def test_convert():
model = cobra.io.load_json_model(join(root_directory, 'escher', 'example_data', 'iJO1366.json'))
# reverse the reaction
model.reactions.get_by_id('GAPD').upper_bound = 0
model.reactions.get_by_id('GAPD').lower_bound = -1000
# map
old_map = {'reactions': {'1': {'bigg_id': 'GAPD',
'label_x': '0',
'label_y': 0,
'segments': {'2': {'from_node_id': '0',
'to_node_id': '1',
'b1': {'x': None, 'y': None},
'b2': None}}},
'2': {'bigg_id': 'PDH'}},
'nodes': {'0': {'node_type': 'multimarker',
'x': 1,
'y': 2},
'1': {'node_type': 'metabolite',
'label_x': 10,
'label_y': 12.2,
'x': 1,
'y': 2,
'bigg_id': 'g3p_c',
'name': 'glycerol-3-phosphate'},
'3': {'node_type': 'metabolite',
'x': 1,
'y': 2,
'bigg_id': 'glc__D_c',
'name': 'D-Glucose'}}}
new_map = convert(old_map, model)
print(new_map)
# no segments: delete reaction
assert '2' not in new_map[1]['reactions']
# reversed the map
for m in new_map[1]['reactions']['1']['metabolites']:
if m['bigg_id'] == 'g3p_c':
assert m['coefficient'] == 1
elif m['bigg_id'] == 'nadh_c':
assert m['coefficient'] == -1
assert new_map[1]['reactions']['1']['reversibility'] == False
# Remove unconnected nodes. These do not make the map invalid, but they are
# annoying.
assert '3' not in new_map[1]['nodes']
# casting
assert type(new_map[1]['reactions']['1']['label_x']) is float
assert new_map[1]['reactions']['1']['segments']['2']['b1'] is None
def main():
# go though the maps
for filepath, output_path, save_model_dir in get_map_dirs(map_paths):
filename = basename(filepath)
logging.info("Converting %s" % filename)
# # debug
# if 'iAF692' not in filename:
# continue
# get the model id
model_id = parse_model(filename)[0]
# check id mapping
try:
model_id = model_id_mapping[model_id]
except KeyError:
pass
# load the model
try:
model = load_bigg_model(model_id)
except NotFoundError as e:
print(filepath, e)
continue
# save the bigg model
if save_model_dir:
save_model(save_model_dir, model, model_id)
# get the id mapping dictionaries. Eventually, this should be available
# in the API.
reaction_id_mapping = get_reaction_mapping(model.id)
metabolite_id_mapping = get_metabolite_mapping(model.id)
gene_id_mapping = None # get_gene_mapping(model_id)
# convert the map
with open(filepath, "r") as f:
map_in = json.load(f)
map_out = convert(
map_in,
model,
map_name=parse_map_name(fix_filename(filename, model_id)),
reaction_id_mapping=reaction_id_mapping,
metabolite_id_mapping=metabolite_id_mapping,
gene_id_mapping=gene_id_mapping,
)
# write
with open(join(output_path, fix_filename(filename, model_id)), "w") as f:
json.dump(map_out, f)
def main():
# go though the maps
for filepath, output_path, save_model_dir in get_map_dirs(map_paths):
filename = basename(filepath)
logging.info('Converting %s' % filename)
# # debug
# if 'iAF692' not in filename:
# continue
# get the model id
model_id = parse_model(filename)[0]
# check id mapping
try:
model_id = model_id_mapping[model_id]
except KeyError:
pass
# load the model
try:
model = load_bigg_model(model_id)
except NotFoundError as e:
print(filepath, e)
continue
# save the bigg model
if save_model_dir:
save_model(save_model_dir, model, model_id)
