3 dNJ@sddljZddlmZddlmZmZmZm Z m Z m Z m Z ddlm Z mZGdd d e ZGd d d e ZGd d d eZGddde ZGddde ZGdddeZGdddeZGddde ZGdddeZGddde ZGddde ZGdddeZGd d!d!eZGd"d#d#e ZGd$d%d%eZGd&d'd'eZGd(d)d)e ZGd*d+d+eZ Gd,d-d-eZ!Gd.d/d/e Z"Gd0d1d1eZ#Gd2d3d3e Z$Gd4d5d5e Z%Gd6d7d7eZ&Gd8d9d9e Z'Gd:d;d;e Z(Gdd?d?e Z*Gd@dAdAe Z+GdBdCdCeZ,GdDdEdEe Z-GdFdGdGe Z.GdHdIdIeZ/GdJdKdKe Z0GdLdMdMe Z1GdNdOdOeZ2GdPdQdQe Z3GdRdSdSe Z4GdTdUdUeZ5GdVdWdWeZ6GdXdYdYe Z7GdZd[d[eZ8Gd\d]d]eZ9Gd^d_d_e Z:Gd`dadaeZ;dS)bN)split_filename)CommandLineInputSpec CommandLinetraits TraitedSpecFileInputMultiPath isdefined)MRTrix3BaseInputSpec MRTrix3Basec@s2eZdZedddd ddZedddd ddd Zd S) BrainMaskInputSpecTz%srzinput diffusion weighted images)existsargstr mandatorypositiondescz brainmask.mifr zoutput brain mask)rrr usedefaultrN)__name__ __module__ __qualname__r in_fileout_filerrA/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/mrtrix3/utils.pyrsrc@seZdZedddZdS)BrainMaskOutputSpecTzthe output response file)rrN)rrrr rrrrrr&src@s$eZdZdZdZeZeZddZ dS) BrainMaska Convert a mesh surface to a partial volume estimation image Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> bmsk = mrt.BrainMask() >>> bmsk.inputs.in_file = 'dwi.mif' >>> bmsk.cmdline # doctest: +ELLIPSIS 'dwi2mask dwi.mif brainmask.mif' >>> bmsk.run() # doctest: +SKIP Zdwi2maskcCs"|jj}tj|jj|d<|S)Nr) output_specgetopabspathinputsr)selfoutputsrrr _list_outputs>s zBrainMask._list_outputsN) rrr__doc___cmdr input_specrr!r(rrrrr *s r c%@seZdZejedddd2dddZedddd3dd d Zejd d d Z ej ddddddddddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/d0d #Z d1S)4MRCatInputSpecT)rz%srzfiles to concatenate)rrrrzconcatenated.mifr zoutput concatenated image)rrrrrz-axis %saspecify axis along which concatenation should be performed. By default, the program will use the last non-singleton, non-spatial axis of any of the input images - in other words axis 3 or whichever axis (greater than 3) of the input images has size greater than one)rrZfloat32Z float32leZ float32beZfloat64Z float64leZ float64beZint64Zuint64Zint64leZuint64leZint64beZuint64beZint32Zuint32Zint32leZuint32leZint32beZuint32beZint16Zuint16Zint16leZuint16leZint16beZuint16beZcfloat32Z cfloat32leZ cfloat32beZcfloat64Z cfloat64leZ cfloat64beZint8Zuint8bitz -datatype %szspecify output image data typeNrr) rrrrListr in_filesrIntaxisEnumdatatyperrrrr,Dshr,c@seZdZedddZdS)MRCatOutputSpecTzthe output concatenated image)rrN)rrrr rrrrrr4sr4c@s$eZdZdZdZeZeZddZ dS)MRCata Concatenate several images into one Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> mrcat = mrt.MRCat() >>> mrcat.inputs.in_files = ['dwi.mif','mask.mif'] >>> mrcat.cmdline # doctest: +ELLIPSIS 'mrcat dwi.mif mask.mif concatenated.mif' >>> mrcat.run() # doctest: +SKIP ZmrcatcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zMRCat._list_outputsN) rrrr)r*r,r+r4r!r(rrrrr5s r5c@sReZdZeddddddZeddddddZeddd d Zed ddddd dZdS)Mesh2PVEInputSpecTz%srz input mesh)rrrrrrzinput reference imagez -first %sz5indicates that the mesh file is provided by FSL FIRST)rrrzmesh2volume.nii.gzr z&output file containing SH coefficients)rrrrrNrr)rrrr r referenceZin_firstrrrrrr6s&r6c@seZdZedddZdS)Mesh2PVEOutputSpecTzthe output response file)rrN)rrrr rrrrrr9sr9c@s$eZdZdZdZeZeZddZ dS)Mesh2PVEa Convert a mesh surface to a partial volume estimation image Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> m2p = mrt.Mesh2PVE() >>> m2p.inputs.in_file = 'surf1.vtk' >>> m2p.inputs.reference = 'dwi.mif' >>> m2p.inputs.in_first = 'T1.nii.gz' >>> m2p.cmdline # doctest: +ELLIPSIS 'mesh2pve -first T1.nii.gz surf1.vtk dwi.mif mesh2volume.nii.gz' >>> m2p.run() # doctest: +SKIP Zmesh2pvecCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zMesh2PVE._list_outputsN) rrrr)r*r6r+r9r!r(rrrrr:s r:c @sFeZdZejddddddddZeddddd d Zedddd dZdS)Generate5ttInputSpecZfslZgifZ freesurferz%srTztissue segmentation algorithm)rrrrrz input image)rrrrrr z output image)rrrrNr7rr) rrrrr2 algorithmr rrrrrrr;sr;c@seZdZedddZdS)Generate5ttOutputSpecTz output image)rrN)rrrr rrrrrr=sr=c@s$eZdZdZdZeZeZddZ dS) Generate5tta Generate a 5TT image suitable for ACT using the selected algorithm Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> gen5tt = mrt.Generate5tt() >>> gen5tt.inputs.in_file = 'T1.nii.gz' >>> gen5tt.inputs.algorithm = 'fsl' >>> gen5tt.inputs.out_file = '5tt.mif' >>> gen5tt.cmdline # doctest: +ELLIPSIS '5ttgen fsl T1.nii.gz 5tt.mif' >>> gen5tt.run() # doctest: +SKIP Z5ttgencCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zGenerate5tt._list_outputsN) rrrr)r*r;r+r=r!r(rrrrr>s r>c@seZdZedddd&ddZedddZed d dZed d dZed ddZedddZ edddZ edddZ edddZ edddZ ejdgdddddZeddddZejd d!d"d#d$dZd%S)'TensorMetricsInputSpecTz%sr zinput DTI image)rrrrrz-fa %szoutput FA file)rrz-adc %szoutput ADC filez-ad %szoutput AD filez-rd %szoutput RD filez-cl %szoutput CL filez-cp %szoutput CP filez-cs %szoutput CS filez -vector %sz#output selected eigenvector(s) filez -value %sz"output selected eigenvalue(s) filez-num %s,zrspecify the desired eigenvalue/eigenvector(s). Note that several eigenvalues can be specified as a number sequence)rrseprz-mask %szEonly perform computation within the specified binary brain mask image)rrrZFAnoneevalz -modulate %sz1how to modulate the magnitude of the eigenvectorsNr)rrrr rout_faout_adcout_adout_rdout_clout_cpout_csout_evecout_evalrr. componentZin_maskr2Zmodulaterrrrr?s>         r?c@sfeZdZeddZeddZeddZeddZeddZeddZ eddZ ed dZ ed dZ d S) TensorMetricsOutputSpeczoutput FA file)rzoutput ADC filezoutput AD filezoutput RD filezoutput CL filezoutput CP filezoutput CS filez#output selected eigenvector(s) filez"output selected eigenvalue(s) fileN) rrrr rDrErFrGrHrIrJrKrLrrrrrN:s        rNc@s$eZdZdZdZeZeZddZ dS) TensorMetricsa Compute metrics from tensors Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> comp = mrt.TensorMetrics() >>> comp.inputs.in_file = 'dti.mif' >>> comp.inputs.out_fa = 'fa.mif' >>> comp.cmdline # doctest: +ELLIPSIS 'tensor2metric -num 1 -fa fa.mif dti.mif' >>> comp.run() # doctest: +SKIP Z tensor2metriccCsL|jj}x:t|jD]*}tt|j|rtjt|j|||<qW|S)N) r!r"listkeysr getattrr%r#r$)r&r'krrrr([s  zTensorMetrics._list_outputsN) rrrr)r*r?r+rNr!r(rrrrrOFs rOc@sTeZdZeddddFddZeddddGdd Zedd d d Zejej d dddZ ej dddddZ ej dddZedddZej dddZej ddddd d!d"d#d$d Zedd%d&d Zej d'd(d)d*d+d,dZej d)d'd(d*d-d.d/d0d1d2d3d4d5d Zejd6d7dZej d8d9dZej d:d;dZej dd?dZedd@dAd Zej dBdCddDZdES)HComputeTDIInputSpecTz%srzinput tractography)rrrrrztdi.mifr zoutput TDI file)rrrrz -template %sz'a referenceimage to be used as template)rrrz-vox %sr@zvoxel dimensions)rrArfloatz unsigned intz -datatype %szspecify output image data type)rrz-deczperform mapping in DEC spacez -dixel %sz\map streamlines todixels within each voxel. Directions are stored