""" 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 QBallMXInputSpec(StdOutCommandLineInputSpec): basistype = traits.Enum('rbf', 'sh', argstr='-basistype %s', desc=('Basis function type. "rbf" to use radial basis functions ' '"sh" to use spherical harmonics'), usedefault=True) scheme_file = File(exists=True, argstr='-schemefile %s', mandatory=True, desc='Specifies the scheme file for the diffusion MRI data') order = traits.Int(argstr='-order %d', units='NA', desc=('Specific to sh. Maximum order of the spherical harmonic series. ' 'Default is 4.')) rbfpointset = traits.Int(argstr='-rbfpointset %d', units='NA', desc=('Specific to rbf. Sets the number of radial basis functions to use. ' 'The value specified must be present in the Pointsets directory. ' 'The default value is 246.')) rbfsigma = traits.Float(argstr='-rbfsigma %f', units='NA', desc=('Specific to rbf. Sets the width of the interpolating basis functions. ' 'The default value is 0.2618 (15 degrees).')) smoothingsigma = traits.Float(argstr='-smoothingsigma %f', units='NA', desc=('Specific to rbf. Sets the width of the smoothing basis functions. ' 'The default value is 0.1309 (7.5 degrees).')) class QBallMXOutputSpec(TraitedSpec): qmat = File(exists=True, desc='Q-Ball reconstruction matrix') class QBallMX(StdOutCommandLine): """ Generates a reconstruction matrix for Q-Ball. Used in LinRecon with the same scheme file to reconstruct data. Example 1 --------- To create a linear transform matrix using Spherical Harmonics (sh). >>> import nipype.interfaces.camino as cam >>> qballmx = cam.QBallMX() >>> qballmx.inputs.scheme_file = 'A.scheme' >>> qballmx.inputs.basistype = 'sh' >>> qballmx.inputs.order = 6 >>> qballmx.run() # doctest: +SKIP Example 2 --------- To create a linear transform matrix using Radial Basis Functions (rbf). This command uses the default setting of rbf sigma = 0.2618 (15 degrees), data smoothing sigma = 0.1309 (7.5 degrees), rbf pointset 246 >>> import nipype.interfaces.camino as cam >>> qballmx = cam.QBallMX() >>> qballmx.inputs.scheme_file = 'A.scheme' >>> qballmx.run() # doctest: +SKIP The linear transform matrix from any of these two examples can then be run over each voxel using LinRecon >>> qballcoeffs = cam.LinRecon() >>> qballcoeffs.inputs.in_file = 'SubjectA.Bfloat' >>> qballcoeffs.inputs.scheme_file = 'A.scheme' >>> qballcoeffs.inputs.qball_mat = 'A_qmat.Bdouble' >>> qballcoeffs.inputs.normalize = True >>> qballcoeffs.inputs.bgmask = 'brain_mask.nii' >>> qballcoeffs.run() # doctest: +SKIP """ _cmd = 'qballmx' input_spec=QBallMXInputSpec output_spec=QBallMXOutputSpec def _list_outputs(self): outputs = self.output_spec().get() outputs['qmat'] = os.path.abspath(self._gen_outfilename()) return outputs def _gen_outfilename(self): _, name , _ = split_filename(self.inputs.scheme_file) return name + '_qmat.Bdouble' class LinReconInputSpec(StdOutCommandLineInputSpec): in_file = File(exists=True, argstr='%s', mandatory=True, position=1, desc='voxel-order data filename') scheme_file = File(exists=True, argstr='%s', mandatory=True, position=2, desc='Specifies the scheme file for the diffusion MRI data') qball_mat = File(exists=True, argstr='%s', mandatory=True, position=3, desc='Linear transformation matrix.') normalize = traits.Bool(argstr='-normalize', desc=('Normalize the measurements and discard ' 'the zero measurements before the linear transform.')) log = traits.Bool(argstr='-log', desc=('Transform the log measurements rather than the ' 'measurements themselves')) bgmask = File(exists=True, argstr='-bgmask %s', desc='background mask') class LinReconOutputSpec(TraitedSpec): recon_data = File(exists=True, desc='Transformed data') class LinRecon(StdOutCommandLine): """ Runs a linear transformation in each voxel. Reads a linear transformation from the matrix file assuming the imaging scheme specified in the scheme file. Performs the linear transformation on the data in every voxel and outputs the result to the standard output. The ouput in every voxel is actually: [exit code, ln(S(0)), p1, ..., pR] where p1, ..., pR are the parameters of the reconstruction. Possible exit codes are: 0. No problems. 