# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""The fsl module provides classes for interfacing with the `FSL
<http://www.fmrib.ox.ac.uk/fsl/index.html>`_ command line tools.  This
was written to work with FSL version 5.0.4.

    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)
"""
from __future__ import print_function, division, unicode_literals, absolute_import
from builtins import str

import os
import numpy as np
import nibabel as nib
import warnings

from ...utils.filemanip import split_filename
from ..base import (traits, TraitedSpec, InputMultiPath, File,
                    isdefined)
from .base import FSLCommand, FSLCommandInputSpec


class PrepareFieldmapInputSpec(FSLCommandInputSpec):
    scanner = traits.String('SIEMENS', argstr='%s', position=1,
                            desc='must be SIEMENS', usedefault=True)
    in_phase = File(exists=True, argstr='%s', position=2, mandatory=True,
                    desc=('Phase difference map, in SIEMENS format range from '
                          '0-4096 or 0-8192)'))
    in_magnitude = File(exists=True, argstr='%s', position=3, mandatory=True,
                        desc='Magnitude difference map, brain extracted')
    delta_TE = traits.Float(2.46, usedefault=True, mandatory=True, argstr='%f',
                            position=-2,
                            desc=('echo time difference of the '
                                  'fieldmap sequence in ms. (usually 2.46ms in'
                                  ' Siemens)'))
    nocheck = traits.Bool(False, position=-1, argstr='--nocheck',
                          usedefault=True,
                          desc=('do not perform sanity checks for image '
                                'size/range/dimensions'))
    out_fieldmap = File(argstr='%s', position=4,
                        desc='output name for prepared fieldmap')


class PrepareFieldmapOutputSpec(TraitedSpec):
    out_fieldmap = File(exists=True, desc='output name for prepared fieldmap')


class PrepareFieldmap(FSLCommand):
    """
    Interface for the fsl_prepare_fieldmap script (FSL 5.0)

    Prepares a fieldmap suitable for FEAT from SIEMENS data - saves output in
    rad/s format (e.g. ```fsl_prepare_fieldmap SIEMENS
    images_3_gre_field_mapping images_4_gre_field_mapping fmap_rads 2.65```).


    Examples
    --------

    >>> from nipype.interfaces.fsl import PrepareFieldmap
    >>> prepare = PrepareFieldmap()
    >>> prepare.inputs.in_phase = "phase.nii"
    >>> prepare.inputs.in_magnitude = "magnitude.nii"
    >>> prepare.inputs.output_type = "NIFTI_GZ"
    >>> prepare.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'fsl_prepare_fieldmap SIEMENS phase.nii magnitude.nii \
.../phase_fslprepared.nii.gz 2.460000'
    >>> res = prepare.run() # doctest: +SKIP


    """
    _cmd = 'fsl_prepare_fieldmap'
    input_spec = PrepareFieldmapInputSpec
    output_spec = PrepareFieldmapOutputSpec

    def _parse_inputs(self, skip=None):
        if skip is None:
            skip = []

        if not isdefined(self.inputs.out_fieldmap):
            self.inputs.out_fieldmap = self._gen_fname(
                self.inputs.in_phase, suffix='_fslprepared')

        if not isdefined(self.inputs.nocheck) or not self.inputs.nocheck:
            skip += ['nocheck']

        return super(PrepareFieldmap, self)._parse_inputs(skip=skip)

    def _list_outputs(self):
        outputs = self.output_spec().get()
        outputs['out_fieldmap'] = self.inputs.out_fieldmap
        return outputs

    def _run_interface(self, runtime):
        runtime = super(PrepareFieldmap, self)._run_interface(runtime)

        if runtime.returncode == 0:
            out_file = self.inputs.out_fieldmap
            im = nib.load(out_file)
            dumb_img = nib.Nifti1Image(np.zeros(im.shape), im.affine,
                                       im.header)
            out_nii = nib.funcs.concat_images((im, dumb_img))
            nib.save(out_nii, out_file)

