def ids_biom(tmpdir_factory, list_of_kos):
fn = tmpdir_factory.mktemp("data").join("ko_list.biom")
observations = list_of_kos + ['K99999']
samples = ('Sample1', 'Sample2')
data = np.array([[1, 1, 0, 0], [1, 0, 1, 0]])
table = Table(data.transpose(), observations, samples)
table.to_json('testing', open(str(fn), 'w'))
return str(fn)
def main(table_loc, otu_list, collapsed_name, output_file, classic=False):
table = load_table(table_loc)
f = open(otu_list)
otus = f.read().strip().split()
otus = set(otus) & set(table.ids(axis="observation"))
table1 = table.filter(otus, axis="observation", inplace=False)
table2 = table.filter(otus, axis="observation", invert=True, inplace=False)
sums1 = table1.sum(axis='sample')
sums2 = table2.sum(axis='sample')
new_table = Table(numpy.array([sums1,sums2]), [collapsed_name, "not_"+collapsed_name], table.ids(axis="sample"), type="otu baptable")
if classic:
# print to tab delimited biom table
open(output_file, 'w').write(new_table.to_tsv())
else:
# print biom table
new_table.to_json("predict_reactions.py", open(output_file, 'w'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_file", help="location of biom table")
parser.add_argument("-m", "--otu_map", help="location of OTU map")
parser.add_argument("-o",
"--output_file",
help="location of output biom table")
args = parser.parse_args()
otu_map = open(args.otu_map, 'U')
otu_map = otu_map.readlines()
otu_map = [i.strip().split() for i in otu_map]
otu_map = {i[0]: i[1:] for i in otu_map}
table = load_table(args.input_file)
new_table = np.zeros((len(otu_map), table.shape[1]))
for i, otu in enumerate(otu_map):
for seq in otu_map[otu]:
new_table[i, ] += table.data(seq, axis="observation")
new_table = Table(new_table, otu_map.keys(), table.ids())
new_table.to_json("dada2_to_otu_table", open(args.output_file, 'w'))
def generaete_biom_file(res_df, o, tg_rank, sampleid):
"""
output result in biom format
"""
import numpy as np
import biom
from biom.table import Table
if biom.__version__ < '2.1.7':
sys.exit("[ERROR] Biom library requires v2.1.7 or above.\n")
target_df = pd.DataFrame()
target_idx = (res_df['LEVEL']==tg_rank)
target_df = res_df.loc[target_idx, ['ABUNDANCE','TAXID']]
target_df['LINEAGE'] = target_df['TAXID'].apply(lambda x: gt.taxid2lineage(x, True, True)).str.split('|')
sample_ids = [sampleid]
data = np.array(target_df['ABUNDANCE']).reshape(len(target_df), 1)
observ_ids = target_df['TAXID']
observ_metadata = [{'taxonomy': x} for x in target_df['LINEAGE'].tolist()]
biom_table = Table(data, observ_ids, sample_ids, observ_metadata, table_id='GOTTCHA2')
biom_table.to_json('GOTTCHA2', direct_io=o)
return True
mode = 'less greedy'
print('Released are the {0} capitalists.'.format(mode))
if not greedy:
print("\tI'll spit out some profit to miximize mine.\n")
else:
print('\tAll profit is mine!\n')
alnNormalized = amplicon.initAlignment(alnString)
wtaProfile = amplicon.winnerTakeAll(alnNormalized,
progress=True,
greedy=greedy,
weight=weight)
print('Winner take all profile found! {0} references survived.'.format(
len(wtaProfile)))
profile = list(wtaProfile.items())
profile.sort(key=lambda x: x[1], reverse=True)
print('Tab-delimited profile wrote to {0}.'.format(tabFile))
with open(tabFile, 'w') as f:
f.write('{0}\t{1}\n'.format('Reference', sampleName))
for item in profile:
if int(item[1]) >= 1:
f.write('{0}\t{1}\n'.format(item[0], int(item[1])))
# Output as biom format
biomTable = Table(np.array([[i[1]] for i in profile]), [i[0] for i in profile],
[sampleName],
table_id='single_sample_biom')
print('Biom JSON format profile wrote to {0}.'.format(biomFile))
with open(biomFile, 'w') as f:
biomTable.to_json('Generated_by_metaSeq', f)
# print "Taxonomy: %s" %Taxonomy
for taxon in observ_ids:
# print taxon
# print Taxonomy[taxon]
observation_metadata.append(Taxonomy[taxon])
#print "observation metadata:\n%s" %observation_metadata
#print len(observation_metadata)
table = Table(data,
observ_ids,
sample_id,
observation_metadata,
sample_metadata,
table_id='Example Table')
print table
out = open(args.prefix + ".biom", "w")
table.to_json('pplacer converted by jplace_to_biom.py v.' + VERSION,
direct_io=out)
out.close()
out = open(args.prefix + ".tsv", "w")
out.write(table.to_tsv(
header_key='taxonomy',
header_value='taxomomy')) #to_json('generaged by test', direct_io=out)
out.close()
print "\n##### DONE! #####\n"
# print "index: %i" %index
ind_taxonomy.append('%s%s' %(syn[levels[index]], taxon[0]['ScientificName']))
# print ind_taxonomy
Taxonomy[taxon[0]['ScientificName']]['taxonomy'] = ind_taxonomy
# print "Taxonomy: %s" %Taxonomy
for taxon in observ_ids:
# print taxon
# print Taxonomy[taxon]
observation_metadata.append(Taxonomy[taxon])
#print "observation metadata:\n%s" %observation_metadata
#print len(observation_metadata)
table = Table(data, observ_ids, sample_id, observation_metadata, sample_metadata, table_id='Example Table')
print table
out=open(args.prefix+".biom","w")
table.to_json('pplacer converted by jplace_to_biom.py v.'+VERSION, direct_io=out)
out.close()
out=open(args.prefix+".tsv","w")
out.write(table.to_tsv(header_key='taxonomy', header_value='taxomomy')) #to_json('generaged by test', direct_io=out)
out.close()
print "\n##### DONE! #####\n"
