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 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 = 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(prop_dict=None):
(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.
global pa
if prop_dict is not None:
prop_dict[props.PERIODS] = 0
pa.add_props(prop_dict)
else:
result = utils.read_props(MODEL_NM)
if result:
pa.add_props(result.props)
else:
utils.ask_for_params(pa)
if pa["user_type"] == props.WEB:
pa["base_dir"] = os.environ['base_dir']
# Now we create a minimal environment for our agents to act within:
env = wm.WolframEnv("Wolfram Env",
pa["grid_width"],
pa["grid_height"],
model_nm=MODEL_NM,
props=pa,
rule_id=pa["rule_id"])
# This env adds agents itself.
return 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)
A script to test our two pop markov capabilities.
"""
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,
A script to test our spatial capabilities.
"""
import indra.utils as utils
import indra.prop_args as props
import indra.spatial_env as se
import models.spatial as sm
# set up some file names:
MODEL_NM = "spatial_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)
# Now we create a minimal environment for our agents to act within:
env = se.SpatialEnv("Test spatial env", 100.0, 100.0,
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(
sm.TestSpatialAgent(name="agent" + str(i),
goal="moving around aimlessly!"))
utils.run_model(env, prog_file, results_file)