""" Change directory to provide relative paths for doctests >>> import os >>> filepath = os.path.dirname( os.path.realpath( __file__ ) ) >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data')) >>> os.chdir(datadir) """ import os from nipype.interfaces.base import (CommandLineInputSpec, CommandLine, traits, TraitedSpec, File, StdOutCommandLine, StdOutCommandLineInputSpec, isdefined) from nipype.utils.filemanip import split_filename class SFPICOCalibDataInputSpec(StdOutCommandLineInputSpec): snr = traits.Float(argstr='-snr %f', units='NA', desc=('Specifies the signal-to-noise ratio of the ' 'non-diffusion-weighted measurements to use in simulations.')) scheme_file = File(exists=True, argstr='-schemefile %s', mandatory=True, desc='Specifies the scheme file for the diffusion MRI data') info_file = File(desc='The name to be given to the information output filename.', argstr='-infooutputfile %s', mandatory=True, genfile=True, hash_files=False) # Genfile and hash_files? trace = traits.Float(argstr='-trace %f', units='NA', desc='Trace of the diffusion tensor(s) used in the test function.') onedtfarange = traits.List(traits.Float, argstr='-onedtfarange %s', minlen=2, maxlen=2, units='NA', desc=('Minimum and maximum FA for the single tensor ' 'synthetic data.')) onedtfastep = traits.Float(argstr='-onedtfastep %f', units='NA', desc=('FA step size controlling how many steps there are ' 'between the minimum and maximum FA settings.')) twodtfarange = traits.List(traits.Float, argstr='-twodtfarange %s', minlen=2, maxlen=2, units='NA', desc=('Minimum and maximum FA for the two tensor ' 'synthetic data. FA is varied for both tensors ' 'to give all the different permutations.')) twodtfastep = traits.Float(argstr='-twodtfastep %f', units='NA', desc=('FA step size controlling how many steps there are ' 'between the minimum and maximum FA settings ' 'for the two tensor cases.')) twodtanglerange = traits.List(traits.Float, argstr='-twodtanglerange %s', minlen=2, maxlen=2, units='NA', desc=('Minimum and maximum crossing angles ' 'between the two fibres.')) twodtanglestep = traits.Float(argstr='-twodtanglestep %f', units='NA', desc=('Angle step size controlling how many steps there are ' 'between the minimum and maximum crossing angles for ' 'the two tensor cases.')) twodtmixmax = traits.Float(argstr='-twodtmixmax %f', units='NA', desc=('Mixing parameter controlling the proportion of one fibre population ' 'to the other. The minimum mixing parameter is (1 - twodtmixmax).')) twodtmixstep = traits.Float(argstr='-twodtmixstep %f', units='NA', desc=('Mixing parameter step size for the two tensor cases. ' 'Specify how many mixing parameter increments to use.')) seed = traits.Float(argstr='-seed %f', units='NA', desc='Specifies the random seed to use for noise generation in simulation trials.') class SFPICOCalibDataOutputSpec(TraitedSpec): PICOCalib = File(exists=True, desc='Calibration dataset') calib_info = File(exists=True, desc='Calibration dataset') class SFPICOCalibData(StdOutCommandLine): """ Generates Spherical Function PICo Calibration Data. SFPICOCalibData creates synthetic data for use with SFLUTGen. The synthetic data is generated using a mixture of gaussians, in the same way datasynth generates data. Each voxel of data models a slightly different fibre configuration (varying FA and fibre- crossings) and undergoes a random rotation to help account for any directional bias in the chosen acquisition scheme. A second file, which stores information about the datafile, is generated along with the datafile. Example 1 --------- To create a calibration dataset using the default settings >>> 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. Example 2 --------- 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 """ _cmd = 'sfpicocalibdata' input_spec=SFPICOCalibDataInputSpec output_spec=SFPICOCalibDataOutputSpec def _list_outputs(self): outputs = self.output_spec().get() outputs['PICOCalib'] = os.path.abspath(self._gen_outfilename()) outputs['calib_info'] = os.path.abspath(self.inputs.info_file) return outputs def _gen_outfilename(self): _, name , _ = split_filename(self.inputs.scheme_file) return name + '_PICOCalib.Bfloat' class SFLUTGenInputSpec(StdOutCommandLineInputSpec): in_file = File(exists=True, argstr='-inputfile %s', mandatory=True, desc='Voxel-order data of the spherical functions peaks.') info_file = File(argstr='-infofile %s', mandatory=True, desc=('The Info file that corresponds to the calibration ' 'datafile used in the reconstruction.')) outputstem = traits.Str('LUT', argstr='-outputstem %s', desc=('Define the name of the generated luts. The form of the filenames will be ' '[outputstem]_oneFibreSurfaceCoeffs.Bdouble and ' '[outputstem]_twoFibreSurfaceCoeffs.Bdouble'), usedefault=True) pdf = traits.Enum('bingham', 'watson', argstr='-pdf %s', desc=('Sets 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.'), usedefault=True) binincsize = traits.Int(argstr='-binincsize %d', units='NA', desc=('Sets the size of the bins. In the case of 2D histograms such as the ' 'Bingham, the bins are always square. Default is 1.')) minvectsperbin = traits.Int(argstr='-minvectsperbin %d', units='NA', desc=('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.')) directmap = traits.Bool(argstr='-directmap', desc=('Use direct mapping between the eigenvalues and the distribution parameters ' 'instead of the log of the eigenvalues.')) order = traits.Int(argstr='-order %d', units='NA', desc=('The order of the polynomial fitting the surface. Order 1 is linear. ' 'Order 2 (default) is quadratic.')) class SFLUTGenOutputSpec(TraitedSpec): lut_one_fibre = File(exists=True, desc='PICo lut for one-fibre model') lut_two_fibres = File(exists=True, desc='PICo lut for two-fibre model') class SFLUTGen(StdOutCommandLine): """ 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 """ _cmd = 'sflutgen' input_spec=SFLUTGenInputSpec output_spec=SFLUTGenOutputSpec def _list_outputs(self): outputs = self.output_spec().get() outputs['lut_one_fibre'] = self.inputs.outputstem + '_oneFibreSurfaceCoeffs.Bdouble' outputs['lut_two_fibres'] = self.inputs.outputstem + '_twoFibreSurfaceCoeffs.Bdouble' return outputs def _gen_outfilename(self): return '/dev/null'