip/pipelines/struct_T1/T1_sienax.py b/bip/pipelines/struct_T1/T1_sienax.py
new file mode 100755
index 0000000..e875933
--- /dev/null
+++ b/bip/pipelines/struct_T1/T1_sienax.py
@@ -0,0 +1,145 @@
+#!/usr/bin/env python
+#
+# T1_sienax.py - Sub-pipeline with the SienaX processing of the T1w.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import os
+import logging
+from fsl import wrappers
+from fsl.transform import affine, flirt
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ T1_fast_pve_0: In,
+ T1_fast_pve_1: In,
+ T1_fast_pve_2: In,
+ T1_to_MNI_linear_mat: In,
+ T1_to_MNI_warp_coef_inv: In,
+ T1_sienax_brain_skull: Out,
+ T1_sienax_to_MNI_linear: Out,
+ T1_sienax_to_MNI_linear_mat: Out,
+ T1_sienax_brain_skull_to_MNI_linear: Out,
+ T1_sienax_segperiph: Out,
+ T1_sienax_segvent: Out,
+ T1_sienax_pve_0_segperiph: Out,
+ T1_sienax_pve_1_segperiph: Out,
+ T1_sienax_report: Out,
+ T1_sienax_brain: Ref,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T1_sienax', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ T1_tmp_mat_1 = tmp_dir + '/tmp_mat_1.mat'
+ T1_tmp_mat_2 = tmp_dir + '/tmp_mat_2.mat'
+ T1_tmp_mat_3 = tmp_dir + '/tmp_mat_3.mat'
+
+ FSLDIR = ctx.FSLDIR
+ FSLDATA = FSLDIR + '/data'
+ FSLMNI = FSLDATA + '/standard'
+
+ MNI = FSLMNI + '/MNI152_T1_1mm.nii.gz'
+ MNI_2mm_brain = FSLMNI + '/MNI152_T1_2mm_brain.nii.gz'
+ MNI_2mm_skull = FSLMNI + '/MNI152_T1_2mm_skull.nii.gz'
+ MNI_2mm_structseg = FSLMNI + '/MNI152_T1_2mm_strucseg.nii.gz'
+ MNI_2mm_segperiph = FSLMNI + '/MNI152_T1_2mm_strucseg_periph.nii.gz'
+
+ report = []
+
+ wrappers.bet(T1, T1_sienax_brain, s=True)
+ os.remove(T1_sienax_brain)
+
+ # These 4 lines are equivalent to sienax's pairreg command:
+ # pairreg ${FSLDIR}/data/standard/MNI152_T1_2mm_brain T1_brain
+ # ${FSLDIR}/data/standard/MNI152_T1_2mm_skull T1_brain_skull
+ # T1_to_MNI_linear.mat
+ wrappers.flirt(src=T1_brain, ref=MNI_2mm_brain, omat=T1_tmp_mat_1,
+ schedule = ctx.FSLDIR + '/etc/flirtsch/pairreg1.sch',
+ interp="trilinear")
+ wrappers.flirt(src=T1_sienax_brain_skull, ref=MNI_2mm_skull,
+ omat=T1_tmp_mat_2, init=T1_tmp_mat_1, interp="trilinear",
+ schedule = ctx.FSLDIR + '/etc/flirtsch/pairreg2.sch')
+ wrappers.fixscaleskew(inmat1=T1_tmp_mat_1, inmat2=T1_tmp_mat_2,
+ omat=T1_tmp_mat_3)
+ wrappers.flirt(src=T1_brain, ref=MNI_2mm_brain,
+ omat=T1_sienax_to_MNI_linear_mat, init=T1_tmp_mat_3,
+ schedule = ctx.FSLDIR + '/etc/flirtsch/pairreg3.sch',
+ interp="trilinear")
+
+ matrix = flirt.readFlirt(T1_to_MNI_linear_mat)
+ scales = affine.decompose(matrix)[0]
+
+ vscale = float(scales[0]) * float(scales[1]) * float(scales[2])
+
+ wrappers.applyxfm(src=T1, ref=MNI, out=T1_sienax_to_MNI_linear,
+ mat=T1_sienax_to_MNI_linear_mat, interp="spline")
+ wrappers.applyxfm(src=T1_sienax_brain_skull, ref=MNI,
+ out=T1_sienax_brain_skull_to_MNI_linear,
+ mat=T1_sienax_to_MNI_linear_mat, interp="trilinear")
+
+ wrappers.applywarp(src=MNI_2mm_segperiph, ref=T1,
+ w=T1_to_MNI_warp_coef_inv, out=T1_sienax_segperiph,
+ rel=True, interp='trilinear')
+ wrappers.fslmaths(T1_sienax_segperiph).thr(0.5).bin().run(T1_sienax_segperiph)
+
+ wrappers.fslmaths(MNI_2mm_structseg).thr(4.5).bin().run(T1_sienax_segvent)
+ wrappers.applywarp(src=T1_sienax_segvent, ref=T1,
+ w=T1_to_MNI_warp_coef_inv, out=T1_sienax_segvent,
+ rel=True, interp='nn')
+
+
+ wrappers.fslmaths(T1_fast_pve_1).mas(T1_sienax_segperiph).run(T1_sienax_pve_1_segperiph)
+ V = wrappers.fslstats(T1_sienax_pve_1_segperiph).m.v.run()
+ xa = float(V[0])
+ xb = float(V[2])
+ uxg = xa * xb
+ xg = xa * xb * vscale
+
+ report.append(f'VSCALING {vscale}')
+ report.append('tissue volume unnormalised-volume')
+ report.append(f'pgrey {xg} {uxg} (peripheral grey)')
+
+
+ wrappers.fslmaths(T1_fast_pve_0).mas(T1_sienax_segperiph).run(T1_sienax_pve_0_segperiph)
+ V = wrappers.fslstats(T1_sienax_pve_0_segperiph).m.v.run()
+ xa = float(V[0])
+ xb = float(V[2])
+ uxg = xa * xb
+ xg = xa * xb * vscale
+ report.append(f'vcsf {xg} {uxg} (ventricular CSF)')
+
+
+ V = wrappers.fslstats(T1_fast_pve_1).m.v.run()
+ xa = float(V[0])
+ xb = float(V[2])
+ ugrey = xa * xb
+ ngrey = xa * xb * vscale
+ report.append(f'GREY {ngrey} {ugrey}')
+
+
+ V = wrappers.fslstats(T1_fast_pve_2).m.v.run()
+ xa = float(V[0])
+ xb = float(V[2])
+ uwhite = xa * xb
+ nwhite = xa * xb * vscale
+ report.append(f'WHITE {nwhite} {uwhite}')
+
+ ubrain = ugrey + uwhite
+ nbrain = ngrey + nwhite
+ report.append(f'BRAIN {nbrain} {ubrain}')
+
+ with open(T1_sienax_report, 'wt', encoding="utf-8") as f:
+ for line in report:
+ f.write(f'{line}\n')
