3 d@sdZddlZddlZddlmZmZmZddlmZm Z m Z m Z m Z m Z mZddlmZmZmZGd d d eZGd d d eZGd ddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZeZeZGdd d eZGd!d"d"eZ Gd#d$d$eZ!Gd%d&d&eZ"Gd'd(d(eZ#Gd)d*d*e!Z$Gd+d,d,e"Z%Gd-d.d.eZ&Gd/d0d0eZ'Gd1d2d2eZ(Gd3d4d4eZ)Gd5d6d6eZ*Gd7d8d8eZ+Gd9d:d:eZ,Gd;d<dd>eZ.Gd?d@d@eZ/GdAdBdBeZ0GdCdDdDeZ1GdEdFdFeZ2GdGdHdHeZ3GdIdJdJeZ4GdKdLdLeZ5GdMdNdNeZ6GdOdPdPeZ7dS)QzThe fsl module provides classes for interfacing with the `FSL `_ command line tools. This was written to work with FSL version 4.1.4. N)fname_presuffixsplit_filenamecopyfile) TraitedSpec isdefinedFile DirectoryInputMultiPathOutputMultiPathtraits) FSLCommandFSLCommandInputSpecInfoc@seZdZeddddddZejdddd dd Zedd d d ddZeddddddZ eddddddZ ej dddZ ej dddZ ej dddZej dddZej dddZej dd dZejd!d"d#Zejd$d%d#Zedd&d'd(Zejd)d*d#Zedd+d,d-Zd.S)/DTIFitInputSpecTz"diffusion weighted image data filez-k %sr)existsdescargstrposition mandatoryZdtifit_z/base_name that all output files will start withz-o %sr)rrr usedefaultzbet binary mask filez-m %srzb vectors filez-r %srz b values filez-b %sz-z %dzmin z)rrz-Z %dzmax zz-y %dzmin yz-Y %dzmax yz-x %dzmin xz-X %dzmax xzsave the elements of the tensorz --save_tensor)rrzoutput sum of squared errorsz--ssezinput counfound regressorsz--cni=%s)rrrz only process small area of brainz --littlebitz--gradnonlin=%szgradient non linearities)rrrN)__name__ __module__ __qualname__r dwir Str base_namemaskbvecsbvalsIntZmin_zZmax_zZmin_yZmax_yZmin_xZmax_xBool save_tensorsseZcniZ little_bit gradnonlinr(r(;/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/fsl/dti.pyrsH  rc@seZdZedddZedddZedddZedddZedddZedddZ edd dZ edd dZ edd dZ edd dZ edd dZedddZdS)DTIFitOutputSpecTz*path/name of file with the 1st eigenvector)rrz*path/name of file with the 2nd eigenvectorz*path/name of file with the 3rd eigenvectorz)path/name of file with the 1st eigenvaluez)path/name of file with the 2nd eigenvaluez)path/name of file with the 3rd eigenvaluez+path/name of file with the mean diffusivityz0path/name of file with the fractional anisotropyz-path/name of file with the mode of anisotropyzDpath/name of file with the raw T2 signal with no diffusion weightingz+path/name of file with the 4D tensor volumez/path/name of file with the summed squared errorN)rrrr ZV1ZV2ZV3ZL1ZL2ZL3ZMDZFAZMOZS0tensorr&r(r(r(r)r*Gs          r*c@s$eZdZdZdZeZeZddZ dS)DTIFitaUse FSL dtifit command for fitting a diffusion tensor model at each voxel Example ------- >>> from nipype.interfaces import fsl >>> dti = fsl.DTIFit() >>> dti.inputs.dwi = 'diffusion.nii' >>> dti.inputs.bvecs = 'bvecs' >>> dti.inputs.bvals = 'bvals' >>> dti.inputs.base_name = 'TP' >>> dti.inputs.mask = 'mask.nii' >>> dti.cmdline 'dtifit -k diffusion.nii -o TP -m mask.nii -r bvecs -b bvals' ZdtifitcCsdddh}|jj|jjd}x,|jD] \}}t|r<|re d?d.Z!e jd@e d+gd?d.Z"e jddAddBdCZ#dDS)GFSLXCommandInputSpecTz --data=%sz"diffusion weighted