3 do@sddljZddlmZddlmZmZmZm Z m Z m Z m Z GdddeZ Gdd d e ZGd d d eZGd d d eZGddde ZGdddeZGdddeZGddde ZGdddeZGdddeZGddde ZGdddeZGdddeZGd d!d!e ZGd"d#d#eZGd$d%d%eZGd&d'd'e ZGd(d)d)eZGd*d+d+eZGd,d-d-e Z Gd.d/d/eZ!Gd0d1d1eZ"Gd2d3d3e Z#Gd4d5d5eZ$Gd6d7d7eZ%Gd8d9d9e Z&Gd:d;d;eZ'Gdd?d?e Z)Gd@dAdAeZ*GdBdCdCeZ+GdDdEdEe Z,GdFdGdGeZ-GdHdIdIeZ.GdJdKdKe Z/GdLdMdMeZ0GdNdOdOeZ1GdPdQdQe Z2GdRdSdSeZ3dS)TN)split_filename)CommandLineInputSpec CommandLinetraits TraitedSpecFileInputMultiPath isdefinedc @seZdZedddd(ddZeddd)ddZejddd d dd d Zej ej dd ddd ddZ ej ej dd d d d ddZ ejddddddddddd Z ejdddddddddddd Zejddddddddddd Zej dd dd d!Zej dd dd"d!Zejd#d d$d Zejd%d d&d Zd'S)*MRConvertInputSpecTz%srzvoxel-order data filename)existsargstr mandatorypositiondesczOutput filename)genfilerrrrz -coord %sz"Extract data only at the coordinates specified. This option specifies the Axis. Must be used in conjunction with extract_at_coordinate.)rrr,z"Extract data only at the coordinates specified. This option specifies the coordinates. Must be used in conjunction with extract_at_axis. Three comma-separated numbers giving the size of each voxel in mm.)rseprminlenmaxlenrz-vox %szBThree comma-separated numbers giving the size of each voxel in mm.Zniifloatcharshortintlongdoublez -output %szI"i.e. Bfloat". Can be "char", "short", "int", "long", "float" or "double"Zmif)rrZ usedefaultzspecify the layout of the data in memory. The actual layout produced will depend on whether the output image format can support it.z -scale %dmmz&Apply scaling to the intensity values.)rrZunitsrz%Apply offset to the intensity values.z-zeroz!Replace all NaN values with zero.z-prszQAssume that the DW gradients are specified in the PRS frame (Siemens DICOM only).N)__name__ __module__ __qualname__r in_file out_filenamerEnumZextract_at_axisListFloatZextract_at_coordinateZ voxel_dimsZoutput_datatype extensionZlayoutZresampleZ offset_biasBoolZreplace_NaN_with_zeroZprsr+r+E/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/mrtrix/preprocess.pyr s r c@seZdZedddZdS)MRConvertOutputSpecTz%path/name of 4D volume in voxel order)r rN)r!r"r#r convertedr+r+r+r,r-qsr-c@s4eZdZdZdZeZeZddZ ddZ ddZ d S) MRConverta Perform conversion between different file types and optionally extract a subset of the input image. If used correctly, this program can be a very useful workhorse. In addition to converting images between different formats, it can be used to extract specific studies from a data set, extract a specific region of interest, flip the images, or to scale the intensity of the images. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> mrconvert = mrt.MRConvert() >>> mrconvert.inputs.in_file = 'dwi_FA.mif' >>> mrconvert.inputs.out_filename = 'dwi_FA.nii' >>> mrconvert.run() # doctest: +SKIP Z mrconvertcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)Nr.) output_specgetinputsr%r opabspath_gen_outfilename)selfoutputsr+r+r, _list_outputss    zMRConvert._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6namer+r+r, _gen_filenameszMRConvert._gen_filenamecCs<t|jj\}}}t|jjr(|jj}n|d|jj}|S)Nz _mrconvert.)rr2r$r r%r))r6_r9outnamer+r+r,r5s   zMRConvert._gen_outfilenameN) r!r"r#__doc___cmdr input_specr-r0r8r:r5r+r+r+r,r/us