def __init__(self, N, width, height, grid=10, seed=None, viewer=False):
"""Initialize the environment methods."""
if seed is None:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed=None)
else:
super(RandomWalkSwarmEnvironmentModel, self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.viewer = viewer
self.schedule = SimultaneousActivation(self)
self.hub = Hub(id=2, location=(0, 0), radius=5)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.site = Sites(id=2, location=(25, 25), radius=5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.agents = []
for i in range(self.num_agents):
a = SwarmAgentRandomWalk(i, self)
self.schedule.add(a)
x = 0
y = 0
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agents.append(a)
if self.viewer:
self.ui = UI((width, height), [self.hub], self.agents, [self.site])
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.foods = []
for site in self.render.objects['sites']:
self.site = site # self.render.objects['sites'][0]
for i in range(self.num_agents):
f = Food(
i, location=self.site.location,
radius=self.site.radius)
f.agent_name = None
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
except KeyError:
pass
if self.viewer:
self.ui = UI(
(self.width, self.height), [self.hub], self.agents,
[self.site], food=self.foods)
class SingleCarryDropReturnSwarmEnvironmentModel(Model):
"""A environment to model swarms."""
def __init__(self, N, width, height, grid=10, seed=None, viewer=False):
if seed is None:
super(SingleCarryDropReturnSwarmEnvironmentModel,
self).__init__(seed=None)
else:
super(SingleCarryDropReturnSwarmEnvironmentModel,
self).__init__(seed)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.viewer = viewer
self.schedule = SimultaneousActivation(self)
self.hub = Hub(id=2, location=(0, 0), radius=5)
self.grid.add_object_to_grid(self.hub.location, self.hub)
self.site = Sites(id=3, location=(-35, -5), radius=5)
self.grid.add_object_to_grid(self.site.location, self.site)
self.agents = []
self.foods = []
for i in range(self.num_agents):
f = Food(i, location=(-35, -5), radius=5)
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
for i in range(self.num_agents):
# a = SwarmAgentRandomSingleCarryDropReturn(i, self)
a = SwarmAgentHandCodedForaging(i, self)
self.schedule.add(a)
x = -45
y = -45
a.location = (x, y)
a.direction = -2.3561944901923448
self.grid.add_object_to_grid((x, y), a)
self.agents.append(a)
if self.viewer:
self.ui = UI((width, height), [self.hub],
self.agents, [self.site],
food=self.foods)
def step(self):
self.schedule.step()
if self.viewer:
self.ui.step()
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = ('/home/aadeshnpn/Documents/BYU/hcmi/swarm/' +
'results/1550156973273193-5000ValidateSForge/')
jname = pfileloc + '1550156973273193-4999.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
# print (len(phenotypes))
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [env.site],
food=env.foods)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
print('foraging percent', env.foraging_percent())
def main():
"""Block main."""
iteration = 9000
# Create a test environment to visualize
env = ViewerModel(50, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/cooperative_transport/1538447335350COT/'
jname = pfileloc + '1538447335350.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
# print (len(phenotypes))
env.create_agents(phenotypes=phenotypes[30:90])
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [env.site],
food=env.foods)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
print('foraging percent', env.foraging_percent())
# print('Execution done')
# Find if food has been deposited in the hub
grid = env.grid
neighbours = grid.get_neighborhood(env.hub.location, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 20, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/thesis/sf/'
jname = pfileloc + '1538473090382007.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents,
env.sites,
food=env.foods,
debris=[])
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
grid = env.grid
neighbours = grid.get_neighborhood(env.hub.location, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
for food in food_objects:
print('food', food.id, food.location)
"""
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/nest_maint/1539014820252NestM/'
jname = pfileloc + '1539014820252.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
print(len(phenotypes))
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [],
food=[],
debris=env.debris)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
print('Cleaning Percentage', env.foraging_percent())
print(len(env.debris_cleaned()))
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.debris = []
for i in range(self.num_agents):
dx, dy = self.random.randint(1, 10, 2)
dx = self.hub.location[0] + dx
dy = self.hub.location[1] + dy
d = Debris(i, location=(dx, dy), radius=5)
d.agent_name = None
self.grid.add_object_to_grid(d.location, d)
self.debris.append(d)
except KeyError:
pass
# Create a place for the agents to drop the derbis
try:
self.obstacles = []
for i in range(1):
dx, dy = self.random.randint(5, 10, 2)
dx = self.hub.location[0] + 25 + dx
dy = self.hub.location[1] + 25 + dy
o = Obstacles(id=i, location=(dx, dy), radius=10)
self.grid.add_object_to_grid(o.location, o)
self.obstacles.append(o)
except AttributeError:
pass
if self.viewer:
self.ui = UI((self.width, self.height), [self.hub],
self.agents, [],
food=[],
derbis=self.debris)
def create_agents(self, random_init=False, phenotypes=None):
"""Initialize agents in the environment."""