# get the id mapping dictionaries. Eventually, this should be available
# in the API.
reaction_id_mapping = get_reaction_mapping(model.id)
metabolite_id_mapping = get_metabolite_mapping(model.id)
gene_id_mapping = None # get_gene_mapping(model_id)
# convert the map
with open(filepath, 'r') as f:
map_in = json.load(f)
map_out = convert(map_in,
model,
map_name=parse_map_name(
fix_filename(filename, model_id)),
reaction_id_mapping=reaction_id_mapping,
metabolite_id_mapping=metabolite_id_mapping,
gene_id_mapping=gene_id_mapping)
# write
with open(join(output_path, fix_filename(filename, model_id)),
'w') as f:
json.dump(map_out, f)
def save_map(filename, out_directory, model_name):
if filename.endswith('.json.gz'):
with gzip.open(filename, "r") as f:
data = json.load(f)
out_file = join(out_directory,
basename(filename).replace('.json.gz', '_map.json'))
elif filename.endswith('.json'):
with open(filename, "r") as f:
data = json.load(f)
out_file = join(out_directory,
basename(filename).replace('.json', '_map.json'))
else:
logging.warn('Not loading file %s' % filename)
# get the cobra model
model = load_model(model_name)
# get the compartment dictionary
df = pd.DataFrame.from_csv("compartment_id_key.csv")
compartment_id_dictionary = {}
for row in df.itertuples(index=True):
compartment_id_dictionary[row[0]] = row[1:3]
# major categories
reactions = []
line_segments = []
text_labels = []
nodes = []
for k, v in data.iteritems():
if k == "MAPREACTION": reactions = v
elif k == "MAPLINESEGMENT": segments = v
elif k == "MAPTEXT": text_labels = v
elif k == "MAPNODE": nodes = v
else: raise Exception('Unrecognized category: %s' % k)
# do the nodes
nodes = parse_nodes(nodes, compartment_id_dictionary)
# do the segments
parse_segments(segments, reactions, nodes)
# do the reactions
reactions = parse_reactions(reactions, model, nodes)
# do the text labels
text_labels = parse_labels(text_labels)
# compile the data
out = {}
out['nodes'] = nodes
out['reactions'] = reactions
out['text_labels'] = text_labels
# translate everything so x > 0 and y > 0
# out = translate_everything(out)
# for export, only keep the necessary stuff
node_keys_to_keep = [
'node_type', 'x', 'y', 'name', 'bigg_id', 'label_x', 'label_y',
'node_is_primary', 'connected_segments'
]
segment_keys_to_keep = ['from_node_id', 'to_node_id', 'b1', 'b2']
reaction_keys_to_keep = [
'segments', 'name', 'reversibility', 'bigg_id', 'label_x', 'label_y',
'metabolites', 'gene_reaction_rule'
]
text_label_keys_to_keep = ['x', 'y', 'text']
for k, node in out['nodes'].iteritems():
only_keep_keys(node, node_keys_to_keep)
for k, reaction in out['reactions'].iteritems():
if 'segments' not in reaction: continue
for k, segment in reaction['segments'].iteritems():
only_keep_keys(segment, segment_keys_to_keep)
only_keep_keys(reaction, reaction_keys_to_keep)
for k, text_label in out['text_labels'].iteritems():
only_keep_keys(text_label, text_label_keys_to_keep)
# get max width and height
min_max = {'x': [inf, -inf], 'y': [inf, -inf]}
for node in nodes.itervalues():
if node['x'] < min_max['x'][0]: min_max['x'][0] = node['x']
if node['x'] > min_max['x'][1]: min_max['x'][1] = node['x']
if node['y'] < min_max['y'][0]: min_max['y'][0] = node['y']
if node['y'] > min_max['y'][1]: min_max['y'][1] = node['y']
width = min_max['x'][1] - min_max['x'][0]
height = min_max['y'][1] - min_max['y'][0]
out['canvas'] = {
'x': min_max['x'][0] - 0.05 * width,
'y': min_max['y'][0] - 0.05 * height,
'width': width + 0.10 * width,
'height': height + 0.10 * height
}
header = {
"schema":
"https://zakandrewking.github.io/escher/escher/jsonschema/1-0-0#",
"homepage": "https://zakandrewking.github.io/escher",
"map_id": basename(filename).replace('.json', '').replace('.gz', ''),
"map_name": "",
"map_description": ""
}
the_map = [header, out]
from escher.convert_map import convert
the_map = convert(the_map, model)
with open(out_file, 'w') as f:
json.dump(the_map, f, allow_nan=False)
model.genes.get_by_id(old_id).remove_from_model()
# update the gene names with the old names
for g in model.genes:
g.name = g.id