asazimuth elevation pairs.z-tod %dz>generate a Track Orientation Distribution (TOD) in each voxel.ZtdilengthZ invlengthZ scalar_mapZscalar_map_conutZfod_ampZ curvaturez -constrast %sz8define the desired form of contrast for the output imagez -image %szkprovide thescalar image map for generating images with 'scalar_map' contrasts, or the SHs image for fod_ampsumminmeanmaxz -stat_vox %szPdefine the statistic for choosing the finalvoxel intesities for a given contrastmedianZ mean_nonzeroZgaussianZends_minZ ends_meanZends_maxZ ends_prodz -stat_tck %szdefine the statistic for choosing the contribution to be made by each streamline as a function of the samples taken along their lengths.z -fwhm_tck %fzdefine the statistic for choosing the contribution to be made by each streamline as a function of the samples taken along their lengthsz -map_zeroaif a streamline has zero contribution based on the contrast & statistic, typically it is not mapped; use this option to still contribute to the map even if this is the case (these non-contributing voxels can then influence the mean value in each voxel of the map)z -upsample %dzMupsample the tracks by some ratio using Hermite interpolation before mapppingz-precisezuse a more precise streamline mapping strategy, that accurately quantifies the length through each voxel (these lengths are then taken into account during TWI calculation)z -ends_onlyz.only map the streamline endpoints to the imagez-tck_weights_in %sz>> import nipype.interfaces.mrtrix3 as mrt >>> tdi = mrt.ComputeTDI() >>> tdi.inputs.in_file = 'dti.mif' >>> tdi.cmdline # doctest: +ELLIPSIS 'tckmap dti.mif tdi.mif' >>> tdi.run() # doctest: +SKIP ZtckmapcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zComputeTDI._list_outputsN) rrrr)r*rTr+r`r!r(rrrrras 4rac@s\eZdZeddddddZedddddd Zedd d d Zedd d d Zej ddddZ dS)TCK2VTKInputSpecTz%srzinput tractography)rrrrrz tracks.vtkr zoutput VTK file)rrrrz -image %szif specified, the properties of this image will be used to convert track point positions from real (scanner) coordinates into image coordinates (in mm).)rrrzif specified, the properties of this image will be used to convert track point positions from real (scanner) coordinates into image coordinates.z -nthreads %dzEnumber of threads. if zero, the number of available cpus will be used)rrr\Nrr) rrrr rrr8Zvoxelrr0r_rrrrrb%s rbc@seZdZeddZdS)TCK2VTKOutputSpeczoutput VTK file)rN)rrrr rrrrrrcBsrcc@s$eZdZdZdZeZeZddZ dS)TCK2VTKa Convert a track file to a vtk format, cave: coordinates are in XYZ coordinates not reference Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> vtk = mrt.TCK2VTK() >>> vtk.inputs.in_file = 'tracks.tck' >>> vtk.inputs.reference = 'b0.nii' >>> vtk.cmdline # doctest: +ELLIPSIS 'tck2vtk -image b0.nii tracks.tck tracks.vtk' >>> vtk.run() # doctest: +SKIP Ztck2vtkcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr([s zTCK2VTK._list_outputsN) rrrr)r*rbr+rcr!r(rrrrrdFs rdc@sleZdZeddddddZedddddZejd d d Zejd d d Z ejddd Z ej ej ddddZ dS)DWIExtractInputSpecTz%srz input image)rrrrrr z output image)rrrrz-bzerozextract b=0 volumes)rrz -no_bzerozextract non b=0 volumesz -singleshellz%extract volumes with a specific shellr@z -shell %sz#specify one or more gradient shells)rArrNrr)rrrr rrrr]ZbzeroZnobzeroZ singleshellr.r^shellrrrrreas rec@seZdZedddZdS)DWIExtractOutputSpecTz output