3 d'~@sddlZddlZddlmZddlmZmZmZmZm Z m Z m Z m Z m Z Gddde ZGdd d eZGd d d e ZGd d d e ZGdddeZGddde ZGddde ZGdddeZGddde ZGddde ZGdddeZGddde ZGddde ZGd d!d!eZGd"d#d#e ZGd$d%d%eZGd&d'd'eZGd(d)d)eZGd*d+d+e Z Gd,d-d-eZ!Gd.d/d/eZ"Gd0d1d1e Z#Gd2d3d3eZ$Gd4d5d5e Z%Gd6d7d7e Z&Gd8d9d9eZ'Gd:d;d;e Z(dS)<N)split_filename) CommandLineInputSpec CommandLinetraits TraitedSpecFileStdOutCommandLineOutputMultiPathStdOutCommandLineInputSpec isdefinedc @s<eZdZeddddddZejdddd d d d d ddd ZdS)Image2VoxelInputSpecTz -4dimage %sz 4d image file)existsargstr mandatorypositiondescfloatcharshortintlongdoublez-outputdatatype %srzI"i.e. Bfloat". Can be "char", "short", "int", "long", "float" or "double")rrr usedefaultN)__name__ __module__ __qualname__r in_filerEnumout_typer"r"B/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/camino/convert.pyrs" rc@seZdZedddZdS)Image2VoxelOutputSpecTz%path/name of 4D volume in voxel order)rrN)rrrr voxel_orderr"r"r"r#r$2sr$c@s,eZdZdZdZeZeZddZ ddZ dS) Image2Voxela Converts Analyze / NIFTI / MHA files to voxel order. Converts scanner-order data in a supported image format to voxel-order data. Either takes a 4D file (all measurements in single image) or a list of 3D images. Examples -------- >>> import nipype.interfaces.camino as cmon >>> img2vox = cmon.Image2Voxel() >>> img2vox.inputs.in_file = '4d_dwi.nii' >>> img2vox.run() # doctest: +SKIP Z image2voxelcCs$|jj}tjj|j|d<|S)Nr%) output_specgetospathabspath_gen_outfilename)selfoutputsr"r"r# _list_outputsKs zImage2Voxel._list_outputscCs"t|jj\}}}|d|jjS)Nz.B)rinputsrr!)r-_namer"r"r#r,PszImage2Voxel._gen_outfilenameN) rrr__doc___cmdr input_specr$r'r/r,r"r"r"r#r&6s r&c@seZdZeddddddZeddddddZejd d d d Zej d ddZ ej dd dd Z ej dd dd Z ej dddZej dddZej dddZej dddZdS)FSL2SchemeInputSpecTz -bvecfile %srz b vector file)rrrrrz -bvalfile %srz b value filez -numscans %dNAzmOutput all measurements numerous (n) times, used when combining multiple scans from the same imaging session.)runitsrz -interleavez4Interleave repeated scans. Only used with -numscans.)rrz -bscale %dzMScaling factor to convert the b-values into different units. Default is 10^6.z-diffusiontime %fzDiffusion timez-flipxz*Negate the x component of all the vectors.z-flipyz*Negate the y component of all the vectors.z-flipzz*Negate the z component of all the vectors.z -usegradmodzqUse the gradient magnitude to scale b. This option has no effect if your gradient directions have unit magnitude.N)rrrr bvec_fileZ bval_filerIntZnumscansBoolZ interleaveFloatZbscaleZ diffusiontimeZflipxZflipyZflipzZ usegradmodr"r"r"r#r6UsD    r6c@seZdZedddZdS)FSL2SchemeOutputSpecTz Scheme file)rrN)rrrr schemer"r"r"r#r=sr=c@s,eZdZdZdZeZeZddZ ddZ dS) FSL2Schemea] Converts b-vectors and b-values from FSL format to a Camino scheme file. Examples -------- >>> import nipype.interfaces.camino as cmon >>> makescheme = cmon.FSL2Scheme() >>> makescheme.inputs.bvec_file = 'bvecs' >>> makescheme.inputs.bvec_file = 'bvals' >>> makescheme.run() # doctest: +SKIP Z fsl2schemecCs$|jj}tjj|j|d<|S)Nr>)r'r(r)r*r+r,)r-r.r"r"r#r/s zFSL2Scheme._list_outputscCst|jj\}}}|dS)Nz.scheme)rr0r9)r-r1r2r"r"r#r,szFSL2Scheme._gen_outfilenameN) rrrr3r4r6r5r=r'r/r,r"r"r"r#r?s  r?c @seZdZejddddddZedddd!d d Zejej d d d d dddZ edddddZ edddddZ eddd ddZ ejdddZejdddZejdddZd S)"VtkStreamlinesInputSpecrawvoxelsz-inputmodel %sz input model type (raw or voxels)T)rrrz < %srz data file)rrrrrzvoxel dimensions in mmz -voxeldims %srmm)rrminlenmaxlenrr8Fz -seedfile %srzimage containing seed points)rrrrz-targetfile %sz.image containing integer-valued target regionsz-scalarfile %sz6image that is in the same physical space as the tractsz -colourorientzBEach point on the streamline is coloured by the local orientation.)rrz-interpolatescalarszYthe scalar value at each point on the streamline is calculated by trilinear interpolationz -interpolateN)rrrrr inputmodelr rListr: voxeldimsZ seed_fileZ target_fileZ scalar_filer;Z colourorientZinterpolatescalarsZ interpolater"r"r"r#r@sPr@c@seZdZedddZdS)VtkStreamlinesOutputSpecTzStreamlines in VTK format)rrN)rrrr vtkr"r"r"r#rKsrKc@s,eZdZdZdZeZeZddZ ddZ dS)VtkStreamlinesaS Use vtkstreamlines to convert raw or voxel format streamlines to VTK polydata Examples -------- >>> import nipype.interfaces.camino as cmon >>> vtk = cmon.VtkStreamlines() >>> vtk.inputs.in_file = 'tract_data.Bfloat' >>> vtk.inputs.voxeldims = [1,1,1] >>> vtk.run() # doctest: +SKIP ZvtkstreamlinescCs$|jj}tjj|j|d<|S)NrL)r'r(r)r*r+r,)r-r.r"r"r#r/s zVtkStreamlines._list_outputscCst|jj\}}}|dS)Nz.vtk)rr0r)r-r1r2r"r"r#r,szVtkStreamlines._gen_outfilenameN) rrrr3r4r@r5rKr'r/r,r"r"r"r#rMs  rMc@s"eZdZejddddddZeddddd d Zejd d d dZ ejdd ddZ ejddddZ ejddddZ ej ejddddddZej ejddddddZej ejddddddZej ejddddddZed d!d"d#Zejd$dd%dZejd&d d'dZed d(d)d#Zejd*d+d,Zej ejd-d.ddd dZed d/d0d#Zejd1d2d,Zejd3d4d,Zed d5d6d#Zejd7d8d,Zed d9d:d#Zejd;d dd,Z!ejd?d@d,Z"ed dAdBd#Z#ejdCdDd,Z$ejdEdFdGdHgdIZ%ejdJdKdGdHgdIZ&ejdLdMdGdHgdIZ'ejdNdOdGdPdHgdIZ(dQS)RProcStreamlinesInputSpecrArBz-inputmodel %sz input model type (raw or voxels)T)rrrz -inputfile %srz data file)rrrrrz-maxtractpoints %dr7zmaximum number of tract points)rr8rz-mintractpoints %dzminimum number of tract pointsz-maxtractlength %drDzmaximum length of tractsz-mintractlength %dzminimum length of tractszdata dimensions in voxelsz -datadims %sr)rrrErFr8zvoxel dimensions in mmz -voxeldims %sz=The coordinates of a single seed point for tractography in mmz-seedpointmm %szAThe coordinates of a single seed point for tractography in voxelsz-seedpointvox %sFz -seedfile %szImage Containing Seed Points)rrrz-regionindex %dz#index of specific region to processz-iterations %dzNumber of streamlines generated for each seed. Not required when outputting streamlines, but needed to create PICo images. The default is 1 if the output is streamlines, and 5000 if the output is connection probability images.z-targetfile %sz Image containing target volumes.z-allowmultitargetsz9Allows streamlines to connect to multiple target volumes.)rraSplits the streamlines at the seed point and computes separate connection probabilities for each segment. Streamline segments are grouped according to their dot product with the vector (X, Y, Z). The ideal vector will be tangential to the streamline trajectory at the seed, such that the streamline projects from the seed along (X, Y, Z) and -(X, Y, Z). However, it is only necessary for the streamline trajectory to not be orthogonal to (X, Y, Z).z-directional %sz-waypointfile %szImage containing waypoints. Waypoints