diff --git a/bip/pipelines/struct_T1/__init__.py b/bip/pipelines/struct_T1/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/bip/pipelines/struct_T1/struct_T1.py b/bip/pipelines/struct_T1/struct_T1.py
new file mode 100755
index 0000000..c9138cc
--- /dev/null
+++ b/bip/pipelines/struct_T1/struct_T1.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+#
+# struct_T1.py - Pipeline with the T1w processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.struct_T1 import T1_gdc, T1_brain_extract, T1_defacing
+from bip.pipelines.struct_T1 import T1_fast, T1_first, T1_sienax, T1_QC_COG
+from bip.pipelines.struct_T1 import T1_QC_CNR_corners, T1_QC_CNR_eyes
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_struct_T1', outdir=logs_dir):
+ pipe(T1_gdc.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_orig_ud_warp')
+ pipe(T1_brain_extract.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_brain')
+ pipe(T1_defacing.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_QC_face_mask_inside_brain_mask')
+ pipe(T1_fast.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_unbiased_brain')
+ pipe(T1_first.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_first_all_fast_firstseg')
+ pipe(T1_sienax.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_sienax_report')
+ pipe(T1_QC_COG.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_QC_COG')
+ pipe(T1_QC_CNR_corners.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_notNorm_QC_CNR_upper')
+ pipe(T1_QC_CNR_eyes.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T1_QC_CNR_eyes')
+
+ return pipe, targets
diff --git a/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_apply_bfc.py b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_apply_bfc.py
new file mode 100755
index 0000000..c9509a9
--- /dev/null
+++ b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_apply_bfc.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+#
+# T2_FLAIR_apply_bfc.py - Sub-pipeline applying the Bias Field Correction
+# to T2 FLAIR
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import logging
+from fsl import wrappers
+from bip.utils import redirect_logging
+from pipe_tree import In, Out, Ref
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T2_FLAIR: In,
+ T2_FLAIR_brain: In,
+ T1_fast_brain_bias: In,
+ logs_dir: Ref,
+ T2_FLAIR_unbiased: Out,
+ T2_FLAIR_unbiased_brain: Out):
+
+ with redirect_logging('T2_FLAIR_apply_bfc', outdir=logs_dir):
+
+ #Apply bias field correction to T2_FLAIR warped
+ if os.path.isfile(T1_fast_brain_bias):
+ wrappers.fslmaths(T2_FLAIR).div(T1_fast_brain_bias).run(T2_FLAIR_unbiased)
+ wrappers.fslmaths(T2_FLAIR_brain).div(T1_fast_brain_bias).run(T2_FLAIR_unbiased_brain)
+ else:
+ print("WARNING: There was no bias field estimation. " +
+ "Bias field correction cannot be applied to T2.")
diff --git a/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_brain_extract.py b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_brain_extract.py
new file mode 100755
index 0000000..2264699
--- /dev/null
+++ b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_brain_extract.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+#
+# T2_FLAIR_brain_extract.py - Sub-pipeline with the brain extraction of T2 FLAIR
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from bip.utils import redirect_logging, tempdir
+from pipe_tree import In, Out, Ref, update_closure
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_brain: In,
+ T1_brain_mask: In,
+ T1_orig_ud: In,
+ T1_orig_ud_to_T1_mat: In,
+ T1_to_MNI_linear_mat: In,
+ T1_to_MNI_warp: In,
+ T2_FLAIR_orig: In,
+ T2_FLAIR_orig_ud: In,
+ T2_FLAIR_orig_ud_warp: In,
+ T2_FLAIR_brain: Out,
+ T2_FLAIR_brain_mask: Out,
+ T2_FLAIR_brain_to_MNI: Out,
+ T2_FLAIR_to_MNI_linear_mat: Out,
+ T2_FLAIR_orig_ud_to_T2_FLAIR_mat: Out,
+ T2_FLAIR: Ref,
+ logs_dir: Ref,
+ tmp_dir: Ref,
+ transform_outputs: Ref("T2_FLAIR*_to_*")):
+
+ with redirect_logging('T2_FLAIR_brain_extract', outdir=logs_dir),\
+ tempdir(tmp_dir),\
+ update_closure(transform_outputs):
+
+ T2_FLAIR_tmp_1_mat = tmp_dir + '/T2_FLAIR_tmp_1.mat'
+ T2_FLAIR_tmp_2_mat = tmp_dir + '/T2_FLAIR_tmp_2.mat'
+
+ #Take T2 to T1 and also the brain mask
+ wrappers.flirt(src=T2_FLAIR_orig_ud, ref=T1_orig_ud,
+ omat=T2_FLAIR_tmp_1_mat, dof = 6)
+ wrappers.concatxfm(atob=T2_FLAIR_tmp_1_mat, btoc=T1_orig_ud_to_T1_mat,
+ atoc=T2_FLAIR_tmp_2_mat)
+
+ #The T2_FLAIR.nii.gz in the output of this
+ #flirt will be overwritten in case of GDC
+ wrappers.flirt(src=T2_FLAIR_orig_ud, ref=T1_brain,
+ refweight=T1_brain_mask, nosearch=True,
+ init=T2_FLAIR_tmp_2_mat, out=T2_FLAIR,
+ omat=T2_FLAIR_orig_ud_to_T2_FLAIR_mat, dof = 6)
+
+ if ctx.gdc != '' :
+ wrappers.applywarp(src=T2_FLAIR_orig, ref=T1_brain, out=T2_FLAIR,
+ w=T2_FLAIR_orig_ud_warp,
+ postmat=T2_FLAIR_orig_ud_to_T2_FLAIR_mat,
+ rel=True, interp='spline')
+
+ copyfile(src=T1_brain_mask, dst=T2_FLAIR_brain_mask)
+ wrappers.fslmaths(T2_FLAIR).mul(T2_FLAIR_brain_mask).run(T2_FLAIR_brain)
+
+ #Generate the linear matrix from T2 to MNI (Needed for defacing)
+ wrappers.concatxfm(atob=T2_FLAIR_orig_ud_to_T2_FLAIR_mat,
+ btoc=T2_FLAIR_tmp_1_mat,
+ atoc=T2_FLAIR_orig_ud_to_MNI_linear_mat)
+ copyfile(src=T1_to_MNI_linear_mat, dst=T2_FLAIR_to_MNI_linear_mat)
+
+ #Generate the non-linearly warped T2 in MNI
+ #(Needed for post-freesurfer processing)
+ if ctx.gdc != '' :
+ wrappers.convertwarp(ref=ctx.MNI, warp1=T2_FLAIR_orig_ud_warp,
+ midmat=T2_FLAIR_orig_ud_to_T2_FLAIR_mat,
+ warp2=T1_to_MNI_warp,
+ out=T2_FLAIR_orig_to_MNI_warp)
+ else:
+ wrappers.convertwrap(ref=ctx.MNI,
+ premat=T2_FLAIR_orig_ud_to_T2_FLAIR_mat,
+ warp1=T1_to_MNI_warp,
+ out=T2_FLAIR_orig_to_MNI_warp)
+ wrappers.applywarp(src=T2_FLAIR_orig, ref=ctx.MNI,
+ out=T2_FLAIR_brain_to_MNI,
+ w=T2_FLAIR_orig_to_MNI_warp,
+ rel=True, interp='spline')
+ wrappers.fslmaths(T2_FLAIR_brain_to_MNI).mul(ctx.MNI_brain_mask).run(T2_FLAIR_brain_to_MNI)
diff --git a/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_defacing.py b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_defacing.py
new file mode 100755
index 0000000..bd4d8ed
--- /dev/null
+++ b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_defacing.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+#
+# T2_FLAIR_defacing.py - Sub-pipeline with defacing of the of T2 FLAIR.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging, tempdir
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T2_FLAIR_orig: In,
+ T2_FLAIR_brain_mask: In,
+ T2_FLAIR_to_MNI_linear_mat: In,
+ T2_FLAIR_orig_ud_to_T2_FLAIR_mat: In,
+ T2_FLAIR_orig_defaced: Out,
+ T2_FLAIR_defacing_mask: Out,
+ T2_FLAIR_orig_defacing_mask: Out,
+ T2_FLAIR: Out,
+ logs_dir: Ref,
+ tmp_dir: Ref):
+
+ with redirect_logging('T2_FLAIR_defacing', outdir=logs_dir),\
+ tempdir(tmp_dir):
+
+ T2_FLAIR_tmp_1 = tmp_dir + '/T2_FLAIR_tmp_1nii.gz'
+ T2_FLAIR_tmp_3_mat = tmp_dir + '/T2_FLAIR_tmp_3.mat'
+
+ BigFoV_mask_mat = 'MNI/MNI_to_MNI_BigFoV_facemask.mat'
+ BigFoV_mask = 'MNI/MNI152_T1_1mm_BigFoV_facemask'
+
+ #TODO: Replace this part with proper call to fsl_deface
+ #Defacing T2_FLAIR_orig
+ wrappers.concatxfm(atob=T2_FLAIR_orig_ud_to_T2_FLAIR_mat,
+ btoc=T2_FLAIR_to_MNI_linear_mat,
+ atoc=T2_FLAIR_tmp_3_mat)
+ wrappers.concatxfm(atob=T2_FLAIR_tmp_3_mat,
+ btoc=ctx.get_data(BigFoV_mask_mat),
+ atoc=T2_FLAIR_tmp_3_mat)
+ wrappers.invxfm(inmat=T2_FLAIR_tmp_3_mat, omat=T2_FLAIR_tmp_3_mat)
+ wrappers.applyxfm(src=ctx.get_data(BigFoV_mask),
+ ref=T2_FLAIR_orig, mat=T2_FLAIR_tmp_3_mat,
+ out=T2_FLAIR_orig_defacing_mask,
+ interp="trilinear")
+ wrappers.fslmaths(T2_FLAIR_orig_defacing_mask).binv().mul(T2_FLAIR_orig).run(T2_FLAIR_orig_defaced)
+
+ #Defacing T2_FLAIR
+ copyfile(src=T2_FLAIR, dst=T2_FLAIR_tmp_1)
+ wrappers.concatxfm(atob=T2_FLAIR_to_MNI_linear_mat,
+ btoc=ctx.get_data(BigFoV_mask_mat),
+ atoc=T2_FLAIR_tmp_3_mat)
+ wrappers.invxfm(inmat=T2_FLAIR_tmp_3_mat, omat=T2_FLAIR_tmp_3_mat)
+ wrappers.applyxfm(src=ctx.get_data(BigFoV_mask),
+ ref=T2_FLAIR, mat=T2_FLAIR_tmp_3_mat,
+ out=T2_FLAIR_defacing_mask, interp="trilinear")
+ wrappers.fslmaths(T2_FLAIR_defacing_mask).binv().mul(T2_FLAIR).run(T2_FLAIR)
diff --git a/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_gdc.py b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_gdc.py
new file mode 100755
index 0000000..b3f00d7
--- /dev/null
+++ b/bip/pipelines/struct_T2_FLAIR/T2_FLAIR_gdc.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+#
+# T2_FLAIR_gdc.py - Sub-pipeline with the Gradient Distortion Correction
+# of the T2 FLAIR.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from shutil import copyfile
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T2_FLAIR_orig: In,
+ T2_FLAIR_orig_ud: Out,
+ T2_FLAIR_orig_ud_warp: Out,
+ logs_dir: Ref,
+ T2_FLAIR_GDC: Ref):
+
+ with redirect_logging('T2_FLAIR_gdc', outdir=logs_dir):
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=T2_FLAIR_GDC, infile=T2_FLAIR_orig,
+ outfile=T2_FLAIR_orig_ud,
+ owarp=T2_FLAIR_orig_ud_warp,gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=True)
+ else:
+ copyfile(src=T2_FLAIR_orig, dst=T2_FLAIR_orig_ud)
diff --git a/bip/pipelines/struct_T2_FLAIR/__init__.py b/bip/pipelines/struct_T2_FLAIR/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bip/pipelines/struct_T2_FLAIR/struct_T2_FLAIR.py b/bip/pipelines/struct_T2_FLAIR/struct_T2_FLAIR.py
new file mode 100755