        return runtime


class TOPUPInputSpec(FSLCommandInputSpec):
    in_file = File(exists=True, mandatory=True,
                   desc='name of 4D file with images', argstr='--imain=%s')
    encoding_file = File(exists=True, mandatory=True,
                         xor=['encoding_direction'],
                         desc='name of text file with PE directions/times',
                         argstr='--datain=%s')
    encoding_direction = traits.List(traits.Enum('y', 'x', 'z', 'x-', 'y-',
                                                 'z-'), mandatory=True,
                                     xor=['encoding_file'],
                                     requires=['readout_times'],
                                     argstr='--datain=%s',
                                     desc=('encoding direction for automatic '
                                           'generation of encoding_file'))
    readout_times = InputMultiPath(traits.Float,
                                   requires=['encoding_direction'],
                                   xor=['encoding_file'], mandatory=True,
                                   desc=('readout times (dwell times by # '
                                         'phase-encode steps minus 1)'))
    out_base = File(desc=('base-name of output files (spline '
                          'coefficients (Hz) and movement parameters)'),
                    name_source=['in_file'], name_template='%s_base',
                    argstr='--out=%s', hash_files=False)
    out_field = File(argstr='--fout=%s', hash_files=False,
                     name_source=['in_file'], name_template='%s_field',
                     desc='name of image file with field (Hz)')
    out_corrected = File(argstr='--iout=%s', hash_files=False,
                         name_source=['in_file'], name_template='%s_corrected',
                         desc='name of 4D image file with unwarped images')
    out_logfile = File(argstr='--logout=%s', desc='name of log-file',
                       name_source=['in_file'], name_template='%s_topup.log',
                       keep_extension=True, hash_files=False)

    # TODO: the following traits admit values separated by commas, one value
    # per registration level inside topup.
    warp_res = traits.Float(10.0, argstr='--warpres=%f',
                            desc=('(approximate) resolution (in mm) of warp '
                                  'basis for the different sub-sampling levels'
                                  '.'))
    subsamp = traits.Int(1, argstr='--subsamp=%d',
                         desc='sub-sampling scheme')
    fwhm = traits.Float(8.0, argstr='--fwhm=%f',
                        desc='FWHM (in mm) of gaussian smoothing kernel')
    config = traits.String('b02b0.cnf', argstr='--config=%s', usedefault=True,
                           desc=('Name of config file specifying command line '
                                 'arguments'))
    max_iter = traits.Int(5, argstr='--miter=%d',
                          desc='max # of non-linear iterations')
    reg_lambda = traits.Float(1.0, argstr='--miter=%0.f',
                              desc=('lambda weighting value of the '
                                    'regularisation term'))
    ssqlambda = traits.Enum(1, 0, argstr='--ssqlambda=%d',
                            desc=('Weight lambda by the current value of the '
                                  'ssd. If used (=1), the effective weight of '
                                  'regularisation term becomes higher for the '
                                  'initial iterations, therefore initial steps'
                                  ' are a little smoother than they would '
                                  'without weighting. This reduces the '
                                  'risk of finding a local minimum.'))
    regmod = traits.Enum('bending_energy', 'membrane_energy',
                         argstr='--regmod=%s',
                         desc=('Regularisation term implementation. Defaults '
                               'to bending_energy. Note that the two functions'
                               ' have vastly different scales. The membrane '
                               'energy is based on the first derivatives and '
                               'the bending energy on the second derivatives. '
                               'The second derivatives will typically be much '
                               'smaller than the first derivatives, so input '
                               'lambda will have to be larger for '
                               'bending_energy to yield approximately the same'
                               ' level of regularisation.'))
    estmov = traits.Enum(1, 0, argstr='--estmov=%d',
                         desc='estimate movements if set')
    minmet = traits.Enum(0, 1, argstr='--minmet=%d',
                         desc=('Minimisation method 0=Levenberg-Marquardt, '
                               '1=Scaled Conjugate Gradient'))
    splineorder = traits.Int(3, argstr='--splineorder=%d',
                             desc=('order of spline, 2->Qadratic spline, '
                                   '3->Cubic spline'))
    numprec = traits.Enum('double', 'float', argstr='--numprec=%s',
                          desc=('Precision for representing Hessian, double '
                                'or float.'))
    interp = traits.Enum('spline', 'linear', argstr='--interp=%s',
                         desc='Image interpolation model, linear or spline.')
    scale = traits.Enum(0, 1, argstr='--scale=%d',
                        desc=('If set (=1), the images are individually scaled'
                              ' to a common mean'))
    regrid = traits.Enum(1, 0, argstr='--regrid=%d',
                         desc=('If set (=1), the calculations are done in a '
                               'different grid'))


class TOPUPOutputSpec(TraitedSpec):
    out_fieldcoef = File(exists=True,
                         desc='file containing the field coefficients')
    out_movpar = File(exists=True, desc='movpar.txt output file')
    out_enc_file = File(desc='encoding directions file output for applytopup')
    out_field = File(desc='name of image file with field (Hz)')
    out_corrected = File(desc='name of 4D image file with unwarped images')
    out_logfile = File(desc='name of log-file')


class TOPUP(FSLCommand):
    """
    Interface for FSL topup, a tool for estimating and correcting
    susceptibility induced distortions. See FSL documentation for
    `reference <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TOPUP>`_,
    `usage examples
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_,
    and `exemplary config files
    <https://github.com/ahheckel/FSL-scripts/blob/master/rsc/fsl/fsl4/topup/b02b0.cnf>`_.

