def update(e_set_config, rebuild=False):
# Create a fresh data model
e_data = dm.EnrichmentData("e_sets")
# Load the specified e_set, if it exists
print "Updating e_set: " + e_set_config.name
e_set = e_data.add_e_set(e_set_config.name)
storage.ensure_e_set_dir(e_set_config.name)
e_set.load()
na = ndex_access.NdexAccess(e_set_config.ndex, e_set_config.account_name, e_set_config.password)
# Find the candidate networks
networks = na.find_networks(e_set_config)
print "Found " + str(len(networks)) + " networks"
# TODO: remove e_sets that are not found in the query
# Figure out which ones to update
networks_to_update = {}
if rebuild:
for network in networks:
network_id = network.get("externalId")
network_name = network.get("name")
networks_to_update[network_name] = network_id
else:
for network in networks:
network_id = network.get("externalId")
network_name = network.get("name")
modification_date = network.get("modificationTime")
id_set = e_set.get_id_set_by_network_id(network_id)
if id_set:
if not id_set.modificationDate == modification_date:
networks_to_update[network_name] = network_id
else:
networks_to_update[network_name] = network_id
print "Updating " + str(len(networks_to_update.keys())) + " networks"
# Do the updates
counter = 0
term_mapper = term2gene_mapper.Term2gene_mapper()
for network_name, network_id in networks_to_update.iteritems():
print network_name.encode("ascii", "ignore") + " : " + network_id.encode("ascii", "ignore")
node_table = na.get_nodes_from_cx(network_id)
term_mapper.add_network_nodes(node_table)
gene_symbols = get_genes_for_network(node_table, term_mapper)
id_set_dict = {
"name": network_name,
"ids": gene_symbols,
"network_id": network_id,
"ndex": e_set_config.ndex,
"e_set": e_set_config.name,
}
id_set = dm.IdentifierSet(id_set_dict)
e_set.add_id_set(id_set)
counter += 1
print str(counter) + " networks indexed."
# now that the updated e_set is ready to save, clear the old cached data
storage.remove_all_id_sets(e_set_config.name)
print "Saving e_set with " + str(len(e_set.get_id_set_names())) + " id_sets"
e_set.save()
else:
print "skipping run sif to cx"
#===================================
#===================================
# Generate similarity files
#===================================
#===================================
na = ndex_access.NdexAccess(upload_to_server, upload_to_username,
upload_to_password)
term_mapper = term2gene_mapper.Term2gene_mapper()
# Create a fresh data model
e_data = dm.EnrichmentData("e_sets")
e_set = e_data.add_e_set('pipeline')
storage.ensure_e_set_dir('pipeline')
counter = 0
if run_generate_gsea_files:
for network_name, network_id in networks_to_update.iteritems():
print str(network_id)
print network_name.encode(
'ascii', 'ignore') + " : " + network_id.encode('ascii', 'ignore')
node_table = na.get_nodes_from_cx(network_id)
term_mapper.add_network_nodes(node_table)
gene_symbols = get_genes_for_network(node_table, term_mapper)
if (len(gene_symbols) < 1):
print gene_symbols
id_set_dict = {
'name': network_name,
'ids': gene_symbols,
'network_id': network_id,
def save(self, alt_grp_path=None):
storage.ensure_e_set_dir(self.name)
for set_name, id_set in self.id_set_map.iteritems():
id_set.save(alt_grp_path)
def save(self):
storage.ensure_e_set_dir(self.name)
for set_name, id_set in self.id_set_map.iteritems():
id_set.save()