# Variable to tell how many agents will have the same phenotype
bound = np.ceil((self.num_agents * 1.0) / len(phenotypes))
j = 0
# Create agents
for i in range(self.num_agents):
# print (i, j, self.xmlstrings[j])
a = ExecutingAgent(i, self, xmlstring=phenotypes[j])
self.schedule.add(a)
# Add the hub to agents memory
a.shared_content['Hub'] = {self.hub}
# Initialize the BT. Since the agents are normal agents just
# use the phenotype
a.construct_bt()
if random_init:
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2,
self.grid.width / 2)
y = self.random.randint(-self.grid.height / 2,
self.grid.height / 2)
else:
try:
x, y = self.hub.location
except AttributeError:
x, y = 0, 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
if (i + 1) % bound == 0:
j += 1
if self.viewer:
self.ui = UI((100, 100), [self.hub],
self.agents,
self.site,
food=self.foods)
class SimModel(Model):
"""A environemnt to model swarms."""
def __init__(
self, N, width, height, grid=10, iter=100000,
xmlstrings=None, seed=None, viewer=False, pname=None,
expname='MSFSimulation', agent='SimAgent'):
"""Initialize the attributes."""
if seed is None:
super(SimModel, self).__init__(seed=None)
else:
super(SimModel, self).__init__(seed)
self.runid = datetime.datetime.now().strftime(
"%s") + str(self.random.randint(1, 1000, 1)[0])
if pname is None:
self.pname = os.getcwd() + '/' + self.runid + expname
else:
self.pname = pname + '/' + self.runid + expname
self.width = width
self.height = height
self.stepcnt = 1
self.iter = iter
self.xmlstrings = xmlstrings
self.viewer = viewer
# Create db connection
connect = Connect('swarm', 'swarm', 'swarm', 'localhost')
self.connect = connect.tns_connect()
# Fill out the experiment table
self.experiment = Experiment(
self.connect, self.runid, N, seed, expname,
iter, width, height, grid, phenotype=xmlstrings[0])
self.experiment.insert_experiment_simulation()
self.sn = self.experiment.sn
# Create a folder to store results
os.mkdir(self.pname)
self.num_agents = N
self.grid = Grid(width, height, grid)
self.schedule = SimultaneousActivation(self)
self.agents = []
bound = np.ceil((self.num_agents * 1.0) / len(self.xmlstrings))
j = 0
# Create agents
for i in range(self.num_agents):
# print (i, j, self.xmlstrings[j])
a = eval(agent)(i, self, xmlstring=self.xmlstrings[j])
self.schedule.add(a)
# Add the agent to a random grid cell
# x = self.random.randint(
# -self.grid.width / 2, self.grid.width / 2)
x = 0
# y = self.random.randint(
# -self.grid.height / 2, self.grid.height / 2)
y = 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
if (i + 1) % bound == 0:
j += 1
def create_environment_object(self, jsondata, obj):
"""Create env from jsondata."""
name = obj.__name__.lower()
temp_list = []
i = 0
for json_object in jsondata[name]:
location = (json_object["x"], json_object["y"])
if "q_value" in json_object:
temp_obj = obj(
i, location, json_object["radius"], q_value=json_object[
"q_value"])
else:
temp_obj = obj(i, location, json_object["radius"])
self.grid.add_object_to_grid(location, temp_obj)
temp_list.append(temp_obj)
i += 1
return temp_list
def build_environment_from_json(self):
"""Build env from jsondata."""
