3 d-@sdZddlZddljZddlmZddlZddl m Z ddl m Z ddl mZdd lmZmZmZmZmZmZmZd d lmZmZmZGd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZ GdddeZ!GdddeZ"Gddde Z#Gd d!d!e"Z$Gd"d#d#eZ%Gd$d%d%eZ&Gd&d'd'eZ'Gd(d)d)eZ(Gd*d+d+eZ)Gd,d-d-eZ*Gd.d/d/eZ+Gd0d1d1eZ,Gd2d3d3eZ-Gd4d5d5eZ.Gd6d7d7eZ/Gd8d9d9eZ0Gd:d;d;eZ1Gdd?d?eZ3Gd@dAdAeZ4GdBdCdCeZ5GdDdEdEeZ6GdFdGdGeZ7GdHdIdIeZ8GdJdKdKeZ9GdLdMdMeZ:GdNdOdOeZ;GdPdQdQeZ`_ command line tools. This was written to work with FSL version 4.1.4. N)warn)load) LooseVersion)split_filename) TraitedSpecFileInputMultiPathOutputMultiPath Undefinedtraits isdefined) FSLCommandFSLCommandInputSpecInfoc@s<eZdZedddddddZedddddd Zejd d d Zejd dd Z ejddd Z ejdddZ ej ddd Z ej dddZejddddZejejdddddd Zejd!d"dZejd#d$dZd=Zejd,d-ed.Zejd/d0ed.Zejd1d2ed.Zejd3d4ed.Zed5d6ed.Zejd7ed8d9Zejd:ed;d9Zd BETInputSpecTzinput file to skull stripz%srF)existsdescargstrposition mandatorycopyfilez#name of output skull stripped imager)rrrgenfile hash_fileszcreate surface outline imagez-o)rrzcreate binary mask imagez-mzcreate skull imagez-sz-nzDon't generate segmented output)rrzfractional intensity thresholdz-f %.2fz-g %.2fz;vertical gradient in fractional intensity threshold (-1, 1)z-r %dmmz head radius)runitsrzcenter of gravity in voxelsz-c %srvoxels)rrminlenmaxlenrz-tz4apply thresholding to segmented brain image and maskz-ez!generate a vtk mesh brain surface functional reduce_biasrobustpadding remove_eyessurfaces t2_guidedz;robust brain centre estimation (iterates BET several times)z-R)rrxorzIimprove BET if FOV is very small in Z (by temporarily padding end slices)z-Zz2eye & optic nerve cleanup (can be useful in SIENA)z-Sz]run bet2 and then betsurf to get additional skull and scalp surfaces (includes registrations)z-Az_as with creating surfaces, when also feeding in non-brain-extracted T2 (includes registrations)z-A2 %sz-Fzapply to 4D fMRI data)rr(rz-Bzbias field and neck cleanupN)r!r"r#r$r%r&r')__name__ __module__ __qualname__r in_fileout_filer Booloutlinemaskskull no_outputFloatfracZvertical_gradientIntZradiusListcenter thresholdmeshZ _xor_inputsr#r$r%r&r'r!r"r:r:B/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/fsl/preprocess.pyrs| rc@seZdZeddZeddZeddZeddZeddZeddZ eddZ ed dZ ed dZ ed dZ ed dZed dZdS) BETOutputSpecz.path/name of skullstripped file (if generated))rz-path/name of binary brain mask (if generated)z(path/name of outline file (if generated)z)path/name of vtk mesh file (if generated)z(path/name of inskull mask (if generated)z0path/name of inskull mesh outline (if generated)z)path/name of outskull mask (if generated)z1path/name of outskull mesh outline (if generated)z(path/name of outskin mask (if generated)z0path/name of outskin mesh outline (if generated)z&path/name of skull mask (if generated)z&path/name of skull file (if generated)N)r)r*r+r r- mask_file outline_filemeshfileinskull_mask_fileinskull_mesh_fileoutskull_mask_fileoutskull_mesh_fileoutskin_mask_fileoutskin_mesh_fileskull_mask_file skull_filer:r:r:r;r<ys           r<csPeZdZdZdZeZeZfddZ fddZ ddZ d d Z d d Z ZS) BETaFSL BET wrapper for skull stripping For complete details, see the `BET Documentation. `_ Examples -------- >>> from nipype.interfaces import fsl >>> btr = fsl.BET() >>> btr.inputs.in_file = 'structural.nii' >>> btr.inputs.frac = 0.7 >>> btr.inputs.out_file = 'brain_anat.nii' >>> btr.cmdline 'bet structural.nii brain_anat.nii -f 0.70' >>> res = btr.run() # doctest: +SKIP Zbetcs$tt|j|}|jr |j||S)N)superrH_run_interfacestderrZraise_exception)selfruntime) __class__r:r;rJs zBET._run_interfacecs2tt|j|||}|dkr.tj|tjdS|S)Nr,)start)rIrH _format_argoprelpathosgetcwd)rLnamespecvalue formatted)rNr:r;rPszBET._format_argcCsF|jj}t| rBt|jjrB|j|jjdd}tj|tjdS|S)NZ_brain)suffix)rO) inputsr-rr, _gen_fnamerQrRrSrT)rLr-r:r:r;_gen_outfilenames zBET._gen_outfilenamecCs |jj}tjj|j|d<tjj|d}tjj|d}||d}t|j j r^|j j srt|j j r|j j r|j fddd||d<t|j j r|j j st|j jr|j jr|j fddi||d <t|j jr|j jr|j fdd i||d <t|j j r|j j r|j fdd i||d <|j fddi||d<|j fddi||d<|j fddi||d<|j fddi||d<|j fddi||d<|j fddi||d<t|j jr|j jr|j fddi||d<t|j jr|j jrt|d<|S)Nr-)basenamecwdz _mesh.vtkF)rY change_extr?rYZ_maskr=Z_overlayr>Z _inskull_maskr@Z _inskull_meshrAZ_outskull_maskrBZ_outskull_meshrCZ _outskin_maskrDZ _outskin_meshrEZ _skull_maskrFZ_skullrG) output_specgetrSpathabspathr\r]dirnamerrZr9r&r[r0r"r/r1r2r )rLoutputsr]r^kwargsr:r:r; _list_outputssB  zBET._list_outputscCs|dkr|jSdS)Nr-)r\)rLrUr:r:r; _gen_filenameszBET._gen_filename)r)r*r+__doc___cmdr input_specr<r`rJrPr\rgrh __classcell__r:r:)rNr;rHs  .rHc@s|eZdZdZeeddddddGddZed d d Zej dd d ddZ ej ddd Z ej ddd Z ejdHddd Zej dd dddZej ddddddZej d d!d"d#d$Zej d%d&d Zedd'd(d)Zeedd*d+d,ddd-Zej d.d/d