    Examples
    --------

    >>> from nipype.interfaces.fsl import TOPUP
    >>> topup = TOPUP()
    >>> topup.inputs.in_file = "b0_b0rev.nii"
    >>> topup.inputs.encoding_file = "topup_encoding.txt"
    >>> topup.inputs.output_type = "NIFTI_GZ"
    >>> topup.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'topup --config=b02b0.cnf --datain=topup_encoding.txt \
--imain=b0_b0rev.nii --out=b0_b0rev_base --iout=b0_b0rev_corrected.nii.gz \
--fout=b0_b0rev_field.nii.gz --logout=b0_b0rev_topup.log'
    >>> res = topup.run() # doctest: +SKIP

    """
    _cmd = 'topup'
    input_spec = TOPUPInputSpec
    output_spec = TOPUPOutputSpec

    def _format_arg(self, name, trait_spec, value):
        if name == 'encoding_direction':
            return trait_spec.argstr % self._generate_encfile()
        if name == 'out_base':
            path, name, ext = split_filename(value)
            if path != '':
                if not os.path.exists(path):
                    raise ValueError('out_base path must exist if provided')
        return super(TOPUP, self)._format_arg(name, trait_spec, value)

    def _list_outputs(self):
        outputs = super(TOPUP, self)._list_outputs()
        del outputs['out_base']
        base_path = None
        if isdefined(self.inputs.out_base):
            base_path, base, _ = split_filename(self.inputs.out_base)
            if base_path == '':
                base_path = None
        else:
            base = split_filename(self.inputs.in_file)[1] + '_base'
        outputs['out_fieldcoef'] = self._gen_fname(base, suffix='_fieldcoef',
                                                   cwd=base_path)
        outputs['out_movpar'] = self._gen_fname(base, suffix='_movpar',
                                                ext='.txt', cwd=base_path)

        if isdefined(self.inputs.encoding_direction):
            outputs['out_enc_file'] = self._get_encfilename()
        return outputs

    def _get_encfilename(self):
        out_file = os.path.join(os.getcwd(),
                                ('%s_encfile.txt' %
                                 split_filename(self.inputs.in_file)[1]))
        return out_file

    def _generate_encfile(self):
        """Generate a topup compatible encoding file based on given directions
        """
        out_file = self._get_encfilename()
        durations = self.inputs.readout_times
        if len(self.inputs.encoding_direction) != len(durations):
            if len(self.inputs.readout_times) != 1:
                raise ValueError(('Readout time must be a float or match the'
                                  'length of encoding directions'))
            durations = durations * len(self.inputs.encoding_direction)

        lines = []
        for idx, encdir in enumerate(self.inputs.encoding_direction):
            direction = 1.0
            if encdir.endswith('-'):
                direction = -1.0
            line = [float(val[0] == encdir[0]) * direction
                    for val in ['x', 'y', 'z']] + [durations[idx]]
            lines.append(line)
        np.savetxt(out_file, np.array(lines), fmt=b'%d %d %d %.8f')
        return out_file

    def _overload_extension(self, value, name=None):
        if name == 'out_base':
            return value
        return super(TOPUP, self)._overload_extension(value, name)


class ApplyTOPUPInputSpec(FSLCommandInputSpec):
    in_files = InputMultiPath(File(exists=True), mandatory=True,
                              desc='name of file with images',
                              argstr='--imain=%s', sep=',')
    encoding_file = File(exists=True, mandatory=True,
                         desc='name of text file with PE directions/times',
                         argstr='--datain=%s')
    in_index = traits.List(
        traits.Int, argstr='--inindex=%s', sep=',',
        desc='comma separated list of indices corresponding to --datain')
    in_topup_fieldcoef = File(exists=True, argstr="--topup=%s", copyfile=False,
                              requires=['in_topup_movpar'],
                              desc=('topup file containing the field '
                                    'coefficients'))
    in_topup_movpar = File(exists=True, requires=['in_topup_fieldcoef'],
                           copyfile=False, desc='topup movpar.txt file')
    out_corrected = File(desc='output (warped) image',
                         name_source=['in_files'],
                         name_template='%s_corrected',
                         argstr='--out=%s')
    method = traits.Enum('jac', 'lsr', argstr='--method=%s',
                         desc=('use jacobian modulation (jac) or least-squares'
                               ' resampling (lsr)'))
    interp = traits.Enum('trilinear', 'spline', argstr='--interp=%s',
                         desc='interpolation method')
    datatype = traits.Enum('char', 'short', 'int', 'float', 'double',
                           argstr='-d=%s', desc='force output data type')