image data file)rrrrz --mask=%sz&brain binary mask file (i.e. from BET)z --bvecs=%szb vectors filez --bvals=%sz b values file.z --logdir=%s)rrrrz --nfibres=%dz-Maximum number of fibres to fit in each voxel)rlowvaluerrrrz --model=%dzfuse monoexponential (1, default, required for single-shell) or multiexponential (2, multi-shell) model)rrz --fudge=%dzARD fudge factoriz --njumps=%dzNum of jumps to be made by MCMC)rrrrz --burnin=%dz3Total num of jumps at start of MCMC to be discarded)rCrDrrrz--burnin_noard=%dz-num of burnin jumps before the ard is imposedz--sampleevery=%dz#Num of jumps for each sample (MCMC)(z--updateproposalevery=%dz?Num of jumps for each update to the proposal density std (MCMC)z --seed=%dz'seed for pseudo random number generatorno_ardall_ardz--noardzTurn ARD off on all fibres)rxorrz--allardzTurn ARD on on all fibresno_spat non_linearcnlinearz--nospatz%Initialise with tensor, not spatiallyz --nonlinearz!Initialise with nonlinear fittingz --cnonlinearz-Initialise with constrained nonlinear fittingz--ricianzuse Rician noise modelingf0_noardf0_ardz--f0zINoise floor model: add to the model an unattenuated signal compartment f0z --f0 --ardf0z --forcedirzJuse the actual directory name given (do not add + to make a new directory))rrrN)rFrG)rIrJrK)$rrrr rr r!r"r logdirr Rangen_fibresEnummodelr#fudgen_jumpsburn_inZburn_in_no_ard sample_everyZupdate_proposal_everyseedZ _xor_inputs1r$rFrGZ _xor_inputs2rIrJrKricianZ _xor_inputs3rLrM force_dirr(r(r(r)rAs    rAc@seZdZeeddddZeeddddZedddZeeddddZedd dZ edd dZ eeddd dZ eeddd dZ d S)FSLXCommandOutputSpecT)rz(Mean of PDD distribution in vector form.)rz-Samples from the distribution on f anisotropyz%Mean of distribution on diffusivity d)rrz$Mean of distribution on f anisotropyz7Mean of distribution on T2wbaseline signal intensity S0zrAr@rZr4rhr= __classcell__r(r()rlr)rcs  rcc@seZdZeddddZeddddZeddddZeddddZeddZ e j dd d d d dd Z e j d d ddddZe jdddZe jdddddZe j ddddddZe j dd ddddZeddddd d d!Ze jd"d#d$dZedd%d&Ze jd"d'd(Zd)S)*BEDPOSTX5InputSpecTz"diffusion weighted image data file)rrrzbet binary mask filezb vectors filez b values filez --logdir=%s)rrrz-n %dz-Maximum number of fibres to fit in each voxel)rrCrDrrrrz -model %dzfuse monoexponential (1, default, required for single-shell) or multiexponential (2, multi-shell) model)rrz-w %dzARD fudge factoriz-j %dzNum of jumps to be made by MCMC)rrrrz-b %dz3Total num of jumps at start of MCMC to be discarded)rCrDrrrz-s %dz#Num of jumps for each sample (MCMC)bedpostxzoutput directoryz%s)rrrrrFz-gz-consider gradient nonlinearities, default offz(grad_dev file, if gradnonlin, -g is True)rrzUse the GPU version of bedpostx)rN)rrrr rr r!r"r rNr rOrPrQrRr#rSrTrUrVrrr$r'grad_devuse_gpur(r(r(r)rwMsX   rwc@seZdZedddZeeddddZedddZeeddddZeeddd dZ eeddd dZ eeddd dZ eeddd dZ eeddd dZ eeddddZdS)BEDPOSTX5OutputSpecTz%Mean of distribution on diffusivity