#  This software and supporting documentation are distributed by
#      Institut Federatif de Recherche 49
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from brainvisa.processes import *
import shfjGlobals
from registration import getTransformationManager

name='Import Diffusion GEMS old sequence'
userLevel=0


orientationSet = [ [  0.707107 , 0.707107 , 0.0      ],
                   [  0.707107 , 0.0      , 0.707107 ],
                   [  0.0      , 0.707107 , 0.707107 ],
                   [  -0.707107, 0.707107 , 0.0      ],
                   [  -0.707107, 0.0      , 0.707107 ],
                   [  0.0      , -0.707107, 0.707107 ] ]


signature = Signature(
  'fileList', ListOf( ReadDiskItem( 'Raw DW Diffusion MR', 
                                    shfjGlobals.aimsVolumeFormats ) ),
  'maximumBValue', Float(),
  'bValueCount', Integer(),
  'nex', Integer(),
  't2_diffusion', WriteDiskItem( 'Raw T2 Diffusion MR', 'GIS image' ),
  'raw_dw_diffusion', WriteDiskItem( 'Raw DW Diffusion MR', 'GIS image' ),
)

def initialization( self ):
  self.maximumBValue = 1000
  self.bValueCount = 5
  self.nex = 1

  self.linkParameters( 'raw_dw_diffusion', 't2_diffusion' )
  self.signature[ 'fileList' ].databaseUserLevel = 2
  self.signature[ 't2_diffusion' ].browseUserLevel = 3
  self.signature[ 'raw_dw_diffusion' ].browseUserLevel = 3

def execution( self, context ):
  baseBValues = [ 0 ] * self.bValueCount
  for i in xrange( self.bValueCount ):
    baseBValues[ i ] = ( i + 1 ) * self.maximumBValue / self.bValueCount
  bValues = [ 0 ] + baseBValues * 6
  bValues = bValues * self.nex
  orientations = [ [ 0, 0, 0 ] ]
  for i in xrange( 6 ):
    orientations = orientations + [ orientationSet[ i ] ] * self.bValueCount
  orientations = orientations * self.nex
  headers = []
  for image in self.fileList:
    header = context.temporary( 'Text file' )
    headers.append( header )
    context.write( _t_('Building meta-info for data %s') % image.fullName() )
    header.setMinf( 'bvalues', bValues )
    header.setMinf( 'diffusion_gradient_orientations', orientations )
    header.setMinf( 'nex', self.nex )

  context.write( _t_('Concatenating data') )
  command = [ 'comistConcatenation',
              '-verbose',
              '-z',
              '-t2', self.t2_diffusion,
              '-dw', self.raw_dw_diffusion,
              '-i' ] + self.fileList + [
              '-m' ] + map( lambda x: x.minfFileName(), headers )
  apply( context.system, command )

  transformManager = getTransformationManager()
  transformManager.createNewReferentialFor( self.t2_diffusion )
  transformManager.copyReferential( self.t2_diffusion,
                                    self.raw_dw_diffusion )