class ApplyTOPUPOutputSpec(TraitedSpec):
    out_corrected = File(exists=True, desc=('name of 4D image file with '
                                            'unwarped images'))


class ApplyTOPUP(FSLCommand):
    """
    Interface for FSL topup, a tool for estimating and correcting
    susceptibility induced distortions.
    `General reference
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ApplytopupUsersGuide>`_
    and `use example
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ExampleTopupFollowedByApplytopup>`_.


    Examples
    --------

    >>> from nipype.interfaces.fsl import ApplyTOPUP
    >>> applytopup = ApplyTOPUP()
    >>> applytopup.inputs.in_files = ["epi.nii", "epi_rev.nii"]
    >>> applytopup.inputs.encoding_file = "topup_encoding.txt"
    >>> applytopup.inputs.in_topup_fieldcoef = "topup_fieldcoef.nii.gz"
    >>> applytopup.inputs.in_topup_movpar = "topup_movpar.txt"
    >>> applytopup.inputs.output_type = "NIFTI_GZ"
    >>> applytopup.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'applytopup --datain=topup_encoding.txt --imain=epi.nii,epi_rev.nii \
--inindex=1,2 --topup=topup --out=epi_corrected.nii.gz'
    >>> res = applytopup.run() # doctest: +SKIP

    """
    _cmd = 'applytopup'
    input_spec = ApplyTOPUPInputSpec
    output_spec = ApplyTOPUPOutputSpec

    def _parse_inputs(self, skip=None):
        if skip is None:
            skip = []

        # If not defined, assume index are the first N entries in the
        # parameters file, for N input images.
        if not isdefined(self.inputs.in_index):
            self.inputs.in_index = list(
                range(1, len(self.inputs.in_files) + 1))

        return super(ApplyTOPUP, self)._parse_inputs(skip=skip)

    def _format_arg(self, name, spec, value):
        if name == 'in_topup_fieldcoef':
            return spec.argstr % value.split('_fieldcoef')[0]
        return super(ApplyTOPUP, self)._format_arg(name, spec, value)


class EddyInputSpec(FSLCommandInputSpec):
    in_file = File(exists=True, mandatory=True, argstr='--imain=%s',
                   desc=('File containing all the images to estimate '
                         'distortions for'))
    in_mask = File(exists=True, mandatory=True, argstr='--mask=%s',
                   desc='Mask to indicate brain')
    in_index = File(exists=True, mandatory=True, argstr='--index=%s',
                    desc=('File containing indices for all volumes in --imain '
                          'into --acqp and --topup'))
    in_acqp = File(exists=True, mandatory=True, argstr='--acqp=%s',
                   desc='File containing acquisition parameters')
    in_bvec = File(exists=True, mandatory=True, argstr='--bvecs=%s',
                   desc=('File containing the b-vectors for all volumes in '
                         '--imain'))
    in_bval = File(exists=True, mandatory=True, argstr='--bvals=%s',
                   desc=('File containing the b-values for all volumes in '
                         '--imain'))
    out_base = traits.Str('eddy_corrected', argstr='--out=%s',
                          usedefault=True,
                          desc=('basename for output (warped) image'))
    session = File(exists=True, argstr='--session=%s',
                   desc=('File containing session indices for all volumes in '
                         '--imain'))
    in_topup_fieldcoef = File(exists=True, argstr="--topup=%s",
                              requires=['in_topup_movpar'],
                              desc=('topup file containing the field '
                                    'coefficients'))
    in_topup_movpar = File(exists=True, requires=['in_topup_fieldcoef'],
                           desc='topup movpar.txt file')

    flm = traits.Enum('linear', 'quadratic', 'cubic', argstr='--flm=%s',
                      desc='First level EC model')

    fwhm = traits.Float(desc=('FWHM for conditioning filter when estimating '
                              'the parameters'), argstr='--fwhm=%s')

    niter = traits.Int(5, argstr='--niter=%s', desc='Number of iterations')