6. Bad data replaced by substitution of zero. The matrix must be R by N+M where N+M is the number of measurements and R is the number of parameters of the reconstruction. The matrix file contains binary double-precision floats. The matrix elements are stored row by row. Example --------- First run QBallMX and create a linear transform matrix using Spherical Harmonics (sh). >>> import nipype.interfaces.camino as cam >>> qballmx = cam.QBallMX() >>> qballmx.inputs.scheme_file = 'A.scheme' >>> qballmx.inputs.basistype = 'sh' >>> qballmx.inputs.order = 4 >>> qballmx.run() # doctest: +SKIP Then run it over each voxel using LinRecon >>> qballcoeffs = cam.LinRecon() >>> qballcoeffs.inputs.in_file = 'SubjectA.Bfloat' >>> qballcoeffs.inputs.scheme_file = 'A.scheme' >>> qballcoeffs.inputs.qball_mat = 'A_qmat.Bdouble' >>> qballcoeffs.inputs.normalize = True >>> qballcoeffs.run() # doctest: +SKIP """ _cmd = 'linrecon' input_spec=LinReconInputSpec output_spec=LinReconOutputSpec def _list_outputs(self): outputs = self.output_spec().get() outputs['recon_data'] = os.path.abspath(self._gen_outfilename()) return outputs def _gen_outfilename(self): _, name , _ = split_filename(self.inputs.scheme_file) return name + '_recondata.Bdouble' class SFPeaksInputSpec(StdOutCommandLineInputSpec): in_file = File(exists=True, argstr='-inputfile %s', mandatory=True, desc='Voxel-order data of spherical functions') inputmodel = traits.Enum('sh', 'maxent', 'rbf', argstr='-inputmodel %s', mandatory=True, desc=('Type of functions input via in_file. Currently supported options are: ' ' sh - Spherical harmonic series. Specify the maximum order of the SH series ' ' with the "order" attribute if different from the default of 4. ' ' maxent - Maximum entropy representations output by MESD. The reconstruction ' ' directions input to MESD must be specified. By default this is the ' ' same set of gradient directions (excluding zero gradients) in the ' ' scheme file, so specify the "schemefile" attribute unless the ' ' "mepointset" attribute was set in MESD. ' ' rbf - Sums of radial basis functions. Specify the pointset with the attribute ' ' "rbfpointset" if different from the default. See QBallMX.')) order = traits.Int(argstr='-order %d', units='NA', desc='Specific to sh. Maximum order of the spherical harmonic series.') scheme_file = File(exists=True, argstr='%s', desc='Specific to maxent. Specifies the scheme file.') rbfpointset = traits.Int(argstr='-rbfpointset %d', units='NA', desc=('Specific to rbf. Sets the number of radial basis functions to use. ' 'The value specified must be present in the Pointsets directory. ' 'The default value is 246.')) mepointset = traits.Int(argstr='-mepointset %d', units='NA', desc=('Use a set of directions other than those in the scheme file for the deconvolution ' 'kernel. The number refers to the number of directions on the unit sphere. ' 'For example, "mepointset = 54" uses the directions in "camino/PointSets/Elec054.txt" ' 'Use this option only if you told MESD to use a custom set of directions with the same ' 'option. Otherwise, specify the scheme file with the "schemefile" attribute.')) numpds = traits.Int(argstr='-numpds %d', units='NA', desc='The largest number of peak directions to output in each voxel.') noconsistencycheck = traits.Bool(argstr='-noconsistencycheck', desc='Turns off the consistency check. The output shows all consistencies as true.') searchradius = traits.Float(argstr='-searchradius %f', units='NA', desc='The search radius in the peak finding algorithm. The default is 0.4 (cf. "density")') density = traits.Int(argstr='-density %d', units='NA', desc=('The number of randomly rotated icosahedra to use in constructing the set of points for ' 'random sampling in the peak finding algorithm. Default is 1000, which works well for very ' 'spiky maxent functions. For other types of function, it is reasonable to set the density ' 'much lower and increase the search radius slightly, which speeds up the computation.')) pointset = traits.Int(argstr='-pointset %d', units='NA', desc=('To sample using an evenly distributed set of points instead. The integer can be ' '0, 1, ..., 7. Index 0 gives 1082 points, 1 