are defined as regions of the image with the same intensity, where 0 is background and any value > 0 is a waypoint.z-truncateloopszThis option allows streamlines to enter a waypoint exactly once. After the streamline leaves the waypoint, it is truncated upon a second entry to the waypoint.z -discardloopszThis option allows streamlines to enter a waypoint exactly once. After the streamline leaves the waypoint, the entire streamline is discarded upon a second entry to the waypoint.z-exclusionfile %szdImage containing exclusion ROIs. This should be an Analyze 7.5 header / image file.hdr and file.img.z-truncateinexclusionzARetain segments of a streamline before entry to an exclusion ROI.z-endpointfile %szcImage containing endpoint ROIs. This should be an Analyze 7.5 header / image file.hdr and file.img.z-resamplestepsize %dapEach point on a streamline is tested for entry into target, exclusion or waypoint volumes. If the length between points on a tract is not much smaller than the voxel length, then streamlines may pass through part of a voxel without being counted. To avoid this, the program resamples streamlines such that the step size is one tenth of the smallest voxel dimension in the image. This increases the size of raw or oogl streamline output and incurs some performance penalty. The resample resolution can be controlled with this option or disabled altogether by passing a negative step size or by passing the -noresample option.z -noresamplezlDisables resampling of input streamlines. Resampling is automatically disabled if the input model is voxels.z -outputtractsz(Output streamlines in raw binary format.z-outputroot %sz%Prepended onto all output file names.z-gzipz$save the output image in gzip formatz -outputcpz>> import nipype.interfaces.camino as cmon >>> proc = cmon.ProcStreamlines() >>> proc.inputs.in_file = 'tract_data.Bfloat' >>> proc.run() # doctest: +SKIP Zprocstreamlinescs,|dkr|j|j|Stt|j|||S)NrO)r_get_actual_outputrootsuperrW _format_arg)r-r2specvalue) __class__r"r#rZszProcStreamlines._format_argcstt|j||g|_dS)N)rYrW__init__rV)r-argskwargs)r]r"r#r^szProcStreamlines.__init__cs|jj}t|rp|j|}t|\}}}tjj|s>tj|t t |j |}t j tjj tj|d|_|St t |j |}|SdS)N*)r0rOr rXrr)r*rmakedirsrYrW_run_interfaceglobjoingetcwdrV)r-ZruntimerOactual_outputrootbasefilenameextZ new_runtime)r]r"r#rcs   zProcStreamlines._run_interfacecCstjjd|}|S)NZprocstream_outfiles)r)r*re)r-rOrgr"r"r#rXsz&ProcStreamlines._get_actual_outputrootcCs.|jj}tjj|j|d<|j|d<|S)NrUrV)r'r(r)r*r+r,rV)r-r.r"r"r#r/s  zProcStreamlines._list_outputscCst|jj\}}}|dS)N_proc)rr0r)r-r1r2r"r"r#r,sz ProcStreamlines._gen_outfilename)rrrr3r4rNr5rTr'rZr^rcrXr/r, __classcell__r"r")r]r#rWs   rWc@sTeZdZeddddddZejdddd d Zejddd dd Zejddd d d Z dS)TractShredderInputSpecTz< %srz tract file)rrrrrz%dr7zinitial offset of offset tractsr)rr8rrz-reads and outputs a group of bunchsize tractszskips space tractsrNrG) rrrr rrr:offsetZ bunchsizespacer"r"r"r#rmsrmc@seZdZedddZdS)TractShredderOutputSpecTzShredded tract file)rrN)rrrr shreddedr"r"r"r#rpsrpc@s,eZdZdZdZeZeZddZ ddZ dS) TractShreddera Extracts bunches of streamlines. tractshredder works in a similar way to shredder, but processes streamlines instead of scalar data. The input is raw streamlines, in the