Zej d0d1d Zej d2d3d Zej dd4d5d6d$Zej d7d8d9d:d$Zej dd;dd?d$Zej d@dAd ZeddBdCd)Zej dDdEd ZdFS)I FASTInputSpecz'Defines inputs (trait classes) for FASTT)rFz6image, or multi-channel set of images, to be segmentedz%sr)rrrrrzbase name of output filesz-o %s)rr z-n %dznumber of tissue-type classes)lowhighrrzoutput estimated bias fieldz-bz,output restored image (bias-corrected image)z-Brrz6int specifying type of image: (1 = T1, 2 = T2, 3 = PD)z-t %dz-I %dz8number of main-loop iterations during bias-field removal(z(bias field smoothing extent (FWHM) in mmz-l %dr)rorprrrg-C6?g?zFinitial segmentation spatial smoothness (during bias field estimation)z-f %.3f)rorprrz4outputs a separate binary image for each tissue typez-gz, initialise using priorsz-a %s)rrr)existzalternative prior imagesz-A %s)rrrr z(turn off PVE (partial volume estimation)z--nopvezdo not remove bias fieldz-Nzuse priors throughoutz-P2z0number of segmentation-initialisation iterationsz-W %dgg?z spatial smoothness for mixeltypez-R %.2fz7number of main-loop iterations after bias-field removalz-O %dzsegmentation spatial smoothnessz-H %.2fzswitch on diagnostic messagesz-vzFilename containing intensitiesz-s %sz#outputs individual probability mapsz-pN)rrr) r)r*r+rir r in_files out_basenamer ZRangenumber_classesr.output_biasfieldoutput_biascorrectedEnumZimg_typeZ bias_itersZ bias_lowpassZinit_seg_smoothsegmentsZinit_transformZ other_priorsno_pveZno_biasZ use_priorsZ segment_itersZ mixel_smoothZiters_afterbiasZhyperverboseZ manual_segprobability_mapsr:r:r:r;rms     rmc@sveZdZdZedddZeeddZeeddZeddZ ed dZ eed dZ eed dZ eed dZ d S)FASTOutputSpecz"Specify possible outputs from FASTTzFpath/name of binary segmented volume file one val for each class _seg)rrzEpath/name of binary segmented volumes one file for each class _seg_x)rzWrestored images (one for each input image) named according to the input images _restorez-path/name of mixeltype volume file _mixeltypez(path/name of partial volume file _pvesegz=path/name of partial volumes files one for each class, _pve_xzEstimated bias field _biasz5filenames, one for each class, for each input, prob_xN)r)r*r+rir tissue_class_mapr tissue_class_filesrestored_image mixeltypepartial_volume_mappartial_volume_files bias_fieldrr:r:r:r;rLs"     rcs4eZdZdZdZeZeZfddZ ddZ Z S)FASTaFSL FAST wrapper for segmentation and bias correction For complete details, see the `FAST Documentation. `_ Examples -------- >>> from nipype.interfaces import fsl >>> fastr = fsl.FAST() >>> fastr.inputs.in_files = 'structural.nii' >>> fastr.inputs.out_basename = 'fast_' >>> fastr.cmdline 'fast -o fast_ -S 1 structural.nii' >>> out = fastr.run() # doctest: +SKIP fastcs0tt|j|||}|dkr,dt||f}|S)Nrwz-S %d %s)rIrrPlen)rLrUrVrWrX)rNr:r;rPszFAST._format_argcCs|jj}t|jjsd}n|jj}i}t|jjrP|jj|d<tj|d<n&|jjd|d<t |d\|d<}}|j fddi||d<|jj rg|d<x2t |D]&}|dj |j fdd |i|qWt|jjr\g|d <t|jjdkr>x`t|jjD]0\}}|d j |j fdd |di|qWn|d j |j fdd i||j fdd i||d<|jjs|j fddi||d<g|d<x4t |D](}|dj |j fdd|i|qW|jjrZg|d<t|jjdkrd?d@dZe jdAdBdCdDZe jdEdFdGdHdIdZe jdJdKdZe jdLdMdZe j dNdOdZe j dPdQdZe j dRdSdTdDZe jdUdBdVdDZe j e jd d dWdXdYdZZ!e j e jd d dWd[d\dZZ"e j e jd d dWd]d^dZZ#e j d_d`dZ$e jdadWdbdDZ%e jdcdWdddDZ&eddedfdgZ'eddhdidgZ(eddjdkdgZ)e j dldmdZ*e j dndodZ+e j dpdqdZ,e j drdsZ-e jdtdudZ.e j ddvdwdZ/edxdydzd{Z0ed|dyd}d{Z1ed~dydd{Z2eddydd{Z3eddydd{Z4e jddydd{Z5e j ddydd{Z6e jdddddydd{Z7e j ddydd{Z8dS)FLIRTInputSpecTz-in %srz input file)rrrrrz-ref %srzreference filez-out %szregistered output filer,z%s_flirtrF)rr name_source name_templaterrz-omat %sz %s_flirt.matz)output affine matrix in 4x4 asciii formatr)rrkeep_extensionrrrrsave_logz %s_flirt.logz output log)rrrequiresrrz-init %szinput 4x4 affine matrix)rrz -applyxfmzuapply transformation supplied by in_matrix_file or uses_qform to use the affine matrix stored in the reference headerz-applyisoxfm %f apply_xfmz+as applyxfm but forces isotropic resampling)rr(rcharshortintfloatdoublez -datatype %szforce output data type mutualinfocorrationormcorrnormmiZleastsqZ labeldiffZbbrz-cost %sz cost functionz-searchcost %sz -usesqformzinitialize using sform or qformz -displayinitzdisplay initial matrixZ quaternionZeulerz -anglerep %sz!representation of rotation angles trilinearZnearestneighboursincsplinez -interp %sz,final interpolation method used in reslicingz -sincwidth %drzfull-width in voxels)rrrZ rectangularZhanningZblackmanz-sincwindow %sz sinc windowz-bins %dznumber of histogram binsz-dof %dz&number of transform degrees of freedomz -noresamplezdo not change input samplingz -forcescalingz'force rescaling even for low-res imagesz-minsampling %frz(set minimum voxel dimension for