r/c @seZdZeedddddddZeddd dd d d Zed dddZej ej dddddddZ ej ej dddd ddZ eddddZ ejdd ddZejdd ddZdS)!DWI2TensorInputSpecT)r z%srzDiffusion-weighted images)rrrrz %s_tensor.mifr$tensorzOutput tensor filenamer)Z name_templateZ name_sourceZ output_namerrrz-grad %szEncoding file supplied as a 4xN text file with each line is in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the b-value in units (1000 s/mm^2). See FSL2MRTrix())rrrz-ignoreslices %s zRequires two values (i.e. [34 1] for [Slice Volume] Ignores the image slices specified when computing the tensor. Slice here means the z coordinate of the slice to be ignored.)rrrrrrz-ignorevolumes %sznRequires two values (i.e. [2 5 6] for [Volumes] Ignores the image volumes specified when computing the tensor.)rrrrrz-mask %szFOnly perform computation within the specified binary brain mask image.)r rrz-quietz7Do not display information messages or progress status.z-debugzDisplay debugging messages.Nrr )r!r"r#r r r$r% encoding_filerr'IntZignore_slice_by_volumeZignore_volumesmaskr*quietdebugr+r+r+r,r@sR r@c@seZdZedddZdS)DWI2TensorOutputSpecTz*path/name of output diffusion tensor image)r rN)r!r"r#r rAr+r+r+r,rHsrHc@seZdZdZdZeZeZdS) DWI2Tensora Converts diffusion-weighted images to tensor images. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> dwi2tensor = mrt.DWI2Tensor() >>> dwi2tensor.inputs.in_file = 'dwi.mif' >>> dwi2tensor.inputs.encoding_file = 'encoding.txt' >>> dwi2tensor.cmdline 'dwi2tensor -grad encoding.txt dwi.mif dwi_tensor.mif' >>> dwi2tensor.run() # doctest: +SKIP Z dwi2tensorN) r!r"r#r=r>r@r?rHr0r+r+r+r,rIsrIc@sNeZdZeddddddZedddddZejd dd d Zejd dd d Z dS)Tensor2VectorInputSpecTz%srzDiffusion tensor image)r rrrrrzOutput vector filename)rrrrz-quietz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nrr ) r!r"r#r r$r%rr*rFrGr+r+r+r,rJsrJc@seZdZedddZdS)Tensor2VectorOutputSpecTzIthe output image of the major eigenvectors of the diffusion tensor image.)r rN)r!r"r#r vectorr+r+r+r,rKsrKc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) Tensor2VectoraG Generates a map of the major eigenvectors of the tensors in each voxel. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> tensor2vector = mrt.Tensor2Vector() >>> tensor2vector.inputs.in_file = 'dwi_tensor.mif' >>> tensor2vector.run() # doctest: +SKIP Z tensor2vectorcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrL)r0r1r2r%r r3r4r5)r6r7r+r+r,r8-s    zTensor2Vector._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:6szTensor2Vector._gen_filenamecCst|jj\}}}|dS)Nz _vector.mif)rr2r$)r6r;r9r+r+r,r5<szTensor2Vector._gen_outfilenameN) r!r"r#r=r>rJr?rKr0r8r:r5r+r+r+r,rMs  rMc@sNeZdZeddddddZedddddZejd dd d Zejd dd d Z dS)$Tensor2FractionalAnisotropyInputSpecTz%srzDiffusion tensor image)r rrrrrz%Output Fractional Anisotropy filename)rrrrz-quietz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nrr ) r!r"r#r r$r%rr*rFrGr+r+r+r,rNAs rNc@seZdZedddZdS)%Tensor2FractionalAnisotropyOutputSpecTzIthe output image of the major eigenvectors of the diffusion tensor image.)r rN)r!r"r#r FAr+r+r+r,rOWsrOc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) Tensor2FractionalAnisotropyaA Generates a map of the fractional anisotropy in each voxel. