# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Provides interfaces to various commands provided by diffusion toolkit

   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 re
from nipype.utils.filemanip import fname_presuffix, split_filename, copyfile
import os
from nipype.utils.misc import isdefined
__docformat__ = 'restructuredtext'

from nipype.interfaces.base import (TraitedSpec, File, traits, CommandLine,
    CommandLineInputSpec)

class DTIReconInputSpec(CommandLineInputSpec):
    DWI = File(desc='Input diffusion volume', argstr='%s',exists=True, mandatory=True,position=1)
    out_prefix = traits.Str("dti", desc='Output file prefix', argstr='%s', usedefault=True,position=2)
    output_type = traits.Enum('nii', 'analyze', 'ni1', 'nii.gz', argstr='-ot %s', desc='output file type', usedefault=True)    
    bvecs = File(exists=True, desc = 'b vectors file',
                argstr='-gm %s', mandatory=True)
    bvals = File(exists=True,desc = 'b values file', mandatory=True)
    n_averages = traits.Int(desc='Number of averages', argstr='-nex %s')
    image_orientation_vectors = traits.List(traits.Float(), minlen=6, maxlen=6, desc="""specify image orientation vectors. if just one argument given,
        will treat it as filename and read the orientation vectors from
        the file. if 6 arguments are given, will treat them as 6 float
        numbers and construct the 1st and 2nd vector and calculate the 3rd
        one automatically.
        this information will be used to determine image orientation,
        as well as to adjust gradient vectors with oblique angle when""", argstr="-iop %f")
    oblique_correction = traits.Bool(desc="""when oblique angle(s) applied, some SIEMENS dti protocols do not
        adjust gradient accordingly, thus it requires adjustment for correct
        diffusion tensor calculation""", argstr="-oc")
    b0_threshold = traits.Float(desc="""program will use b0 image with the given threshold to mask out high
        background of fa/adc maps. by default it will calculate threshold
        automatically. but if it failed, you need to set it manually.""", argstr="-b0_th")
    
    
class DTIReconOutputSpec(TraitedSpec):
    ADC = File(exists=True)
    B0 = File(exists=True)
    L1 = File(exists=True)
    L2 = File(exists=True)
    L3 = File(exists=True)
    exp = File(exists=True)
    FA = File(exists=True)
    FA_color = File(exists=True)
    tensor = File(exists=True)
    V1 = File(exists=True)
    V2 = File(exists=True)
    V3 = File(exists=True)

class DTIRecon(CommandLine):
    """Use dti_recon to generate tensors and other maps
    """
    
    input_spec=DTIReconInputSpec
    output_spec=DTIReconOutputSpec
    
    _cmd = 'dti_recon'
    
    def _create_gradient_matrix(self, bvecs_file, bvals_file):
        _gradient_matrix_file = 'gradient_matrix.txt'
        bvals = [val for val in  re.split('\s+', open(bvals_file).readline().strip())]
        bvecs_f = open(bvecs_file)
        bvecs_x = [val for val in  re.split('\s+', bvecs_f.readline().strip())]
        bvecs_y = [val for val in  re.split('\s+', bvecs_f.readline().strip())]
        bvecs_z = [val for val in  re.split('\s+', bvecs_f.readline().strip())]
        bvecs_f.close()
        gradient_matrix_f = open(_gradient_matrix_file, 'w')
        for i in range(len(bvals)):
            gradient_matrix_f.write("%s, %s, %s, %s\n"%(bvecs_x[i], bvecs_y[i], bvecs_z[i], bvals[i]))
        gradient_matrix_f.close()
        return _gradient_matrix_file
    
    def _format_arg(self, name, spec, value):
        if name == "bvecs":
            new_val = self._create_gradient_matrix(self.inputs.bvecs, self.inputs.bvals)
            return super(DTIRecon, self)._format_arg("bvecs", spec, new_val)
        return super(DTIRecon, self)._format_arg(name, spec, value)

    def _list_outputs(self):
        out_prefix = self.inputs.out_prefix
        output_type = self.inputs.output_type

