def assemble_cx(stmts_with_counts):
for channel, (stmts, ev_counts,
source_counts) in stmts_with_counts.items():
cxa = CxAssembler(stmts, network_name='%s INDRA interactome' % channel)
cxa.make_model()
add_semantic_hub_layout(cxa.cx, channel)
model_id = cxa.upload_model()
def upload_to_ndex(self):
"""Upload the assembled model as CX to NDEx, creates new network."""
if not self.assembled_stmts:
self.run_assembly()
cxa = CxAssembler(self.assembled_stmts, network_name=self.name)
cxa.make_model()
model_uuid = cxa.upload_model()
self.ndex_network = model_uuid
return model_uuid
def upload_ndex_network(kinase, stmts):
network_set_id = '9d9d4f66-e3da-11eb-b666-0ac135e8bacf'
name = '%s INDRA network' % kinase
cxa = CxAssembler(stmts, name)
cxa.make_model()
add_semantic_hub_layout(cxa.cx, kinase)
network_id = cxa.upload_model(private=False)
# Style setting already done as part of upload
# ndex_client.set_style(network_id)
ndex_client.add_to_network_set(network_id, network_set_id)
return network_id
def share_model_ndex():
"""Upload the model to NDEX"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_str = body.get('stmts')
stmts_json = json.loads(stmts_str)
stmts = stmts_from_json(stmts_json["statements"])
ca = CxAssembler(stmts)
for n, v in body.items():
ca.cx['networkAttributes'].append({'n': n, 'v': v, 'd': 'string'})
ca.make_model()
network_id = ca.upload_model(private=False)
return {'network_id': network_id}
def share_model_ndex():
"""Upload the model to NDEX"""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_str = body.get('stmts')
stmts_json = json.loads(stmts_str)
stmts = stmts_from_json(stmts_json["statements"])
ca = CxAssembler(stmts)
for n, v in body.items():
ca.cx['networkAttributes'].append({'n': n, 'v': v, 'd': 'string'})
ca.make_model()
network_id = ca.upload_model(private=False)
return {'network_id': network_id}
def create_upload_model(model_name, full_name, indra_stmts, ndex_id=None):
"""Make and upload an EMMAA model from a list of INDRA Statements.
Parameters
----------
short_name : str
Short name of the model to use on S3.
full_name : str
Human-readable model name to use in EMMAA dashboard.
indra_stmts : list of indra.statement
INDRA Statements to be used to populate the EMMAA model.
ndex_id : str
UUID of the network corresponding to the model on NDex. If provided,
the NDex network will be updated with the latest model content.
If None (default), a new network will be created and the UUID stored
in the model config files on S3.
"""
emmaa_stmts = to_emmaa_stmts(indra_stmts, datetime.datetime.now(), [])
# Get updated CX content for the INDRA Statements
cxa = CxAssembler(indra_stmts)
cx_str = cxa.make_model()
# If we don't have an NDex ID, create network and upload to Ndex
if ndex_id is None:
ndex_id = cxa.upload_model(private=False)
print(f'NDex ID for {model_name} is {ndex_id}.')
# If the NDEx ID is provided, update the existing network
else:
ndex_client.update_network(cx_str, ndex_id)
# Create the config dictionary
config_dict = {'ndex': {'network': ndex_id}, 'search_terms': []}
# Create EMMAA model
emmaa_model = EmmaaModel(model_name, config_dict)
emmaa_model.add_statements(emmaa_stmts)
# Upload model to S3 with config as YAML and JSON
emmaa_model.save_to_s3()
s3_client = boto3.client('s3')
config_json = json.dumps(config_dict)
s3_client.put_object(Body=config_json.encode('utf8'),
Key='models/%s/config.json' % model_name,
Bucket='emmaa')
config_json = json.dumps(config_dict)
s3_client.put_object(Body=config_json.encode('utf8'),
Key='models/%s/config.json' % model_name,
Bucket='emmaa')
def create_upload_model(model_name, indra_stmts, config_file):
"""Make and upload an EMMAA model from a list of INDRA Statements.
Parameters
----------
model_name : str
Name of the model to use on S3.
indra_stmts : list of indra.statement
INDRA Statements to be used to populate the EMMAA model.
config_file : str
Path to the local config.json file.
"""
emmaa_stmts = to_emmaa_stmts(indra_stmts, datetime.datetime.now(), [],
{'internal': True})
# Load config information
with open(config_file, 'rt') as f:
config_json = json.load(f)
# If there is no ndex entry in the config, create a new network and update
# the config file with the NDex network ID
if 'ndex' not in config_json:
cxa = CxAssembler(indra_stmts)
cx_str = cxa.make_model()
ndex_id = cxa.upload_model(private=False)
print(f'NDex ID for {model_name} is {ndex_id}.')
config_json['ndex'] = {'network': ndex_id}
updated_config_file = f'{config_file}.updated'
with open(updated_config_file, 'wt') as f:
json.dump(config_json, f, indent=2)
# If the NDEx ID is provided we don't need to update the existing network
# because this will occur as part of the model assembly/update procedure
# on EMMAA itself.
# Create the config dictionary
# Create EMMAA model
emmaa_model = EmmaaModel(model_name, config_json)
emmaa_model.add_statements(emmaa_stmts)
# Upload model to S3
emmaa_model.save_to_s3()
# Upload config JSON
s3_client = boto3.client('s3')
save_config_to_s3(model_name, config_json)
def post(self):
"""Upload the model to NDEX.
Parameters
----------
statements : list[indra.statements.Statement.to_json()]
A list of INDRA Statements to assemble.
Returns
-------
network_id : str
ID of uploaded NDEx network.
"""
args = request.json
stmts_json = args.get('statements')
stmts = stmts_from_json(stmts_json)
ca = CxAssembler(stmts)
for n, v in args.items():
ca.cx['networkAttributes'].append({'n': n, 'v': v, 'd': 'string'})
ca.make_model()
network_id = ca.upload_model(private=False)
return {'network_id': network_id}
import sys
import pickle
from indra.databases import ndex_client
from indra.assemblers.cx import CxAssembler
from indra.assemblers.cx.hub_layout import add_semantic_hub_layout
if __name__ == '__main__':
kinase = sys.argv[1]
with open('dark_kinase_statements_assembled.pkl', 'rb') as fh:
stmts = pickle.load(fh)
cxa = CxAssembler(stmts[kinase])
cxa.make_model()
add_semantic_hub_layout(cxa.cx, kinase)
model_id = cxa.upload_model()
ndex_client.set_style(model_id)
