def run():
(prog_file, log_file, prop_file,
results_file) = utils.gen_file_names(MODEL_NM)
# We store basic parameters in a "property" file; this allows us to save
# multiple parameter sets, which is important in simulation work.
# We can read these in from file or set them here.
pa = utils.read_props(MODEL_NM)
if pa is None:
pa = props.PropArgs(MODEL_NM, logfile=log_file, props=None)
# Now we create a minimal environment for our agents to act within:
env = ge.GridEnv("Obstacle env",
pa.get("grid_height"),
pa.get("grid_width"),
torus=False,
model_nm=MODEL_NM,
postact=True,
props=pa)
# Now we loop creating multiple agents with numbered names
# based on the loop variable:
for i in range(pa.get("num_moving_agents")):
env.add_agent(
om.ObstacleAgent(name="agent" + str(i),
goal="Avoiding obstacles!",
max_move=4,
tolerance=2))
for i in range(pa.get("num_obstacles")):
env.add_agent(om.Obstacle(name="obstacle" + str(i)))
utils.run_model(env, prog_file, results_file)
def run():
(prog_file, log_file, prop_file,
results_file) = utils.gen_file_names(MODEL_NM)
# We store basic parameters in a "property" file; this allows us to save
# multiple parameter sets, which is important in simulation work.
# We can read these in from file or set them here.
pa = utils.read_props(MODEL_NM)
if pa is None:
pa = props.PropArgs(MODEL_NM, logfile=log_file, props=None)
# Now we create a minimal environment for our agents to act within:
env = itpm.TwoPopEnv("Test grid env",
pa.get("grid_width"),
pa.get("grid_height"),
torus=False,
model_nm=MODEL_NM,
postact=True,
props=pa)
# Now we loop creating multiple agents with numbered names
# based on the loop variable:
# Get Number of Followers
for i in range(pa.get("num_agents")):
env.add_agent(
tpm.TestFollower(name="Follower" + str(i),
goal="taking up a grid space!",
max_move=1))
# Get Number of Hipsters
for i in range(pa.get("num_agents")):
env.add_agent(
tpm.TestLeader(name="Leader" + str(i),
goal="taking up a grid space!",
max_move=1))
utils.run_model(env, prog_file, results_file)
def run():
(prog_file, log_file, prop_file,
results_file) = utils.gen_file_names(MODEL_NM)
# We store menu parameters in a
# "property" file; this allows us to save
# multiple parameter sets, which is important in simulation work.
# We can read these in from file or set them here.
pa = utils.read_props(MODEL_NM)
if pa is None:
pa = props.PropArgs(MODEL_NM, logfile=log_file, props=None)
# Now we create a minimal environment for our agents to act within:
env = mm.MenuEnv(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")):
env.add_agent(
mm.MenuAgent(name="agent" + str(i),
goal="testing our menu capabilities!"))
utils.run_model(env, prog_file, results_file)
"""
import logging
import indra.node as node
import indra.prop_args as props
import predprey_model as ppm
MODEL_NM = "predprey_model"
PROG_NM = MODEL_NM + ".py"
LOG_FILE = MODEL_NM + ".txt"
read_props = False
if read_props:
pa = props.PropArgs.read_props(MODEL_NM, "predprey.props")
else:
pa = props.PropArgs(MODEL_NM, logfile=LOG_FILE, props=None)
pa.set("model", MODEL_NM)
pa.set("num_foxes", 16)
pa.set("num_rabbits", 48)
pa.set("num_zombies", 6)
pa.set("fox_repro_age", 11)
pa.set("rabbit_repro_age", 3.6)
pa.set("fox_life_force", 32.8)
pa.set("rabbit_life_force", 22.0)
pa.set("fox_max_move", 12.8)
pa.set("rabbit_max_move", 10.2)
pa.set("fox_decay_rate", 5.8)
pa.set("rabbit_decay_rate", 3.98)
pa.set("fox_max_detect", 40.0)
env = ppm.PredPreyEnv("meadow", 50.0, 50.0)
import indra.utils as utils
import indra.prop_args as props
import indra.two_pop_markov as itpm
import two_pop_markov_model as tpm
# set up some file names:
MODEL_NM = "two_pop_markov_model"
(prog_file, log_file, prop_file, results_file) = utils.gen_file_names(MODEL_NM)
# We store basic parameters in a "property" file; this allows us to save
# multiple parameter sets, which is important in simulation work.
# We can read these in from file or set them here.
pa = utils.read_props(MODEL_NM)
if pa is None:
pa = props.PropArgs(MODEL_NM, logfile=log_file, props=None)
utils.get_grid_dims(pa, 6)
utils.get_agent_num(pa, "num_agents", "agents", 16)
# Now we create a minimal environment for our agents to act within:
env = itpm.TwoPopEnv(
"Test two pop Markov env",
pa.get("grid_width"),
pa.get("grid_height"),
preact=True,
postact=True,
trans_str="0.5 0.5; 0.5 0.5",
model_nm=MODEL_NM,
torus=False,
)