class Collection:
def __init__(self, folder, color_file, gh_vectors=False):
"""
A class to create a collection of individuals based on the genetic information extracted from the
generative grasshopper model.
:param folder: The location where the genetic information of the individuals
are stored.
:param color_file: The location where the color file for the collection was
stored.
"""
#the location where individuals were stored
self.folder = folder
#the location where the color file was stored
self.color_file = color_file
#adding files that include individuals' genetic information to the collection
self.points = glob.glob(folder + "\points\*.npy", recursive=False)
self.heights = glob.glob(folder + "\heights\*.npy", recursive=False)
self.splits = glob.glob(folder + "\splits\*.npy", recursive=False)
#couple of sanity checks to make sure a proper collection location was passed as an argument
if (len(self.points)) == len(self.heights) == len(self.splits) == 0:
raise ValueError(f"Collection folder is empty.")
if(len(self.points) != len(self.heights) != len(self.splits)):
raise ValueError(f"A collection requires the same number of points, heights, and splits values.")
#properties of the collection
self.collection = {'points': self.points, 'heights': self.heights, 'splits': self.splits}
self.collection_length = len(self.points)
#extract genome information from grasshopper parameters embedded in the filenames
if(gh_vectors):
self.parameter_vectors = [point.split('\\')[-1].split('_')[:-1][1::2] for point in self.points]
#the color map for later translation of genotypes to visual representation (heightmaps) of the phenotype
self.cmap = util.create_cmap(color_file)class Individual:
def __init__(self, collection, id_, cmap, size):
"""
A class to create an individual, along with its properties, out of the provided collection.
:param id_: The indice of the individual in the collection
:param cmap: The color gradient map that represents heights into colors, as it was extracted from
the grasshopper model.
:param size: The extend of the bounding box of an individual, in meters. Used to generate appropriate
image outputs.
"""
self.collection = collection
self.id_ = id_
self.size = size
self.parent_ids = None
self.grid_position = None
# get individual's genome properties from collection
self.points = np.load(collection.points[id_]).flatten()
self.heights = np.load(collection.heights[id_]).flatten()
self.splits = np.load(collection.splits[id_]).flatten()
# generate phenotype
self.colors = np.array([util.height_to_color(cmap, height) for height in np.clip(self.heights, 0, 100)])
self.polygons = geom.create_shapely_polygons(self.points, self.splits)
# calcualte 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, geom.get_features(self.footprints, self.heights)))
self.centroids = np.array(geom.centroids(self.polygons))
self.std = util.calc_std(self.heights)
self.dangerous = None
self.sitting = None
def draw_image(self):
_, image = geom.draw_polygons(self.polygons, self.colors, self.size)
return image
def save_to_disk(self, fname):
data = {'polygons': self.polygons, 'heights': self.heights,
'colors': self.colors, 'footprints:': self.footprints,
'features': self.features, 'parent_id': self.parent_ids,
'grid_position': self.grid_position}
with open(fname, 'wb') as file:
pickle.dump(data, file)class Offspring:
def __init__(self, polygons, colors, heights, size, parent_ids):
"""
A class to create an offspring, along with its properties, out of the crossover or mutation
of individuals.
:param polygons: The polygons of the evolved individual.
:param colors: The color of the evolved individual.
:param heights: The heights of the evolved individual.
:param size: The extend of the bounding box of an individual, in meters. Used to generate appropriate
image outputs.
"""
# assign genome
self.colors = colors
self.polygons = polygons
self.heights= heights
self.size = size
self.parent_ids = parent_ids
#assign position on the map
self.grid_position = None
#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, geom.get_features(self.footprints, self.heights)))
self.std = util.calc_std(self.heights)
self.centroids = np.array(geom.centroids(self.polygons))
self.dangerous = None
self.sitting = None
self.fi_fitness = None
def draw_image(self):
_, image = geom.draw_polygons(self.polygons, self.colors, self.size)
return image
def save_to_disk(self, fname):
data = {'polygons': self.polygons, 'heights': self.heights,
'colors': self.colors, 'footprints:': self.footprints,
'features': self.features, 'parent_id': self.parent_ids,
'grid_position': self.grid_position}
with open(fname, 'wb') as file:
pickle.dump(data, file)