jsondata = JsonData.load_json_file(filename)
# Create a instance of JsonData to store object that
# needs to be sent to UI
self.render = JsonData()
self.render.objects = {}
for name in jsondata.keys():
obj = eval(name.capitalize())
self.render.objects[name] = self.create_environment_object(
jsondata, obj)
self.hub = self.render.objects['hub'][0]
try:
self.foods = []
for site in self.render.objects['sites']:
self.site = site # self.render.objects['sites'][0]
for i in range(self.num_agents):
f = Food(
i, location=self.site.location,
radius=self.site.radius)
f.agent_name = None
self.grid.add_object_to_grid(f.location, f)
self.foods.append(f)
except KeyError:
pass
if self.viewer:
self.ui = UI(
(self.width, self.height), [self.hub], self.agents,
[self.site], food=self.foods)
def step(self):
"""Step through the environment."""
# Gather info from all the agents
# self.gather_info()
# Next step
self.schedule.step()
# Increment the step count
self.stepcnt += 1
if self.viewer:
self.ui.step()
def find_higest_performer(self):
"""Find the best agent."""
fitness = self.agents[0].individual[0].fitness
fittest = self.agents[0]
for agent in self.agents:
if agent.individual[0].fitness > fitness:
fittest = agent
return fittest
def find_higest_food_collector(self):
"""Find the best agent to collect food."""
fitness = self.agents[0].food_collected
fittest = self.agents[0]
for agent in self.agents:
if agent.food_collected > fitness:
fittest = agent
return fittest
def detect_food_moved(self):
"""Detect food moved."""
grid = self.grid
food_loc = self.site.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
# print (food_objects)
return food_objects
def food_in_hub(self):
"""Find amount of food in hub."""
grid = self.grid
food_loc = self.hub.location
neighbours = grid.get_neighborhood(food_loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return len(food_objects)
def food_in_loc(self, loc):
"""Find amount of food in hub."""
grid = self.grid
neighbours = grid.get_neighborhood(loc, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
return food_objects
class ViewerModel(ForagingModel):
"""A environemnt to test swarm behavior performance."""
def __init__(self,
N,
width,
height,
grid=10,
iter=100000,
seed=None,
name="TestSForge",
viewer=True):
"""Initialize the attributes."""
super(ViewerModel, self).__init__(N, width, height, grid, iter, seed,
name, viewer)
def create_agents(self, random_init=False, phenotypes=None):
"""Initialize agents in the environment."""
# Variable to tell how many agents will have the same phenotype
bound = np.ceil((self.num_agents * 1.0) / len(phenotypes))
j = 0
# Create agents
for i in range(self.num_agents):
# print (i, j, self.xmlstrings[j])
a = ExecutingAgent(i, self, xmlstring=phenotypes[j])
self.schedule.add(a)
# Add the hub to agents memory
a.shared_content['Hub'] = {self.hub}
# Initialize the BT. Since the agents are normal agents just
# use the phenotype
a.construct_bt()
if random_init:
# Add the agent to a random grid cell
x = self.random.randint(-self.grid.width / 2,
self.grid.width / 2)
y = self.random.randint(-self.grid.height / 2,
self.grid.height / 2)
else:
try:
x, y = self.hub.location
except AttributeError:
x, y = 0, 0
a.location = (x, y)
self.grid.add_object_to_grid((x, y), a)
a.operation_threshold = 2 # self.num_agents // 10
self.agents.append(a)
if (i + 1) % bound == 0:
j += 1
if self.viewer:
self.ui = UI((100, 100), [self.hub],
self.agents,
self.site,
food=self.foods)
def step(self):
"""Step through the environment."""
# Next step
self.schedule.step()
# Increment the step count
self.stepcnt += 1
# If viewer required do take a step in UI
if self.viewer:
self.ui.step()