samplingz-paddingsize %dz0for applyxfm: interpolates outside image by sizedegreesz -searchrx %sz&search angles along x-axis, in degrees)rr rrrz -searchry %sz&search angles along y-axis, in degreesz -searchrz %sz&search angles along z-axis, in degreesz -nosearchz)set all angular searches to ranges 0 to 0z-coarsesearch %dzcoarse search delta anglez-finesearch %dzfine search delta anglez -schedule %szreplaces default schedule)rrrz -refweight %sz#File for reference weighting volumez -inweight %szFile for input weighting volumez-noclampzdo not use intensity clampingz -noresampblurz#do not use blurring on downsamplingz-2Dz$use 2D rigid body mode - ignores dofzsave to log file)rz -verbose %dzverbose mode, 0 is leastz-setbackground %fz5use specified background value for points outside FOVz -wmseg %sz5.0.0z`_ To print out the command line help, use: fsl.FLIRT().inputs_help() Examples -------- >>> from nipype.interfaces import fsl >>> from nipype.testing import example_data >>> flt = fsl.FLIRT(bins=640, cost_func='mutualinfo') >>> flt.inputs.in_file = 'structural.nii' >>> flt.inputs.reference = 'mni.nii' >>> flt.inputs.output_type = "NIFTI_GZ" >>> flt.cmdline # doctest: +ELLIPSIS 'flirt -in structural.nii -ref mni.nii -out structural_flirt.nii.gz -omat structural_flirt.mat -bins 640 -searchcost mutualinfo' >>> res = flt.run() #doctest: +SKIP ZflirtFNc sVtt|j||d}|jjrR|j rRt|jd}|j|j dWdQRXd|_|S)N)rMneeded_outputsa T) rIraggregate_outputsrZr _log_writtenopenrwritestdout)rLrMrreZ text_file)rNr:r;rs  zFLIRT.aggregate_outputscsd|dkr g}|jjr&|jj r&d|j_|jjrH|jjp<|jj rHtd|jdtt |j |dS)NrzIArgument apply_xfm requires in_matrix_file or uses_qform arguments to runr)skip) rZrrrrr RuntimeErrorrrIr _parse_inputs)rLr)rNr:r;rs zFLIRT._parse_inputs)NN)N) r)r*r+rirjrrkrr`rrrrlr:r:)rNr;rs rc@seZdZejdddddZdS)ApplyXFMInputSpecTz -applyxfmzuapply transformation supplied by in_matrix_file or uses_qform to use the affine matrix stored in the reference header)rr usedefaultN)r)r*r+r r.rr:r:r:r;rs rc@seZdZdZeZdS)ApplyXFMa}Currently just a light wrapper around FLIRT, with no modifications ApplyXFM is used to apply an existing tranform to an image Examples -------- >>> import nipype.interfaces.fsl as fsl >>> from nipype.testing import example_data >>> applyxfm = fsl.preprocess.ApplyXFM() >>> applyxfm.inputs.in_file = example_data('structural.nii') >>> applyxfm.inputs.in_matrix_file = example_data('trans.mat') >>> applyxfm.inputs.out_file = 'newfile.nii' >>> applyxfm.inputs.reference = example_data('mni.nii') >>> applyxfm.inputs.apply_xfm = True >>> result = applyxfm.run() # doctest: +SKIP N)r)r*r+rirrkr:r:r:r;r src @s<eZdZeddddddZeddddd Zejd d d d dddddZej dddZ ej dddZ ej dddZ ej dddZej dddZej dddZej dd dZedd!d"d#Zejd$d%d&d'd(dZejd)d*dZejd+d,dZejd-d.dZejd/d0dZejd1d2dZejd3d4dZejd5d6dZedd7d8d#Zd9S):MCFLIRTInputSpecTrz-in %sztimeseries to motion-correct)rrrrrz-out %sz file to writeF)rrrrrZwoodsrrrZ leastsquaresz-cost %szcost function to optimize)rrz-bins %dznumber of histogram binsz-dof %dz)degrees of freedom for the transformationz -refvol %dzvolume to align frames toz -scaling %.2fzscaling factor to usez -smooth %.2fz&smoothing factor for the cost functionz -rotation %dz&scaling factor for rotation tolerancesz -stages %dz:stages (if 4, perform final search with sinc interpolationz-init %szinital transformation matrix)rrrrnnrz -%s_finalz'interpolation method for transformationz-gdtzrun search on gradient imagesz-edgezrun search on contour imagesz-meanvolzregister to mean volumez-statsz%produce variance and std. dev. imagesz-matszsave transformation matricesz-plotszsave transformation parametersz-rmsabs -rmsrelz save rms displacement parametersz -reffile %sz"target image for motion correctionN)r)r*r+r r,r-r r|rr5rrZref_volr3ZscalingZsmoothZrotationZstagesinit interpolationr.Z use_gradientZ use_contourmean_vol stats_imgs save_mats save_plotssave_rmsref_filer:r:r:r;r$s\    rc@sleZdZedddZedddZedddZedddZedddZe eddd d Z e eddd d Z d S) MCFLIRTOutputSpecTzmotion-corrected timeseries)rrzvariance imagezstandard deviation imagez(mean timeseries image (if mean_vol=True)z text-file with motion parameters)rztransformation matrices)rz-absolute and relative displacement parametersN) r)r*r+r r- variance_imgstd_imgmean_imgpar_filer mat_file rms_filesr:r:r:r;r_s     rcsDeZdZdZdZeZeZfddZ ddZ ddZ d d Z Z S) MCFLIRTaFSL MCFLIRT wrapper for within-modality motion correction For complete details, see the `MCFLIRT Documentation. `_ Examples -------- >>> from nipype.interfaces import fsl >>> mcflt = fsl.MCFLIRT() >>> mcflt.inputs.in_file = 'functional.nii' >>> mcflt.inputs.cost = 'mutualinfo' >>> mcflt.inputs.out_file = 