index 0000000..2c08a11
--- /dev/null
+++ b/bip/pipelines/struct_T2_FLAIR/struct_T2_FLAIR.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+#
+# struct_T2_FLAIR.py - Pipeline with the T2 FLAIR processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.struct_T2_FLAIR import T2_FLAIR_gdc, T2_FLAIR_brain_extract
+from bip.pipelines.struct_T2_FLAIR import T2_FLAIR_defacing, T2_FLAIR_apply_bfc
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_struct_T2_FLAIR', outdir=logs_dir):
+ pipe(T2_FLAIR_gdc.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T2_FLAIR_orig_ud_warp')
+ pipe(T2_FLAIR_brain_extract.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T2_FLAIR_brain')
+ pipe(T2_FLAIR_defacing.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T2_FLAIR_defacing_mask')
+ pipe(T2_FLAIR_apply_bfc.run, submit=dict(jobtime=200),
+ kwargs={'ctx' : ctx})
+ targets.append('T2_FLAIR_unbiased_brain')
+
+ return pipe, targets
diff --git a/bip/pipelines/struct_asl/asl_get_IDPs.py b/bip/pipelines/struct_asl/asl_get_IDPs.py
new file mode 100755
index 0000000..4d19117
--- /dev/null
+++ b/bip/pipelines/struct_asl/asl_get_IDPs.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+#
+# asl_get_IDPs.py - Sub-pipeline generating the IDPs of the ASL pipeline.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613,R1718
+#
+
+import logging
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ region_analysis_gm_csv: In,
+ logs_dir: Ref,
+ ASL_region_analysis_dir: Ref,
+ ASL_IDPs: Out):
+
+ with redirect_logging('asl_get_IDPs', outdir=logs_dir):
+
+ result = ctx.subject
+
+ asl_format_file = ctx.get_data("asl/asl_format.txt")
+
+ with open(asl_format_file, 'rt', encoding="utf-8") as f:
+ asl_format = [x.strip().split() for x in f.readlines()]
+
+ regions = set([x[0] for x in asl_format])
+
+ for region in regions:
+ file_name_1 = ASL_region_analysis_dir + "/" + region + ".csv"
+ file_name_2 = ASL_region_analysis_dir + "/" + region + ".txt"
+
+ with open(file_name_1, 'rt', encoding="utf-8") as f:
+ file_data = f.read()
+
+ file_data = file_data.replace(", ", "_-_")
+ file_data = file_data.replace('"', "")
+ file_data = file_data.replace(" ", "_")
+
+ with open(file_name_2, 'wt', encoding="utf-8") as f:
+ f.write(file_data)
+
+
+ for elem in asl_format:
+
+ fil = elem[0]
+ row = elem[1]
+ column = elem[2]
+
+ file_name = ASL_region_analysis_dir + "/" + fil + ".txt"
+
+ with open(file_name, 'rt', encoding="utf-8") as f:
+ file_data = [x.strip().split(",") for x in f.readlines()]
+
+ for line in file_data:
+ if line[0] == row:
+ val = line[int(column)]
+ if val == "":
+ val = "NaN"
+
+ result += " " + val
+
+ with open(ASL_IDPs, 'wt', encoding="utf-8") as f:
+ f.write(result + "\n")
+
\ No newline at end of file
diff --git a/bip/pipelines/struct_asl/asl_proc.py b/bip/pipelines/struct_asl/asl_proc.py
new file mode 100755
index 0000000..94d3361
--- /dev/null
+++ b/bip/pipelines/struct_asl/asl_proc.py
@@ -0,0 +1,167 @@
+#!/usr/bin/env python
+#
+# asl_proc.py - Sub-pipeline with the ASL main processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613
+#
+
+import os
+import glob
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+from bip.utils import redirect_logging
+from bip.commands.bb_read_json_field import bb_read_json_field
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain: In,
+ T1_to_MNI_warp: In,
+ fieldmap_fout_to_T1_brain_rad: In,
+ ASL_M0: In,
+ ASL_M0_json: In,
+ logs_dir: Ref,
+ ASL_GDC: Ref,
+ OXASL_ra_dir: Ref,
+ T1_fast_prefix: Ref,
+ ASL_PLD_prefix: Ref,
+ ASL_raw_dir: Ref,
+ ASL_M0_ud: Out,
+ ASL_M0_ud_warp: Out,
+ ASL_DATA_wrongorder: Out,
+ ASL_DATA: Out,
+ ASL_DATA_diff: Out,
+ ASL_DATA_diff_mean: Out,
+ ASL_control: Out,
+ ASL_label: Out,
+ CALIB: Out,
+ CALIB_json: Out,
+ ASL_qc_modelfit: Out,
+ ASL_mni_right_amygdala: Out,
+ ASL_masks_right_amygdala: Out,
+ ASL_native_right_amygdala: Out,
+ ASL_struct_90_wm: Out,
+ ASL_calib_M0: Out,
+ ASL_struct_ACBV_calib: Out,
+ ASL_std_ACBV_calib: Out,
+ region_analysis_gm_csv: Out):
+
+ with redirect_logging('asl_proc', outdir=logs_dir):
+
+ BBASL_ROI_DIR = ctx.get_data("asl/ukb_rois/")
+
+ ###############################################
+ # Initial check for Phase Encoding Directions #
+ ###############################################
+ list_PEDs = []
+
+ for fil in glob.glob(ASL_raw_dir + "/*.json"):
+ list_PEDs.append(bb_read_json_field(fileName=fil,
+ fieldName="PhaseEncodingDirection"))
+
+ if len(list_PEDs) != 12:
+ error_msg = "ERROR: Wrong phase-encoding-direction in some of the "
+ error_msg += "files (or wrong number of files). "
+ error_msg += "ASL will not be processed."