model.id = model_id
cobra.io.save_json_model(model, model_path)
print 'Found model %s %s' % (the_organism, model_path)
break
if the_organism is None:
print 'Could not find model'
exit()
# apply model to maps
map_org = join('maps', the_organism)
for map_file in listdir(map_org):
if map_file.startswith('.'):
continue
map_path = join(map_org, map_file)
print 'Writing map %s' % map_path
with open(map_path, 'r') as f:
map = json.load(f)
for r in map[1]['reactions'].itervalues():
r['gene_reaction_rule'] = re.sub('\.[0-9]+', '', r['gene_reaction_rule'])
for g in r['genes']:
g['bigg_id'] = g['bigg_id'].split('.')[0]
new_map = convert(map, model) # TODO the gene names are not being fixed here
# import ipdb; ipdb.set_trace()
with open(map_path, 'w') as f:
json.dump(new_map, f)
def save_map(filename, out_directory, model_name):
if filename.endswith('.json.gz'):
with gzip.open(filename, "r") as f:
data = json.load(f)
out_file = join(out_directory, basename(filename).replace('.json.gz', '_map.json'))
elif filename.endswith('.json'):
with open(filename, "r") as f:
data = json.load(f)
out_file = join(out_directory, basename(filename).replace('.json', '_map.json'))
else:
logging.warn('Not loading file %s' % filename)
# get the cobra model
model = load_model(model_name)
# get the compartment dictionary
df = pd.DataFrame.from_csv("compartment_id_key.csv")
compartment_id_dictionary = {}
for row in df.itertuples(index=True):
compartment_id_dictionary[row[0]] = row[1:3]
# major categories
reactions = []; line_segments = []; text_labels = []; nodes = []
for k, v in data.iteritems():
if k=="MAPREACTION": reactions = v
elif k=="MAPLINESEGMENT": segments = v
elif k=="MAPTEXT": text_labels = v
elif k=="MAPNODE": nodes = v
else: raise Exception('Unrecognized category: %s' % k)
# do the nodes
nodes = parse_nodes(nodes, compartment_id_dictionary)
# do the segments
parse_segments(segments, reactions, nodes)
# do the reactions
reactions = parse_reactions(reactions, model, nodes)
# do the text labels
text_labels = parse_labels(text_labels)
# compile the data
out = {}
out['nodes'] = nodes
out['reactions'] = reactions
out['text_labels'] = text_labels
# translate everything so x > 0 and y > 0
# out = translate_everything(out)
# for export, only keep the necessary stuff
node_keys_to_keep = ['node_type', 'x', 'y', 'name', 'bigg_id', 'label_x',
'label_y', 'node_is_primary', 'connected_segments']
segment_keys_to_keep = ['from_node_id', 'to_node_id', 'b1', 'b2']
reaction_keys_to_keep = ['segments', 'name', 'reversibility',
'bigg_id', 'label_x', 'label_y', 'metabolites',
'gene_reaction_rule']
text_label_keys_to_keep = ['x', 'y', 'text']
for k, node in out['nodes'].iteritems():
only_keep_keys(node, node_keys_to_keep)
for k, reaction in out['reactions'].iteritems():
if 'segments' not in reaction: continue
for k, segment in reaction['segments'].iteritems():
only_keep_keys(segment, segment_keys_to_keep)
only_keep_keys(reaction, reaction_keys_to_keep)
for k, text_label in out['text_labels'].iteritems():
only_keep_keys(text_label, text_label_keys_to_keep)
# get max width and height
min_max = {'x': [inf, -inf], 'y': [inf, -inf]}
for node in nodes.itervalues():
if node['x'] < min_max['x'][0]: min_max['x'][0] = node['x']
if node['x'] > min_max['x'][1]: min_max['x'][1] = node['x']
if node['y'] < min_max['y'][0]: min_max['y'][0] = node['y']
if node['y'] > min_max['y'][1]: min_max['y'][1] = node['y']
width = min_max['x'][1] - min_max['x'][0]
height = min_max['y'][1] - min_max['y'][0]
out['canvas'] = { 'x': min_max['x'][0] - 0.05 * width,
'y': min_max['y'][0] - 0.05 * height,
'width': width + 0.10 * width,
'height': height + 0.10 * height}
header = {
"schema": "https://zakandrewking.github.io/escher/escher/jsonschema/1-0-0#",
"homepage": "https://zakandrewking.github.io/escher",
"map_id": basename(filename).replace('.json', '').replace('.gz', ''),
"map_name": "",
"map_description": ""
}
the_map = [header, out]
from escher.convert_map import convert
the_map = convert(the_map, model)
with open(out_file, 'w') as f: json.dump(the_map, f, allow_nan=False)