d)rr)rz$Mean of distribution on f anisotropy)rz7Mean of distribution on T2wbaseline signal intensity S0zMean of distribution on phizMean of distribution on thetaz&Samples from the distribution on thetaz$Samples from the distribution on phiz`_. .. note:: Consider using :func:`niflow.nipype1.workflows.fsl.dmri.create_bedpostx_pipeline` instead. Example ------- >>> from nipype.interfaces import fsl >>> bedp = fsl.BEDPOSTX5(bvecs='bvecs', bvals='bvals', dwi='diffusion.nii', ... mask='mask.nii', n_fibres=1) >>> bedp.cmdline 'bedpostx bedpostx -b 0 --burnin_noard=0 --forcedir -n 1 -j 5000 -s 1 --updateproposalevery=40' rxTc s&tt|jf||jj|jddS)Nrz)rgr__init__r1Zon_trait_change _cuda_update)r9r1)rlr(r)rszBEDPOSTX5.__init__cCs(t|jjr|jjrd|_n|j|_dS)NZ bedpostx_gpu)rr1rzr? _default_cmd)r9r(r(r)rszBEDPOSTX5._cuda_updatecs tjj|jj}tjj|s&tj|t|jj\}}}t |jjtjj |d|t|jj \}}}t |jj tjj |d|t |jj tjj |dt |jj tjj |dt|jjrt|jj\}}}t |jjtjj |d|tt|j|}|d|_|S)NZnodif_brain_maskdatar"r!ryz .bedpostX)rdrnror1rrrmakedirsrr rjoinrr"r!rryrgrrhrf)r9rkZ subjectdirr/extretval)rlr(r)rhs    zBEDPOSTX5._run_interfacecCsv|jj}|jj}ddddddddg}d d g}x |D]}|j||jd ||<q6Wx|D] }g||<qXWxtd |d D]}|dj|jd ||jd |dj|jd||jd |dj|jd||jd |dj|jd||jd |dj|jd||jd |dj|jd||jd |dj|jd||jd |dj|jd||jd qzW|S)Nr~rrr|r}r^rr[r]r_)rmrzmerged_th%dsampleszmerged_f%dsampleszmerged_ph%dsampleszmean_th%dsampleszmean_ph%dsampleszmean_f%dsampleszdyads%dzdyads%d_dispersion)r4r5r1rPr8rfrprq)r9r;rPrsrtr<rur(r(r)r=s@    zBEDPOSTX5._list_outputs)rrrr>r?rrwr@r{r4Z _can_resumerrrhr=rvr(r()rlr)rs  rc@seZdZeddddZdS)XFibres5InputSpecTz--gradnonlin=%sz$gradient file corresponding to slice)rrrN)rrrr r'r(r(r(r)rsrc@seZdZdZdZeZeZdS)XFibres5z\ Perform model parameters estimation for local (voxelwise) diffusion parameters ZxfibresN) rrrr>r?rr@rZr4r(r(r(r)rsrc @s<eZdZeeddddZeeddddZeeddddZej dddddZ eddd dd Z ej eddej eddej ej ejd d d d dddZeedddddZeddddZejdddZeddddZedddddZejdddddZejdd d!ddZejd"d#dZejd$d%dZedd&d'dZedd(d)d*Zedd+d,d*Zed-d.d/Zejd0d1d2dd3Zejd4d5d/Z ej!d6d7d/Z"ej!d8d9d/Z#ejd:d;d/Z$ej!dd?d/Z&ejd@dAd/Z'ej(dBdCdDd dEdFd/Z)ejdGdHd/Z*ejdIdJd/Z+ejdKdLd/Z,ejdMdNd/Z-ej(dBdCdDdOdPdZ.dQS)RProbTrackXBaseInputSpecT)r)rZmergedz(the rootname/base_name for samples filesz --samples=%s)rrrz'bet binary mask file in diffusion spacez-m %s)rrrrr)Zminlenmaxlenz4seed volume(s), or voxel(s) or freesurfer label filez --seed=%s)rrr)ZexitszClist of target masks - required for seeds_to_targets classificationz--targetmasks=%s)rrz[waypoint mask or ascii list of waypoint masks - only keep paths going through ALL the masksz--waypoints=%s)rrrznactivate network mode - only keep paths going through at least one seed mask (required if multiple seed masks)z --networkzUreference vol to define seed space in simple mode - diffusion space assumed if absentz --seedref=%sz--dir=%sz%directory to put the final volumes in)rrrgenfilezOuse the actual directory name