format produced by track or procstreamlines. The program first makes an initial offset of offset tracts. It then reads and outputs a group of bunchsize tracts, skips space tracts, and repeats until there is no more input. Examples -------- >>> import nipype.interfaces.camino as cmon >>> shred = cmon.TractShredder() >>> shred.inputs.in_file = 'tract_data.Bfloat' >>> shred.inputs.offset = 0 >>> shred.inputs.bunchsize = 1 >>> shred.inputs.space = 2 >>> shred.run() # doctest: +SKIP Z tractshreddercCs$|jj}tjj|j|d<|S)Nrq)r'r(r)r*r+r,)r-r.r"r"r#r/s zTractShredder._list_outputscCst|jj\}}}|dS)N _shredded)rr0r)r-r1r2r"r"r#r,szTractShredder._gen_outfilenameN) rrrr3r4rmr5rpr'r/r,r"r"r"r#rrs rrc@s@eZdZeddddddZeddddd Zedd dd d dZd S)DT2NIfTIInputSpecTz -inputfile %srz tract file)rrrrrz-outputroot %srz=filename root prepended onto the names of three output files.)rrZgenfilerz -header %srz< A Nifti .nii or .hdr file containing the header informationN)rrrr r output_rootZ header_filer"r"r"r#rt!s"rtc@s0eZdZedddZedddZedddZdS)DT2NIfTIOutputSpecTz!diffusion tensors in NIfTI format)rrz5exit codes from Camino reconstruction in NIfTI formatz9estimated lns0 from Camino reconstruction in NIfTI formatN)rrrr dtexitcodelns0r"r"r"r#rv:s   rvc@s<eZdZdZdZeZeZddZ ddZ ddZ d d Z d S) DT2NIfTIz Converts camino tensor data to NIfTI format Reads Camino diffusion tensors, and converts them to NIFTI format as three .nii files. Zdt2niicCsT|jj}|j}tjj|d|d<tjj|d|d<tjj|d|d<|S)Nzdt.niirwz exitcode.niirxzlns0.niiry)r'r(_gen_outputrootr)r*r+)r-r.rur"r"r#r/Qs  zDT2NIfTI._list_outputscCs|jS)N)r{)r-r"r"r#r,YszDT2NIfTI._gen_outfilenamecCs|jj}t|s|jd}|S)Nru)r0rur _gen_filename)r-rur"r"r#r{\s zDT2NIfTI._gen_outputrootcCs&|dkr"t|jj\}}}|d}|S)Nrur1)rr0r)r-r2r1rir"r"r#r|bszDT2NIfTI._gen_filenameN) rrrr3r4rtr5rvr'r/r,r{r|r"r"r"r#rzFsrzc@sreZdZeddddddZeddddZed dd dZed dd dZej d ddZ ej dddZ ej dddZ dS)NIfTIDT2CaminoInputSpecTz -inputfile %srzA NIFTI-1 dataset containing diffusion tensors. The tensors are assumed to be in lower-triangular order as specified by the NIFTI standard for the storage of symmetric matrices. This file should be either a .nii or a .hdr file.)rrrrrz-s0 %szFile containing the unweighted signal for each voxel, may be a raw binary file (specify type with -inputdatatype) or a supported image file.)rrrz-lns0 %szFile containing the log of the unweighted signal for each voxel, may be a raw binary file (specify type with -inputdatatype) or a supported image file.z -bgmask %szBinary valued brain / background segmentation, may be a raw binary file (specify type with -maskdatatype) or a supported image file.z-scaleslope %szA value v in the diffusion tensor is scaled to v * s + i. This is applied after any scaling specified by the input image. Default is 1.0.)rrz-scaleinter %szA value v in the diffusion tensor is scaled to v * s + i. This is applied after any scaling specified by the input image. Default is 0.0.z-uppertriangular %sz6Specifies input in upper-triangular (VTK style) order.N)rrrr rZs0_fileZ lns0_fileZbgmaskrr< scaleslope scaleinterr;Zuppertriangularr"r"r"r#r}is6r}c@seZdZeddZdS)NIfTIDT2CaminoOutputSpecz'diffusion tensors data in Camino format)rN)rrrr out_filer"r"r"r#rsrc@s,eZdZdZdZeZeZddZ ddZ dS)NIfTIDT2Caminoav Converts