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)