    method = traits.Enum('jac', 'lsr', argstr='--resamp=%s',
                         desc=('Final resampling method (jacobian/least '
                               'squares)'))
    repol = traits.Bool(False, argstr='--repol',
                        desc='Detect and replace outlier slices')
    num_threads = traits.Int(1, usedefault=True, nohash=True,
                             desc="Number of openmp threads to use")


class EddyOutputSpec(TraitedSpec):
    out_corrected = File(exists=True,
                         desc=('4D image file containing all the corrected '
                               'volumes'))
    out_parameter = File(exists=True,
                         desc=('text file with parameters definining the '
                               'field and movement for each scan'))


class Eddy(FSLCommand):
    """
    Interface for FSL eddy, a tool for estimating and correcting eddy
    currents induced distortions. `User guide
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Eddy/UsersGuide>`_ and
    `more info regarding acqp file
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/Faq#How_do_I_know_what_to_put_into_my_--acqp_file>`_.

    Examples
    --------

    >>> from nipype.interfaces.fsl import Eddy
    >>> eddy = Eddy()
    >>> eddy.inputs.in_file = 'epi.nii'
    >>> eddy.inputs.in_mask  = 'epi_mask.nii'
    >>> eddy.inputs.in_index = 'epi_index.txt'
    >>> eddy.inputs.in_acqp  = 'epi_acqp.txt'
    >>> eddy.inputs.in_bvec  = 'bvecs.scheme'
    >>> eddy.inputs.in_bval  = 'bvals.scheme'
    >>> eddy.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'eddy --acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \
--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \
--out=.../eddy_corrected'
    >>> res = eddy.run() # doctest: +SKIP

    """
    _cmd = 'eddy'
    input_spec = EddyInputSpec
    output_spec = EddyOutputSpec

    _num_threads = 1

    def __init__(self, **inputs):
        super(Eddy, self).__init__(**inputs)
        self.inputs.on_trait_change(self._num_threads_update, 'num_threads')

        if not isdefined(self.inputs.num_threads):
            self.inputs.num_threads = self._num_threads
        else:
            self._num_threads_update()

    def _num_threads_update(self):
        self._num_threads = self.inputs.num_threads
        if not isdefined(self.inputs.num_threads):
            if 'OMP_NUM_THREADS' in self.inputs.environ:
                del self.inputs.environ['OMP_NUM_THREADS']
        else:
            self.inputs.environ['OMP_NUM_THREADS'] = str(
                self.inputs.num_threads)

    def _format_arg(self, name, spec, value):
        if name == 'in_topup_fieldcoef':
            return spec.argstr % value.split('_fieldcoef')[0]
        if name == 'out_base':
            return spec.argstr % os.path.abspath(value)
        return super(Eddy, self)._format_arg(name, spec, value)

    def _list_outputs(self):
        outputs = self.output_spec().get()
        outputs['out_corrected'] = os.path.abspath(
            '%s.nii.gz' % self.inputs.out_base)
        outputs['out_parameter'] = os.path.abspath(
            '%s.eddy_parameters' % self.inputs.out_base)
        return outputs


class SigLossInputSpec(FSLCommandInputSpec):
    in_file = File(mandatory=True,
                   exists=True,
                   argstr='-i %s',
                   desc='b0 fieldmap file')
    out_file = File(argstr='-s %s',
                    desc='output signal loss estimate file',
                    genfile=True)

    mask_file = File(exists=True,
                     argstr='-m %s',
                     desc='brain mask file')
    echo_time = traits.Float(argstr='--te=%f',
                             desc='echo time in seconds')
    slice_direction = traits.Enum('x', 'y', 'z',
                                  argstr='-d %s',
                                  desc='slicing direction')


class SigLossOuputSpec(TraitedSpec):
    out_file = File(exists=True,
                    desc='signal loss estimate file')


class SigLoss(FSLCommand):
    """
    Estimates signal loss from a field map (in rad/s)

    Examples
    --------

    >>> from nipype.interfaces.fsl import SigLoss
    >>> sigloss = SigLoss()
    >>> sigloss.inputs.in_file = "phase.nii"
    >>> sigloss.inputs.echo_time = 0.03
    >>> sigloss.inputs.output_type = "NIFTI_GZ"
    >>> sigloss.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'sigloss --te=0.030000 -i phase.nii -s .../phase_sigloss.nii.gz'
    >>> res = sigloss.run() # doctest: +SKIP


