def is_an_agent_can_be_there_faster(agent,target): """ si un autre agent peut atteindre avant la cellule visée """ pop = get_population(agent) agents = p.get_agents(pop) i=0 already_found = False while i < len(agents) and not already_found: if agents[i] != agent and target in accecible_positions(agents[i]): already_found = (u.eucl_dist(get_pos(agents[i]),target) < u.eucl_dist(get_pos(agent),target)) i+=1 return already_found
def is_an_agent_can_be_there_faster(agent, target):
"""
si un autre agent peut atteindre avant la cellule visée
"""
pop = get_population(agent)
agents = p.get_agents(pop)
i = 0
already_found = False
while i < len(agents) and not already_found:
if agents[i] != agent and target in accecible_positions(agents[i]):
already_found = (u.eucl_dist(get_pos(agents[i]), target) <
u.eucl_dist(get_pos(agent), target))
i += 1
return already_found
def add_capacity_gaussian(env, max_capacity_factor, center, disp):
"""
Add capacity to a given set of cells of the environment.
The distribution of capacity follows the shape of a multi-
variate (bi-dimensional) normal probability distribution
function (gaussian PDF) with a given center and dispersion.
The max_capacity_factor represents the maximum level of the
added capacity (at the center of the distribution) and is
a multiplicative factor of the maximum capacity property of
the environment.
"""
# Set the max capacity to a given factor (e.g. "1.0" for 100%
# of the maximum capacity defined for the environment)
max_capacity = get_max_capacity(env) * max_capacity_factor
(cx, cy) = center
# Compute the minimum capacity to assign to a cell.
min_capacity = max_capacity * 1e-3
# Compute the cell distance from the center at which to add
# capacity.
max_dist = math.ceil(disp * math.sqrt(-2 * math.log(min_capacity)))
for x in range(cx - max_dist, cx + max_dist):
for y in range(cx - max_dist, cx + max_dist):
capacity = max_capacity * math.exp(-0.5 * (u.eucl_dist((x, y), center) / disp) ** 2)
cell = get_cell(env, (x, y))
c.add_capacity(cell, capacity)
def add_capacity_gaussian(env, max_capacity_factor, center, disp):
"""
Add capacity to a given set of cells of the environment.
The distribution of capacity follows the shape of a multi-
variate (bi-dimensional) normal probability distribution
function (gaussian PDF) with a given center and dispersion.
The max_capacity_factor represents the maximum level of the
added capacity (at the center of the distribution) and is
a multiplicative factor of the maximum capacity property of
the environment.
"""
# Set the max capacity to a given factor (e.g. "1.0" for 100%
# of the maximum capacity defined for the environment)
max_capacity = get_max_capacity(env) * max_capacity_factor
(cx, cy) = center
# Compute the minimum capacity to assign to a cell.
min_capacity = max_capacity * 1E-3
# Compute the cell distance from the center at which to add
# capacity.
max_dist = math.ceil(disp * math.sqrt(-2 * math.log(min_capacity)))
for x in range(cx - max_dist, cx + max_dist):
for y in range(cx - max_dist, cx + max_dist):
capacity = max_capacity * math.exp(-0.5 * (u.eucl_dist(
(x, y), center) / disp)**2)
cell = get_cell(env, (x, y))
c.add_capacity(cell, capacity)
