3 Yd[V@sddljZddlZddlmZddlmZddl m Z m Z m Z m Z mZmZmZejdZGdd d e ZGd d d eZGd d d e ZGddde ZGdddeZGddde ZGddde ZGdddeZGddde ZddZGdddeZGdddeZGd d!d!e ZGd"d#d#e Z Gd$d%d%eZ!Gd&d'd'e Z"Gd(d)d)e Z#Gd*d+d+eZ$Gd,d-d-e Z%Gd.d/d/e Z&Gd0d1d1eZ'Gd2d3d3e Z(dS)4N)logging)split_filename)CommandLineInputSpec CommandLine BaseInterfacetraitsFile TraitedSpec isdefinedznipype.interfacec@s^eZdZeddddddZedddddZedd ddd dZejd d d Z ej ddddZ dS)$DWI2SphericalHarmonicsImageInputSpecTz%srzDiffusion-weighted images)existsargstr mandatorypositiondesczOutput filename)genfilerrrz-grad %szGradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the b-value in units (1000 s/mm^2). See FSL2MRTrixz-lmax %szset the maximum harmonic order for the output series. By default, the program will use the highest possible lmax given the number of diffusion-weighted images.)rrz -normaliserz(normalise the DW signal to the b=0 image)rrrN) __name__ __module__ __qualname__r in_file out_filename encoding_filer Floatmaximum_harmonic_orderBool normaliser!r!R/var/www/html/virt/lib/python3.6/site-packages/nipype/interfaces/mrtrix/tensors.pyr s$r c@seZdZedddZdS)%DWI2SphericalHarmonicsImageOutputSpecTzSpherical harmonics image)rrN)rrrr spherical_harmonics_imager!r!r!r"r#0sr#c@s4eZdZdZdZeZeZddZ ddZ ddZ d S) DWI2SphericalHarmonicsImagea Convert base diffusion-weighted images to their spherical harmonic representation. This program outputs the spherical harmonic decomposition for the set measured signal attenuations. The signal attenuations are calculated by identifying the b-zero images from the diffusion encoding supplied (i.e. those with zero as the b-value), and dividing the remaining signals by the mean b-zero signal intensity. The spherical harmonic decomposition is then calculated by least-squares linear fitting. Note that this program makes use of implied symmetries in the diffusion profile. First, the fact the signal attenuation profile is real implies that it has conjugate symmetry, i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero. Therefore, this program only computes the even elements. Note that the spherical harmonics equations used here differ slightly from those conventionally used, in that the (-1)^m factor has been omitted. This should be taken into account in all subsequent calculations. Each volume in the output image corresponds to a different spherical harmonic component, according to the following convention: * [0] Y(0,0) * [1] Im {Y(2,2)} * [2] Im {Y(2,1)} * [3] Y(2,0) * [4] Re {Y(2,1)} * [5] Re {Y(2,2)} * [6] Im {Y(4,4)} * [7] Im {Y(4,3)} Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> dwi2SH = mrt.DWI2SphericalHarmonicsImage() >>> dwi2SH.inputs.in_file = 'diffusion.nii' >>> dwi2SH.inputs.encoding_file = 'encoding.txt' >>> dwi2SH.run() # doctest: +SKIP Zdwi2SHcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)Nr$) output_specgetinputsrr opabspath_gen_outfilename)selfoutputsr!r!r" _list_outputs_s   z)DWI2SphericalHarmonicsImage._list_outputscCs|dkr|jSdSdS)Nr)r+)r,namer!r!r" _gen_filenamejsz)DWI2SphericalHarmonicsImage._gen_filenamecCst|jj\}}}|dS)Nz_SH.mif)rr(r)r,_r/r!r!r"r+psz,DWI2SphericalHarmonicsImage._gen_outfilenameN) rrr__doc___cmdr input_specr#r&r.r0r+r!r!r!r"r%4s% r%c@seZdZedddd#ddZedddd$ddZeddd%d d Zedd dd d Zeddddd Zeddd&dd Z e j dddZ e j dddZe j dddZe j dddZe jdddZeddd'dd Ze jddd d!Zd"S)(*ConstrainedSphericalDeconvolutionInputSpecTz%srzdiffusion-weighted image)rrrrrrzdthe diffusion-weighted signal response function for a