'moco.nii' >>> mcflt.cmdline 'mcflirt -in functional.nii -cost mutualinfo -out moco.nii' >>> res = mcflt.run() # doctest: +SKIP Zmcflirtcs2|dkr|dkrdS|j|Stt|j|||S)Nrr)rrIrrP)rLrUrVrW)rNr:r;rPs  zMCFLIRT._format_argc Cs|jj}|j|d<tjj|d}t|jjr|jjrt t j t dkr|j |dd|d|d<|j |dd|d|d<n0|j |dd|d |d<|j |dd |d |d<t|jj o|jj rt t j t dkr|j |dd |d|d <n|j |dd |d |d <t|jjr|jjrtjj|d\}}tjj||d}t|jjj\}}}}g|d<x.t|D]"}|djtjj|d|qzWt|jjr|jjr|dd|d<t|jjr|jjr|d}|d|dg|d<|S)Nr-z6.0.0z _variance.ext)r^rz _sigma.extrZ _variance)rYr^Z_sigmaz _mean_reg.extrZ _mean_regz.matrzMAT_%04dz.parrz_abs.rmsz_rel.rmsr)_outputsrar\rSrbrdrrZrrrversionr[rrsplitjoinrr,shaperrrr) rLre output_dirrfilenameZ matpathnameZ timepointstoutfiler:r:r;rgs@   "zMCFLIRT._list_outputscCs|dkr|jSdS)Nr-)r\)rLrUr:r:r;rhszMCFLIRT._gen_filenamecCsP|jj}t|rtjj|}t| rDt|jjrD|j|jjdd}tjj|S)NZ_mcf)rY) rZr-rrSrbrealpathr,r[rc)rLr-r:r:r;r\s  zMCFLIRT._gen_outfilename)r)r*r+rirjrrkrr`rPrgrhr\rlr:r:)rNr;rks 8rc @s&eZdZedddddZedddddZedddd Zedd d d Zej edd d dddddZ ej ej edddZ edddddZej ej eddddZej ej eddddZej ej eddddZej ej ed d!ddZed"d#dddZej ejd$d%edd d&d'dZedd(d)d Zedd*d+d Zej d,d-gd.d/Zej d0d1gd2d/Zej ejd3dd4d5gd6d7d8Zej ejd3dd9d:gd;d7d8Zej dd?dZejd@dAdZejdBdCdZ ej ej!dDdEd7dFZ"ej ej!dGdHd7dFZ#ej$ej!ej!ej!dIdJdZ%ej!dKdLdZ&ej ej!dMdNd7dFZ'ej ej!dOdPd7dFZ(ejdQdRdSdTdZ)ej ejdUdVd7dFZ*ej dWdXdZ+ej$ejejdYdZdZ,ej d[d\dZ-ejd]d^d_d`dadbdcdddZ.ej!dedfdZ/ej$ej!ej!ej!dgdhdZ0ejdidjdZ1ej dkdlgdmd/Z2ej ejd3ddndogdpd7d8Z3ejdqdrdsdtdZ4duS)vFNIRTInputSpecTz--ref=%szname of reference image)rrrrz--in=%szname of input imagez--aff=%sz(name of file containing affine transform)rrrz --inwarp=%sz0name of file containing initial non-linear warps)rz --intin=%sFrrz_name of file/files containing initial intensity mapping usually generated by previous fnirt run)rrrr rz --cout=%sz3name of output file with field coefficients or true)rrz --iout=%szname of output image)rrrrz --fout=%sz&name of output file with field or true)rrrz --jout=%szWname of file for writing out the Jacobian of the field (for diagnostic or VBM purposes)z --refout=%szPname of file for writing out intensity modulated --ref (for diagnostic purposes)z --intout=%szEname of files for writing information pertaining to intensity mappingz --logout=%szName of log-fileZT1_2_MNI152_2mmZFA_2_FMRIB58_1mmz --config=%sz5Name of config file specifying command line argumentsz --refmask=%sz)name of file with mask in reference spacez --inmask=%sz+name of file with mask in input image spacez--applyrefmask=0 apply_refmaskz,Skip specified refmask if set, default false)rr(rz--applyinmask=0 apply_inmaskz+skip specified inmask if set, default falserz--applyrefmask=%s skip_refmaskzAlist of iterations to use reference mask on (1 to use, 0 to skip),)rr(rsepz--applyinmask=%s skip_inmaskz=list of iterations to use input mask on (1 to use, 0 to skip)z --imprefm=0zAskip implicit masking based on value in --ref image. Default = 0z --impinm=0z@skip implicit masking based on value in --in image. Default = 0z--imprefval=%fz.Value to mask out in --ref image. Default =0.0z --impinval=%fz-Value to mask out in --in image. Default =0.0z --miter=%sz9Max # of non-linear iterations list, default [5, 5, 5, 5])rrrz --subsamp=%sz/sub-sampling scheme, list, default [4, 2, 1, 1]z--warpres=%d,%d,%dz\(approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10, 10, 10z--splineorder=%dz?Order of spline, 2->Qadratic spline, 3->Cubic spline. Default=3z --infwhm=%szPFWHM (in mm) of gaussian smoothing kernel for input volume, default [6, 4, 2, 2]z --reffwhm=%szNFWHM (in mm) of gaussian smoothing kernel for ref volume, default [4, 2, 0, 0]Zmembrane_energyZbending_energyz --regmod=%sz_Model for regularisation of warp-field [membrane_energy bending_energy], default bending_energyz --lambda=%szhWeight of regularisation, default depending on --ssqlambda and --regmod switches. See user documetation.z --ssqlambda=0z=If true, lambda is not weighted by current ssq, default falsez--jacrange=%f,%fz;Allowed range of Jacobian determinants, default 0.01, 100.0z --refderivzBIf true, ref image is used to calculate derivatives. Default falsenoneZ global_linearZglobal_non_linearZ local_linearZglobal_non_linear_with_biasZlocal_non_linearz --intmod=%szModel for intensity-mappingz --intorder=%dz5Order of poynomial for mapping intensities, default 5z--biasres=%d,%d,%dzPResolution (in mm) of bias-field modelling local intensities, default 50, 50, 50z--biaslambda=%fz6Weight of regularisation for bias-field, default 10000z --estint=0apply_intensity_mappingz-Skip estimate intensity-mapping default falsez --estint=%sskip_intensity_mappingzQList of subsampling levels