def accessibility(self): """ Accessibility matrix (transitive closure). @rtype: dictionary @return: Accessibility information for each node. """ return accessibility.accessibility(self)
def accessibility(self): """ Accessibility matrix (transitive closure). @rtype: list @return: Accessibility matrix """ return accessibility.accessibility(self)
def accessibility(self): """ Accessibility matrix (transitive closure). @rtype: dictionary @return: Accessibility information for each node. """ return accessibility.accessibility(self)
def accessibility(self):
"""
Accessibility matrix (transitive closure).
@rtype: list
@return: Accessibility matrix
"""
return accessibility.accessibility(self)
def interest(building_type, scenario, maps, settlement_seeds, size,
parcel_size):
# type: (BuildingType, str, Maps, object, object, object) -> object
def river_interest():
if maps.fluid_map.has_river:
return close_distance(maps.fluid_map.river_distance[x, z],
lambdas["RiverDistance"])
return 0
def ocean_interest():
if maps.fluid_map.has_ocean:
return close_distance(maps.fluid_map.ocean_distance[x, z],
lambdas["OceanDistance"])
return 0
def lava_interest():
if maps.fluid_map.has_lava:
return obstacle(maps.fluid_map.lava_distance[x, z],
lambdas["LavaObstacle"])
return 0
_interest_map = np.zeros(size)
print("Compute accessibility map")
accessibility_map = accessibility(building_type, scenario,
maps.road_network, size)
print("Compute sociability map")
sociability_map = sociability(building_type, scenario, settlement_seeds,
size)
extendability_map = extendability(size, parcel_size)
scenario_dict = BUILDING_ENCYCLOPEDIA[scenario]
lambdas = {
criteria: scenario_dict[criteria][building_type.name]
for criteria in scenario_dict
if building_type.name in scenario_dict[criteria]
}
for x, z, in product(range(size[0]), range(size[1])):
interest_functions = np.array([
accessibility_map[x][z], sociability_map[x][z],
river_interest(),
ocean_interest(),
lava_interest()
])
if min(interest_functions) == -1 or extendability_map[x][z] == -1:
interest_score = 0
else:
weights = np.array(lambdas["Weighting_factors"])
weighted_score = interest_functions.dot(weights) / sum(weights)
interest_score = max(0, weighted_score)
_interest_map[x][z] = interest_score
return _interest_map, accessibility_map, sociability_map
def accessibility(self):
"""
Accessibility matrix (transitive closure).
@rtype: dictionary
@return: Accessibility information for each node.
"""
access_ = accessibility.accessibility(self.graph)
access = {}
for each in access_.keys():
if (each[1] == 'n'):
access[each[0]] = []
for other in access_[each]:
if (other[1] == 'n'):
access[each[0]].append(other[0])
return access
