class TestSpaceNonToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, False, -30, -30, 100, 100)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_2 = (70, 20)
pos_3 = (-30, -20)
assert self.space.get_distance(pos_2, pos_3) == 107.70329614269008
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 0
def test_bounds(self):
'''
Test positions outside of boundary
'''
for i, pos in enumerate(OUTSIDE_POSITIONS):
a = MockAgent(len(self.agents) + i, None)
with self.assertRaises(Exception):
self.space.place_agent(a, pos)
a = self.agents[0]
for pos in OUTSIDE_POSITIONS:
assert self.space.out_of_bounds(pos)
with self.assertRaises(Exception):
self.space.move_agent(a, pos)
class TestSpaceToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, True, -30, -30, 100, 100)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_1 = (-30, -30)
pos_2 = (70, 20)
assert self.space.get_distance(pos_1, pos_2) == 0
pos_3 = (-30, -20)
assert self.space.get_distance(pos_1, pos_3) == 10
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 1
class TestSpaceToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, True, -30, -30)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_agent_matching(self):
'''
Ensure that the agents are all placed and indexed properly.
'''
for i, agent in self.space._index_to_agent.items():
assert agent.pos == tuple(self.space._agent_points[i, :])
assert i == self.space._agent_to_index[agent]
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_1 = (-30, -30)
pos_2 = (70, 20)
assert self.space.get_distance(pos_1, pos_2) == 0
pos_3 = (-30, -20)
assert self.space.get_distance(pos_1, pos_3) == 10
pos_4 = (20, -5)
pos_5 = (20, -15)
assert self.space.get_distance(pos_4, pos_5) == 10
pos_6 = (-30, -29)
pos_7 = (21, -5)
assert self.space.get_distance(pos_6, pos_7) == np.sqrt(49**2 + 24**2)
def test_heading(self):
pos_1 = (-30, -30)
pos_2 = (70, 20)
self.assertEqual((0, 0), self.space.get_heading(pos_1, pos_2))
pos_1 = (65, -25)
pos_2 = (-25, -25)
self.assertEqual((10, 0), self.space.get_heading(pos_1, pos_2))
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 1
def test_bounds(self):
'''
Test positions outside of boundary
'''
boundary_agents = []
for i, pos in enumerate(OUTSIDE_POSITIONS):
a = MockAgent(len(self.agents) + i, None)
boundary_agents.append(a)
self.space.place_agent(a, pos)
for a, pos in zip(boundary_agents, OUTSIDE_POSITIONS):
adj_pos = self.space.torus_adj(pos)
assert a.pos == adj_pos
a = self.agents[0]
for pos in OUTSIDE_POSITIONS:
assert self.space.out_of_bounds(pos)
self.space.move_agent(a, pos)
class TestSpaceNonToroidal(unittest.TestCase):
"""
Testing a toroidal continuous space.
"""
def setUp(self):
"""
Create a test space and populate with Mock Agents.
"""
self.space = ContinuousSpace(70, 20, False, -30, -30)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
"""
Ensure that the agents are all placed properly.
"""
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_agent_matching(self):
"""
Ensure that the agents are all placed and indexed properly.
"""
for i, agent in self.space._index_to_agent.items():
assert agent.pos == tuple(self.space._agent_points[i, :])
assert i == self.space._agent_to_index[agent]
def test_distance_calculations(self):
"""
Test toroidal distance calculations.
"""
pos_2 = (70, 20)
pos_3 = (-30, -20)
assert self.space.get_distance(pos_2, pos_3) == 107.70329614269008
def test_heading(self):
pos_1 = (-30, -30)
pos_2 = (70, 20)
self.assertEqual((100, 50), self.space.get_heading(pos_1, pos_2))
pos_1 = (65, -25)
pos_2 = (-25, -25)
self.assertEqual((-90, 0), self.space.get_heading(pos_1, pos_2))
def test_neighborhood_retrieval(self):
"""
Test neighborhood retrieval
"""
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 0
def test_bounds(self):
"""
Test positions outside of boundary
"""
for i, pos in enumerate(OUTSIDE_POSITIONS):
a = MockAgent(len(self.agents) + i, None)
with self.assertRaises(Exception):
self.space.place_agent(a, pos)
a = self.agents[0]
for pos in OUTSIDE_POSITIONS:
assert self.space.out_of_bounds(pos)
with self.assertRaises(Exception):
self.space.move_agent(a, pos)
class BoidModel(Model):
'''
Flocker model class. Handles agent creation, placement and scheduling.
