def __init__(self, methodName, prop_file="models/fashion_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = fm.Society("Society",
self.pa["grid_height"],
self.pa["grid_width"],
torus=False,
model_nm=MODEL_NM,
props=self.pa)
for i in range(self.pa["num_followers"]):
self.env.add_agent(
fm.Follower("follower" + str(i), "Looking like hipsters",
self.pa["fmax_move"], self.pa["variability"]))
for i in range(self.pa["num_hipsters"]):
self.env.add_agent(
fm.Hipster("hipster" + str(i), "Looking trendy",
self.pa["hmax_move"], self.pa["variability"]))
self.env.add_agent(
fm.Hipster("hipster for tracking", "Looking trendy",
self.pa["hmax_move"], self.pa["variability"]))
def __init__(self,
methodName,
prop_file="models/forestfire_for_test.props"):
super().__init__(methodName=methodName)
pa = props.read_props(MODEL_NM, prop_file)
grid_x = pa["grid_width"]
grid_y = pa["grid_height"]
density = pa["density"]
# Now we create a forest environment for our agents to act within:
self.env = fm.ForestEnv(grid_x,
grid_y,
density,
pa["strike_freq"],
pa["regen_period"],
model_nm=MODEL_NM,
torus=False,
props=pa)
num_agents = int(grid_x * grid_y * density)
for i in range(num_agents):
self.env.add_agent(fm.Tree(name="tree" + str(i)))
self.env.add_agent(fm.Tree(name="tree for tracking"))
def __init__(self, methodName, prop_file="models/fashion_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = fm.Society("Society",
self.pa["grid_height"],
self.pa["grid_width"],
torus=False,
model_nm=MODEL_NM,
props=self.pa)
for i in range(self.pa["num_followers"]):
self.env.add_agent(fm.Follower("follower" + str(i),
"Looking like hipsters",
self.pa["fmax_move"],
self.pa["variability"]))
for i in range(self.pa["num_hipsters"]):
self.env.add_agent(fm.Hipster("hipster" + str(i), "Looking trendy",
self.pa["hmax_move"],
self.pa["variability"]))
self.env.add_agent(fm.Hipster("hipster for tracking", "Looking trendy",
self.pa["hmax_move"],
self.pa["variability"]))
def __init__(self, methodName, prop_file="models/forestfire_for_test.props"):
super().__init__(methodName=methodName)
pa = props.read_props(MODEL_NM, prop_file)
grid_x = pa["grid_width"]
grid_y = pa["grid_height"]
density = pa["density"]
# Now we create a forest environment for our agents to act within:
self.env = fm.ForestEnv(grid_x,
grid_y,
density,
pa["strike_freq"],
pa["regen_period"],
model_nm=MODEL_NM,
torus=False,
props=pa)
num_agents = int(grid_x * grid_y * density)
for i in range(num_agents):
self.env.add_agent(fm.Tree(name="tree" + str(i)))
self.env.add_agent(fm.Tree(name="tree for tracking"))
def __init__(self, methodName, prop_file="models/wolfsheep_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = wsm.Meadow("Meadow",
self.pa["grid_width"],
self.pa["grid_height"],
model_nm=MODEL_NM,
preact=True,
postact=True,
props=self.pa)
for i in range(self.pa["num_wolves"]):
self.env.add_agent(wsm.Wolf("wolf" + str(i), "Eating sheep",
self.pa["wolf_repro"],
self.pa["wolf_lforce"],
rand_age=True))
for i in range(self.pa["num_sheep"]):
self.env.add_agent(wsm.Sheep("sheep" + str(i), "Reproducing",
self.pa["sheep_repro"],
self.pa["sheep_lforce"],
rand_age=True))
self.env.add_agent(wsm.Sheep("sheep for tracking", "Reproducing",
self.pa["sheep_repro"],
self.pa["sheep_lforce"],
rand_age=True))
def __init__(self, methodName, prop_file="models/party_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
if self.pa["user_type"] == props.WEB:
self.pa["base_dir"] = os.environ['base_dir']
# Now we create an environment for our agents to act within:
self.env = pm.PartyEnv("A cocktail party",
self.pa["grid_width"],
self.pa["grid_height"],
model_nm=self.pa.model_nm,
props=self.pa)
for i in range(self.pa["num_men"]):
self.env.add_agent(
pm.Man(name="Man" + str(i),
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
for i in range(self.pa["num_women"]):
self.env.add_agent(
pm.Woman(name="Woman" + str(i),
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
self.env.add_agent(
pm.Woman(name="Woman for tracking",
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
def __init__(self, methodName, prop_file="models/grid_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
if self.pa["user_type"] == props.WEB:
self.pa["base_dir"] = os.environ["base_dir"]
# Now we create a minimal environment for our agents to act within:
self.env = ge.GridEnv("Test grid env",
self.pa["grid_width"],
self.pa["grid_height"],
torus=False,
model_nm=MODEL_NM,
preact=True,
postact=True,
props=self.pa)
for i in range(self.pa["num_agents"]):
self.env.add_agent(gm.TestGridAgent(name="agent" + str(i),
goal="taking up a grid space!"))
self.env.add_agent(gm.TestGridAgent(name="agent for tracking",
goal="taking up a grid space!"))
