3 d,@sddlZddlmZddlmZmZmZmZmZGdddeZ Gdd d eZ Gd d d eZ Gd d d eZ GdddeZ GdddeZdS)N)split_filename)traits TraitedSpecFileStdOutCommandLineStdOutCommandLineInputSpecc@seZdZejddddZedddddZed d ddd d Zejd dddZ ej ejddddddZ ejddddZ ej ejddddddZ ejddddZej ejddddddZejddddZejddddZejddd dZejd!dd"dZd#S)$SFPICOCalibDataInputSpecz-snr %fNAzgSpecifies the signal-to-noise ratio of the non-diffusion-weighted measurements to use in simulations.)argstrunitsdescTz-schemefile %sz4Specifies the scheme file for the diffusion MRI data)existsr mandatoryrz8The name to be given to the information output filename.z-infooutputfile %sF)rr rZgenfileZ hash_filesz -trace %fz;Trace of the diffusion tensor(s) used in the test function.z-onedtfarange %srz>> import nipype.interfaces.camino as cam >>> calib = cam.SFPICOCalibData() >>> calib.inputs.scheme_file = 'A.scheme' >>> calib.inputs.snr = 20 >>> calib.inputs.info_file = 'PICO_calib.info' >>> calib.run() # doctest: +SKIP The default settings create a large dataset (249,231 voxels), of which 3401 voxels contain a single fibre population per voxel and the rest of the voxels contain two fibre-populations. The amount of data produced can be varied by specifying the ranges and steps of the parameters for both the one and two fibre datasets used. To create a custom calibration dataset >>> import nipype.interfaces.camino as cam >>> calib = cam.SFPICOCalibData() >>> calib.inputs.scheme_file = 'A.scheme' >>> calib.inputs.snr = 20 >>> calib.inputs.info_file = 'PICO_calib.info' >>> calib.inputs.twodtfarange = [0.3, 0.9] >>> calib.inputs.twodtfastep = 0.02 >>> calib.inputs.twodtanglerange = [0, 0.785] >>> calib.inputs.twodtanglestep = 0.03925 >>> calib.inputs.twodtmixmax = 0.8 >>> calib.inputs.twodtmixstep = 0.1 >>> calib.run() # doctest: +SKIP This would provide 76,313 voxels of synthetic data, where 3401 voxels simulate the one fibre cases and 72,912 voxels simulate the various two fibre cases. However, care should be taken to ensure that enough data is generated for calculating the LUT. # doctest: +SKIP ZsfpicocalibdatacCs8|jj}tjj|j|d<tjj|jj|d<|S)Nrr) output_specgetospathabspath_gen_outfilenameinputsr)selfoutputsrrr _list_outputss zSFPICOCalibData._list_outputscCst|jj\}}}|dS)Nz_PICOCalib.Bfloat)rr&r)r'_namerrrr%sz SFPICOCalibData._gen_outfilenameN) rrr__doc___cmdr input_specrr r)r%rrrrr|s 2rc@seZdZedddddZeddddZejdd d dd Zej d d dddd Z ej ddddZ ej ddddZ ejdddZej ddddZdS)SFLUTGenInputSpecTz -inputfile %sz2Voxel-order data of the spherical functions peaks.)rr rrz -infofile %szVThe Info file that corresponds to the calibration datafile used in the reconstruction.)r rrZLUTz-outputstem %szDefine the name of the generated luts. The form of the filenames will be [outputstem]_oneFibreSurfaceCoeffs.Bdouble and [outputstem]_twoFibreSurfaceCoeffs.Bdouble)r rZ usedefaultZbinghamZwatsonz-pdf %sakSets the distribution to use for the calibration. The default is the Bingham distribution, which allows elliptical probability density contours. Currently supported options are: * bingham -- The Bingham distribution, which allows elliptical probability density contours. * watson -- The Watson distribution. This distribution is rotationally symmetric. z-binincsize %dr zwSets the size of the bins. In the case of 2D histograms such as the Bingham, the bins are always square. Default is 1.)r r rz-minvectsperbin %da!Specifies the minimum number of fibre-orientation estimates a bin must contain before it is used in the lut line/surface generation. Default is 50. If you get the error "no fibre-orientation estimates in histogram!", the calibration data set is too small to get enough samples in any of the histogram bins. You can decrease the minimum number per bin to get things running in quick tests, but the sta- tistics will not be reliable and for serious applications, you need to increase the size of the calibration data set until the error goes.z -directmapzqUse direct mapping between the eigenvalues and the distribution parameters instead of the log of the eigenvalues.)r rz -order %dzcThe order of the polynomial fitting the surface. Order 1 is linear. Order 2 (default) is quadratic.N)rrrrin_filerrStr outputstemEnumZpdfZIntZ binincsizeZminvectsperbinZBoolZ directmaporderrrrrr/sF  r/c@s$eZdZedddZedddZdS)SFLUTGenOutputSpecTzPICo lut for one-fibre model)rrzPICo lut for two-fibre modelN)rrrr lut_one_fibrelut_two_fibresrrrrr5s r5c@s,eZdZdZdZeZeZddZ ddZ dS)SFLUTGenau Generates PICo lookup tables (LUT) for multi-fibre methods such as PASMRI and Q-Ball. SFLUTGen creates the lookup tables for the generalized multi-fibre implementation of the PICo tractography algorithm. The outputs of this utility are either surface or line coefficients up to a given order. The calibration can be performed for different distributions, such as the Bingham and Watson distributions. This utility uses calibration data generated from SFPICOCalibData and peak information created by SFPeaks. The utility outputs two lut's, ``*_oneFibreSurfaceCoeffs.Bdouble`` and ``*_twoFibreSurfaceCoeffs.Bdouble``. Each of these files contains big-endian doubles as standard. The format of the output is:: dimensions (1 for Watson, 2 for Bingham) order (the order of the polynomial) coefficient_1 coefficient_2 ... coefficient_N In the case of the Watson, there is a single set of coefficients, which are ordered:: constant, x, x^2, ..., x^order. In the case of the Bingham, there are two sets of coefficients (one for each surface), ordered so that:: for j = 1 to order for k = 1 to order coeff_i = x^j * y^k where j+k < order Example ------- To create a calibration dataset using the default settings >>> import nipype.interfaces.camino as cam >>> lutgen = cam.SFLUTGen() >>> lutgen.inputs.in_file = 'QSH_peaks.Bdouble' >>> lutgen.inputs.info_file = 'PICO_calib.info' >>> lutgen.run() # doctest: +SKIP ZsflutgencCs0|jj}|jjd|d<|jjd|d<|S)Nz_oneFibreSurfaceCoeffs.Bdoubler6z_twoFibreSurfaceCoeffs.Bdoubler7)r r!r&r2)r'r(rrrr)Js zSFLUTGen._list_outputscCsdS)Nz /dev/nullr)r'rrrr%TszSFLUTGen._gen_outfilenameN) rrrr,r-r/r.r5r r)r%rrrrr8s 0 r8)r"Zutils.filemaniprbaserrrrr r rrr/r5r8rrrrs  hCP