'''
def __init__(self,
target,
predators=False,
mouse=True,
population=50,
width=100,
height=100,
speed=2,
vision=10,
separation=1,
collision_separation=30,
cohere=0.05,
separate=1.25,
match=0.4):
'''
Create a new Flockers model.
Args:
population: Number of Boids
width, height: Size of the space.
speed: How fast should the Boids move.
vision: How far around should each Boid look for its neighbors
separation: What's the minimum distance each Boid will attempt to
keep from any other
cohere, separate, match: factors for the relative importance of
the three drives. '''
self.target = target
self.predators = predators
self.mouse = mouse
self.population = population
self.vision = vision
self.speed = speed
self.separation = separation
self.collision_separation = collision_separation
self.schedule = RandomActivation(self)
self.space = ContinuousSpace(width, height, True)
self.factors = dict(cohere=cohere, separate=separate, match=match)
self.make_agents()
self.running = True
def update_target(self, target):
self.target = target
def make_agents(self):
'''
Create self.population agents, with random positions and starting headings.
'''
for i in range(50):
x = random.random() * self.space.x_max
y = random.random() * self.space.y_max
center_x = self.space.x_max / 2
center_y = self.space.y_max / 2
pos = np.array((x, y))
center = np.array((center_x, center_y))
velocity = np.random.random(2) * 2 - 1
velocity = np.zeros(2) + self.space.get_distance(pos, center)
velocity[0] *= self.target[0]
velocity[1] *= self.target[1]
boid = Boid(i, self, pos, self.speed, velocity, self.vision,
self.separation, self.collision_separation, "boid.png",
10, **self.factors)
self.space.place_agent(boid, pos)
self.schedule.add(boid)
for i in range(4):
x = random.random() * self.space.x_max
y = random.random() * self.space.y_max
pos = np.array((x, y))
obstacle = Obstacle(i + self.population, self, pos, 30)
obstacle2 = Obstacle(i + self.population + 5, self, pos, 4)
self.space.place_agent(obstacle, pos)
self.space.place_agent(obstacle2, pos)
self.schedule.add(obstacle)
self.schedule.add(obstacle2)
if self.predators:
x = random.random() * self.space.x_max
y = random.random() * self.space.y_max
pos = np.array((x, y))
velocity = np.random.random(2) * 2 - 1
predator = Predator(2003, self, pos, self.speed + 0.1, velocity,
self.vision + 5, 12, self.collision_separation,
"predator.png")
self.space.place_agent(predator, pos)
self.schedule.add(predator)
def step(self):
self.schedule.step()
class BoidFlockers(Model):
'''
A Mesa implementation of flocking agents inspired by https://doi.org/10.1109/IROS.2014.6943105.
'''
def __init__(self, formation, population, width, height, vision, min_dist,
flock_vel, accel_time, equi_dist, repulse_max, repulse_spring,
align_frict, align_slope, align_min, wall_decay, wall_frict,
form_shape, form_track, form_decay, wp_tolerance):
'''
Create a new Flockers model.
Args:
population: Number of agents.
width, height: Size of the space.
vision: How far around should each agents look for its neighbors.
min_dist: Minimum allowed distance between agents before a collision occurs. This is only used for statistics.