+ print(error_msg)
+ # TODO: Make unusable this modality
+ # bb_make_unusable ASL $src "2 Wrong_acquisition"
+
+ ##################
+ # Pre-processing #
+ ##################
+ list_fils_control = glob.glob(ASL_PLD_prefix + "*_control.nii.gz")
+ list_fils_control.sort()
+ list_fils_label = glob.glob(ASL_PLD_prefix + "*_label.nii.gz")
+ list_fils_label.sort()
+
+ wrappers.fslmerge("t", ASL_control, *list_fils_control)
+ wrappers.fslmerge("t", ASL_label, *list_fils_label)
+ wrappers.fslmerge("t", ASL_DATA_wrongorder, ASL_label, ASL_control)
+
+ os.symlink("../raw/ASL_M0.nii.gz", CALIB)
+ os.symlink("../raw/ASL_M0.json", CALIB_json)
+
+ TE = bb_read_json_field(fileName=CALIB_json, fieldName="EchoTime",
+ rounding = 3, multFactor=1000)
+
+ #Gradient distortion correction applied to the M0
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=ASL_GDC, infile=CALIB, outfile=ASL_M0_ud,
+ owarp=ASL_M0_ud_warp,gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=False)
+ else:
+ copyfile(src=CALIB, dst=ASL_M0_ud)
+
+
+ ##############
+ # Processing #
+ ##############
+ # Use asl_file to get into alternating tag-control
+ wrappers.asl_file(data=ASL_DATA_wrongorder, out=ASL_DATA, ntis=5,
+ iaf="tcb")
+
+ # PWI generation - this is more of a sanity check than anything else
+ # now use ASL file to get difference images
+ wrappers.asl_file(data=ASL_DATA, out=ASL_DATA_diff,
+ mean=ASL_DATA_diff_mean, ntis=5, iaf="tc", diff=True)
+
+
+ #######################
+ # PERFUSION ANALSYSIS #
+ #######################
+
+ raa_list = [BBASL_ROI_DIR + "MNI_seg_max_prob_masked_RandL.nii.gz",
+ BBASL_ROI_DIR + "HO_L_Cerebral_WM_thr80.nii.gz",
+ BBASL_ROI_DIR + "HO_R_Cerebral_WM_thr80.nii.gz",
+ BBASL_ROI_DIR + "VascularTerritories_ero.nii.gz"]
+ raa_labels_list = [BBASL_ROI_DIR + "MNI_seg_max_prob_masked_RandL.txt",
+ BBASL_ROI_DIR + "HO_L_Cerebral_WM_thr80.txt",
+ BBASL_ROI_DIR + "HO_R_Cerebral_WM_thr80.txt",
+ BBASL_ROI_DIR + "VascularTerritories_ero.txt"]
+
+ wrappers.oxford_asl(data=ASL_DATA,
+ out=OXASL_ra_dir,
+ c=CALIB,
+ s=T1,
+ regfrom_method="pwi",
+ reg_init_bbr=True,
+ sbrain=T1_brain,
+ fastsrc=T1_fast_prefix,
+ warp=T1_to_MNI_warp,
+ fmap=fieldmap_fout_to_T1_brain_rad,
+ fmapmag=T1,
+ fmapmagbrain=T1_brain,
+ gdcwarp=ASL_M0_ud_warp,
+ iaf="tc",
+ ibf="rpt",
+ mc="on",
+ tis=[2.2, 2.6, 3.0, 3.4, 3.8],
+ casl=True,
+ bolus=1.8,
+ fixbolus=True,
+ cgain=10,
+ spatial=True,
+ tr=5,
+ te=TE,
+ echospacing=0.0005,
+ pedir="x",
+ gm_thresh=0.7,
+ cmethod="voxel",
+ nofmapreg=True,
+ region_analysis=True,
+ region_analysis_atlas= raa_list,
+ region_analysis_atlas_labels= raa_labels_list,
+ region_analysis_save_rois=True,
+ qc_output=True)
diff --git a/bip/pipelines/struct_asl/struct_asl.py b/bip/pipelines/struct_asl/struct_asl.py
new file mode 100755
index 0000000..2f172e0
--- /dev/null
+++ b/bip/pipelines/struct_asl/struct_asl.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+#
+# struct_asl.py - Pipeline with the ASL processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.struct_asl import asl_proc
+from bip.pipelines.struct_asl import asl_get_IDPs
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_struct_asl', outdir=logs_dir):
+ pipe(asl_proc.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('region_analysis_gm_csv')
+ pipe(asl_get_IDPs.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('ASL_IDPs')
+
+ return pipe, targets
diff --git a/bip/pipelines/struct_swMRI/struct_swMRI.py b/bip/pipelines/struct_swMRI/struct_swMRI.py
new file mode 100755
index 0000000..6bb4c65
--- /dev/null
+++ b/bip/pipelines/struct_swMRI/struct_swMRI.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+#
+# struct_swMRI.py - Pipeline with the swMRI processing.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from bip.utils import redirect_logging
+from bip.pipelines.struct_swMRI import swMRI_proc
+
+log = logging.getLogger(__name__)
+
+def add_to_pipeline(ctx, pipe, tree, targets):
+
+ logs_dir=tree.get('logs_dir')
+
+ with redirect_logging('pipe_struct_swMRI', outdir=logs_dir):