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> tensor2FA = mrt.Tensor2FractionalAnisotropy() >>> tensor2FA.inputs.in_file = 'dwi_tensor.mif' >>> tensor2FA.run() # doctest: +SKIP Z tensor2FAcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrP)r0r1r2r%r r3r4r5)r6r7r+r+r,r8os    z)Tensor2FractionalAnisotropy._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:xsz)Tensor2FractionalAnisotropy._gen_filenamecCst|jj\}}}|dS)Nz_FA.mif)rr2r$)r6r;r9r+r+r,r5~sz,Tensor2FractionalAnisotropy._gen_outfilenameN) r!r"r#r=r>rNr?rOr0r8r:r5r+r+r+r,rQ^s  rQc@sNeZdZeddddddZedddddZejd dd d Zejd dd d Z dS)!Tensor2ApparentDiffusionInputSpecTz%srzDiffusion tensor image)r rrrrrz%Output Fractional Anisotropy filename)rrrrz-quietz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nrr ) r!r"r#r r$r%rr*rFrGr+r+r+r,rRs rRc@seZdZedddZdS)"Tensor2ApparentDiffusionOutputSpecTzIthe output image of the major eigenvectors of the diffusion tensor image.)r rN)r!r"r#r ADCr+r+r+r,rSsrSc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) Tensor2ApparentDiffusionaN Generates a map of the apparent diffusion coefficient (ADC) in each voxel Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> tensor2ADC = mrt.Tensor2ApparentDiffusion() >>> tensor2ADC.inputs.in_file = 'dwi_tensor.mif' >>> tensor2ADC.run() # doctest: +SKIP Z tensor2ADCcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrT)r0r1r2r%r r3r4r5)r6r7r+r+r,r8s    z&Tensor2ApparentDiffusion._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:sz&Tensor2ApparentDiffusion._gen_filenamecCst|jj\}}}|dS)Nz_ADC.mif)rr2r$)r6r;r9r+r+r,r5sz)Tensor2ApparentDiffusion._gen_outfilenameN) r!r"r#r=r>rRr?rSr0r8r:r5r+r+r+r,rUs  rUc@sTeZdZeedddddddZeddddd Zejd dd d Z ejd ddd Z dS)MRMultiplyInputSpecT)r z%srzInput images to be multiplied)rrrrrzOutput image filename)rrrrz-quietz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nrr ) r!r"r#r r in_filesr%rr*rFrGr+r+r+r,rVsrVc@seZdZedddZdS)MRMultiplyOutputSpecTz&the output image of the multiplication)r rN)r!r"r#r out_filer+r+r+r,rXsrXc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) MRMultiplya Multiplies two images. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> MRmult = mrt.MRMultiply() >>> MRmult.inputs.in_files = ['dwi.mif', 'dwi_WMProb.mif'] >>> MRmult.run() # doctest: +SKIP ZmrmultcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrY)r0r1r2r%r r3r4r5)r6r7r+r+r,r8s    zMRMultiply._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:szMRMultiply._gen_filenamecCst|jjd\}}}|dS)Nrz _MRMult.mif)rr2rW)r6r;r9r+r+r,r5szMRMultiply._gen_outfilenameN) r!r"r#r=r>rVr?rXr0r8r:r5r+r+r+r,rZs  rZc@sDeZdZeedddddddZejddd d Zejd dd d Z d S)MRTrixViewerInputSpecT)r z%srzInput images to be viewed)rrrrz-quietrz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nr) r!r"r#r r rWrr*rFrGr+r+r+r,r[sr[c@s eZdZdS)MRTrixViewerOutputSpecN)r!r"r#r+r+r+r,r\sr\c@s$eZdZdZdZeZeZddZ dS) MRTrixViewera Loads the input images in the MRTrix Viewer. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> MRview = mrt.MRTrixViewer() >>> MRview.inputs.in_files = 'dwi.mif' >>> MRview.run() # doctest: +SKIP ZmrviewcCsdS)Nr+)r6r+r+r,r8&szMRTrixViewer._list_outputsN) r!r"r#r=r>r[r?r\r0r8r+r+r+r,r]s  r]c@seZdZeddddddZdS)MRTrixInfoInputSpecTz%srzInput images to be read)r rrrrNr)r!r"r#r r$r+r+r+r,r^*s r^c@s eZdZdS)MRTrixInfoOutputSpecN)r!r"r#r+r+r+r,r_4sr_c@s$eZdZdZdZeZeZddZ dS) MRTrixInfoa, Prints out relevant header information found in the image specified. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> MRinfo = mrt.MRTrixInfo() >>> MRinfo.inputs.in_file = 'dwi.mif' >>> MRinfo.run() # doctest: +SKIP ZmrinfocCsdS)Nr+)r6r+r+r,r8IszMRTrixInfo._list_outputsN) r!r"r#r=r>r^r?r_r0r8r+r+r+r,r`8s  r`c@s`eZdZeddddddZeddddddZedddd d Zedd ddd dZej d ddZ dS) GenerateWhiteMatterMaskInputSpecTz%srzDiffusion-weighted images)r rrrrrzBinary brain maskrz$Output WM probability image filename)rrrrz-grad %szGradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the b-value in units (1000 s/mm^2). See FSL2MRTrixz -margin %szqSpecify the width of the margin on either side of the image to be used to estimate the noise level (default = 10))rrNrr ) r!r"r#r r$Z binary_maskout_WMProb_filenamerCrr(Znoise_level_marginr+r+r+r,raMs,rac@seZdZedddZdS)!GenerateWhiteMatterMaskOutputSpecTWMprobabilitymap)r rN)r!r"r#r rer+r+r+r,rdksrdc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) GenerateWhiteMatterMaskad Generates a white matter probability mask from the DW images. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> genWM = mrt.GenerateWhiteMatterMask() >>> genWM.inputs.in_file = 'dwi.mif' >>> genWM.inputs.encoding_file = 'encoding.txt' >>> genWM.run() # doctest: +SKIP Z gen_WM_maskcCs"|jj}tj|j|d<|S)Nre)r0r1r3r4r5)r6r7r+r+r,r8s z%GenerateWhiteMatterMask._list_outputscCs|dkr|jSdSdS)Nrc)r5)r6r9r+r+r,r:sz%GenerateWhiteMatterMask._gen_filenamecCst|jj\}}}|dS)Nz _WMProb.mif)rr2r$)r6r;r9r+r+r,r5sz(GenerateWhiteMatterMask._gen_outfilenameN) r!r"r#r=r>rar?rdr0r8r:r5r+r+r+r,rfos rfc@sleZdZeddddddZedddddZejd d d Zej d dd dZ ej ddddZ ej ddddZ dS)ErodeInputSpecTz%srzInput mask image to be eroded)r rrrrrzOutput image filename)rrrrz -npass %sz!the number of passes (default: 1))rrz-dilatez$Perform dilation rather than erosion)rrrz-quietz7Do not display information messages or progress status.z-debugzDisplay debugging messages.Nrr ) r!r"r#r r$r%rrDZnumber_of_passesr*ZdilaterFrGr+r+r+r,rgs"  rgc@seZdZedddZdS)ErodeOutputSpecTzthe output image)r rN)r!r"r#r rYr+r+r+r,rhsrhc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) Erodea Erode (or dilates) a mask (i.e. binary) image Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> erode = mrt.Erode() >>> erode.inputs.in_file = 'mask.mif' >>> erode.run() # doctest: +SKIP ZerodecCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrY)r0r1r2r%r r3r4r5)r6r7r+r+r,r8s    zErode._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:szErode._gen_filenamecCst|jj\}}}|dS)Nz _erode.mif)rr2r$)r6r;r9r+r+r,r5szErode._gen_outfilenameN) r!r"r#r=r>rgr?rhr0r8r:r5r+r+r+r,ris  ric@seZdZeddddddZedddddZejd d d Zejd d d Z ej ddddZ ej ddddZ ej ddddZ ej ddddZdS)ThresholdInputSpecTz%srz!The input image to be thresholded)r rrrrrzThe output binary image mask.)rrrrz-abs %sz.Specify threshold value as absolute intensity.)rrz -percent %szQSpecify threshold value as a percentage of the peak intensity in the input image.z-invertzInvert