-
Notifications
You must be signed in to change notification settings - Fork 2
/
Node_coupling.py
185 lines (170 loc) · 8.27 KB
/
Node_coupling.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
import pandas as pd
import geopandas as gpd
from scipy import spatial
import geopandas
from shapely.geometry import Polygon, Point
from shapely.ops import cascaded_union
def Node_coupling(Distribution_centers_df, