def evolve(self, offspring):
# Add/Replace the information to the location's genome
# temporary values
temp_heights = self.heights[offspring.added]
temp_status = self.status[offspring.added]
temp_colors = self.colors[offspring.added]
if(len(offspring.dropped)):
self.grid_ids = np.delete(self.grid_ids, offspring.dropped)
self.heights = np.delete(self.heights, offspring.dropped)
self.status = np.delete(self.status, offspring.dropped)
self.colors = np.delete(self.colors, offspring.dropped, axis=0)
self.polygons = np.delete(self.polygons, offspring.dropped)
if(len(offspring.added)):
self.grid_ids = np.append(self.grid_ids, np.array([offspring.grid_ids[0] for building in offspring.added]))
self.heights = np.append(self.heights, temp_heights)
self.status = np.append(self.status, temp_status)
self.colors = np.append(self.colors, temp_colors, axis=0)
indices = []
for id_ in offspring.added:
indx = np.where(offspring.building_ids == id_)[0].item()
indices.append(indx)
polygons = move_polygons(offspring.polygons[indices], from_grid=0, to_grid=offspring.grid_ids[0])
self.polygons = np.append(self.polygons, polygons)
self.evolved_grids.append(offspring.grid_ids[0])
self.footprints = np.array([polygon.area for polygon in self.polygons])
self.centroids = np.array(centroids(self.polygons))
self.features = dict(zip(self.feature_names, get_features(self.footprints, self.heights)))
self.num_buildings = len(self.heights)
def __init__(self, location, grid_id, size=(750, 750)):
"""
A class that creates the collection of grid-individuals out of a provided location.
Args:
collection (Collection): A collection of individuals.
id_ (int): The indice of the individual in the collection.
cmap (list): The color gradient map that represents heights into colors.
size ([type]): The extend of the bounding box of an individual, in meters. Used to generate appropriate
image outputs.
"""
self.grid_id = grid_id
self.size = size
self.grid_position = None
# get genome properties from collection
building_ids = []
for id_ in location.neighbours[grid_id]:
building_ids.append(np.where(location.grid_ids == id_))
self.building_ids = np.concatenate(building_ids, axis=1).ravel()
self.polygons = move_neighbour_polygons(location.polygons[self.building_ids], self.grid_id)
self.heights = location.heights[self.building_ids]
self.colors = location.colors[self.building_ids]
self.footprints = location.footprints[self.building_ids]
self.status = location.status[self.building_ids]
self.centroids = location.centroids[self.building_ids]
# calcualte features and descriptors
self.feature_names = ['FSI', 'GSI', 'OSR', 'Mean_height', 'Tare']
self.features = dict(zip(self.feature_names, get_features(self.footprints, self.heights)))
self.dangerous = None
self.sitting = None
def __init__(self, polygons, colors, heights, grid_ids, status, building_ids, added, dropped, size=(250, 250)):
"""
A class to create an offspring, along with its properties, out of the crossover or mutation
of individuals.
Args:
polygons (list): The polygons of the evolved individual.
colors (list): The colors of the evolved individual.
heights (list): The heights of the evolved individual.
size (tuple): The extend of the bounding box of an individual, in meters. Used to generate appropriate
image outputs.
"""
# assign genome
self.polygons = polygons
self.colors = colors
self.heights = heights
self.grid_ids = grid_ids
self.status = status
self.building_ids = building_ids
self.added = added
self.dropped = dropped
self.size = size
#calculate phenotype
self.footprints = np.array([polygon.area for polygon in self.polygons])
self.feature_names = ['FSI', 'GSI', 'OSR', 'Mean_height', 'Tare']
self.features = dict(zip(self.feature_names, get_features(self.footprints, self.heights, boundary=self.size)))
self.centroids = np.array(centroids(self.polygons))
self.dangerous = None
self.sitting = None
self.fi_fitness = None
def __init__(self, case_folder, color_file, size=(2500, 2500), init=True,
polygons=None, heights=None, colors=None, status=None, grid_ids=None,
grid_occupancy=None, grids_dangerous=None, grids_sitting=None, dangerous=None, sitting=None,
evolved_buildings=None, evolved_grids=None, neighbours=None):
"""
A class to create an individual representing a whole geographical location,
including the information of all individual buildings.
Args:
folder (string): The folder where the genetic information of the location is stored.
color_file (string): The location where the color file for the collection was stored.
Raises:
ValueError: I can't find anything in the selected folder.
ValueError: A location requires the same number of points, heights, splits, and grid id values.
"""
if(init):
self.folder = case_folder
self.color_file = color_file
self.cmap = create_cmap(self.color_file)
self.size = size
self.evolved_buildings = []
self.evolved_grids = []
# get the location's initial genetic information
self.points = np.load(self.folder + r"\points\harvard_init.npy").flatten()
self.heights = np.load(self.folder + r"\heights\harvard_init.npy").flatten()
self.splits = np.load(self.folder + r"\splits\harvard_init.npy").flatten()
self.grid_ids = np.load(self.folder + r"\grid_ids\harvard_init.npy").flatten()
self.neighbour_ids = np.load(self.folder + r"\neighbour_ids\harvard_init.npy").flatten()
self.neighbour_id_splits = np.load(self.folder + r"\neighbour_id_splits\harvard_init.npy").flatten()
self.status = np.load(self.folder + r"\status\harvard_init.npy").flatten()
self.grid_occupancy = np.load(self.folder + r"\grid_occupancy\harvard_init.npy").flatten()
# Genotype
self.colors = np.array([height_to_color(self.cmap, height) for height in np.clip(self.heights, 0, 100)]).astype(int)
self.polygons = create_shapely_polygons(self.points, self.splits, self.grid_ids)
self.neighbours = np.split(self.neighbour_ids, np.cumsum(self.neighbour_id_splits))
#couple of sanity checks to make sure a proper individual was passed
if (len(self.points)) == len(self.heights) == len(self.splits) == len(self.grid_ids) == len(self.status) == 0:
raise ValueError(f"I can't find anything in the selected folder")
if(len(self.points) != len(self.heights) != len(self.splits) != len(self.status) != len(self.grid_ids)):
raise ValueError(f"A location requires the same number of points, heights, splits, \
and grid id values.")
else:
self.polygons = polygons
self.heights = heights
self.grid_ids = grid_ids
self.neighbours = neighbours
self.status = status
self.grid_occupancy = grid_occupancy
self.colors = colors
self.color_file = color_file
self.cmap = create_cmap(self.color_file)
self.size = size
self.evolved_buildings = evolved_buildings
self.evolved_grids = evolved_grids
# Features and descriptors
self.footprints = np.array([polygon.area for polygon in self.polygons])
self.feature_names = ['FSI', 'GSI', 'OSR', 'Mean_height', 'Tare']
self.features = dict(zip(self.feature_names, get_features(self.footprints, self.heights)))
self.centroids = np.array(centroids(self.polygons))
self.num_buildings = len(self.heights)
self.num_occupied_grids = len(self.grid_occupancy)
self.grids_dangerous = grids_dangerous
self.grids_sitting = grids_sitting
self.dangerous = dangerous
self.sitting = sitting