#  This software and supporting documentation are distributed by
#      Institut Federatif de Recherche 49
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from brainvisa.processes import *
import shfjGlobals
try:
  from connectomist.GemsOrientationSets import gemsOrientationSets
except:
  pass
from registration import getTransformationManager

name='Import Diffusion SHFJ (GEMS scanner)'
userLevel=0

def validation():
  try:
    from connectomist.GemsOrientationSets import gemsOrientationSets
  except:
    raise ValidationError( 'connectomist.GemsOrientationSets module is not present or does not work.' )

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

def initialization( self ):
  self.nex = 1
  self.linkParameters( 'raw_dw_diffusion', 't2_diffusion' )
  self.setOptional( 'ignoreFileIndices' )
  self.signature[ 'ignoreFileIndices' ].userLevel = 2
  self.signature[ 'fileList' ].databaseUserLevel = 2
  self.signature[ 't2_diffusion' ].browseUserLevel = 3
  self.signature[ 'raw_dw_diffusion' ].browseUserLevel = 3
  
def execution( self, context ):
  volumeDimension = self.fileList[ 0 ].get( 'volume_dimension' )
  #print 'volumeDimension'
  #print volumeDimension
  sliceCount = int( volumeDimension[ 2 ] )
  maxOrientationCountByStep = int( 512 ) / sliceCount - 1
  
  # case with only one image in list
  if maxOrientationCountByStep >= self.orientationCount:

    if len( self.fileList ) != 1:
      raise RuntimeError( _t_('There should be exactly one image in fileList' ) )

    image = self.fileList[ 0 ]
    header = context.temporary( 'Text file' )
    
    bValues = [ 0 ] + [ self.bValue ] * self.orientationCount
    orientations = [ [ 0, 0, 0 ] ] + gemsOrientationSets[ 
                                  self.orientationCount ]

    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 )

    command = [ 'comistConcatenation',
                '-verbose',
                '-t2', self.t2_diffusion,
                '-dw', self.raw_dw_diffusion,
                '-i', image,
                '-m', header.minfFileName() ]
  
  # there are several images corresponding to several subset(s) of orientations
  else:
    if self.ignoreFileIndices:
      files = string.join( \
        map( lambda x, fileList=self.fileList: fileList[ x ].fullPath()+'<br>', 
             self.ignoreFileIndices ) )
      answer = context.ask( _t_( 'The following images will be ignored :<p><code>%s</code>') % files, _t_('Ok'),_t_('Cancel') )
      if answer != 0:
        return
    acqStepCount = self.orientationCount / maxOrientationCountByStep
    if self.orientationCount % maxOrientationCountByStep != 0:
      acqStepCount = acqStepCount + 1
            
    if len( self.fileList ) != acqStepCount:
      raise RuntimeError( _t_('There should be excatly %d image in fileList' ) %
                          acqStepCount )

    headers = []
    fileList = []
    for step in range( acqStepCount ):
      image = self.fileList[ step ]
      if self.ignoreFileIndices and step in self.ignoreFileIndices:
        context.warning( _t_( 'Image <em>%s</em> ignored' ) % image.fullPath() )
      else:
        fileList.append( image )
        header = context.temporary( 'Text file' )
        headers.append( header )
        orientationCountForStep =maxOrientationCountByStep
        if ( step == acqStepCount - 1 ) and ( self.orientationCount %
                                              maxOrientationCountByStep != 0  ):
          orientationCountForStep = ( self.orientationCount -
                                      maxOrientationCountByStep *
                                      ( acqStepCount - 1 ) )
        firstOrientationIndex = step * maxOrientationCountByStep

        bValues = [ 0 ] + [ self.bValue ] * orientationCountForStep
        orientations = [ [ 0, 0, 0 ] ] +  gemsOrientationSets[
                         self.orientationCount ][
                         firstOrientationIndex:
                         firstOrientationIndex + orientationCountForStep]

        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 )

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