image)rrN)rrrr rrrrrrgssrgc@s$eZdZdZdZeZeZddZ dS) DWIExtractaf Extract diffusion-weighted volumes, b=0 volumes, or certain shells from a DWI dataset Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> dwiextract = mrt.DWIExtract() >>> dwiextract.inputs.in_file = 'dwi.mif' >>> dwiextract.inputs.bzero = True >>> dwiextract.inputs.out_file = 'b0vols.mif' >>> dwiextract.inputs.grad_fsl = ('bvecs', 'bvals') >>> dwiextract.cmdline # doctest: +ELLIPSIS 'dwiextract -bzero -fslgrad bvecs bvals dwi.mif b0vols.mif' >>> dwiextract.run() # doctest: +SKIP Z dwiextractcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zDWIExtract._list_outputsN) rrrr)r*rer+rgr!r(rrrrrhws rhc@seZdZeddddddZeddddddd Zejejd d d d Z ejej dddd Z ejejdddd Z ejej dddd Z edddddZedddddZdS)MRConvertInputSpecTz%srz input image)rrrrrzdwi.mifr z output image)rrrrr z -coord %sz)extract data at the specified coordinates)rArrr@z-vox %szchange the voxel dimensionsz-axes %sz"specify the axes that will be usedz -scaling %sz"specify the data scaling parameterz-json_import %sFz8import data from a JSON file into header key-value pairs)rrrrz-json_export %szAexport data from an image header key-value pairs into a JSON fileNrr)rrrr rrrr.r0Zcoordr^ZvoxZaxesZscalingZ json_import json_exportrrrrrisHric@s<eZdZedddZedddZedddZedddZdS)MRConvertOutputSpecTz output image)rrzAexported data from an image header key-value pairs in a JSON filezexport bvec file in FSL formatN)rrrr rrkout_bvecout_bvalrrrrrls   rlc@s$eZdZdZdZeZeZddZ dS) MRConverta Perform conversion between different file types and optionally extract a subset of the input image Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> mrconvert = mrt.MRConvert() >>> mrconvert.inputs.in_file = 'dwi.nii.gz' >>> mrconvert.inputs.grad_fsl = ('bvecs', 'bvals') >>> mrconvert.cmdline # doctest: +ELLIPSIS 'mrconvert -fslgrad bvecs bvals dwi.nii.gz dwi.mif' >>> mrconvert.run() # doctest: +SKIP Z mrconvertcCsp|jj}tj|jj|d<|jjr8tj|jj|d<|jjrRtj|jj|d<|jjrltj|jj|d<|S)Nrrkrmrn) r!r"r#r$r%rrkrmrn)r&r'rrrr(s zMRConvert._list_outputsN) rrrr)r*rir+rlr!r(rrrrros roc@sleZdZeddddddZeddddddZeddddd dZed ddddd d Zej dd dddddZ dS)TransformFSLConvertInputSpecTz%sr zFLIRT input image)rrrrrrzFLIRT reference imagerz"FLIRT output transformation matrixztransform_mrtrix.txtz-output transformed affine in mrtrix3's format)rrrrr flirt_importz"import transform from FSL's FLIRT.)rrrrrNrr) rrrr rr8Z in_transform out_transformrr]rqrrrrrps@rpc@seZdZedddZdS)TransformFSLConvertOutputSpecTz-output transformed affine in mrtrix3's format)rrN)rrrr rrrrrrrssrsc@s$eZdZdZdZeZeZddZ dS)TransformFSLConvertzU Perform conversion between FSL's transformation matrix format to mrtrix3's. ZtransformconvertcCs"|jj}tj|jj|d<|S)Nrr)r!r"r#r$r%rr)r&r'rrrr(%s z!TransformFSLConvert._list_outputsN) rrrr)r*rpr+rsr!r(rrrrrts rtc@seZdZeedddddddZeddd dd Zejd dd d Z edd dddZ ejdddd Z edddddZ edddddZ edddddZejdddd Zejdddd Zejdddd ZdS)!MRTransformInputSpecT)rz%srzInput images to be transformed)rrrrr z Output image)Zgenfilerrrz-inversez.Invert the specified transform before using it)rrrz -linear %saSpecify a linear transform to apply, in the form of a 3x4 or 4x4 ascii file. Note the standard reverse convention is used, where the transform maps points in the template image to the moving image. Note that the reverse convention is still assumed even if no -template image