biom = Biom( generated_by="grinder", matrix_type="sparse" )
# Set observations count
for sample_name in args.samples:
biom.add_sample( sample_name )
fh_abund = open( args.samples[sample_name] )
for line in fh_abund: # Content format: "# rank<TAB>seq_id<TAB>rel_abund_perc"
if not line.startswith('#'):
fields = line.strip().split()
try:
biom.add_observation( fields[1] )
except: # already exist
pass
biom.change_count( fields[1], sample_name, int(float(fields[2])*100000000000000) )################## depend de la precision grinder
fh_abund.close()
# Set taxonomy metadata
fh_classif = FastaIO( args.affiliation )
for record in fh_classif:
try:
metadata = biom.get_observation_metadata( record.id )
if metadata is None or not metadata.has_key( taxonomy_key ):
taxonomy = getCleanedTaxonomy(record.description)
biom.add_metadata( record.id, taxonomy_key, taxonomy, "observation" )
except ValueError: # is not in BIOM
pass
fh_classif.close()
# Write BIOM
BiomIO.write( args.output, biom )
biom.add_sample(sample_name)
fh_abund = open(args.samples[sample_name])
for line in fh_abund: # Content format: "# rank<TAB>seq_id<TAB>rel_abund_perc"
if not line.startswith('#'):
fields = line.strip().split()
try:
biom.add_observation(fields[1])
except: # already exist
pass
biom.change_count(
fields[1], sample_name,
int(float(fields[2]) * 100000000000000
)) ################## depend de la precision grinder
fh_abund.close()
# Set taxonomy metadata
fh_classif = FastaIO(args.affiliation)
for record in fh_classif:
try:
metadata = biom.get_observation_metadata(record.id)
if metadata is None or not metadata.has_key(taxonomy_key):
taxonomy = getCleanedTaxonomy(record.description)
biom.add_metadata(record.id, taxonomy_key, taxonomy,
"observation")
except ValueError: # is not in BIOM
pass
fh_classif.close()
# Write BIOM
BiomIO.write(args.output, biom)