+ pipe(swMRI_proc.run, submit=dict(jobtime=200), kwargs={'ctx' : ctx})
+ targets.append('SWI')
+
+ return pipe, targets
diff --git a/bip/pipelines/struct_swMRI/swMRI_gdc.py b/bip/pipelines/struct_swMRI/swMRI_gdc.py
new file mode 100755
index 0000000..d9c2880
--- /dev/null
+++ b/bip/pipelines/struct_swMRI/swMRI_gdc.py
@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+#
+# swMRI_gdc.py - Sub-pipeline with Gradient Distortion Correction of the swMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import logging
+from shutil import copyfile
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1_orig: In,
+ T1_orig_ud: Out,
+ T1_orig_ud_warp: Out,
+ logs_dir: Ref,
+ T1_GDC: Ref):
+
+ with redirect_logging('T1_gdc', outdir=logs_dir):
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
+ owarp=T1_orig_ud_warp,gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=True)
+ else:
+ copyfile(src=T1_orig, dst=T1_orig_ud)
diff --git a/bip/pipelines/struct_swMRI/swMRI_proc.py b/bip/pipelines/struct_swMRI/swMRI_proc.py
new file mode 100755
index 0000000..86c5cd3
--- /dev/null
+++ b/bip/pipelines/struct_swMRI/swMRI_proc.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+#
+# swMRI_proc.py - Sub-pipeline with the main processing of swMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613,R0912
+#
+
+import os
+import logging
+from shutil import copyfile
+from fsl import wrappers
+from pipe_tree import In, Out, Ref
+from bip.utils import redirect_logging
+from bip.pipelines.struct_swMRI.swMRI_proc_fnc import combine_magnitude_coils
+from bip.pipelines.struct_swMRI.swMRI_proc_fnc import gen_filtered_phase
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+ T1: In,
+ T1_brain_mask: In,
+ SWI_MAG_TE1_orig: In,
+ SWI_MAG_TE2_orig: In,
+ SWI_num_coils: In,
+ SWI_complex_phase: In,
+ logs_dir: Ref,
+ SWI_MAG_TE1_C01: Ref,
+ SOS: Ref,
+ MAG_TE1_dir: Ref,
+ MAG_TE2_dir: Ref,
+ PHA_TE2_dir: Ref,
+ R2star: Out,
+ T2star: Out,
+ T2star_to_T1: Out,
+ SOS_TE1: Out,
+ SOS_TE2: Out,
+ SOS_ratio: Out,
+ SWI: Out,
+ SWI_brain_mask: Out,
+ SWI_GDC: Out,
+ SWI_MAG_TE1: Out,
+ SWI_TOTAL_MAG_orig_ud_warp: Out,
+ SWI_to_T1: Out,
+ SWI_to_T1_mat: Out,
+ T1_to_SWI_mat: Out,
+ filtered_phase: Out):
+
+ with redirect_logging('swMRI_proc', outdir=logs_dir):
+
+ with open(SWI_num_coils, 'r', encoding="utf-8") as f:
+ num_coils = int(f.read())
+ with open(SWI_complex_phase, 'r', encoding="utf-8") as f:
+ complex_phase = int(f.read())
+ if complex_phase == 0:
+ complex_phase = False
+ else:
+ complex_phase = True
+
+ MAG_TE1 = SWI_MAG_TE1_orig
+ if os.path.exists(SWI_MAG_TE2_orig):
+ MAG_TE2 = SWI_MAG_TE2_orig
+ else:
+ MAG_TE2 = ""
+
+ if num_coils > 0:
+ if os.path.exists(SWI_MAG_TE1_C01):
+ combine_magnitude_coils(MAG_TE1, MAG_TE2, MAG_TE1_dir,
+ MAG_TE2_dir, num_coils, SOS_TE1,
+ SOS_TE2, SOS_ratio, R2star, T2star)
+
+ # Generate filtered_phase and SWI
+ gen_filtered_phase(ctx, MAG_TE2_dir, PHA_TE2_dir, SWI_MAG_TE2_orig,
+ filtered_phase, SWI)
+
+ if ctx.gdc != '':
+ #Calculate and apply the Gradient Distortion Unwarp
+ # TODO: Review the "half=True" in next version
+ gradient_unwarp_apply(WD=SWI_GDC, infile=SWI_MAG_TE1_orig,
+ outfile=SWI_MAG_TE1,
+ owarp=SWI_TOTAL_MAG_orig_ud_warp,
+ gradcoeff=ctx.gdc,
+ vendor='siemens', nojac=True, half=False)
+ else:
+ copyfile(src=SWI_MAG_TE1_orig, dst=SWI_MAG_TE1)
+
+ wrappers.flirt(src=SWI_MAG_TE1, ref=T1, omat=SWI_to_T1_mat, dof=6,
+ out=SWI_to_T1, interp="trilinear")
+
+ wrappers.invxfm(inmat=SWI_to_T1_mat, omat=T1_to_SWI_mat)
+
+ #TODO: BINARISE BRAIN MASK?
+ wrappers.applyxfm(src=T1_brain_mask, ref=SWI_MAG_TE1,
+ mat=T1_to_SWI_mat, out=SWI_brain_mask,
+ interp="trilinear")
+
+ if ctx.gdc != '':
+ wrappers.applywarp(src=T2star, ref=T1, w=SWI_TOTAL_MAG_orig_ud_warp,
+ out=T2star_to_T1, postmat=SWI_to_T1_mat,
+ rel=True, interp='trilinear')
+ else:
+ wrappers.applyxfm(src=T2star, ref=T1, mat=SWI_to_T1_mat,
+ out=T2star_to_T1, interp="trilinear")
+
+ wrappers.fslmaths(T2star_to_T1).mul(T1_brain_mask).run(T2star_to_T1)
+
+ #TODO: Change name of SWI to venogram?
