def setUp(self):
print('In method "{}"...'.format(self._testMethodName))
self.params = {'r': .1, 'd': .02, 'w': 0.5, 'tau': 12, 'q': .0001}
self.plume_structure = plume_structures.Gaussian2D(**self.params)
self.src_density = 0.01
self.agent_search_radius = 10
self.env_random = environments.Environment2d(self.plume_structure,
self.src_density,
self.agent_search_radius)
src_positions_fixed = np.array([[-3.1, 0], [5.1, 3.2], [-8.9, -2.7]])
self.env_fixed = environments.Environment2d(self.plume_structure,
self.src_density,
self.agent_search_radius,
src_positions_fixed)
src_positions_single = np.array([[5.1, 3.2]])
self.env_single_src = environments.Environment2d(
self.plume_structure, self.src_density, self.agent_search_radius,
src_positions_single)
def setUp(self):
print('In method "{}"...'.format(self._testMethodName))
self.params = {'r': .1,
'd': .02,
'w': 0.5,
'tau': 12,
'q': .0001}
self.plume_structure = plume_structures.Gaussian2D(**self.params)
self.src_density = 0.1
self.agent_search_radius = 10
self.env = environments.Environment2d(self.plume_structure,
self.src_density,
self.agent_search_radius,
src_positions='random')
src_positions_single = np.array([[-1., 1]])
self.env_single_src = environments.Environment2d(self.plume_structure,
self.src_density,
self.agent_search_radius,
src_positions=src_positions_single)
def main():
plume_structure = plume_structures.Gaussian2D(**PARAMS)
agent_search_radius = SPEED * SEARCH_TIME_MAX
env = environments.Environment2d(plume_structure, SRC_DENSITY,
agent_search_radius, SRC_POSITIONS)
_, ax = plt.subplots(1, 1, facecolor='white')
draw_background = True
for theta in THETAS[1:3]:
agent = search_agent.LinearSearcher(theta=theta, speed=SPEED)
trial = simulation.Trial2d(env, agent, SEARCH_TIME_MAX, DT)
trial.run(with_plot=True,
ax=ax,
draw_every=20,
draw_background=draw_background)
draw_background = False
plt.show(block=True)
search_agent.LinearSearcher(theta=theta, speed=SPEED) for theta in THETAS
]
envs = []
sim = simulation.Simulation(plume_structure,
agents=agents,
n_environments=N_ENVIRONMENTS)
plume_detected = np.zeros((N_ENVIRONMENTS, len(THETAS)))
search_times = np.nan * np.ones((N_ENVIRONMENTS, len(THETAS)), dtype=float)
for e_ctr in range(N_ENVIRONMENTS):
print(e_ctr)
# make new environment
env = environments.Environment2d(plume_structure, SRC_DENSITY,
AGENT_SEARCH_RADIUS, SRC_POSITIONS)
envs += [env]
for a_ctr, agent in enumerate(agents):
# set agent's starting position back to zero
agent.reset()
trial = simulation.Trial2d(env, agent, SEARCH_TIME_MAX, DT)
trial.run()
if trial.plume_detected:
plume_detected[e_ctr, a_ctr] = 1
plume_detected_n = plume_detected.sum(0)
plume_detected_prob = plume_detected.sum(0) / N_ENVIRONMENTS
fig, ax = plt.subplots(1, 1, facecolor='white')