    """
    input_spec = SigLossInputSpec
    output_spec = SigLossOuputSpec
    _cmd = 'sigloss'

    def _list_outputs(self):
        outputs = self.output_spec().get()
        outputs['out_file'] = self.inputs.out_file
        if ((not isdefined(outputs['out_file'])) and
                (isdefined(self.inputs.in_file))):
            outputs['out_file'] = self._gen_fname(self.inputs.in_file,
                                                  suffix='_sigloss')
        return outputs

    def _gen_filename(self, name):
        if name == 'out_file':
            return self._list_outputs()['out_file']
        return None


class EpiRegInputSpec(FSLCommandInputSpec):
    epi = File(exists=True, argstr='--epi=%s', mandatory=True,
               position=-4, desc='EPI image')
    t1_head = File(exists=True, argstr='--t1=%s', mandatory=True,
                   position=-3, desc='wholehead T1 image')
    t1_brain = File(exists=True, argstr='--t1brain=%s', mandatory=True,
                    position=-2, desc='brain extracted T1 image')
    out_base = traits.String("epi2struct", desc='output base name',
                             argstr='--out=%s',
                             position=-1, usedefault=True)
    fmap = File(exists=True, argstr='--fmap=%s',
                desc='fieldmap image (in rad/s)')
    fmapmag = File(exists=True, argstr='--fmapmag=%s',
                   desc='fieldmap magnitude image - wholehead')
    fmapmagbrain = File(exists=True, argstr='--fmapmagbrain=%s',
                        desc='fieldmap magnitude image - brain extracted')
    wmseg = File(exists=True, argstr='--wmseg=%s',
                 desc='white matter segmentation of T1 image, has to be named \
                 like the t1brain and end on _wmseg')
    echospacing = traits.Float(argstr='--echospacing=%f',
                               desc='Effective EPI echo spacing  \
                               (sometimes called dwell time) - in seconds')
    pedir = traits.Enum('x', 'y', 'z', '-x', '-y', '-z', argstr='--pedir=%s',
                        desc='phase encoding direction, dir = x/y/z/-x/-y/-z')

    weight_image = File(exists=True, argstr='--weight=%s',
                        desc='weighting image (in T1 space)')
    no_fmapreg = traits.Bool(False, argstr='--nofmapreg',
                             desc='do not perform registration of fmap to T1 \
                        (use if fmap already registered)')
    no_clean = traits.Bool(True, argstr='--noclean', usedefault=True,
                           desc='do not clean up intermediate files')


class EpiRegOutputSpec(TraitedSpec):
    out_file = File(exists=True,
                    desc='unwarped and coregistered epi input')
    out_1vol = File(exists=True,
                    desc='unwarped and coregistered single volume')
    fmap2str_mat = File(exists=True,
                        desc='rigid fieldmap-to-structural transform')
    fmap2epi_mat = File(exists=True,
                        desc='rigid fieldmap-to-epi transform')
    fmap_epi = File(exists=True, desc='fieldmap in epi space')
    fmap_str = File(exists=True, desc='fieldmap in structural space')
    fmapmag_str = File(exists=True,
                       desc='fieldmap magnitude image in structural space')
    epi2str_inv = File(exists=True,
                       desc='rigid structural-to-epi transform')
    epi2str_mat = File(exists=True,
                       desc='rigid epi-to-structural transform')
    shiftmap = File(exists=True, desc='shiftmap in epi space')
    fullwarp = File(exists=True,
                    desc='warpfield to unwarp epi and transform into \
                    structural space')
    wmseg = File(exists=True,
                 desc='white matter segmentation used in flirt bbr')
    wmedge = File(exists=True, desc='white matter edges for visualization')


class EpiReg(FSLCommand):
    """

    Runs FSL epi_reg script for simultaneous coregistration and fieldmap
    unwarping.

    Examples
    --------

    >>> from nipype.interfaces.fsl import EpiReg
    >>> epireg = EpiReg()
    >>> epireg.inputs.epi='epi.nii'
    >>> epireg.inputs.t1_head='T1.nii'
    >>> epireg.inputs.t1_brain='T1_brain.nii'
    >>> epireg.inputs.out_base='epi2struct'
    >>> epireg.inputs.fmap='fieldmap_phase_fslprepared.nii'
    >>> epireg.inputs.fmapmag='fieldmap_mag.nii'
    >>> epireg.inputs.fmapmagbrain='fieldmap_mag_brain.nii'
    >>> epireg.inputs.echospacing=0.00067
    >>> epireg.inputs.pedir='y'
    >>> epireg.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'epi_reg --echospacing=0.000670 --fmap=fieldmap_phase_fslprepared.nii \
--fmapmag=fieldmap_mag.nii --fmapmagbrain=fieldmap_mag_brain.nii --noclean \
--pedir=y --epi=epi.nii --t1=T1.nii --t1brain=T1_brain.nii --out=epi2struct'
    >>> epireg.run() # doctest: +SKIP