given - i.e. do not add + to make a new directoryz --forcedirzoutputs path distributionsz--opdz8correct path distribution for the length of the pathwaysz--pdzOutputs seeds to targetsz--os2tz fibthresh), 2 - to sample in proportion fibres (with f>fibthresh) to f, 3 - to sample ALL populations at random (even if feZdZejdddddddZeddd d Zedd d d Zd S)ProbTrackXInputSpecsimpleZ two_mask_symmseedmaskzhoptions: simple (single seed voxel), seedmask (mask of seed voxels), twomask_symm (two bet binary masks)z --mode=%sT)rrrz@second bet binary mask (in diffusion space) in twomask_symm modez --mask2=%s)rrrz4Freesurfer-type surface descriptor (in ascii format)z --mesh=%sN) rrrr rQmoder Zmask2Zmeshr(r(r(r)rsrc@s^eZdZedddZeeddddZedddZej eddddZ ej eddd dZ d S) ProbTrackXOutputSpecTz6path/name of a text record of the command that was run)rr)rz]path/name of a 3D image file containing the output connectivity distribution to the seed mask)rzpath/name of a text file containing a single number corresponding to the total number of generated tracts that have not been rejected by inclusion/exclusion mask criteriaz/a list with all generated seeds_to_target fileszPFiles describing all of the tract samples. Generated only if verbose is set to 2N) rrrr logr fdt_paths way_totalr rtargetsparticle_filesr(r(r(r)rs rcsTeZdZdZdZeZeZfddZ fddZ fddZ d d Z d d Z ZS) ProbTrackXa Use FSL probtrackx for tractography on bedpostx results Examples -------- >>> from nipype.interfaces import fsl >>> pbx = fsl.ProbTrackX(samples_base_name='merged', mask='mask.nii', seed='MASK_average_thal_right.nii', mode='seedmask', xfm='trans.mat', n_samples=3, n_steps=10, force_dir=True, opd=True, os2t=True, target_masks = ['targets_MASK1.nii', 'targets_MASK2.nii'], thsamples='merged_thsamples.nii', fsamples='merged_fsamples.nii', phsamples='merged_phsamples.nii', out_dir='.') >>> pbx.cmdline 'probtrackx --forcedir -m mask.nii --mode=seedmask --nsamples=3 --nsteps=10 --opd --os2t --dir=. --samples=merged --seed=MASK_average_thal_right.nii --targetmasks=targets.txt --xfm=trans.mat' Z probtrackxc stjdttt|jf|S)Nz7Deprecated: Please use create_bedpostx_pipeline instead)warningswarnDeprecationWarningrgrr)r9r1)rlr(r)r/szProbTrackX.__init__csxtdt|jjdD]}t|jj|d\}}}t|jj|d|jjd||ddt|jj|d\}}}t|jj|d|jjd||ddt|jj|d\}}}t|jj|d|jjd||ddqWt |jj r.t dd}x |jj D]}|j d |qW|j t|jjtrt d d}xL|jjD]@}t|tr|j d d jd d |Dn|j d |qRW|j tt|j|}|jr|j||S)Nrz _th%dsamplesF)copyz _ph%dsamplesz _f%dsamplesz targets.txtwz%s z seeds.txt cSsg|] }t|qSr()str).0sr(r(r) Tsz-ProbTrackX._run_interface..)rplenr1rbrrrrar\rropenwriteclose isinstancerWlistrrgrrhrirj)r9rkrur/rftargetrW)rlr(r)rh6s@      zProbTrackX._run_interfacecsl|dkr*t|r*d}tt|j|||gS|dkrTt|trTd}tt|j|||Stt|j|||SdS)Nrz targets.txtrWz seeds.txt)rrgr _format_argrr)r9namespecrDfname)rlr(r)r^szProbTrackX._format_argcs~|jj}t|jjs$|jdn|jjtjjtjj d|d<t|jj dkrt |jj t rt |jj dt rg|d<xd|jj