def main():
try:
search_dir = argv[1]
model_name = argv[2]
except:
raise Exception('Not enough arguments.')
model = load_model(model_name)
ids = [id_for_new_id_style(x.id) for x in model.reactions]
scores = []; size=len(listdir(search_dir))
for i, path in enumerate(listdir(search_dir)):
# progress
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("%d / %d" % (i + 1, size))
sys.stdout.flush()
if path.endswith('.gz'):
f = gzip.open(join(search_dir, path), 'r')
else:
f = open(join(search_dir, path), 'r')
# (1) Compare the metabolite count
m = json.load(f)
try:
met_count = len(m['MAPNODE'])
reaction_count = len(m['MAPREACTION'])
# diff = abs(len(m['MAPNODE']) - metabolite_count)
except KeyError:
continue
# (2) Compare the reaction ids to the cobra model
# f.seek(0)
num_matches = 0
try:
reactions = m['MAPREACTION']
except KeyError:
continue
for reaction in reactions:
try:
an_id = reaction['REACTIONABBREVATION']
except KeyError:
continue
if id_for_new_id_style(an_id) in ids:
num_matches = num_matches + 1
# quit if not > 90%
frac = 0.9
if num_matches < frac*len(reactions): continue
scores.append((join(search_dir, path),
float(num_matches) / len(reactions),
met_count, reaction_count))
f.close()
scores = sorted(scores, key=itemgetter(2), reverse=True)
scores = sorted(scores, key=itemgetter(1))
outfile = '%s_maps.tsv' % model_name
print
print 'saving to %s' % outfile
(pd
.DataFrame(scores, columns=['path', 'score', 'n_metabolites', 'n_reactions'])
.to_csv(outfile, sep='\t'))
def parse_nodes(nodes, compartment_id_key):
for node in nodes:
# assign new keys
try_assignment(node, 'MAPOBJECT_ID', 'object_id',
cast=str, require=True)
try_assignment(node, 'MAPNODENODETYPE', 'node_type',
cast=lambda x: str(x).lower(), require=True)
try_assignment(node, 'MAPNODEPOSITIONX', 'x',
cast=float, require=True)
try_assignment(node, 'MAPNODEPOSITIONY', 'y',
cast=float, require=True)
if node['node_type'] == 'metabolite':
try_assignment(node, 'MAPNODELABELPOSITIONX', 'label_x',
cast=float, fallback=node['x'])
try_assignment(node, 'MAPNODELABELPOSITIONY', 'label_y',
cast=float, fallback=node['y'])
try_assignment(node, 'MOLECULEABBREVIATION', 'bigg_id',
cast=lambda x: id_for_new_id_style(x, is_metabolite=True),
fallback='')
try_assignment(node, 'MOLECULEOFFICIALNAME', 'name',
cast=str, fallback='')
try_assignment(node, 'MAPNODEISPRIMARY', 'node_is_primary',
cast=lambda x: True if x=='Y' else False, fallback=False)
try_assignment(node, 'MAPNODECOMPARTMENT_ID', 'compartment_id', cast=int)
try_assignment(node, 'MAPNODECOMPARTMENT_ID', 'compartment_name',
cast=lambda x: compartment_id_key[int(x)][0])
try_assignment(node, 'MAPNODECOMPARTMENT_ID', 'compartment_letter',
cast=lambda x: compartment_id_key[int(x)][1])
if node['bigg_id'] != '' and node['compartment_letter'] is not None:
node['bigg_id'] = "%s_%s" % (node['bigg_id'],
node['compartment_letter'])
# Make into dictionary
return {a['object_id']: a for a in nodes
if a['node_type'] in ['metabolite', 'multimarker', 'midmarker']}
def parse_nodes(nodes, compartment_id_key):
for node in nodes:
# assign new keys
try_assignment(node,
'MAPOBJECT_ID',
'object_id',
cast=str,
require=True)
try_assignment(node,
'MAPNODENODETYPE',
'node_type',
cast=lambda x: str(x).lower(),
require=True)
try_assignment(node, 'MAPNODEPOSITIONX', 'x', cast=float, require=True)
try_assignment(node, 'MAPNODEPOSITIONY', 'y', cast=float, require=True)
if node['node_type'] == 'metabolite':
try_assignment(node,
'MAPNODELABELPOSITIONX',
'label_x',
cast=float,
fallback=node['x'])
try_assignment(node,
'MAPNODELABELPOSITIONY',
'label_y',
cast=float,
fallback=node['y'])
try_assignment(
node,
'MOLECULEABBREVIATION',
'bigg_id',
cast=lambda x: id_for_new_id_style(x, is_metabolite=True),
fallback='')
try_assignment(node,
'MOLECULEOFFICIALNAME',
'name',
cast=str,
fallback='')
try_assignment(node,
'MAPNODEISPRIMARY',
'node_is_primary',
cast=lambda x: True if x == 'Y' else False,
fallback=False)
try_assignment(node,
'MAPNODECOMPARTMENT_ID',
'compartment_id',
cast=int)
try_assignment(node,
'MAPNODECOMPARTMENT_ID',
'compartment_name',
cast=lambda x: compartment_id_key[int(x)][0])
try_assignment(node,
'MAPNODECOMPARTMENT_ID',
'compartment_letter',
cast=lambda x: compartment_id_key[int(x)][1])
if node['bigg_id'] != '' and node['compartment_letter'] is not None:
node['bigg_id'] = "%s_%s" % (node['bigg_id'],
node['compartment_letter'])
# Make into dictionary
return {
a['object_id']: a
for a in nodes
if a['node_type'] in ['metabolite', 'multimarker', 'midmarker']
}
def main():
try:
search_dir = argv[1]
model_name = argv[2]
except:
raise Exception('Not enough arguments.')
model = load_model(model_name)
ids = [id_for_new_id_style(x.id) for x in model.reactions]
scores = []
size = len(listdir(search_dir))
for i, path in enumerate(listdir(search_dir)):
# progress
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("%d / %d" % (i + 1, size))
sys.stdout.flush()
if path.endswith('.gz'):
f = gzip.open(join(search_dir, path), 'r')
else:
f = open(join(search_dir, path), 'r')
# (1) Compare the metabolite count
m = json.load(f)
try:
met_count = len(m['MAPNODE'])
reaction_count = len(m['MAPREACTION'])
# diff = abs(len(m['MAPNODE']) - metabolite_count)
except KeyError:
continue
# (2) Compare the reaction ids to the cobra model
# f.seek(0)
num_matches = 0
try:
reactions = m['MAPREACTION']
except KeyError:
continue
for reaction in reactions:
try:
an_id = reaction['REACTIONABBREVATION']
except KeyError:
continue
if id_for_new_id_style(an_id) in ids:
num_matches = num_matches + 1
# quit if not > 90%
frac = 0.9
if num_matches < frac * len(reactions): continue
scores.append((join(search_dir,
path), float(num_matches) / len(reactions),
met_count, reaction_count))
f.close()
scores = sorted(scores, key=itemgetter(2), reverse=True)
scores = sorted(scores, key=itemgetter(1))
outfile = '%s_maps.tsv' % model_name
print
print 'saving to %s' % outfile
(pd.DataFrame(scores,
columns=['path', 'score', 'n_metabolites',
'n_reactions']).to_csv(outfile, sep='\t'))