single fibre population (see EstimateResponse)rzOutput filename)rrrrz-mask %szEonly perform computation within the specified binary brain mask image)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 -filter %sza text file containing the filtering coefficients for each even harmonic order.the linear frequency filtering parameters used for the initial linear spherical deconvolution step (default = [ 1 1 1 0 0 ]).z -lambda %szathe regularisation parameter lambda that controls the strength of the constraint (default = 1.0).)rrz-lmax %szset the maximum harmonic order for the output series. By default, the program will use the highest possible lmax given the number of diffusion-weighted images.z -threshold %szthe threshold below which the amplitude of the FOD is assumed to be zero, expressed as a fraction of the mean value of the initial FOD (default = 0.1)z -niter %szIthe maximum number of iterations to perform for each voxel (default = 50)z-debugzDisplay debugging messages.z-directions %sza text file containing the [ el az ] pairs for the directions: Specify the directions over which to apply the non-negativity constraint (by default, the built-in 300 direction set is used)z -normalisez(normalise the DW signal to the b=0 image)rrrNrrrr)rrrr rZ response_filer mask_imagerZ filter_filer rZ lambda_valueIntrZthreshold_valueZ iterationsrdebugdirections_filer r!r!r!r"r5us`r5c@seZdZedddZdS)+ConstrainedSphericalDeconvolutionOutputSpecTzSpherical harmonics image)rrN)rrrr r$r!r!r!r"r;sr;c@s4eZdZdZdZeZeZddZ ddZ ddZ d S) !ConstrainedSphericalDeconvolutionaH Perform non-negativity constrained spherical deconvolution. Note that this program makes use of implied symmetries in the diffusion profile. First, the fact the signal attenuation profile is real implies that it has conjugate symmetry, i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero. Therefore, this program only computes the even elements. Note that the spherical harmonics equations used here differ slightly from those conventionally used, in that the (-1)^m factor has been omitted. This should be taken into account in all subsequent calculations. Each volume in the output image corresponds to a different spherical harmonic component, according to the following convention: * [0] Y(0,0) * [1] Im {Y(2,2)} * [2] Im {Y(2,1)} * [3] Y(2,0) * [4] Re {Y(2,1)} * [5] Re {Y(2,2)} * [6] Im {Y(4,4)} * [7] Im {Y(4,3)} Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> csdeconv = mrt.ConstrainedSphericalDeconvolution() >>> csdeconv.inputs.in_file = 'dwi.mif' >>> csdeconv.inputs.encoding_file = 'encoding.txt' >>> csdeconv.run() # doctest: +SKIP ZcsdeconvcCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)Nr$)r&r'r(rr r)r*r+)r,r-r!r!r"r.s   z/ConstrainedSphericalDeconvolution._list_outputscCs|dkr|jSdSdS)Nr)r+)r,r/r!r!r"r0sz/ConstrainedSphericalDeconvolution._gen_filenamecCst|jj\}}}|dS)Nz_CSD.mif)rr(r)r,r1r/r!r!r"r+sz2ConstrainedSphericalDeconvolution._gen_outfilenameN) rrrr2r3r5r4r;r&r.r0r+r!r!r!r"r<s r<c@seZdZeddddddZeddddddZedddd d Zedd dd d dZej dddZ ej dddZ ej dddZ ej dddZdS)EstimateResponseForSHInputSpecTz%srzDiffusion-weighted images)rrrrrrzEonly perform computation within the specified binary brain mask image)rrrrrrzOutput 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-lmax %szset the maximum harmonic order for the output series. By default, the program will use the highest possible lmax given the number of diffusion-weighted images.)rrz -normalisez(normalise the DW signal to the b=0 imagez-quietz7Do not display information messages or progress status.z-debugzDisplay debugging messages.Nr6rr)rrrr rr7rrr r8rrr quietr9r!r!r!r"r=s6  r=c@seZdZedddZdS)EstimateResponseForSHOutputSpecTzSpherical harmonics image)rrN)rrrr responser!r!r!r"r?sr?c@s4eZdZdZdZeZeZddZ ddZ ddZ d S) EstimateResponseForSHa Estimates the fibre response function for use in spherical deconvolution. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> estresp = mrt.EstimateResponseForSH() >>> estresp.inputs.in_file = 'dwi.mif' >>> estresp.inputs.mask_image = 'dwi_WMProb.mif' >>> estresp.inputs.encoding_file = 'encoding.txt' >>> estresp.run() # doctest: +SKIP Zestimate_responsecCsN|jj}|jj|d<t|ds8tj|j|d<ntj|d|d<|S)Nr@)r&r'r(rr r)r*r+)r,r-r!r!r"r./s    z#EstimateResponseForSH._list_outputscCs|dkr|jSdSdS)Nr)r+)r,r/r!r!r"r08sz#EstimateResponseForSH._gen_filenamecCst|jj\}}}|dS)Nz_ER.txt)rr(r)r,r1r/r!r!r"r+>sz&EstimateResponseForSH._gen_outfilenameN) rrrr2r3r=r4r?r&r.r0r+r!r!r!r"rAs  rAc Cs.tj|}tj|}tj|dtj|dkr:tj|}|rf|dddf |dddf<tjd|r|dddf |dddf<tjd|r|dddf |dddf<tjdtjtj|tjtj|tjtj||f}t|\}} }t|\}} }| d| d} tj| || S) Nrrz&Inverting b-vectors in the x directionz&Inverting b-vectors in the y directionrz&Inverting b-vectors in the z directionr1z.txt) npZloadtxtshapeZ transposeifloggerinfoZvstackrZsavetxt) bvec_file bval_fileinvert_xinvert_yinvert_zZbvecsZbvalsencodingr1bvecbvalout_encoding_filer!r!r" concat_filesCs*       rOc@sdeZdZeddddZeddddZejddddZejddddZ ejddd dZ edd d Z d S) FSL2MRTrixInputSpecTz"FSL b-vectors file (3xN text file))rrrz!FSL b-values file (1xN text file)Fz&Inverts the b-vectors along the x-axis)Z usedefaultrz&Inverts the b-vectors along the y-axisz&Inverts the b-vectors along the z-axiszOutput encoding filename)rrN) rrrr rFrGr rrHrIrJrNr!r!r!r"rP[s     rPc@seZdZeddZdS)FSL2MRTrixOutputSpeczThe gradient 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 gradientand b gives the b-value in units (1000 s/mm^2).)rN)rrrr rr!r!r!r"rQnsrQc@s8eZdZdZeZeZddZddZ ddZ dd Z d S) FSL2MRTrixaN Converts separate b-values and b-vectors from text files (FSL style) into a 4xN text file in which 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). Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> fsl2mrtrix = mrt.FSL2MRTrix() >>> fsl2mrtrix.inputs.bvec_file = 'bvecs' >>> fsl2mrtrix.inputs.bval_file = 'bvals' >>> fsl2mrtrix.inputs.invert_y = True >>> fsl2mrtrix.run() # doctest: +SKIP cCs(t|jj|jj|jj|jj|jj}|S)N)rOr(rFrGrHrIrJ)r,ZruntimerKr!r!r"_run_interfaces zFSL2MRTrix._run_interfacecCs$|jj}tj|jd|d<|S)NrNr)r&r'r)r*r0)r,r-r!r!r"r.s zFSL2MRTrix._list_outputscCs|dkr|jSdSdS)NrN)r+)r,r/r!r!r"r0szFSL2MRTrix._gen_filenamecCs4t|jj\}}}t|jj\}}}|d|dS)Nr1z.txt)rr(rFrG)r,r1rLrMr!r!r"r+szFSL2MRTrix._gen_outfilenameN) rrrr2rPr4rQr&rSr.r0r+r!r!r!r"rRus rRc@szeZdZejdddddZejdddZejd d dZej d d dZ ej d ddZ ej dddZ e dgddddddZdS)GenerateDirectionsInputSpecTz%srz%the number of directions to generate.)rrrrz -power %sz0specify exponent to use for repulsion power law.)rrz -niter %sz4specify the maximum number of iterations to perform.z-infozDisplay information messages.z-quietz7do not display information messages or progress status.z-debugzDisplay debugging messages.num_dirszdirections_%d.txtFrz=the text file to write the directions to, as [ az el ] pairs.) name_source name_templater hash_filesrrNrr)rrrr r8rUrpowerZniterr display_info quiet_display display_debugr out_filer!r!r!r"rTs(   rTc@seZdZedddZdS)GenerateDirectionsOutputSpecTzdirections file)rrN)rrrr r]r!r!r!r"r^sr^c@seZdZdZdZeZeZdS)GenerateDirectionsa3 generate a set of directions evenly distributed over a hemisphere. Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> gendir = mrt.GenerateDirections() >>> gendir.inputs.num_dirs = 300 >>> gendir.run() # doctest: +SKIP ZgendirN) rrrr2r3rTr4r^r&r!r!r!r"r_s r_c @seZdZedddd!ddZedddd"ddZeddd d Zejd d d Z ej ej ddddddZ ej ddd Z ejddd Zejddd Zejddd Zeddddd#ddgdZd S)$FindShPeaksInputSpecTz%srz#the input image of SH coefficients.)rrrrrrz:the set of directions to use as seeds for the peak findingz -peaks %szZthe program will try to find the peaks that most closely match those in the image provided)rrrz-num %sz-the number of peaks to extract (default is 3))rrz -direction %s a!phi theta. the direction of a peak to estimate. The algorithm will attempt to find the same number of peaks as have been specified using this option phi: the azimuthal angle of the direction (in degrees). theta: the elevation angle of the direction (in degrees, from the vertical z-axis))rsepminlenmaxlenrz -threshold %szBonly peak amplitudes greater than the threshold will be consideredz-infozDisplay information messages.z-quietz7do not display information messages or progress status.z-debugzDisplay debugging messages.z%s_peak_dirs.mifFrzithe output image. Each volume corresponds to the x, y & z component of each peak direction vector in turnr)rWkeep_extensionrrXrrrVNr6rr)rrrr rr: peaks_imager r8 num_peaksListrpeak_directionsZpeak_thresholdrrZr[r\r]r!r!r!r"r`sP  r`c@seZdZedddZdS)FindShPeaksOutputSpecTzPeak directions image)rrN)rrrr r]r!r!r!r"rj srjc@seZdZdZdZeZeZdS) FindShPeaksa identify the orientations of the N largest peaks of a SH profile Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> shpeaks = mrt.FindShPeaks() >>> shpeaks.inputs.in_file = 'csd.mif' >>> shpeaks.inputs.directions_file = 'dirs.txt' >>> shpeaks.inputs.num_peaks = 2 >>> shpeaks.run() # doctest: +SKIP Z find_SH_peaksN) rrrr2r3r`r4rjr&r!r!r!r"rks rkc @seZdZeddddddZeddddZejd d d Zej ej d d ddddZ ej ddd Z ej ddd Zej ddd ZedddddddgdZdS)Directions2AmplitudeInputSpecTz%srzothe input directions image. Each volume corresponds to the x, y & z component of each direction vector in turn.)rrrrrz -peaks %szZthe program will try to find the peaks that most closely match those in the image provided)rrrz-num %sz-the number of peaks to extract (default is 3))rrz -direction %sraa!phi theta. the direction of a peak to estimate. The algorithm will attempt to find the same number of peaks as have been specified using this option phi: the azimuthal angle of the direction (in degrees). theta: the elevation angle of the direction (in degrees, from the vertical z-axis))rrbrcrdrz-infozDisplay information messages.z-quietz7do not display information messages or progress status.z-debugzDisplay debugging messages.z%s_amplitudes.mifFrzthe output amplitudes imager)rWrerrXrrrVNrr)rrrr rrfr r8rgrhrrirrZr[r\r]r!r!r!r"rl$s>  rlc@seZdZedddZdS)Directions2AmplitudeOutputSpecTzamplitudes image)rrN)rrrr r]r!r!r!r"rmMsrmc@seZdZdZdZeZeZdS)Directions2Amplitudea6 convert directions image to amplitudes Example ------- >>> import nipype.interfaces.mrtrix as mrt >>> amplitudes = mrt.Directions2Amplitude() >>> amplitudes.inputs.in_file = 'peak_directions.mif' >>> amplitudes.run() # doctest: +SKIP Zdir2ampN) rrrr2r3rlr4rmr&r!r!r!r"rnQs rn))os.pathpathr)ZnumpyrBrZutils.filemaniprbaserrrr r r r getLoggerrDr r#r%r5r;r<r=r?rArOrPrQrRrTr^r_r`rjrkrlrmrnr!r!r!r"s6   $ AA:$'04)