to apply intensity mapping for (0 to skip, 1 to apply)rrz --numprec=%szCPrecision for representing Hessian, double or float. Default doubleN)5r)r*r+r rr, affine_fileZ inwarp_filer r6in_intensitymap_fileEitherr.fieldcoeff_file warped_file field_file jacobian_filemodulatedref_fileout_intensitymap_filelog_filer| config_fileZ refmask_fileZ inmask_filerrrrZskip_implicit_ref_maskingZskip_implicit_in_maskingr3Z refmask_valZ inmask_valr5Zmax_nonlin_iterZsubsampling_schemeTupleZwarp_resolutionZ spline_orderZin_fwhmZref_fwhmZregularization_modelZregularization_lambdaZskip_lambda_ssqZjacobian_rangeZderive_from_refZintensity_mapping_modelZintensity_mapping_orderZbiasfield_resolutionZbias_regularization_lambdarrZhessian_precisionr:r:r:r;rsf       rc@s^eZdZedddZedddZeddZeddZeddZe j ed d d d Z ed dZ d S)FNIRTOutputSpecTzfile with field coefficients)rrz warped imagezfile with warp field)rz%file containing Jacobian of the fieldz)file containing intensity modulated --refrz5files containing info pertaining to intensity mapping)rr rzName of log-fileN) r)r*r+r rrrrrr r6rrr:r:r:r;rs     rcsdeZdZdZdZeZeZddddddd d Z d d Z fd dZ ddZ ddZ eddZZS)FNIRTaFSL FNIRT wrapper for non-linear registration For complete details, see the `FNIRT Documentation. `_ Examples -------- >>> from nipype.interfaces import fsl >>> from nipype.testing import example_data >>> fnt = fsl.FNIRT(affine_file=example_data('trans.mat')) >>> res = fnt.run(ref_file=example_data('mni.nii', in_file=example_data('structural.nii')) #doctest: +SKIP T1 -> Mni153 >>> from nipype.interfaces import fsl >>> fnirt_mprage = fsl.FNIRT() >>> fnirt_mprage.inputs.in_fwhm = [8, 4, 2, 2] >>> fnirt_mprage.inputs.subsampling_scheme = [4, 2, 1, 1] Specify the resolution of the warps >>> fnirt_mprage.inputs.warp_resolution = (6, 6, 6) >>> res = fnirt_mprage.run(in_file='structural.nii', ref_file='mni.nii', warped_file='warped.nii', fieldcoeff_file='fieldcoeff.nii')#doctest: +SKIP We can check the command line and confirm that it's what we expect. >>> fnirt_mprage.cmdline #doctest: +SKIP 'fnirt --cout=fieldcoeff.nii --in=structural.nii --infwhm=8,4,2,2 --ref=mni.nii --subsamp=4,2,1,1 --warpres=6,6,6 --iout=warped.nii' ZfnirtZwarpedfieldZfield_jacobianZ modulatedZintmapzlog.txtZ fieldwarp)rrrrrrrcCs|jj}xt|jjD]\}}t|j|}d}|d kr|jdrJd}t|rdt j j |||<q|j |jj d||d||<nDt|rt|tr|r|j |jj d||d||<nt j j |||<|dkot||rtj||}||d |g||<qW|S) NTrrz.txtFr)rYr_rz%s.txt)rr)r`ralistfilemapitemsgetattrrZendswithrrSrbrcr[r, isinstanceboolrintensitymap_file_basename)rLrekeyrYZinvalr_r]r:r:r;rgs.    zFNIRT._list_outputscs|dkrX|dkr|j|}dd|D}tt|dksJtdj|||j|dS|t|jjkr||j|j|St t |j |||S) NrrcSsg|]}tj|qSr:)rr).0vr:r:r; 4sz%FNIRT._format_arg..rz$Found different basenames for {}: {}r)rr) rgrsetAssertionErrorformatrrrkeysrIrrP)rLrUrVrW)rNr:r;rP0s  zFNIRT._format_argcCs|dkr|j|SdS)Nrr)rr)rg)rLrUr:r:r;rh=s zFNIRT._gen_filenamec Csfyt|d}Wn tk r.td|YnXx(t|jjjD]}|jd|qDW|jdS)zWrites out currently set options to specified config file XX TODO : need to figure out how the config file is written Parameters ---------- configfile : /path/to/configfile zw+zunable to create config_file %sz%s N) rIOErrorprintrrZrarrclose)rLZ configfileZfiditemr:r:r; write_configBs zFNIRT.write_configcCs>x8ttjjdgD] }|j|r|dt| SqW|S)zRemoves valid intensitymap extensions from `f`, returning a basename that can refer to both intensitymap files. z.txtN)rrZftypesvaluesrr)clsrextr:r:r;rTs z FNIRT.intensitymap_file_basename)r)r*r+rirjrrkrr`rrgrPrhr  classmethodrrlr:r:)rNr;rs  rc @seZdZeddddddZeddddd d Zedd dd d dZeddddZej ddgddZ ej ddgd4ddZ ej dddddddd Z ej d!d"d Zejej d#ejd$d%d Zedd&d'dZedd(d)dZedd*d+dZej d,d-d.d/d0d5d1d2Zd3S)6ApplyWarpInputSpecTz--in=%srzimage to be warped)rrrrrz--out=%srzoutput filenameF)rrrrrz--ref=%srzreference imagez --warp=%szfile containing warp field)rrrz--absrelwarpz'treat warp field as absolute: x' = w(x))rr(rz--relabswarpz+treat warp field as relative: x' = x + w(x))rr(rrrrrrrz --datatype=%sz5Force output data type [char short int float double].)rrz--superz4intermediary supersampling of output, default is offrz--superlevel=%szTlevel of intermediary supersampling, a for 'automatic' or integer level. Default = 2z --premat=%sz*filename for pre-transform (affine matrix)z --postmat=%sz+filename for post-transform (affine matrix)z --mask=%sz,filename for mask image (in reference space)rrrrz --interp=%szinterpolation method)rrrNrv)r)r*r+r r,r-rrr r.rrr|rZ supersamplerr5 superlevelZprematZpostmatr=rr:r:r:r;r`s|  rc@seZdZedddZdS)ApplyWarpOutputSpecTzWarped output file)rrN)r)r*r+r r-r:r:r:r;rsrcs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) ApplyWarpaFSL's applywarp wrapper to