import sys, collections, itertools, os.path, optparse

optParser = optparse.OptionParser( 
   
   usage = "python %prog [options] <in.gtf> <out.gff>",
   
   description=
      "Script to prepare annotation for DEXSeq." +
      "This script takes an annotation file in Ensembl GTF format" +
      "and outputs a 'flattened' annotation file suitable for use " +
      "with the count_in_exons.py script ",
      
   epilog = 
      "Written by Simon Anders (sanders@fs.tum.de), European Molecular Biology " +
      "Laboratory (EMBL). (c) 2010. Released under the terms of the GNU General " +
      "Public License v3. Part of the 'DEXSeq' package." )

optParser.add_option( "-r", "--aggregate", type="choice", dest="aggregate",
   choices = ( "no", "yes" ), default = "yes",
   help = "'yes' or 'no'. Indicates whether two or more genes sharing an exon should be merged into an 'aggregate gene'. If 'no', the exons that can not be assiged to a single gene are ignored." )

(opts, args) = optParser.parse_args()

if len( args ) != 2:
   sys.stderr.write( "Script to prepare annotation for DEXSeq.\n\n" )
   sys.stderr.write( "Usage: python %s <in.gtf> <out.gff>\n\n" % os.path.basename(sys.argv[0]) )
   sys.stderr.write( "This script takes an annotation file in Ensembl GTF format\n" )
   sys.stderr.write( "and outputs a 'flattened' annotation file suitable for use\n" )
   sys.stderr.write( "with the count_in_exons.py script.\n" )
   sys.exit(1)

try:
   import HTSeq
except ImportError:
   sys.stderr.write( "Could not import HTSeq. Please install the HTSeq Python framework\n" )   
   sys.stderr.write( "available from http://www-huber.embl.de/users/anders/HTSeq\n" )   
   sys.exit(1)




gtf_file = args[0]
out_file = args[1]

aggregateGenes = opts.aggregate == "yes"

# Step 1: Store all exons with their gene and transcript ID 
# in a GenomicArrayOfSets

exons = HTSeq.GenomicArrayOfSets( "auto", stranded=True )
for f in HTSeq.GFF_Reader( gtf_file ):
   if f.type != "exon":
      continue
   f.attr['gene_id'] = f.attr['gene_id'].replace( ":", "_" )
   exons[f.iv] += ( f.attr['gene_id'], f.attr['transcript_id'] )


# Step 2: Form sets of overlapping genes

# We produce the dict 'gene_sets', whose values are sets of gene IDs. Each set
# contains IDs of genes that overlap, i.e., share bases (on the same strand).
# The keys of 'gene_sets' are the IDs of all genes, and each key refers to
# the set that contains the gene.
# Each gene set forms an 'aggregate gene'.

if aggregateGenes == True:
   gene_sets = collections.defaultdict( lambda: set() )
   for iv, s in exons.steps():
      # For each step, make a set, 'full_set' of all the gene IDs occuring
      # in the present step, and also add all those gene IDs, whch have been
      # seen earlier to co-occur with each of the currently present gene IDs.
      full_set = set()
      for gene_id, transcript_id in s:
         full_set.add( gene_id )
         full_set |= gene_sets[ gene_id ]
      # Make sure that all genes that are now in full_set get associated
      # with full_set, i.e., get to know about their new partners
      for gene_id in full_set:
         assert gene_sets[ gene_id ] <= full_set
         gene_sets[ gene_id ] = full_set


# Step 3: Go through the steps again to get the exonic sections. Each step
# becomes an 'exonic part'. The exonic part is associated with an
# aggregate gene, i.e., a gene set as determined in the previous step, 
# and a transcript set, containing all transcripts that occur in the step.
# The results are stored in the dict 'aggregates', which contains, for each
# aggregate ID, a list of all its exonic_part features.

aggregates = collections.defaultdict( lambda: list() )
for iv, s in exons.steps( ):
   # Skip empty steps
   if len(s) == 0:
      continue
   gene_id = list(s)[0][0]
   ## if aggregateGenes=FALSE, ignore the exons associated to more than one gene ID
   if aggregateGenes == False:
      check_set = set()
      for geneID, transcript_id in s:
         check_set.add( geneID )
      if( len( check_set ) > 1 ):
         continue
      else:
         aggregate_id = gene_id
   # Take one of the gene IDs, find the others via gene sets, and
   # form the aggregate ID from all of them   
   else:
      assert set( gene_id for gene_id, transcript_id in s ) <= gene_sets[ gene_id ] 
      aggregate_id = '+'.join( gene_sets[ gene_id ] )
   # Make the feature and store it in 'aggregates'
   f = HTSeq.GenomicFeature( aggregate_id, "exonic_part", iv )   
   f.source = os.path.basename( sys.argv[0] )
#   f.source = "camara"
   f.attr = {}
   f.attr[ 'gene_id' ] = aggregate_id
   transcript_set = set( ( transcript_id for gene_id, transcript_id in s ) )
   f.attr[ 'transcripts' ] = '+'.join( transcript_set )
   aggregates[ aggregate_id ].append( f )


# Step 4: For each aggregate, number the exonic parts

aggregate_features = []
for l in aggregates.values():
   for i in xrange( len(l)-1 ):
      assert l[i].name == l[i+1].name, str(l[i+1]) + " has wrong name"
      assert l[i].iv.end <= l[i+1].iv.start, str(l[i+1]) + " starts too early"
      if l[i].iv.chrom != l[i+1].iv.chrom:
         raise ValueError, "Same name found on two chromosomes: %s, %s" % ( str(l[i]), str(l[i+1]) )
      if l[i].iv.strand != l[i+1].iv.strand:
         raise ValueError, "Same name found on two strands: %s, %s" % ( str(l[i]), str(l[i+1]) )
   aggr_feat = HTSeq.GenomicFeature( l[0].name, "aggregate_gene", 
      HTSeq.GenomicInterval( l[0].iv.chrom, l[0].iv.start, 
         l[-1].iv.end, l[0].iv.strand ) )
   aggr_feat.source = os.path.basename( sys.argv[0] )
   aggr_feat.attr = { 'gene_id': aggr_feat.name }
   for i in xrange( len(l) ):
      l[i].attr['exonic_part_number'] = "%03d" % ( i+1 )
   aggregate_features.append( aggr_feat )
      
      
# Step 5: Sort the aggregates, then write everything out

aggregate_features.sort( key = lambda f: ( f.iv.chrom, f.iv.start ) )

fout = open( out_file, "w" ) 
for aggr_feat in aggregate_features:
   fout.write( aggr_feat.get_gff_line() )
   for f in aggregates[ aggr_feat.name ]:
      fout.write( f.get_gff_line() )

fout.close()