def __init__(self, methodName, prop_file="models/party_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
if self.pa["user_type"] == props.WEB:
self.pa["base_dir"] = os.environ['base_dir']
# Now we create an environment for our agents to act within:
self.env = pm.PartyEnv("A cocktail party",
self.pa["grid_width"],
self.pa["grid_height"],
model_nm=self.pa.model_nm,
props=self.pa)
for i in range(self.pa["num_men"]):
self.env.add_agent(pm.Man(name="Man" + str(i),
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
for i in range(self.pa["num_women"]):
self.env.add_agent(pm.Woman(name="Woman" + str(i),
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
self.env.add_agent(pm.Woman(name="Woman for tracking",
goal="A good party.",
tol=0.5,
max_detect=self.pa['max_detect']))
def __init__(self, methodName, prop_file="models/fmarket_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = fm.FinMarket("Financial Market",
self.pa["grid_height"],
self.pa["grid_width"],
torus=False,
model_nm=MODEL_NM,
props=self.pa)
for i in range(self.pa["num_followers"]):
self.env.add_agent(fm.ChartFollower("follower" + str(i),
"Following trend",
self.pa["fmax_move"],
self.pa["variability"]))
for i in range(self.pa["num_vinvestors"]):
self.env.add_agent(fm.ValueInvestor("value_inv" + str(i), "Buying value",
self.pa["vmax_move"],
self.pa["variability"]))
self.env.add_agent(fm.ValueInvestor("value_inv for tracking", "Buying value",
self.pa["vmax_move"],
self.pa["variability"]))
def read_props(model_nm):
"""
A prop file to read must be our first arg, if it exists.
"""
if len(sys.argv) > 1:
poss_props = sys.argv[1]
if not poss_props.startswith('-'): # not a property but a prop file
return prop_args.read_props(model_nm, poss_props)
return None
def __init__(self, methodName, prop_file="models/coop_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = cm.CoopEnv(model_nm=MODEL_NM, props=self.pa)
for i in range(self.pa["num_agents"]):
self.env.add_agent(cm.CoopAgent('agent' + str(i), 5, 0))
self.env.add_agent(cm.CoopAgent('agent for tracking', 5, 0))
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_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/fmarket_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = fm.FinMarket("Financial Market",
self.pa["grid_height"],
self.pa["grid_width"],
torus=False,
model_nm=MODEL_NM,
props=self.pa)
for i in range(self.pa["num_followers"]):
self.env.add_agent(
fm.ChartFollower("follower" + str(i), "Following trend",
self.pa["fmax_move"], self.pa["variability"]))
for i in range(self.pa["num_vinvestors"]):
self.env.add_agent(
fm.ValueInvestor("value_inv" + str(i), "Buying value",
self.pa["vmax_move"], self.pa["variability"]))
self.env.add_agent(
fm.ValueInvestor("value_inv for tracking", "Buying value",
self.pa["vmax_move"], self.pa["variability"]))
def __init__(self, methodName, prop_file="models/hiv_for_test.props"):
super().__init__(methodName=methodName)
self.pa = props.read_props(MODEL_NM, prop_file)
# Now we create a forest environment for our agents to act within:
self.env = hiv.People("People", self.pa["grid_width"],
self.pa["grid_height"], model_nm=MODEL_NM,
preact=True, postact=True, props=self.pa)
self.max_ppl = self.pa["grid_width"] * self.pa["grid_height"]
if self.pa["ini_ppl"] > self.max_ppl:
self.ini_ppl = self.max_ppl
else:
self.ini_ppl = self.pa["ini_ppl"]
self.ini_infected_ppl = round(INI_INFECTED_PCT * self.ini_ppl)
self.ini_healthy_ppl = self.ini_ppl - self.ini_infected_ppl
self.coup_tend = numpy.random.normal(self.pa["avg_coup_tend"],
STD_COUP_TEND, self.ini_ppl)
self.test_freq = numpy.random.normal(self.pa["avg_test_freq"],
STD_TEST_FREQ, self.ini_ppl)
self.commitment = numpy.random.normal(self.pa["avg_commitment"],
STD_COMMITMENT, self.ini_ppl)
self.condom_use = numpy.random.normal(self.pa["avg_condom_use"],
STD_CONDOM_USE, self.ini_ppl)
for i in range(self.ini_ppl):
if self.coup_tend[i] < 0:
self.coup_tend[i] = 0
elif self.coup_tend[i] > 10:
self.coup_tend[i] = 10
if self.test_freq[i] < 0:
self.test_freq[i] = 0
elif self.test_freq[i] > 2:
self.test_freq[i] = 2
if self.commitment[i] < 1:
self.commitment[i] = 1
elif self.commitment[i] > 200:
self.commitment[i] = 200
if self.condom_use[i] < 0:
self.condom_use[i] = 0
elif self.condom_use[i] > 10:
self.condom_use[i] = 10
for i in range(self.ini_infected_ppl):
rand_inf_len = random.randint(0, hiv.SYMPTOMS_SHOW-1)
new_agent = hiv.Person(name="person" + str(i),
infected=True,
infection_length=rand_inf_len,
initiative=i,
coupling_tendency=self.coup_tend[i],
test_frequency=self.test_freq[i],
commitment=self.commitment[i],
condom_use=self.condom_use[i])
self.env.add_agent(new_agent)
for i in range(self.ini_healthy_ppl):
j = self.ini_infected_ppl+i
new_agent = hiv.Person(name="person"+str(j),
infected=False, infection_length=0,
initiative=j,
coupling_tendency=self.coup_tend[j],
test_frequency=self.test_freq[j],
commitment=self.commitment[j],
condom_use=self.condom_use[j])
self.env.add_agent(new_agent)