NIFTI-1 diffusion tensors to Camino format. The program reads the NIFTI header but does not apply any spatial transformations to the data. The NIFTI intensity scaling parameters are applied. The output is the tensors in Camino voxel ordering: [exit, ln(S0), dxx, dxy, dxz, dyy, dyz, dzz]. The exit code is set to 0 unless a background mask is supplied, in which case the code is 0 in brain voxels and -1 in background voxels. The value of ln(S0) in the output is taken from a file if one is supplied, otherwise it is set to 0. NOTE FOR FSL USERS - FSL's dtifit can output NIFTI tensors, but they are not stored in the usual way (which is using NIFTI_INTENT_SYMMATRIX). FSL's tensors follow the ITK / VTK "upper-triangular" convention, so you will need to use the -uppertriangular option to convert these correctly. Zniftidt2caminocCs|jj}|jd|d<|S)Nr)r'r(r|)r-r.r"r"r#r/s zNIfTIDT2Camino._list_outputscCs|dkrt|jj\}}}|S)Nr)rr0r)r-r2r1rir"r"r#r|szNIfTIDT2Camino._gen_filenameN) rrrr3r4r}r5rr'r/r|r"r"r"r#rs rc @seZdZeddddddZeddddd Zedd d d d Zedd d dd Zeddd dd Zeddd dd Z eddd dd Z eddd dd Z e j e jddd d ddZe j e jddd d ddZe j e jdd d ddd Ze j e jd!d d d"dd#Ze jd$d%d&d'Ze jd(d)d*d+d,d-d.d/d0dd1 Ze jd2d%d3d'Ze j e jd4ddd5d%d#Ze jd6d%d7d'Ze jd8d%d9d'Ze jd:d;d<Ze jd=d>d<Ze jd?d@d<ZdAS)BAnalyzeHeaderInputSpecTz< %srzTensor-fitted data filename)rrrrrz%srz>Camino scheme file (b values / vectors, see camino.fsl2scheme))rrrrz-readheader %srzReads header information from file and prints to stdout. If this option is not specified, then the program writes a header based on the other arguments.z-printimagedims %szEPrints image data and voxel dimensions as Camino arguments and exits.z-printprogargs %szPrints data dimension (and type, if relevant) arguments for a specific Camino program, where prog is one of shredder, scanner2voxel, vcthreshselect, pdview, track.z-printintelbyteorder %sz5Prints 1 if the header is little-endian, 0 otherwise.z-printbigendian %sz2Prints 1 if the header is big-endian, 0 otherwise.z-initfromheader %szReads header information from file and intializes a new header with the values read from the file. You may replace any combination of fields in the new header by specifying subsequent options.zdata dimensions in voxelsz -datadims %srB)rrrErFr8zvoxel dimensions in mmz -voxeldims %srDz -centre %szPVoxel specifying origin of Talairach coordinate system for SPM, default [0 0 0].)rrErFr8rz -picoseed %sz;Voxel specifying the seed (for PICo maps), default [0 0 0].)rrErFrr8z -nimages %dr7z,Number of images in the img file. Default 1.)rr8rZbyterz[u]shortz[u]intrcomplexrz -datatype %szThe char datatype is 8 bit (not the 16 bit char of Java), as specified by the Analyze 7.5 standard. The byte, ushort and uint types are not part of the Analyze specification but are supported by SPM.)rrrz -offset %dzAccording to the Analyze 7.5 standard, this is the byte offset in the .img file at which voxels start. This value can be negative to specify that the absolute value is applied for every image in the file.z-gl %sz?Minimum and maximum greylevels. Stored as shorts in the header.z-scaleslope %dzUIntensities in the image are scaled by this factor by SPM and MRICro. Default is 1.0.z-scaleinter %dzAConstant to add to