def run(prop_dict=None):
(prog_file, log_file, prop_file,
results_file) = utils.gen_file_names(MODEL_NM)
# now we run some tests:
# test prop_args as an iterable:
for prop, val in pa.items():
print(prop + ": " + str(val))
# test that props work as a dictionary:
if "num_agents" in pa:
print("In is working!")
# test what pa["num_agents"] is:
num_agents = pa["num_agents"]
print("num_agents = " + str(num_agents))
# make sure we can get props length:
print("Props length = " + str(len(pa)))
# Now we create a minimal environment for our agents to act within:
env = bm.BasicEnv(model_nm=MODEL_NM, props=pa)
# Now we loop creating multiple agents
# with numbered names based on the loop variable:
for i in range(num_agents):
env.add_agent(bm.BasicAgent(name="agent" + str(i), goal="acting up!"))
return utils.run_model(env, prog_file, results_file)
def __init__(self, methodName, prop_file="models/basic_for_test.props"):
super().__init__(methodName=methodName)
pa = props.read_props(MODEL_NM, prop_file)
# if result:
# pa.overwrite_props_from_dict(result.props)
# else:
# print("Oh-oh, no props to read in!")
# exit(1)
# Now we create a minimal environment for our agents to act within:
self.env = bm.BasicEnv(model_nm=MODEL_NM, props=pa)
# Now we loop creating multiple agents
# with numbered names based on the loop variable:
for i in range(pa.props.get("num_agents").val):
self.env.add_agent(
bm.BasicAgent(name="agent" + str(i), goal="acting up!"))
self.env.add_agent(
bm.BasicAgent(name="agent for tracking", goal="acting up!"))
def __init__(self, methodName, prop_file="models/basic.props"):
super().__init__(methodName=methodName)
result = props.read_props(MODEL_NM, prop_file)
if result:
pa.add_props(result.props)
else:
print("Oh-oh, no props to read in!")
exit(1)
# Now we create a minimal environment for our agents to act within:
self.env = bm.BasicEnv(model_nm=MODEL_NM, props=pa)
# Now we loop creating multiple agents
# with numbered names based on the loop variable:
for i in range(pa.get("num_agents")):
self.env.add_agent(
bm.BasicAgent(name="agent" + str(i), goal="acting up!"))
self.env.add_agent(
bm.BasicAgent(name="agent for tracking", goal="acting up!"))
self.session_id = random.randint(1, 10)
def run(prop_dict=None):
# We need to create props before we import the basic model,
# as our choice of display_method is dependent on the user_type.
pa = props.PropArgs.create_props(MODEL_NM, prop_dict)
import models.basic as bm
import indra.utils as utils
(prog_file, log_file, prop_file,
results_file) = utils.gen_file_names(MODEL_NM)
# test prop_args as an iterable:
for prop, val in pa.items():
print(prop + ": " + str(val))
# test that props work as a dictionary:
if "num_agents" in pa:
print("In is working!")
# test what pa["num_agents"] is:
num_agents = pa["num_agents"]
print("num_agents = " + str(num_agents))
# make sure we can get props length:
print("Props length = " + str(len(pa)))
# Now we create a minimal environment for our agents to act within:
env = bm.BasicEnv(model_nm=MODEL_NM, props=pa)
# Now we loop creating multiple agents
# with numbered names based on the loop variable:
for i in range(num_agents):
env.add_agent(bm.BasicAgent(name="agent" + str(i),
goal="acting up!"))
return utils.run_model(env, prog_file, results_file)