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# Eclipse SUMO, Simulation of Urban MObility; see https://eclipse.dev/sumo
# Copyright (C) 2016-2023 German Aerospace Center (DLR) and others.
# SUMOPy module
# Copyright (C) 2012-2021 University of Bologna - DICAM
# This program and the accompanying materials are made available under the
# terms of the Eclipse Public License 2.0 which is available at
# https://www.eclipse.org/legal/epl-2.0/
# This Source Code may also be made available under the following Secondary
# Licenses when the conditions for such availability set forth in the Eclipse
# Public License 2.0 are satisfied: GNU General Public License, version 2
# or later which is available at
# https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
# SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
# @file CREATE_ZONES.py
# @author Joerg Schweizer
# @date 2012
# CREATE GRID ZONES
def create_zones(n_zones, x_min, x_max, y_min, y_max):
zone_binx = np.int(np.sqrt(n_zones))
zone_biny = np.int(np.sqrt(n_zones))
zones = myscenario.landuse.zones
zones.clear()
x_bin = (x_max-x_min)/(zone_binx)
y_bin = (y_max-y_min)/(zone_biny)
zone_name = 1
for i in range(zone_binx):
for j in range(zone_biny):
shape = [[x_min+i*x_bin, y_min+j*y_bin, 0.], [x_min+i*x_bin, y_min+(j+1)*y_bin, 0.], [x_min+(i+1)*x_bin, y_min+(
j+1)*y_bin, 0.], [x_min+(i+1)*x_bin, y_min+j*y_bin, 0.], [x_min+i*x_bin, y_min+j*y_bin, 0.]]
zones.make(zonename=zone_name, coord=np.zeros(3, dtype=np.float32), shape=shape, id_landusetype=6)
zone_name += 1
ids_zone = zones.get_ids()
for id_zone in ids_zone:
zones.identify_zonearea(id_zone)
# ~ if zones.areas[id_zone] == 0:
#~ zones.del_element(id_zone)
ids_zone = zones.get_ids()
return ids_zone
# CREATE ZONES by CLUSTER OF POINTS
print('k-value cluster')
# k-value cluster
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html
features = np.array(origin_dest_points)
print('features', features)
# Whiten data
st_dev_x = np.std(features[:][0])
st_dev_y = np.std(features[:][1])
print('st_dev_x', st_dev_x)
print('st_dev_y', st_dev_y)
whitened = whiten(features)
# Find 2 clusters in the data
codebook, distortion = kmeans(features, n_zones)
print('codebook', codebook)
features[:, 0] = features[:, 0]*st_dev_x
features[:, 1] = features[:, 1]*st_dev_y
codebook[:, 0] = codebook[:, 0]*st_dev_x
codebook[:, 1] = codebook[:, 1]*st_dev_y
# Plot whitened data and cluster centers in red
plt.scatter(features[:, 0], features[:, 1])
plt.scatter(codebook[:, 0], codebook[:, 1], c='r')
# Create and plot zones Voronoy
vor = Voronoi(codebook)
fig = voronoi_plot_2d(vor)
plt.show()
# Create zones ON SUMOPY
# myscenario.landuse.zones.clear()
vertices = vor.vertices
regions = vor.regions
print(vertices)
print(regions)
zone_name = 1
for region in regions:
if region != []:
shape = []
for vertice in region:
shape.append([vertices[vertice][0], vertices[vertice][1], 0.])
ids_zone = myscenario.landuse.zones.get_ids()
print('n_zones =', len(ids_zone))
print(shape)
zone_name += 1
# np.concatenate(shape).astype(None)
print(shape)
myscenario.landuse.zones.make(zonename='zone_name', coord=np.zeros(
3, dtype=np.float32), shape=shape, id_landusetype=6)
ids_zone = myscenario.landuse.zones.get_ids()
zones_shape = myscenario.landuse.zones.shapes[ids_zone]
# ~ #plt.scatter(zones_shape[0], zones_shape[1])
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