D]:}|djtjj|jddj d d |Dd d qWntjj|jdd d |d<t|jjr@g|d <xD|jjD]8}|d jtjj|jdtjj|dd d qWt|jjrz|jjdkrzfdd t|jjD|d<|S)Nrrzprobtrackx.logrTrrz fdt_paths_%sr/cSsg|] }t|qSr()r)rrr(r(r)r{sz,ProbTrackX._list_outputs..)rmr0rZ seeds_to_rrcs&g|]}tjjtjjd|qS)z particle%d)rdrnror)rru)rrr(r)rsr)r4r5rr1rr _gen_filenamerdrnrorrrrWrrqr8rsplitrrpr)r9r;rWrr()rrr)r=hs>    zProbTrackX._list_outputscCsD|dkrtjS|dkr@t|jjtrr?rr@rr4rrhrr=rrvr(r()rlr)rs  ( 2rc@seZdZejdddZeddddZejdd d d d Z ejd ddZ ejdddZ ejdddZ ej ddd ZejdddgdZeddddZejddddgdZeddd dZedd!d"dZej d#d$d Zejd%d&dZedd'd(dZedd)d*dZejd+d,d-d.d/d0d Zd1S)2ProbTrackX2InputSpeczErack from a list of voxels (seed must be a ASCII list of coordinates)z--simple)rrTz)Other mask for binning tract distributionz --fopd=%s)rrrORZANDz --waycond=%sz2Waypoint condition. Either "AND" (default) or "OR")rrzVReject streamlines that do not hit waypoints in given order. Only valid if waycond=ANDz --wayorderz7Apply waypoint conditions to each half tract separatelyz--onewayconditionz(Output matrix1 - SeedToSeed Connectivityz --omatrix1z--distthresh1=%.3fzMDiscards samples (in matrix1) shorter than this threshold (in mm - default=0)z!Output matrix2 - SeedToLowResMaskz --omatrix2target2)rrrequireszVLow resolution binary brain mask for storing connectivity distribution in matrix2 modez --target2=%sz(Output matrix3 (NxN connectivity matrix)z --omatrix3target3 lrtarget3zBMask used for NxN connectivity matrix (or Nxn if lrtarget3 is set)z --target3=%sz2Column-space mask used for Nxn connectivity matrixz--lrtarget3=%sz--distthresh3=%.3fzMDiscards samples (in matrix3) shorter than this threshold (in mm - default=0)z=Output matrix4 - DtiMaskToSeed (special Oxford Sparse Format)z --omatrix4z/Mask for columns of matrix4 (default=seed mask)z --colmask4=%szBrain mask in DTI spacez --target4=%sZcaretZ freesurferfirstZvoxz--meshspace=%szSMesh reference space - either "caret" (default) or "freesurfer" or "first" or "vox"N)rrrr r$rr ZfopdrQZwaycondZwayorderZonewayconditionomatrix1rZ distthresh1omatrix2romatrix3rrZ distthresh3Zomatrix4Zcolmask4Ztarget4Z meshspacer(r(r(r)rsz   rc@sHeZdZedddZedddZedddZedddZedddZdS) ProbTrackX2OutputSpecTz1the network matrix generated by --omatrix1 option)rrz,Output matrix1.dot - SeedToSeed Connectivityz0lookup_tractspace generated by --omatrix2 optionz%Output matrix2.dot - SeedToLowResMaskz(Output matrix3 - NxN connectivity matrixN) rrrr network_matrix matrix1_dotlookup_tractspace matrix2_dot matrix3_dotr(r(r(r)r s    rcs,eZdZdZdZeZeZfddZ Z S) ProbTrackX2aUse FSL probtrackx2 for tractography on bedpostx results Examples -------- >>> from nipype.interfaces import fsl >>> pbx2 = fsl.ProbTrackX2() >>> pbx2.inputs.seed = 'seed_source.nii.gz' >>> pbx2.inputs.thsamples = 'merged_th1samples.nii.gz' >>> pbx2.inputs.fsamples = 'merged_f1samples.nii.gz' >>> pbx2.inputs.phsamples = 'merged_ph1samples.nii.gz' >>> pbx2.inputs.mask = 'nodif_brain_mask.nii.gz' >>> pbx2.inputs.out_dir = '.' >>> pbx2.inputs.n_samples = 3 >>> pbx2.inputs.n_steps = 10 >>> pbx2.cmdline 'probtrackx2 --forcedir -m nodif_brain_mask.nii.gz --nsamples=3 --nsteps=10 --opd --dir=. --samples=merged --seed=seed_source.nii.gz' Z probtrackx2cstt|j}t|jjs$tj}n|jj}tjj tjj |d|d<t|jj rtjj tjj |d|d<tjj tjj |d|d<t|jj rtjj tjj |d|d<tjj tjj |d |d <t|jj rtjj tjj |d |d <|S) NZwaytotalrZmatrix_seeds_to_all_targetsrzfdt_matrix1.dotrz$lookup_tractspace_fdt_matrix2.nii.gzrzfdt_matrix2.dotrzfdt_matrix3.dotr)rgrr=rr1rrrdrernrorrrr)r9r;rr)rlr(r)r=/s&     zProbTrackX2._list_outputs) rrrr>r?rr@rr4r=rvr(r()rlr)rs rc@seZdZedddddZedddddZedd d ddZedd d d Zedddd Zedddd Z edddd Z e j dddddddZ edddd Zedddd ZdS) VecRegInputSpecTz-i %sz)filename for input vector or tensor field)rrrrz-o %sz5filename for output registered vector or tensor fieldF)rrr hash_filesz-r %sz&filename for reference (target) volumez-t %sz)filename for affine transformation matrix)rrrz-w %sz5filename for 4D warp field for nonlinear registrationz --rotmat=%szjfilename for secondary affine matrix if set, this will be used for the rotation of the vector/tensor fieldz --rotwarp=%szgfilename for secondary warp field if set, this will be used for the rotation of the vector/tensor fieldZnearestneighbourZ trilinearZsincZsplinez --interp=%szLinterpolation method : nearestneighbour, trilinear (default), sinc or spline)rrz-m %szbrain mask in input spacez --refmask=%szAbrain mask in output space (useful for speed up of nonlinear reg)N)rrrr in_fileout_fileZref_volZ affine_matZ warp_fieldZ rotation_matZ rotation_warpr rQ interpolationr Zref_maskr(r(r(r)rPsR rc@seZdZedddZdS)VecRegOutputSpecTz?path/name of filename for the registered vector or tensor field)rrN)rrrr rr(r(r(r)rsrcs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) VecRegaUse FSL vecreg for registering vector data For complete details, see the FDT Documentation Example ------- >>> from nipype.interfaces import fsl >>> vreg = fsl.VecReg(in_file='diffusion.nii', affine_mat='trans.mat', ref_vol='mni.nii', out_file='diffusion_vreg.nii') >>> vreg.cmdline 'vecreg -t trans.mat -i diffusion.nii -o diffusion_vreg.nii -r mni.nii' ZvecregcsLt|jjsr?rr@rr4rhr=rrvr(r()rlr)rs  rc@s8eZdZejedddddddZejdddd dZd S) ProjThreshInputSpecT)rz%sza list of input volumesr)rrrrz%dzLthreshold indicating minimum number of seed voxels entering this mask regionrN) rrrr rr in_filesr# thresholdr(r(r(r)rsrc@s eZdZejeddddZdS)ProjThreshOuputSpecT)rz-path/name of output volume after thresholding)rN)rrrr rr out_filesr(r(r(r)rsrc@s$eZdZdZdZeZeZddZ dS) ProjThreshaUse FSL proj_thresh for thresholding some outputs of probtrack For complete details, see the FDT Documentation Example ------- >>> from nipype.interfaces import fsl >>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii'] >>> pThresh = fsl.ProjThresh(in_files=ldir, threshold=3) >>> pThresh.cmdline 'proj_thresh seeds_to_M1.nii seeds_to_M2.nii 3' Z proj_threshcCs^|jj}g|d<xD|jjD]8}tjj|\}}|dj|j||dj |jj dqW|S)Nrz_proj_seg_thr_{})rmr0) r4r5r1rrdrnrrqr8formatr)r9r;rrmrr(r(r)r=s zProjThresh._list_outputsN) rrrr>r?rr@rr4r=r(r(r(r)rs rc@s8eZdZejedddddddZeddddd d Zd S) FindTheBiggestInputSpecT)rz%sz-a list of input volumes or a singleMatrixFiler)rrrrz$file with the resulting segmentationrF)rrrrrN)rrrr rr rrr(r(r(r)rsrc@seZdZeddddZdS)FindTheBiggestOutputSpecTz%sz+output file indexed in order of input files)rrrN)rrrr rr(r(r(r)rsrcs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) FindTheBiggesta$ Use FSL find_the_biggest for performing hard segmentation on the outputs of connectivity-based thresholding in probtrack. For complete details, see the `FDT Documentation. `_ Example ------- >>> from nipype.interfaces import fsl >>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii'] >>> fBig = fsl.FindTheBiggest(in_files=ldir, out_file='biggestSegmentation') >>> fBig.cmdline 'find_the_biggest seeds_to_M1.nii seeds_to_M2.nii biggestSegmentation' Zfind_the_biggestcs.t|jjs|jddd|j_tt|j|S)NbiggestSegmentationr)r0)rr1rr8rgrrh)r9rk)rlr(r)rh1s zFindTheBiggest._run_interfacecCsN|jj}|jj|d<t|ds6|jddd|d<tjj|d|d<|S)Nrrr)r0) r4r5r1rrr8rdrnro)r9r;r(r(r)r=6s    zFindTheBiggest._list_outputscCs|dkr|j|SdSdS)Nr)r=)r9rr(r(r)r>s zFindTheBiggest._gen_filename) rrrr>r?rr@rr4rhr=rrvr(r()rlr)rs rc@seZdZedddddZdddgZejded d Zej d d Z edd dZ eddgddZ ejdddgddZ edddZeddddZeddddZedd ZejejedddZdS) TractSkeletonInputSpecTz-i %sz&input image (typcially mean FA volume))rrrrr distance_map data_filez-p %.3f %s %s %s %szproject data onto skeleton)rrrzskeleton threshold value)rzdistance map image)rruse_cingulum_maskz*mask in which to use alternate search rule)rrHrsearch_mask_filez5perform alternate search using built-in cingulum mask)rrHrz-4D data to project onto skeleton (usually FA)z-a %sz'4D non-FA data to project onto skeleton)rrrz-s %szalternate skeleton to usez"input data projected onto skeletonz-o %szwrite out skeleton image)rrN)rrrr rZ _proj_inputsr r$ project_datarrrrrr alt_data_fileZ alt_skeletonprojected_datar skeleton_filer(r(r(r)rEs8      rc@s eZdZeddZeddZdS)TractSkeletonOutputSpecz"input data projected onto skeleton)rztract skeleton imageN)rrrr rrr(r(r(r)rks rcs4eZdZdZdZeZeZfddZ ddZ Z S) TractSkeletonaUse FSL's tbss_skeleton to skeletonise an FA image or project arbitrary values onto a skeleton. There are two ways to use this interface. To create a skeleton from an FA image, just supply the ``in_file`` and set ``skeleton_file`` to True (or specify a skeleton filename. To project values onto a skeleton, you must set ``project_data`` to True, and then also supply values for ``threshold``, ``distance_map``, and ``data_file``. The ``search_mask_file`` and ``use_cingulum_mask`` inputs are also used in data projection, but ``use_cingulum_mask`` is set to True by default. This mask controls where the projection algorithm searches within a circular space around a tract, rather than in a single perpindicular direction. Example ------- >>> import nipype.interfaces.fsl as fsl >>> skeletor = fsl.TractSkeleton() >>> skeletor.inputs.in_file = "all_FA.nii.gz" >>> skeletor.inputs.skeleton_file = True >>> skeletor.run() # doctest: +SKIP Z tbss_skeletoncs|dkrvt|r|r|j}t|jr6|jr6tjd}n|j}t|jsT|jd}n|j}|j|j |j ||j |fSn.|dkrt |t r|j|jdS|j|Stt|j|||S)NrzLowerCingulum_1mm.nii.gzrr)rr1rrZstandard_imagerrr=rrrrrboolrgrr)r9rrrD_si mask_fileZ proj_file)rlr(r)rs(      zTractSkeleton._format_argcCs|jj}|j}t|jrf|jrf|j}||d<t|sf|j}t|jrN|j}t|dt j dd|d<t|j r|j r|j |d<t |j t rt|jdt j dd|d<|S)NrZ _skeletonisedT)r0newpathuse_extrZ _skeleton)r4r5r1rrrrrrrdrerrrr)r9r;rZ proj_dataZstemr(r(r)r=s"    zTractSkeleton._list_outputs) rrrr>r?rr@rr4rr=rvr(r()rlr)rqs  rc@s^eZdZedddddZeddddZejdd d Zej ejed d d dZ edddd dZ dS)DistanceMapInputSpecTz--in=%sz&image to calculate distance values for)rrrrz --mask=%sz$binary mask to contrain calculations)rrrz--invertzinvert input image)rrz --localmax=%sz"write an image of the local maximaF)rrrz--out=%szdistance map to write)rrrrN) rrrr rrr r$Z invert_inputrlocal_max_filerr(r(r(r)rs  rc@s"eZdZedddZeddZdS)DistanceMapOutputSpecTz+value is distance to nearest nonzero voxels)rrzimage of local maxima)rN)rrrr rrr(r(r(r)rs rcs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) DistanceMapa%Use FSL's distancemap to generate a map of the distance to the nearest nonzero voxel. Example ------- >>> import nipype.interfaces.fsl as fsl >>> mapper = fsl.DistanceMap() >>> mapper.inputs.in_file = "skeleton_mask.nii.gz" >>> mapper.run() # doctest: +SKIP Z distancemapcs8|dkr$t|tr$|j|jdStt|j|||S)Nr)rrrr=rgrr)r9rrrD)rlr(r)rs zDistanceMap._format_argcCs|jj}|j}|j|d<t|js@t|jddtjd|d<tj j |d|d<t|j r|j |d<t |j t rt|jddtjd|d<tj j |d|d<|S)NrZ_dstmapT)r0rrrZ_lclmax)r4r5r1rrrrrdrernrorrr)r9r;rr(r(r)r=s      zDistanceMap._list_outputscCs|dkr|jdSdS)Nr)r=)r9rr(r(r)rs zDistanceMap._gen_filename) rrrr>r?rr@rr4rr=rrvr(r()rlr)rs  rc@s\eZdZedddddZedddddZeddddZedd ddd d Zej d d ddZ dS)MakeDyadicVectorsInputSpecTrz%s)rrrrrr)rrrr[rF)rrrrzOthe {perc}% angle of the output cone of uncertainty (output will be in degrees)rz%f)rrrN) rrrr Z theta_volZphi_volr r:r rpercr(r(r(r)r src@s eZdZeddZeddZdS)MakeDyadicVectorsOutputSpecT)rN)rrrr r[ dispersionr(r(r(r)rs rc@s$eZdZdZdZeZeZddZ dS)MakeDyadicVectorsziCreate vector volume representing mean principal diffusion direction and its uncertainty (dispersion)Zmake_dyadic_vectorscCs8|jj}|j|jj|d<|j|jjdd|d<|S)Nr[Z _dispersion)r0r)r4r5r8r1r:)r9r;r(r(r)r=(s  zMakeDyadicVectors._list_outputsN) rrrr>r?rr@rr4r=r(r(r(r)r s r)8r>rdrZutils.filemaniprrrbaserrr r r r r rrrrr*r,rArZrcrwr{rrrZXFibresZBEDPOSTXrrrrrrrrrrrrrrrrrrrrrrrrrr(r(r(r)s\$ /)2:p 3"e 9A0#*&M0