        outputs = self.output_spec().get()
        outputs['ADC'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_adc.'+ output_type))
        outputs['B0'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_b0.'+ output_type))
        outputs['L1'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_e1.'+ output_type))
        outputs['L2'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_e2.'+ output_type))
        outputs['L3'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_e3.'+ output_type))
        outputs['exp'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_exp.'+ output_type))
        outputs['FA'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_fa.'+ output_type))
        outputs['FA_color'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_fa_color.'+ output_type))
        outputs['tensor'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_tensor.'+ output_type))
        outputs['V1'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_v1.'+ output_type))
        outputs['V2'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_v2.'+ output_type))
        outputs['V3'] = os.path.abspath(fname_presuffix("",  prefix=out_prefix, suffix='_v3.'+ output_type))

        return outputs
    
class DTITrackerInputSpec(CommandLineInputSpec):
    tensor_file = File(exists=True, desc="reconstructed tensor file")
    input_type = traits.Enum('nii', 'analyze', 'ni1', 'nii.gz', desc="""input and output file type. accepted values are:
        analyze -> analyze format 7.5
        ni1     -> nifti format saved in seperate .hdr and .img file
        nii     -> nifti format with one .nii file
        nii.gz  -> nifti format with compression
        default type is 'nii'""", argstr = "-it %s")
    tracking_method = traits.Enum('fact', 'rk2', 'tl', 'sl', desc="""fact -> use FACT method for tracking. this is the default method.
        rk2  -> use 2nd order runge-kutta method for tracking.
        tl   -> use tensorline method for tracking.
        sl   -> use interpolated streamline method with fixed step-length""", argstr="-%s")
    step_length = traits.Float(desc="""set step length, in the unit of minimum voxel size.
        default value is 0.5 for interpolated streamline method
        and 0.1 for other methods""", argstr="-l %f")
    angle_threshold = traits.Float(desc="set angle threshold. default value is 35 degree", argstr="-at %f")
    angle_threshold_weight = traits.Float(desc="set angle threshold weighting factor. weighting will be be applied \
        on top of the angle_threshold", argstr = "-atw %f")
    random_seed = traits.Int(desc = "use random location in a voxel instead of the center of the voxel \
          to seed. can also define number of seed per voxel. default is 1", argstr="-rseed")
    invert_x = traits.Bool(desc="invert x component of the vector", argstr = "-ix")
    invert_y = traits.Bool(desc="invert y component of the vector", argstr = "-iy")
    invert_z = traits.Bool(desc="invert z component of the vector", argstr = "-iz")
    swap_xy = traits.Bool(desc="swap x & y vectors while tracking", argstr = "-sxy")
    swap_yz = traits.Bool(desc="swap y & z vectors while tracking", argstr = "-syz")
    swap_zx = traits.Bool(desc="swap x & z vectors while tracking", argstr = "-szx")
    mask1_file = File(desc="first mask image", mandatory=True, argstr="-m %s", position=2)
    mask1_threshold = traits.Float(desc="threshold value for the first mask image, if not given, the program will \
        try automatically find the threshold", position=3)
    mask2_file = File(desc="second mask image", argstr="-m2 %s", position=4)
    mask2_threshold = traits.Float(desc="threshold value for the second mask image, if not given, the program will \
        try automatically find the threshold", position=5)
    input_data_prefix = traits.Str("dti", desc="for internal naming use only", position=0, argstr="%s", usedefault=True)
    output_file = File("tracks.trk", "file containing tracks", argstr="%s", position=1, usedefault=True)
    output_mask = File(desc="output a binary mask file in analyze format", argstr="-om %s")
    primary_vector = traits.Enum('v2', 'v3', desc = "which vector to use for fibre tracking: v2 or v3. If not set use v1", argstr="-%s")

class DTITrackerOutputSpec(TraitedSpec):
    track_file = File(exists=True)
    mask_file = File(exists=True)
    
class DTITracker(CommandLine):
    input_spec=DTITrackerInputSpec
    output_spec=DTITrackerOutputSpec
    
    _cmd = 'dti_tracker'
    
    def _run_interface(self, runtime):
        _, _, ext = split_filename(self.inputs.tensor_file)
        copyfile(self.inputs.tensor_file, os.path.abspath(self.inputs.input_data_prefix + "_tensor" + ext), copy=False)
        
        return super(DTITracker, self)._run_interface(runtime)
    
    def _list_outputs(self):
        outputs = self.output_spec().get()
        outputs['track_file'] = os.path.abspath(self.inputs.output_file)
        if isdefined(self.inputs.output_mask) and self.inputs.output_mask:
            outputs['mask_file'] = os.path.abspath(self.inputs.output_mask)
        
        return outputs