apply the results of a FNIRT registration Examples -------- >>> from nipype.interfaces import fsl >>> from nipype.testing import example_data >>> aw = fsl.ApplyWarp() >>> aw.inputs.in_file = example_data('structural.nii') >>> aw.inputs.ref_file = example_data('mni.nii') >>> aw.inputs.field_file = 'my_coefficients_filed.nii' #doctest: +SKIP >>> res = aw.run() #doctest: +SKIP Z applywarpcs*|dkr|jt|Stt|j|||S)Nr)rstrrIrrP)rLrUrVrW)rNr:r;rPszApplyWarp._format_argcCsH|jj}t|jjs0|j|jjdd|d<ntjj |jj|d<|S)NZ_warp)rYr-) rrarrZr-r[r,rSrbrc)rLrer:r:r;rgs   zApplyWarp._list_outputscCs|dkr|j|SdS)Nr-)rg)rLrUr:r:r;rhs zApplyWarp._gen_filename) r)r*r+rirjrrkrr`rPrgrhrlr:r:)rNr;rs rc@seZdZeddddddZeddddd Zejd d d Zej d dd Z ej dddddd Z ejddd Z eddddZej ddd ZeddddZdS)SliceTimerInputSpecTz--in=%srzfilename of input timeseries)rrrrrz--out=%szfilename of output timeseriesF)rrrrz--downz!slice indexing from top to bottom)rrz --repeat=%fz"Specify TR of data - default is 3srrrz--direction=%dz=direction of slice acquisition (x=1, y=2, z=3) - default is zz--oddzuse interleaved acquisitionz --tcustom=%szHslice timings, in fractions of TR, range 0:1 (default is 0.5 = no shift))rrrz --tglobalz>shift in fraction of TR, range 0:1 (default is 0.5 = no shift)z --ocustom=%sz^filename of single-column custom interleave order file (first slice is referred to as 1 not 0)N)r)r*r+r r,r-r r.Z index_dirr3Ztime_repetitionr|Zslice_directionZ interleavedZcustom_timingsZ global_shiftZ custom_orderr:r:r:r;rs@ rc@seZdZedddZdS)SliceTimerOutputSpecTzslice time corrected file)rrN)r)r*r+r slice_time_corrected_filer:r:r:r;r src@s,eZdZdZdZeZeZddZ ddZ dS) SliceTimera\FSL slicetimer wrapper to perform slice timing correction Examples -------- >>> from nipype.interfaces import fsl >>> from nipype.testing import example_data >>> st = fsl.SliceTimer() >>> st.inputs.in_file = example_data('functional.nii') >>> st.inputs.interleaved = True >>> result = st.run() #doctest: +SKIP Z slicetimercCsB|jj}|jj}t|s.|j|jjdd}tjj ||d<|S)NZ_st)rYr) rrarZr-rr[r,rSrbrc)rLrer-r:r:r;rg"s  zSliceTimer._list_outputscCs|dkr|jdSdS)Nr-r)rg)rLrUr:r:r;rh*s zSliceTimer._gen_filenameN) r)r*r+rirjrrkrr`rgrhr:r:r:r;rs  rc@seZdZeddddddZejddddd Zejdd dd d Zej d dd d dddZ ej ddd ddddZ ej ej eddejddddddZeddddddZdS)SUSANInputSpecTz%srzfilename of input timeseries)rrrrrz%.10frzlbrightness threshold and should be greater than noise level and less than contrast of edges to be preserved.)rrrrrz=fwhm of smoothing, in mm, gets converted using sqrt(8*log(2))z%drqz within-plane (2) or fully 3D (3))rrrrrzVwhether to use a local median filter in the cases where single-point noise is detected)rrzdetermines whether the smoothing area (USAN) is to be found from secondary images (0, 1 or 2). A negative value for any brightness threshold will auto-set the threshold at 10% of the robust range)r rrrrzoutput file nameF)rrrrrNrv)r)r*r+r r,r r3Zbrightness_thresholdfwhmr|Z dimensionZ use_medianr6rusansr-r:r:r:r;r0sVrc@seZdZedddZdS)SUSANOutputSpecTzsmoothed output file)rrN)r)r*r+r smoothed_filer:r:r:r;r#nsr#cs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) SUSANa)FSL SUSAN wrapper to perform smoothing For complete details, see the `SUSAN Documentation. `_ Examples -------- >>> from nipype.interfaces import fsl >>> from nipype.testing import example_data >>> anatfile # doctest: +SKIP anatomical.nii # doctest: +SKIP >>> sus = fsl.SUSAN() >>> sus.inputs.in_file = example_data('structural.nii') >>> sus.inputs.brightness_threshold = 2000.0 >>> sus.inputs.fwhm = 8.0 >>> result = sus.run() # doctest: +SKIP Zsusancs|dkr*|jt|tjdtjdS|dkrv|s:dStt|g}x"|D]\}}|j|d|gqNWdj|St t |j |||S)Nr!rr"0z%.10f ) rrnpsqrtlogrrextendrrIr%rP)rLrUrVrWZarglistrZthresh)rNr:r;rPs" zSUSAN._format_argcCsB|jj}|jj}t|s.|j|jjdd}tjj ||d<|S)NZ_smooth)rYr$) rrarZr-rr[r,rSrbrc)rLrer-r:r:r;rgs  zSUSAN._list_outputscCs|dkr|jdSdS)Nr-r$)rg)rLrUr:r:r;rhs zSUSAN._gen_filename) r)r*r+rirjrrkr#r`rPrgrhrlr:r:)rNr;r%rs r%c @seZdZeddddZeddddZeddddZedd d dZed d d gdgdZedddgdgdZ e j ddddZ e j dddZe j dddZe j dddZe j dddZe j dd dZe j d!d"dZe j d#d$dZe j d%d&dZe j d'd(dZe jd)d*dZe jd+d,dZe j d-d.dZe j d/d0dZe jd1d2d3d4d5d6d7d8dZe j d9d:dZe j d;dZe j d?dgd@d>Z eddAdBdZ!e j ddCdDdZ"e j ddEgdFdGZ#edHdIdZ$e j dJdKgdLdMZ%e j ddNgdOdGZ&edPdQdZ'e j ddRdSgdTdMZ(dUS)VFUGUEInputSpecTz--in=%szfilename of input volume)rrrz--loadshift=%sz'filename for reading pixel shift volumez --phasemap=%szfilename for input phase imagez --loadfmap=%sz%filename for loading fieldmap (rad/s)z --unwarp=%sz$apply unwarping and save as filenamerr,)rrr(rz --warp=%sz*apply forward warping and save as filename unwarped_fileFz*apply forward warping