gives 1922, 2 gives 3002, 3 gives 4322, ' '4 gives 5882, 5 gives 8672, 6 gives 12002, 7 gives 15872.')) pdthresh = traits.Float(argstr='-pdthresh %f', units='NA', desc=('Base threshold on the actual peak direction strength divided by the mean of the ' 'function. The default is 1.0 (the peak must be equal or greater than the mean).')) stdsfrommean = traits.Float(argstr='-stdsfrommean %f', units='NA', desc=('This is the number of standard deviations of the function to be added to the ' '"pdthresh" attribute in the peak directions pruning.')) class SFPeaksOutputSpec(TraitedSpec): peaks = File(exists=True, desc='Peaks of the spherical functions.') class SFPeaks(StdOutCommandLine): """ Finds the peaks of spherical functions. This utility reads coefficients of the spherical functions and outputs a list of peak directions of the function. It computes the value of the function at each of a set of sample points. Then it finds local maxima by finding all points at which the function is larger than for any other point within a fixed search radius (the default is 0.4). The utility then uses Powell's algorithm to optimize the position of each local maximum. Finally the utility removes duplicates and tiny peaks with function value smaller than some threshold, which is the mean of the function plus some number of standard deviations. By default the program checks for con- sistency with a second set of starting points, but skips the optimization step. To speed up execution, you can turn off the con- sistency check by setting the noconsistencycheck flag to True. By default, the utility constructs a set of sample points by randomly rotating a unit icosahedron repeatedly (the default is 1000 times, which produces a set of 6000 points) and concatenating the lists of vertices. The 'pointset = ' attribute can tell the utility to use an evenly distributed set of points (index 0 gives 1082 points, 1 gives 1922, 2 gives 4322, 3 gives 8672, 4 gives 15872, 5 gives 32762, 6 gives 72032), which is quicker, because you can get away with fewer points. We estimate that you can use a factor of 2.5 less evenly distributed points than randomly distributed points and still expect similar performance levels. The output for each voxel is: - exitcode (inherited from the input data). - ln(A(0)) - number of peaks found. - flag for consistency with a repeated run (number of directions is the same and the directions are the same to within a threshold.) - mean(f). - std(f). - direction 1 (x, y, z, f, H00, H01, H10, H11). - direction 2 (x, y, z, f, H00, H01, H10, H11). - direction 3 (x, y, z, f, H00, H01, H10, H11). H is the Hessian of f at the peak. It is the matrix: [d^2f/ds^2 d^2f/dsdt] [d^2f/dtds d^2f/dt^2] = [H00 H01] [H10 H11] where s and t are orthogonal coordinates local to the peak. By default the maximum number of peak directions output in each voxel is three. If less than three directions are found, zeros are output for later directions. The peaks are ordered by the value of the function at the peak. If more than the maximum number of directions are found only the strongest ones are output. The maximum number can be changed setting the 'numpds' attribute. The utility can read various kinds of spherical function, but must be told what kind of function is input using the 'inputmodel' attribute. The description of the 'inputmodel' attribute lists additional information required by SFPeaks for each input model. Example --------- First run QBallMX and create a linear transform matrix using Spherical Harmonics (sh). >>> import nipype.interfaces.camino as cam >>> sf_peaks = cam.SFPeaks() >>> sf_peaks.inputs.in_file = 'A_recon_params.Bdouble' >>> sf_peaks.inputs.inputmodel = 'sh' >>> sf_peaks.inputs.order = 4 >>> sf_peaks.inputs.density = 100 >>> sf_peaks.inputs.searchradius = 1.0 >>> sf_peaks.run() # doctest: +SKIP """ _cmd = 'sfpeaks' input_spec=SFPeaksInputSpec output_spec=SFPeaksOutputSpec def _list_outputs(self): outputs = self.output_spec().get() outputs['peaks'] = os.path.abspath(self._gen_outfilename()) return outputs def _gen_outfilename(self): _, name , _ = split_filename(self.inputs.in_file) return name + '_peaks.Bdouble'