+ for fil in [R2star, T2star, SOS_TE1, SOS_TE2, SOS_ratio, filtered_phase,
+ SWI]:
+ if os.path.exists(fil):
+ if ctx.gdc != '':
+ wrappers.applywarp(src=fil, ref=fil, out=fil, rel=True,
+ w=SWI_TOTAL_MAG_orig_ud_warp,
+ interp='trilinear')
+
+ wrappers.fslmaths(fil).mul(SWI_brain_mask).run(fil)
diff --git a/bip/pipelines/struct_swMRI/swMRI_proc_fnc.py b/bip/pipelines/struct_swMRI/swMRI_proc_fnc.py
new file mode 100755
index 0000000..141b89c
--- /dev/null
+++ b/bip/pipelines/struct_swMRI/swMRI_proc_fnc.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+#
+# swMRI_proc_fnc.py - Functions used by the main processing of swMRI.
+#
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+# Author: Michiel Cottaar <michiel.cottaar@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+#
+
+import glob
+import nibabel as nib
+from fsl import wrappers
+import numpy as np
+import numpy.fft as ft
+from bip.commands.bb_read_json_field import bb_read_json_field
+
+def combine_magnitude_coils(MAG_TE1, MAG_TE2, MAG_TE1_dir, MAG_TE2_dir,
+ num_coils, SOS_TE1, SOS_TE2, SOS_ratio, R2star,
+ T2star):
+
+ MAG_TE1_J = MAG_TE1.replace('.nii.gz', '.json')
+ MAG_TE2_J = MAG_TE2.replace('.nii.gz', '.json')
+
+ TE1 = bb_read_json_field(fileName=MAG_TE1_J,
+ fieldName="EchoTime",
+ rounding = 3, multFactor=1000)
+ TE2 = bb_read_json_field(fileName=MAG_TE2_J,
+ fieldName="EchoTime",
+ rounding = 3, multFactor=1000)
+
+ TE_DIFF = TE2 - TE1
+
+ if not num_coils == 0:
+
+ files_TE1 = glob.glob(MAG_TE1_dir + '/*.nii.gz')
+ wrappers.fslmaths(files_TE1[0]).mul(0).run(SOS_TE1)
+ for file_TE1 in files_TE1:
+ wrappers.fslmaths(file_TE1).sqr().add(SOS_TE1).run(SOS_TE1)
+ wrappers.fslmaths(SOS_TE1).sqrt().run(SOS_TE1)
+
+ files_TE2 = glob.glob(MAG_TE2_dir + '/*.nii.gz')
+ wrappers.fslmaths(files_TE2[0]).mul(0).run(SOS_TE2)
+ for file_TE2 in files_TE2:
+ wrappers.fslmaths(file_TE2).sqr().add(SOS_TE2).run(SOS_TE2)
+ wrappers.fslmaths(SOS_TE2).sqrt().run(SOS_TE2)
+
+ wrappers.fslmaths(SOS_TE1).div(SOS_TE2).run(SOS_ratio)
+
+ else:
+ wrappers.fslmaths(MAG_TE1).div(MAG_TE2).run(SOS_ratio)
+
+ wrappers.fslmaths(SOS_ratio).log().div(TE_DIFF).run(R2star)
+ wrappers.fslmaths(R2star).recip().uthr(80).thr(5).kernel("2D").fmedian().kernel("sphere", "3").dilM().run(T2star)
+
+
+def gen_filtered_phase(ctx, magImgDir, phaImgDir, magImgFileName, fp_fn,SWI_fn):
+
+ magImgFiles=glob.glob(magImgDir + '/*.nii.gz')
+ phaImgFiles=glob.glob(phaImgDir + '/*.nii.gz')
+ magImgFiles.sort()
+ phaImgFiles.sort()
+
+ chaDim = len(magImgFiles)
+
+ # read one data set for getting the header
+ magImg = nib.load(magImgFileName)
+ magImgNOBIAS = magImg
+ [xDim, yDim, zDim] = magImg.header['dim'][1:4]
+
+ # Loading Hanning filter of 96 dim (actually, 97 dim because of symmetry)
+ w = np.loadtxt(ctx.get_data('swMRI/hann_filter.txt'), delimiter=",")
+ filterLP = np.zeros((xDim,yDim))
+ indX = slice(int(xDim/2+1-96/2)-1, int(xDim/2+1+96/2),1)
+ indY = slice(int(yDim/2+1-96/2)-1, int(yDim/2+1+96/2),1)
+ filterLP[indX, indY] = w
+
+ SOSImg = np.zeros((xDim,yDim,zDim))
+ complexAvg = np.zeros((xDim,yDim,zDim))
+
+ # Loop over channels
+ for chaInd in range(chaDim):
+ magImgFileName = magImgFiles[chaInd]
+ phaImgFileName = phaImgFiles[chaInd]
+
+ magImg_nib = nib.load(magImgFileName)
+ phaImg_nib = nib.load(phaImgFileName)
+
+ magImg = magImg_nib.get_fdata()
+ phaImg = phaImg_nib.get_fdata()
+
+ phaImg = -np.pi * (phaImg - 2048) / 2048
+
+ complexImg = magImg * np.exp(1j * phaImg)
+ phaseHP = np.zeros((xDim,yDim,zDim))
+
+ for zInd in range(zDim):
+ complexFFT = ft.fftshift(ft.fft2(ft.ifftshift(complexImg[:,:,zInd])))
+ complexFFT = complexFFT * filterLP
+ complexImgLP = ft.fftshift(ft.ifft2(ft.ifftshift(complexFFT)))
+ phaseHP[:,:,zInd] = np.angle(complexImg[:,:,zInd] * np.conj(complexImgLP))
+
+ complexAvg = complexAvg + magImg * np.exp(1j * phaseHP)
+ SOSImg = SOSImg + (magImg * magImg)
+
+ SOSImg = np.power(SOSImg, 0.5)
+ fp = np.angle(complexAvg)
+
+ maskSWI = np.zeros((xDim,yDim,zDim))
+ maskSWI[np.where(fp>0)] = 1
+ maskSWI[np.where(fp<=0)] = 1 + fp[np.where(fp<=0)] / np.pi
+ maskSWI = maskSWI * maskSWI * maskSWI * maskSWI
+
+ # Using prescan-normalised mag image instead of bias-fielded SOS
+ SWI = magImgNOBIAS.get_fdata() * maskSWI
+
+ fp_tmp = nib.Nifti1Image(fp, magImgNOBIAS.affine, magImgNOBIAS.header)