output binary mask)rrrz-nanz Replace all zero values with NaNz-quietz7Do not display information messages or progress status.z-debugzDisplay debugging messages.Nrr )r!r"r#r r$r%rr(Zabsolute_threshold_valueZpercentage_threshold_valuer*invertZreplace_zeros_with_NaNrFrGr+r+r+r,rjs*  rjc@seZdZedddZdS)ThresholdOutputSpecTzThe output binary image mask.)r rN)r!r"r#r rYr+r+r+r,rlsrlc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) Thresholda' Create bitwise image by thresholding image intensity. By default, the threshold level is determined using a histogram analysis to cut out the background. Otherwise, the threshold intensity can be specified using command line options. Note that only the first study is used for thresholding. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> thresh = mrt.Threshold() >>> thresh.inputs.in_file = 'wm_mask.mif' >>> thresh.run() # doctest: +SKIP thresholdcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrY)r0r1r2r%r r3r4r5)r6r7r+r+r,r8 s    zThreshold._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:szThreshold._gen_filenamecCst|jj\}}}|dS)Nz _thresh.mif)rr2r$)r6r;r9r+r+r,r5szThreshold._gen_outfilenameN) r!r"r#r=r>rjr?rlr0r8r:r5r+r+r+r,rms rmc@sNeZdZeddddddZedddddZejd dd d Zejd dd d Z dS)MedianFilter3DInputSpecTz%srzInput images to be smoothed)r rrrrrzOutput image filename)rrrrz-quietz7Do not display information messages or progress status.)rrrz-debugzDisplay debugging messages.Nrr ) r!r"r#r r$r%rr*rFrGr+r+r+r,rosroc@seZdZedddZdS)MedianFilter3DOutputSpecTzthe output image)r rN)r!r"r#r rYr+r+r+r,rp2srpc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) MedianFilter3Da Smooth images using a 3x3x3 median filter. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> median3d = mrt.MedianFilter3D() >>> median3d.inputs.in_file = 'mask.mif' >>> median3d.run() # doctest: +SKIP Zmedian3DcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)NrY)r0r1r2r%r r3r4r5)r6r7r+r+r,r8Gs    zMedianFilter3D._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:PszMedianFilter3D._gen_filenamecCst|jj\}}}|dS)Nz _median3D.mif)rr2r$)r6r;r9r+r+r,r5VszMedianFilter3D._gen_outfilenameN) r!r"r#r=r>ror?rpr0r8r:r5r+r+r+r,rq6s  rqc@seZdZeedddddddZeddd dd Zejd dd d Z edd dddZ ejdddd Z edddddZ edddddZ edddddZejdddd Zejdddd Zejdddd ZdS)!MRTransformInputSpecT)r z%srzInput images to be transformed)rrrrrz Output image)rrrrz-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.)r rrrz-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 )r!r"r#r r rWr%rr*rkZlinear_transformZreplace_transformZtransformation_fileZtemplate_imageZreference_imageZflip_xrFrGr+r+r+r,rr[sXrrc@seZdZedddZdS)MRTransformOutputSpecTz&the output image of the transformation)r rN)r!r"r#r rYr+r+r+r,rssrsc@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)NrY)r0r1r2r%r r3r4r5)r6r7r+r+r,r8s    zMRTransform._list_outputscCs|dkr|jSdSdS)Nr%)r5)r6r9r+r+r,r:szMRTransform._gen_filenamecCst|jjd\}}}|dS)Nrz_MRTransform.mif)rr2rW)r6r;r9r+r+r,r5szMRTransform._gen_outfilenameN) r!r"r#r=r>rrr?rsr0r8r:r5r+r+r+r,rts  rt)4os.pathpathr3Zutils.filemaniprbaserrrrr r r r r-r/r@rHrIrJrKrMrNrOrQrRrSrUrVrXrZr[r\r]r^r_r`rardrfrgrhrirjrlrmrorprqrrrsrtr+r+r+r,sR  $ ^/E%%%% "%*%=