the image intensities. Used by SPM and MRIcro.z-description %szZShort description - No spaces, max length 79 bytes. Will be null terminated automatically.)rrz-intelbyteorderz1Write header in intel byte order (little-endian).z-networkbyteorderzUWrite header in network byte order (big-endian). This is the default for new headers.N) rrrr rZ scheme_fileZ readheaderZprintimagedimsZ printprogargsZprintintelbyteorderZprintbigendianZinitfromheaderrrIr:Z data_dimsr<Z voxel_dimsZcentreZpicoseedZnimagesr datatypernZ greylevelsr~rString descriptionr;ZintelbyteorderZnetworkbyteorderr"r"r"r#rs rc@seZdZedddZdS)AnalyzeHeaderOutputSpecTzAnalyze header)rrN)rrrr headerr"r"r"r#rsrc@s,eZdZdZdZeZeZddZ ddZ dS) AnalyzeHeadera Create or read an Analyze 7.5 header file. Analyze image header, provides support for the most common header fields. Some fields, such as patient_id, are not currently supported. The program allows three nonstandard options: the field image_dimension.funused1 is the image scale. The intensity of each pixel in the associated .img file is (image value from file) * scale. Also, the origin of the Talairach coordinates (midline of the anterior commisure) are encoded in the field data_history.originator. These changes are included for compatibility with SPM. All headers written with this program are big endian by default. Example ------- >>> import nipype.interfaces.camino as cmon >>> hdr = cmon.AnalyzeHeader() >>> hdr.inputs.in_file = 'tensor_fitted_data.Bdouble' >>> hdr.inputs.scheme_file = 'A.scheme' >>> hdr.inputs.data_dims = [256,256,256] >>> hdr.inputs.voxel_dims = [1,1,1] >>> hdr.run() # doctest: +SKIP Z analyzeheadercCs$|jj}tjj|j|d<|S)Nr)r'r(r)r*r+r,)r-r.r"r"r#r/s zAnalyzeHeader._list_outputscCst|jj\}}}|dS)Nz.hdr)rr0r)r-r1r2r"r"r#r,szAnalyzeHeader._gen_outfilenameN) rrrr3r4rr5rr'r/r,r"r"r"r#rs rc@sTeZdZeddddddZejdddd d Zejddd dd Zejddd d d Z dS)ShredderInputSpecTz< %srzraw binary data file)rrrrrz%dr7zinitial offset of offset bytesr)rr8rrz,reads and outputs a chunk of chunksize byteszskips space bytesrNrG) rrrr rrr:rn chunksizeror"r"r"r#rsrc@seZdZedddZdS)ShredderOutputSpecTzShredded binary data file)rrN)rrrr rqr"r"r"r#rsrc@s,eZdZdZdZeZeZddZ ddZ dS)ShredderaH Extracts periodic chunks from a data stream. Shredder makes an initial offset of offset bytes. It then reads and outputs chunksize bytes, skips space bytes, and repeats until there is no more input. If the chunksize is negative, chunks of size chunksize are read and the byte ordering of each chunk is reversed. The whole chunk will be reversed, so the chunk must be the same size as the data type, otherwise the order of the values in the chunk, as well as their endianness, will be reversed. Examples -------- >>> import nipype.interfaces.camino as cam >>> shred = cam.Shredder() >>> shred.inputs.in_file = 'SubjectA.Bfloat' >>> shred.inputs.offset = 0 >>> shred.inputs.chunksize = 1 >>> shred.inputs.space = 2 >>> shred.run() # doctest: +SKIP ZshreddercCs$|jj}tjj|j|d<|S)NZ shredded_file)r'r(r)r*r+r,)r-r.r"r"r#r/s zShredder._list_outputscCst|jj\}}}|dS)Nrs)rr0r)r-r1r2r"r"r#r,szShredder._gen_outfilenameN) rrrr3r4rr5rr'r/r,r"r"r"r#rs r))r)rdZutils.filemaniprrhrrrrr r r r r rr$r&r6r=r?r@rKrMrNrTrWrmrprrrtrvrzr}rrrrrrrrr"r"r"r#s@ , 5:!<$ #2%P'