"""
    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 = <index>' 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'


