def test_prune_influence_map():
kin = Agent('Kin', db_refs={'HGNC': '1'})
phos = Agent('Phos', db_refs={'HGNC': '2'})
subs = Agent('Substrate', db_refs={'HGNC': '3'})
st1 = Phosphorylation(kin, subs)
st2 = Dephosphorylation(phos, subs)
pa = PysbAssembler()
pa.add_statements([st1, st2])
pa.make_model(policies='one_step')
mc = ModelChecker(pa.model, [st1])
mc.prune_influence_map()
im = mc.get_im()
assert len(im.nodes()) == 3
assert len(im.edges()) == 2
with open('korkut_pysb.pkl', 'wb') as f:
print("Pickling PySB model")
pickle.dump(pa.model, f)
"""
with open('korkut_pysb.pkl', 'rb') as f:
print("Unpickling PySB model")
model = pickle.load(f)
# Preprocess and assemble the pysb model
#model = assemble_pysb(combined_stmts, data_genes, '')
rerun = True
if rerun:
mc = ModelChecker(model, all_data_stmts, agent_obs)
mc.prune_influence_map()
# Iterate over each drug/ab statement subset
results = []
for drug_name, ab_dict in data_stmts.items():
agent_values = agent_data[drug_name]
for ab, stmt_list in ab_dict.items():
value = data_values[drug_name][ab]
# For each subset, check statements; if any of them checks
# out, we're good and can move on to the next group
print("-- Checking the effect of %s on %s --" % (drug_name, ab))
relation = 'positive' if value > 1 else 'negative'
path_found = 0
paths = []
for stmt in stmt_list:
print("Checking: %s" % stmt)