    """
    _cmd = 'epi_reg'
    input_spec = EpiRegInputSpec
    output_spec = EpiRegOutputSpec

    def _list_outputs(self):
        outputs = self.output_spec().get()
        outputs['out_file'] = os.path.join(os.getcwd(),
                                           self.inputs.out_base + '.nii.gz')
        if (not (isdefined(self.inputs.no_fmapreg) and
                 self.inputs.no_fmapreg) and isdefined(self.inputs.fmap)):
            outputs['out_1vol'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_1vol.nii.gz')
            outputs['fmap2str_mat'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_fieldmap2str.mat')
            outputs['fmap2epi_mat'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_fieldmaprads2epi.mat')
            outputs['fmap_epi'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_fieldmaprads2epi.nii.gz')
            outputs['fmap_str'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_fieldmaprads2str.nii.gz')
            outputs['fmapmag_str'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_fieldmap2str.nii.gz')
            outputs['shiftmap'] = os.path.join(
                os.getcwd(),
                self.inputs.out_base + '_fieldmaprads2epi_shift.nii.gz')
            outputs['fullwarp'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_warp.nii.gz')
            outputs['epi2str_inv'] = os.path.join(
                os.getcwd(), self.inputs.out_base + '_inv.mat')

        outputs['epi2str_mat'] = os.path.join(
            os.getcwd(), self.inputs.out_base + '.mat')
        outputs['wmedge'] = os.path.join(
            os.getcwd(), self.inputs.out_base + '_fast_wmedge.nii.gz')
        outputs['wmseg'] = os.path.join(
            os.getcwd(), self.inputs.out_base + '_fast_wmseg.nii.gz')

        return outputs


#######################################
# deprecated interfaces
#######################################


class EPIDeWarpInputSpec(FSLCommandInputSpec):
    mag_file = File(exists=True,
                    desc='Magnitude file',
                    argstr='--mag %s', position=0, mandatory=True)
    dph_file = File(exists=True,
                    desc='Phase file assumed to be scaled from 0 to 4095',
                    argstr='--dph %s', mandatory=True)
    exf_file = File(exists=True,
                    desc='example func volume (or use epi)',
                    argstr='--exf %s')
    epi_file = File(exists=True,
                    desc='EPI volume to unwarp',
                    argstr='--epi %s')
    tediff = traits.Float(2.46, usedefault=True,
                          desc='difference in B0 field map TEs',
                          argstr='--tediff %s')
    esp = traits.Float(0.58, desc='EPI echo spacing',
                       argstr='--esp %s', usedefault=True)
    sigma = traits.Int(2, usedefault=True, argstr='--sigma %s',
                       desc="2D spatial gaussing smoothing \
                       stdev (default = 2mm)")
    vsm = traits.String(genfile=True, desc='voxel shift map',
                        argstr='--vsm %s')
    exfdw = traits.String(desc='dewarped example func volume', genfile=True,
                          argstr='--exfdw %s')
    epidw = traits.String(desc='dewarped epi volume', genfile=False,
                          argstr='--epidw %s')
    tmpdir = traits.String(genfile=True, desc='tmpdir',
                           argstr='--tmpdir %s')
    nocleanup = traits.Bool(True, usedefault=True, desc='no cleanup',
                            argstr='--nocleanup')
    cleanup = traits.Bool(desc='cleanup',
                          argstr='--cleanup')


class EPIDeWarpOutputSpec(TraitedSpec):
    unwarped_file = File(desc="unwarped epi file")
    vsm_file = File(desc="voxel shift map")
    exfdw = File(desc="dewarped functional volume example")
    exf_mask = File(desc="Mask from example functional volume")


class EPIDeWarp(FSLCommand):
    """
    Wraps the unwarping script `epidewarp.fsl
    <http://surfer.nmr.mgh.harvard.edu/fswiki/epidewarp.fsl>`_.

    .. warning:: deprecated in FSL, please use
      :func:`nipype.workflows.dmri.preprocess.epi.sdc_fmb` instead.