is supplied.)rrrrz-replacezareplace the current transform by that specified, rather than applying it to the current transformz -transform %sz8The transform to apply, in the form of a 4x4 ascii file.z -template %sz>Reslice the input image to match the specified template image.z -reference %szin case the transform supplied maps from the input image onto a reference image, use this option to specify the reference. Note that this implicitly sets the -replace option.z-flipxa+assume the transform is supplied assuming a coordinate system with the x-axis reversed relative to the MRtrix convention (i.e. x increases from right to left). This is required to handle transform matrices produced by FSL's FLIRT command. This is only used in conjunction with the -reference option.z-quietz7Do not display information messages or progress status.z-debugzDisplay debugging messages.Nrr)rrrr r r/rrr]invertZlinear_transformZreplace_transformZtransformation_fileZtemplate_imageZreference_imageZflip_xquietdebugrrrrru+s`ruc@seZdZedddZdS)MRTransformOutputSpecTz&the output image of the transformation)rrN)rrrr rrrrrrymsryc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) MRTransformz Apply spatial transformations or reslice images Example ------- >>> MRxform = MRTransform() >>> MRxform.inputs.in_files = 'anat_coreg.mif' >>> MRxform.run() # doctest: +SKIP Z mrtransformcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)Nr)r!r"r%rr r#r$_gen_outfilename)r&r'rrrr(s    zMRTransform._list_outputscCs|dkr|jSdSdS)Nr)r{)r&namerrr _gen_filenameszMRTransform._gen_filenamecCst|jjd\}}}|dS)Nrz_MRTransform.mif)rr%r/)r&_r|rrrr{szMRTransform._gen_outfilenameN) rrrr)r*rur+ryr!r(r}r{rrrrrzqs  rzc@sheZdZeddddddZedddddZejd d d d d ddddddddddddZej ddddZ dS) MRMathInputSpecTz%srz input image)rrrrrr z output image)rrrrrYr[rWproductZrmsZnormvarZstdrXrZZabsmaxZmagmaxrz,operation to computer along a specified axis)rrrrrz-axis %dz,specfied axis to perform the operation along)rrNr7rr) rrrr rrrr2Z operationr0r1rrrrrs,rc@seZdZedddZdS)MRMathOutputSpecTz output image)rrN)rrrr rrrrrrsrc@s$eZdZdZdZeZeZddZ dS)MRMathas Compute summary statistic on image intensities along a specified axis of a single image Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> mrmath = mrt.MRMath() >>> mrmath.inputs.in_file = 'dwi.mif' >>> mrmath.inputs.operation = 'mean' >>> mrmath.inputs.axis = 3 >>> mrmath.inputs.out_file = 'dwi_mean.mif' >>> mrmath.inputs.grad_fsl = ('bvecs', 'bvals') >>> mrmath.cmdline # doctest: +ELLIPSIS 'mrmath -axis 3 -fslgrad bvecs bvals dwi.mif mean dwi_mean.mif' >>> mrmath.run() # doctest: +SKIP ZmrmathcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zMRMath._list_outputsN) rrrr)r*rr+rr!r(rrrrrs rc @seZdZeddddddZejejejejfddddd gd Zejej ej ej fd dd d d gd Z ejej ej ej fdddd dgd Z ej ddddddddZ edddgddddZdS)MRResizeInputSpecTz%srzinput DWI image)rrrrrz-size %d,%d,%dz2Number of voxels in each dimension of output image voxel_size scale_factor)rrrxorz-voxel %g,%g,%gz-Desired voxel size in mm for the output image image_sizez-scale %g,%g,%gz7Scale factors to rescale the image by in each dimensionZcubicZnearestZlinearZsincz -interp %szjset the interpolation method to use when resizing (choices: nearest, linear, cubic, sinc. Default: cubic).)rrrz %s_resizedrr zthe output resized DWI image)r name_template name_sourceZkeep_extensionrrNrr)rrrr