'''
# set parameters
self.population = population
self.space = ContinuousSpace(width, height, torus=False)
self.vision = vision
self.min_dist = min_dist
self.params = dict(formation=formation,
population=population,
flock_vel=flock_vel,
accel_time=accel_time,
equi_dist=equi_dist,
repulse_max=repulse_max,
repulse_spring=repulse_spring,
align_frict=align_frict,
align_slope=align_slope,
align_min=align_min,
wall_decay=wall_decay,
wall_frict=wall_frict,
form_shape=form_shape,
form_track=form_track,
form_decay=form_decay,
wp_tolerance=wp_tolerance)
# data collection for plots
self.datacollector = DataCollector(
model_reporters={
"Minimum Distance": minimum_distance,
"Maximum Distance": maximum_distance,
"Average Distance": average_distance,
"Collisions": collisions,
"Messags Distributed": messages_distributed,
"Messags Centralized": messages_centralized
})
# execute agents sequentially in a random order
self.schedule = RandomActivation(self)
# place agents
self.make_agents()
# pairwise distances
self.agent_distances()
# run model
self.running = True
# collect initial data sample
self.datacollector.collect(self)
def agent_distances(self):
'''
Compute the pairwise distances between all agents.
'''
self.density = [
self.space.get_distance(pair[0].pos, pair[1].pos)
for pair in it.combinations(self.schedule.agent_buffer(), 2)
]
def make_agents(self):
'''
Create self.population agents and place it at the center of the environment.
'''
s = np.floor(np.sqrt(self.population))
for i in range(self.population):
x = self.space.center[
0] - self.min_dist * s + 2 * self.min_dist * (i % s)
y = self.space.center[1] - self.min_dist * np.floor(
self.population / s) + 2 * self.min_dist * np.floor(i / s)
pos = np.array((x, y))
velocity = np.array([0, 1.0])
boid = Boid(i, self, pos, velocity, self.vision, **self.params)
self.space.place_agent(boid, pos)
self.schedule.add(boid)
def step(self):
'''
Execute one step of the simulation.
'''
self.schedule.step()
self.agent_distances()
self.datacollector.collect(self)
class TestSpaceToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, True, -30, -30, 100, 100)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_1 = (-30, -30)
pos_2 = (70, 20)
assert self.space.get_distance(pos_1, pos_2) == 0
pos_3 = (-30, -20)
assert self.space.get_distance(pos_1, pos_3) == 10
def test_heading(self):
pos_1 = (-30, -30)
pos_2 = (70, 20)
self.assertEqual((0, 0), self.space.get_heading(pos_1, pos_2))
pos_1 = (65, -25)
pos_2 = (-25, -25)
self.assertEqual((10, 0), self.space.get_heading(pos_1, pos_2))
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 1
def test_bounds(self):
'''
Test positions outside of boundary
'''
boundary_agents = []
for i, pos in enumerate(OUTSIDE_POSITIONS):
a = MockAgent(len(self.agents) + i, None)
boundary_agents.append(a)
self.space.place_agent(a, pos)
for a, pos in zip(boundary_agents, OUTSIDE_POSITIONS):
adj_pos = self.space.torus_adj(pos)
assert a.pos == adj_pos
a = self.agents[0]
for pos in OUTSIDE_POSITIONS:
assert self.space.out_of_bounds(pos)
self.space.move_agent(a, pos)
class TestSpaceNonToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, False, -30, -30)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_agent_matching(self):
'''
Ensure that the agents are all placed and indexed properly.
'''
for i, agent in self.space._index_to_agent.items():
assert agent.pos == tuple(self.space._agent_points[i, :])
assert i == self.space._agent_to_index[agent]
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_2 = (70, 20)
pos_3 = (-30, -20)
assert self.space.get_distance(pos_2, pos_3) == 107.70329614269008
def test_heading(self):
pos_1 = (-30, -30)
pos_2 = (70, 20)
self.assertEqual((100, 50), self.space.get_heading(pos_1, pos_2))
pos_1 = (65, -25)
pos_2 = (-25, -25)
self.assertEqual((-90, 0), self.space.get_heading(pos_1, pos_2))
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 0
def test_bounds(self):
'''
Test positions outside of boundary
'''
for i, pos in enumerate(OUTSIDE_POSITIONS):
a = MockAgent(len(self.agents) + i, None)
with self.assertRaises(Exception):
self.space.place_agent(a, pos)
a = self.agents[0]
for pos in OUTSIDE_POSITIONS:
assert self.space.out_of_bounds(pos)
with self.assertRaises(Exception):
self.space.move_agent(a, pos)