    Examples
    --------

    >>> from nipype.interfaces.fsl import EPIDeWarp
    >>> dewarp = EPIDeWarp()
    >>> dewarp.inputs.epi_file = "functional.nii"
    >>> dewarp.inputs.mag_file = "magnitude.nii"
    >>> dewarp.inputs.dph_file = "phase.nii"
    >>> dewarp.inputs.output_type = "NIFTI_GZ"
    >>> dewarp.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
    'epidewarp.fsl --mag magnitude.nii --dph phase.nii --epi functional.nii \
--esp 0.58 --exfdw .../exfdw.nii.gz --nocleanup --sigma 2 --tediff 2.46 \
--tmpdir .../temp --vsm .../vsm.nii.gz'
    >>> res = dewarp.run() # doctest: +SKIP


    """
    _cmd = 'epidewarp.fsl'
    input_spec = EPIDeWarpInputSpec
    output_spec = EPIDeWarpOutputSpec

    def __init__(self, **inputs):
        warnings.warn(("Deprecated: Please use "
                       "nipype.workflows.dmri.preprocess.epi.sdc_fmb instead"),
                      DeprecationWarning)
        return super(EPIDeWarp, self).__init__(**inputs)

    def _run_interface(self, runtime):
        runtime = super(EPIDeWarp, self)._run_interface(runtime)
        if runtime.stderr:
            self.raise_exception(runtime)
        return runtime

    def _gen_filename(self, name):
        if name == 'exfdw':
            if isdefined(self.inputs.exf_file):
                return self._gen_fname(self.inputs.exf_file,
                                       suffix="_exfdw")
            else:
                return self._gen_fname("exfdw")
        if name == 'epidw':
            if isdefined(self.inputs.epi_file):
                return self._gen_fname(self.inputs.epi_file,
                                       suffix="_epidw")
        if name == 'vsm':
            return self._gen_fname('vsm')
        if name == 'tmpdir':
            return os.path.join(os.getcwd(), 'temp')
        return None

    def _list_outputs(self):
        outputs = self.output_spec().get()
        if not isdefined(self.inputs.exfdw):
            outputs['exfdw'] = self._gen_filename('exfdw')
        else:
            outputs['exfdw'] = self.inputs.exfdw
        if isdefined(self.inputs.epi_file):
            if isdefined(self.inputs.epidw):
                outputs['unwarped_file'] = self.inputs.epidw
            else:
                outputs['unwarped_file'] = self._gen_filename('epidw')
        if not isdefined(self.inputs.vsm):
            outputs['vsm_file'] = self._gen_filename('vsm')
        else:
            outputs['vsm_file'] = self._gen_fname(self.inputs.vsm)
        if not isdefined(self.inputs.tmpdir):
            outputs[
                'exf_mask'] = self._gen_fname(cwd=self._gen_filename('tmpdir'),
                                              basename='maskexf')
        else:
            outputs['exf_mask'] = self._gen_fname(cwd=self.inputs.tmpdir,
                                                  basename='maskexf')
        return outputs


class EddyCorrectInputSpec(FSLCommandInputSpec):
    in_file = File(exists=True, desc='4D input file', argstr='%s', position=0,
                   mandatory=True)
    out_file = File(desc='4D output file', argstr='%s', position=1,
                    name_source=['in_file'], name_template='%s_edc',
                    output_name='eddy_corrected')
    ref_num = traits.Int(0, argstr='%d', position=2, desc='reference number',
                         mandatory=True, usedefault=True)


class EddyCorrectOutputSpec(TraitedSpec):
    eddy_corrected = File(exists=True,
                          desc='path/name of 4D eddy corrected output file')


class EddyCorrect(FSLCommand):
    """

    .. warning:: Deprecated in FSL. Please use
      :class:`nipype.interfaces.fsl.epi.Eddy` instead

    Example
    -------

    >>> from nipype.interfaces.fsl import EddyCorrect
    >>> eddyc = EddyCorrect(in_file='diffusion.nii',
    ...                     out_file="diffusion_edc.nii", ref_num=0)
    >>> eddyc.cmdline # doctest: +IGNORE_UNICODE
    'eddy_correct diffusion.nii diffusion_edc.nii 0'

    """
    _cmd = 'eddy_correct'
    input_spec = EddyCorrectInputSpec
    output_spec = EddyCorrectOutputSpec

    def __init__(self, **inputs):
        warnings.warn(("Deprecated: Please use nipype.interfaces.fsl.epi.Eddy "
                       "instead"), DeprecationWarning)
        return super(EddyCorrect, self).__init__(**inputs)

    def _run_interface(self, runtime):
        runtime = super(EddyCorrect, self)._run_interface(runtime)
        if runtime.stderr:
            self.raise_exception(runtime)
        return runtime