rrTupler0rr^rrr2 interpolationrrrrrrsF   rc@seZdZedddZdS)MRResizeOutputSpeczthe output resized DWI imageT)rrN)rrrr rrrrrrsrc@seZdZdZdZeZeZdS)MRResizea Resize an image by defining the new image resolution, voxel size or a scale factor. If the image is 4D, then only the first 3 dimensions can be resized. Also, if the image is down-sampled, the appropriate smoothing is automatically applied using Gaussian smoothing. For more information, see Example ------- >>> import nipype.interfaces.mrtrix3 as mrt Defining the new image resolution: >>> image_resize = mrt.MRResize() >>> image_resize.inputs.in_file = 'dwi.mif' >>> image_resize.inputs.image_size = (256, 256, 144) >>> image_resize.cmdline # doctest: +ELLIPSIS 'mrresize -size 256,256,144 -interp cubic dwi.mif dwi_resized.mif' >>> image_resize.run() # doctest: +SKIP Defining the new image's voxel size: >>> voxel_resize = mrt.MRResize() >>> voxel_resize.inputs.in_file = 'dwi.mif' >>> voxel_resize.inputs.voxel_size = (1, 1, 1) >>> voxel_resize.cmdline # doctest: +ELLIPSIS 'mrresize -interp cubic -voxel 1,1,1 dwi.mif dwi_resized.mif' >>> voxel_resize.run() # doctest: +SKIP Defining the scale factor of each image dimension: >>> scale_resize = mrt.MRResize() >>> scale_resize.inputs.in_file = 'dwi.mif' >>> scale_resize.inputs.scale_factor = (0.5,0.5,0.5) >>> scale_resize.cmdline # doctest: +ELLIPSIS 'mrresize -interp cubic -scale 0.5,0.5,0.5 dwi.mif dwi_resized.mif' >>> scale_resize.run() # doctest: +SKIP ZmrresizeN) rrrr)r*rr+rr!rrrrrs$rc@sFeZdZeddddddZeddddddZed d gdddd d ZdS)SHConvInputSpecTz%srzinput ODF image)rrrrrrzThe response function)rrrrrz %s_shconv.mifrr zthe output spherical harmonics)rrrrrrNr7rr)rrrr rresponserrrrrr/s&rc@seZdZedddZdS)SHConvOutputSpecTz.the output convoluted spherical harmonics file)rrN)rrrr rrrrrrIsrc@s$eZdZdZdZeZeZddZ dS)SHConva Convolve spherical harmonics with a tissue response function. Useful for checking residuals of ODF estimates. Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> sh = mrt.SHConv() >>> sh.inputs.in_file = 'csd.mif' >>> sh.inputs.response = 'response.txt' >>> sh.cmdline 'shconv csd.mif response.txt csd_shconv.mif' >>> sh.run() # doctest: +SKIP ZshconvcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(cs zSHConv._list_outputsN) rrrr)r*rr+rr!r(rrrrrMs rc@sTeZdZeddddddZeddddddZed d gdddd d ZejdddZ dS)SH2AmpInputSpecTz%srzinput ODF image)rrrrrrzSThe gradient directions along which to sample the spherical harmonics MRtrix format)rrrrrz %s_amp.mifrr zthe output spherical harmonics)rrrrrrz -nonnegativez#cap all negative amplitudes to zero)rrNr7rr) rrrr rZ directionsrrr]Z nonnegativerrrrris*rc@seZdZedddZdS)SH2AmpOutputSpecTz.the output convoluted spherical harmonics file)rrN)rrrr rrrrrrsrc@s$eZdZdZdZeZeZddZ dS)SH2Ampa Sample spherical harmonics on a set of gradient orientations. Useful for checking residuals of ODF estimates. Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> sh = mrt.SH2Amp() >>> sh.inputs.in_file = 'sh.mif' >>> sh.inputs.directions = 'grads.txt' >>> sh.cmdline 'sh2amp sh.mif grads.txt sh_amp.mif' >>> sh.run() # doctest: +SKIP Zsh2ampcCs"|jj}tj|jj|d<|S)Nr)r!r"r#r$r%r)r&r'rrrr(s zSH2Amp._list_outputsN) rrrr)r*rr+rr!r(rrrrrs r)<os.pathpathr#Zutils.filemaniprbaserrrrr r r r rrrr r,r4r5r6r9r:r;r=r>r?rNrOrTr`rarbrcrdrergrhrirlrorprsrtruryrzrrrrrrrrrrrrrrrrs`  $ A, |@/ !(B$-+