instead of unwarping)rrz--dwelltoasym=%.10fz set the dwell to asym time ratio)rrz --dwell=%.10fzKset the EPI dwell time per phase-encode line - same as echo spacing - (sec)z --asym=%.10fz0set the fieldmap asymmetric spin echo time (sec)z--medianzapply 2D median filteringz --despikezapply a 2D de-spiking filterz--nofillz0do not apply gap-filling measure to the fieldmapz --noextendz5do not apply rigid-body extrapolation to the fieldmapz--smooth2=%.2fz.apply 2D Gaussian smoothing of sigma N (in mm)z--smooth3=%.2fz.apply 3D Gaussian smoothing of sigma N (in mm)z --poly=%dz#apply polynomial fitting of order Nz --fourier=%dz-apply Fourier (sinusoidal) fitting of order Nz--pavaz$apply monotonic enforcement via PAVAz--despikethreshold=%sz2specify the threshold for de-spiking (default=3.0)xyzzx-zy-zz-z--unwarpdir=%sz*specifies direction of warping (default y)z --phaseconjz)apply phase conjugate method of unwarpingz--icorr shift_in_filezAapply intensity correction to unwarping (pixel shift method only))rrrz --icorronlyzapply intensity correction onlyz --mask=%szfilename for loading valid maskz --nokspacez"do not use k-space forward warpingsave_unmasked_shiftzwrite pixel shift volume)r(rz--saveshift=%sz&filename for saving pixel shift volumez --unmaskshift save_shiftz2saves the unmasked shiftmap when using --saveshift)rr(rsave_unmasked_fmapzwrite field map volumez --savefmap=%sz$filename for saving fieldmap (rad/s)z --unmaskfmap save_fmapz1saves the unmasked fieldmap when using --savefmapN))r)r*r+r r,r2phasemap_in_file fmap_in_filer.rr r.forward_warpingr3Zdwell_to_asym_ratioZ dwell_timeZ asym_se_timeZmedian_2dfilterZdespike_2dfilterZ no_gap_fillZ no_extendZsmooth2dZsmooth3dr5Z poly_orderZ fourier_orderZpavaZdespike_thresholdr|Zunwarp_directionZphase_conjugateZicorrZ icorr_onlyr=Znokspacer4shift_out_filer3r6 fmap_out_filer5r:r:r:r;r-s                 r-c@s4eZdZeddZeddZeddZeddZdS)FUGUEOutputSpecz unwarped file)rzforward warped filezvoxel shift map filez fieldmap fileN)r)r*r+r r.rr:r;r:r:r:r;r<%s   r<cs.eZdZdZdZeZeZdfdd Z Z S)FUGUEaA FSL FUGUE set of tools for EPI distortion correction `FUGUE `_ is, most generally, a set of tools for EPI distortion correction. Distortions may be corrected for 1. improving registration with non-distorted images (e.g. structurals), or 2. dealing with motion-dependent changes. FUGUE is designed to deal only with the first case - improving registration. Examples -------- Unwarping an input image (shift map is known): >>> from nipype.interfaces.fsl.preprocess import FUGUE >>> fugue = FUGUE() >>> fugue.inputs.in_file = 'epi.nii' >>> fugue.inputs.mask_file = 'epi_mask.nii' >>> fugue.inputs.shift_in_file = 'vsm.nii' # Previously computed with fugue as well >>> fugue.inputs.unwarp_direction = 'y' >>> fugue.inputs.output_type = "NIFTI_GZ" >>> fugue.cmdline # doctest: +ELLIPSIS 'fugue --in=epi.nii --mask=epi_mask.nii --loadshift=vsm.nii --unwarpdir=y --unwarp=epi_unwarped.nii.gz' >>> fugue.run() #doctest: +SKIP Warping an input image (shift map is known): >>> from nipype.interfaces.fsl.preprocess import FUGUE >>> fugue = FUGUE() >>> fugue.inputs.in_file = 'epi.nii' >>> fugue.inputs.forward_warping = True >>> fugue.inputs.mask_file = 'epi_mask.nii' >>> fugue.inputs.shift_in_file = 'vsm.nii' # Previously computed with fugue as well >>> fugue.inputs.unwarp_direction = 'y' >>> fugue.inputs.output_type = "NIFTI_GZ" >>> fugue.cmdline # doctest: +ELLIPSIS 'fugue --in=epi.nii --mask=epi_mask.nii --loadshift=vsm.nii --unwarpdir=y --warp=epi_warped.nii.gz' >>> fugue.run() #doctest: +SKIP Computing the vsm (unwrapped phase map is known): >>> from nipype.interfaces.fsl.preprocess import FUGUE >>> fugue = FUGUE() >>> fugue.inputs.phasemap_in_file = 'epi_phasediff.nii' >>> fugue.inputs.mask_file = 'epi_mask.nii' >>> fugue.inputs.dwell_to_asym_ratio = (0.77e-3 * 3) / 2.46e-3 >>> fugue.inputs.unwarp_direction = 'y' >>> fugue.inputs.save_shift = True >>> fugue.inputs.output_type = "NIFTI_GZ" >>> fugue.cmdline # doctest: +ELLIPSIS 'fugue --dwelltoasym=0.9390243902 --mask=epi_mask.nii --phasemap=epi_phasediff.nii --saveshift=epi_phasediff_vsm.nii.gz --unwarpdir=y' >>> fugue.run() #doctest: +SKIP ZfugueNc s&|dkr g}t|jj}t|jj}t|jj}| rJ| rJ| rJtdt|jjsd|ddg7}nZ|jjr|dg7}|jjd}d|_ d|_ d|_ n(|dg7}|jjd}d|_ d|_ d|_ t|jj sft|jj o|jj }t|jjo|jj}|p|rX|jjd}d|_ |rd|_ n$|r,d |_ n|r:d |_ ntd|rPd |_ nd |_ n|d ddg7}t|jjst|jjo|jj}t|jjo|jj} |s| r|jjd}d|_ |rd |_ n$|rd |_ n|rd|_ ntd| rd|_ nd|_ n|dddg7}tt|j|dS)NzCEither phasemap_in_file, shift_in_file or fmap_in_file must be set.r.rz %s_warpedr,z %s_unwarpedr:r8r7r2z%s_vsm_unmaskedz%s_vsmr4r3r;z%s_fieldmap_unmaskedz %s_fieldmapr6r5)r)rrZr7r2r8rr,r9ZtraitrrZ output_namer:r4r3r;r6r5rIr=r) rLrZ input_phaseZ input_vsmZ input_fmapZ trait_specZvsm_save_maskedZvsm_save_unmaskedZfmap_save_maskedZfmap_save_unmasked)rNr:r;rqsx             zFUGUE._parse_inputs)N) r)r*r+rirjr-rkr<r`rrlr:r:)rNr;r=,s ?r=c@s eZdZedddddgddZeddddgd dZedd ddgd dZedd d ddZej dddZ ej dddZ ej ddgddZ ej dddgddZejdddZedddd Zej d!d"dZej d#d$dZed%d&dd'Zed(d)dd'Zed*d+dd'Zej d,d-dZd.S)/PRELUDEInputSpecTz --complex=%smagnitude_file phase_filezcomplex phase input volume)rrrr(rz--abs=%scomplex_phase_filezfile containing magnitude imagez --phase=%szraw phase filez --unwrap=%szfile containing unwrapepd phaseF)rrrrz--numphasesplit=%dz!number of phase partitions to use)rrz --labelslicesz-does label processing in 2D (slice at a time)z--sliceslabelprocess2dz+does all processing in 2D (slice at a time))rr(rz --force3D process2dz#forces all processing to be full 3Dz--thresh=%.10fzintensity threshold for maskingz --mask=%szfilename of mask input volume)rrrz --start=%dz)first image number to process (default 0)z--end=%dz+final image number to process (default Inf)z --savemask=%szsaving the mask volume)rrrz --rawphase=%szsaving the raw phase outputz --labels=%szsaving the area labels outputz --removerampsz$remove phase ramps during unwrappingN)r)r*r+r rAr?r@unwrapped_phase_filer r5Znum_partitionsr.rBrCZ process3dr3r8r=rOendZ savemask_fileZ rawphase_fileZ label_fileZ removerampsr:r:r:r;r>sf         r>c@seZdZedddZdS)PRELUDEOutputSpecTzunwrapped phase file)rrN)r)r*r+r rDr:r:r:r;rF srFcs<eZdZdZeZeZdZfddZ ddZ ddZ Z S) PRELUDEz}FSL prelude wrapper for phase unwrapping Examples -------- Please insert examples for use of this command Zpreludec stt|jf|tddS)Nz;This has not been fully tested. Please report any failures.)rIrG__init__r)rLrf)rNr:r;rH2szPRELUDE.__init__cCsn|jj}|jj}t|sZt|jjr<|j|jjdd}nt|jjrZ|j|jjdd}tj j ||d<|S)NZ _unwrapped)rYZ_phase_unwrappedrD) rrarZrDrr@r[rArSrbrc)rLrer-r:r:r;rg6s   zPRELUDE._list_outputscCs|dkr|jdSdS)NrD)rg)rLrUr:r:r;rhCs zPRELUDE._gen_filename) r)r*r+rir>rkrFr`rjrHrgrhrlr:r:)rNr;rG$s  rGc @seZdZeddd'ddddZedddd(d d dd Zejd dd dZejddddZ ejddddZ ej ddddgdddddZ ej ddddZejejddddd d!Zedd"d#d$d%Zd&S))FIRSTInputSpecTrFz-i %szinput data file)rrrrrrZ segmentedrz-o %szoutput data file)rrrrrrz-vzUse verbose logging.)rrrz-bz1Input structural image is already brain-extractedz-drautorrmethod_as_numerical_thresholdz-m %srqzlMethod must be one of auto, fast, none, or it can be entered using the 'method_as_numerical_threshold' input)r(rrrrz-m %.4fz[Specify a numerical threshold value or use the 'method' input to choose auto, fast, or nonez-s %srrzRuns only on the specified structures (e.g. L_Hipp, R_HippL_Accu, R_Accu, L_Amyg, R_AmygL_Caud, R_Caud, L_Pall, R_PallL_Puta, R_Puta, L_Thal, R_Thal, BrStem)rrrrrr z-a %szFAffine matrix to use (e.g. img2std.mat) (does not re-run registration))rrrrNrrv)r)r*r+r r,r-r r.rZbrain_extractedZ no_cleanupr|methodr3rKr6Strlist_of_specific_structuresrr:r:r:r;rIIsbrIc@sHeZdZeeddddZeeddddZedddZedddZd S) FIRSTOutputSpecT)rz-VTK format meshes for each subcortical region)rz!bvars for each subcortical regionzT3D image file containing the segmented regions as integer values. Uses CMA labelling)rrz=4D image file containing a single volume per segmented regionN) r)r*r+r r vtk_surfacesbvarsoriginal_segmentationssegmentation_filer:r:r:r;rOsrOc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) FIRSTagFSL run_first_all wrapper for segmentation of subcortical volumes http://www.fmrib.ox.ac.uk/fsl/first/index.html Examples -------- >>> from nipype.interfaces import fsl >>> first = fsl.FIRST() >>> first.inputs.in_file = 'structural.nii' >>> first.inputs.out_file = 'segmented.nii' >>> res = first.run() #doctest: +SKIP Z run_first_allcCs|jj}t|jjr"|jj}n"ddddddddd d d d d ddg}|jd|d<|jd|d<|jd||d<|jd||d<|S)NZL_HippZR_HippZL_AccuZR_AccuZL_AmygZR_AmygZL_CaudZR_CaudZL_PallZR_PallZL_PutaZR_PutaZL_ThalZR_ThalZBrStemrRrSrPrQ)r`rarrZrNr[_gen_mesh_names)rLre structuresr:r:r;rgs.   zFIRST._list_outputscCst|jj\}}}d}t|jjrJ|jjdkrJd}|jjrJ|jjdkrJd}t|jjrnd|jj}|jdd}|dkrtj d||fS|d krtj d ||fSdS) NrrrJz%.4f.rrRz%s_all_%s_origsegs.nii.gzrSz%s_all_%s_firstseg.nii.gz) rrZr-rrLrNrKreplacerQrc)rLr]rboutnamerrLZthresr:r:r;r[s   zFIRST._gen_fnamec Cst|jj\}}}|dkrRt}x,|D]$}|d|d}|jtj|q&W|S|dkrt} x,|D]$}|d|d} | jtj| qfW| SdS)NrP-z _first.vtkrQz _first.bvars)rrZr-rrrQrc) rLrUrVrbprefixrZvtksstructZvtkrQZbvarr:r:r;rUs  zFIRST._gen_mesh_namesN) r)r*r+rirjrIrkrOr`rgr[rUr:r:r:r;rTsrT)=rirSos.pathrbrQwarningsrnumpyr)ZnibabelrrrZutils.filemaniprbaserr r r r r rrrrrr<rHrmrrrrrrrrrrrrrrrrrrrrr#r%r-r<r=r>rFrGrIrOrTr:r:r:r;sb     $ \f^&mc6 ; e|Q'0 >2/F%G