+ SWI_tmp = nib.Nifti1Image(SWI, magImgNOBIAS.affine, magImgNOBIAS.header)
+ nib.save(fp_tmp, fp_fn)
+ nib.save(SWI_tmp, SWI_fn)
diff --git a/bip/utils.py b/bip/utils.py
new file mode 100755
index 0000000..adf57f1
--- /dev/null
+++ b/bip/utils.py
@@ -0,0 +1,116 @@
+import os
+import os.path as op
+import socket
+import shutil
+import inspect
+import logging
+import traceback
+import contextlib
+import functools as ft
+from fsl import wrappers
+
+
+def create_formatter():
+ """Create a logging.Formatter. """
+ return logging.Formatter('%(asctime)s '
+ '%(filename)s '
+ '%(lineno)4d: '
+ '%(funcName)s - '
+ '%(message)s',
+ datefmt='%m/%d/%Y %H:%M:%S')
+
+
+def setup_logging(fname, level=logging.INFO):
+ """Set up BIP logging. Intended to be called once from main
+ at application startup.
+ """
+
+ log = logging.getLogger('bip')
+ handler = logging.FileHandler(fname)
+ formatter = create_formatter()
+
+ # Add handler/formatter references
+ # to the top level logger so the
+ # redirect_logging function can
+ # manipulate them.
+ log.formatter = formatter
+ log.handler = handler
+
+ handler.setFormatter(formatter)
+ log.addHandler(handler)
+ log.setLevel(level)
+
+
+def log_bip(logobj, level, msg):
+ """Log a message, associating it with the nearest line in the
+ bip python codebase in the call stack.
+ """
+ frames = inspect.stack()[1:]
+ for i, frame in enumerate(frames):
+ if 'bip' in frame[1]:
+ break
+ logobj.log(level, msg.strip(), stacklevel=i + 2)
+
+
+@contextlib.contextmanager
+def redirect_logging(name, outdir='.', level=logging.INFO):
+ """Context manager which temporarily redirects logging. The following log
+ files are created:
+
+ - outdir/name.log: contains log messages, commands that are executed
+ through fsl.wrappers functions, and standard output/error of those
+ commands.
+ """
+
+ fname = op.join(outdir, name)
+
+ with open(f'{fname}.log', 'wt', buffering=1,
+ encoding="utf-8") as stdout:
+ log = logging.getLogger('bip')
+ handler = logging.StreamHandler(stdout)
+ handler.setFormatter(create_formatter())
+ log.addHandler(handler)
+ log.setLevel(level)
+
+ # If running within a single process, we
+ # want to temporarily suppress log messages
+ # from the main log file (assumed to have
+ # been configured with setup_logging).
+ if getattr(log, 'handler', None):
+ log.removeHandler(log.handler)
+
+ # Log all fsl.wrappers calls via log_bip
+ logcmd = ft.partial(log_bip, log, level)
+
+ # Redirect fsl.wrappers stdout/stderr to
+ # the same
+ with wrappers.wrapperconfig(log={'tee' : False,
+ 'stdout' : stdout,
+ 'stderr' : stdout,
+ 'cmd' : logcmd}):
+ try:
+ log.info(f'Started {name}')
+ log.info(f'Host {socket.gethostname()}')
+ yield
+ except Exception as e:
+ traceback.print_exception(type(e), e, e.__traceback__,
+ file=stdout)
+ raise e
+ finally:
+ log.info(f'Finished {name}')
+ log.removeHandler(handler)
+ # Restore main log handler
+ if getattr(log, 'handler', None):
+ log.addHandler(log.handler)
+
+
+@contextlib.contextmanager
+def tempdir(dirName):
+ os.makedirs(dirName, exist_ok=True)
+ yield dirName
+ for fileName in os.listdir(dirName):
+ filePath = os.path.join(dirName, fileName)
+ try:
+ shutil.rmtree(filePath)
+ except OSError:
+ os.remove(filePath)
diff --git a/init_vars b/init_vars
new file mode 100755
index 0000000..28f0319
--- /dev/null
+++ b/init_vars
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+export BB_BIN_DIR="$SWdir/bip"
+export FSLDIR="$SWdir/fsl"
+
+source $FSLDIR/etc/fslconf/fsl.sh
+source $FSLDIR/bin/activate ukb
+
+export FSLOUTPUTTYPE="NIFTI_GZ"
+export FSLSUB_CONF="$SWdir/bip/fsl_sub.yml"
+
+unset PYTHONPATH
+unset LC_ALL
+unset LC_CTYPE
+
+alias runbip="$BB_BIN_DIR/bip/main.py"
+PS1="(ukb) \! "`whoami`@`hostname|cut -d. -f1`@$(basename $(tty))" | \t | \w $ " ;
+export PS1
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..35dd721
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+oxasl
+oxasl-deblur
+pickle-mixin
+pickleshare
+niicat
+ipython
+jinja2
+seaborn
+jsonschema
+python-utils
+scikit-image
+scikit-learn
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..8bc548e
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,11 @@
+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,
+ packages=find_packages(),
+ include_package_data=True)
--
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