#  This software and supporting documentation are distributed by
#      Institut Federatif de Recherche 49
#      CEA/NeuroSpin, Batiment 145,
#      91191 Gif-sur-Yvette cedex
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#
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from brainvisa.processes import *
import registration

name = 'Diffusion Maps'
userLevel = 1

signature=Signature(
  'dw_diffusion', ReadDiskItem( 'DW Diffusion MR',
                                'Aims readable volume formats' ),
  'diffusion_model', ReadDiskItem( 'Diffusion Model', 'Bucket' ),
  'apparent_diffusion_coefficient', WriteDiskItem( 
                                               'Apparent Diffusion Coefficient',
                                               'Aims writable volume formats' ),
  'fractional_anisotropy', WriteDiskItem( 'Fractional Anisotropy',
                                          'Aims writable volume formats' ),
  'volume_ratio', WriteDiskItem( 'Volume Ratio', 'Aims writable volume formats' ),
  'standard_deviation', WriteDiskItem( 'Diffusion Standard Deviation',
                                       'Aims writable volume formats' ),
  'parallel_diffusion_coefficient', WriteDiskItem(
                                       'Parallel Diffusion Coefficient',
                                       'Aims writable volume formats' ),
  'transverse_diffusion_coefficient', WriteDiskItem(
                                       'Transverse Diffusion Coefficient',
                                       'Aims writable volume formats' ),
  'diffusion_weighted_t2', WriteDiskItem( 'Diffusion Weighted T2',
                                          'Aims writable volume formats' ),
  'maximum_eigenvector', WriteDiskItem( 'Maximum Eigenvector', 'GIS image' ),
  'RGB_eigenvector', WriteDiskItem( 'RGB Eigenvector', 'Aims writable volume formats' ),
)


def initialization( self ):
  self.linkParameters( 'diffusion_model', 'dw_diffusion' )
  self.linkParameters( 'apparent_diffusion_coefficient', 'diffusion_model' )
  self.linkParameters( 'fractional_anisotropy', 'apparent_diffusion_coefficient' )
  self.linkParameters( 'volume_ratio', 'fractional_anisotropy' )
  self.linkParameters( 'standard_deviation', 'volume_ratio' )
  self.linkParameters( 'parallel_diffusion_coefficient', 'standard_deviation' )
  self.linkParameters( 'transverse_diffusion_coefficient', 'parallel_diffusion_coefficient' )
  self.linkParameters( 'diffusion_weighted_t2', 'transverse_diffusion_coefficient' )
  self.linkParameters( 'maximum_eigenvector', 'diffusion_weighted_t2' )
  self.linkParameters( 'RGB_eigenvector', 'diffusion_weighted_t2' )
  
  self.setOptional( 'apparent_diffusion_coefficient', 
                    'fractional_anisotropy', 
                    'volume_ratio',
                    'standard_deviation',
                    'parallel_diffusion_coefficient',
                    'transverse_diffusion_coefficient',
                    'diffusion_weighted_t2',
                    'maximum_eigenvector',
                    'RGB_eigenvector' )

def execution( self, context ):
  x,y,z,t = self.diffusion_model.get( 'volume_dimension' )
  if self.diffusion_model.type.isA("Diffusion model"): # in ( getDiskItemType( 'DTI Model' ),getDiskItemType( 'Diffusion model' ) ):
    command = [ 'comistScalarMap',
                '-verbose',
                '-i', self.diffusion_model,
                '-m' ]
    outputFiles = []
    if self.apparent_diffusion_coefficient is not None:
      command.append( 'adc' )
      outputFiles.append( self.apparent_diffusion_coefficient )
    if self.fractional_anisotropy is not None:
      command.append( 'fa' )
      outputFiles.append( self.fractional_anisotropy )
    if self.volume_ratio is not None:
      command.append( 'vr' )    
      outputFiles.append( self.volume_ratio )
    if self.standard_deviation is not None:
      command.append( 'stddev' )    
      outputFiles.append( self.standard_deviation )
    if self.parallel_diffusion_coefficient is not None:
      command.append( 'lambdaparallel' )    
      outputFiles.append( self.parallel_diffusion_coefficient )
    if self.transverse_diffusion_coefficient is not None:
      command.append( 'lambdatransverse' )    
      outputFiles.append( self.transverse_diffusion_coefficient )
    if outputFiles:
      command += [ '-x', x, '-y', y, '-z', z, '-o' ] + outputFiles
      context.system( *command )
      
    command = [ 'comistOrientationMap',
                '-verbose',
                '-i', self.diffusion_model, '-m' ]
    outputFiles = []
    if self.maximum_eigenvector is not None:
      command.append( 'maxeigenvector' )
      outputFiles.append( self.maximum_eigenvector )
    if self.RGB_eigenvector is not None:
      command.append( 'rgb' )
      outputFiles.append( self.RGB_eigenvector )
    if outputFiles:
      command += [ '-x', x, '-y', y, '-z', z, '-o' ] + outputFiles
      context.system( *command )      
    
    command = [ 'comistWeightedT2',
                '-verbose',
                '-dw', self.dw_diffusion,
                '-o', self.diffusion_weighted_t2 ]
    context.system( *command )      

    # Set referentials of new objects
    trManager = registration.getTransformationManager()
    trManager.copyReferential( self.dw_diffusion, self.apparent_diffusion_coefficient )
    trManager.copyReferential( self.dw_diffusion, self.fractional_anisotropy )
    trManager.copyReferential( self.dw_diffusion, self.volume_ratio )
    trManager.copyReferential( self.dw_diffusion, self.standard_deviation )
    trManager.copyReferential( self.dw_diffusion, self.parallel_diffusion_coefficient )
    trManager.copyReferential( self.dw_diffusion, self.transverse_diffusion_coefficient )
    trManager.copyReferential( self.dw_diffusion, self.diffusion_weighted_t2 )
    trManager.copyReferential( self.dw_diffusion, self.maximum_eigenvector )
    trManager.copyReferential( self.dw_diffusion, self.RGB_eigenvector )
    
  else:
    raise RuntimeError( _t_('Type %s is not supported') % 
                        ( self.diffusion_model.type.name ) )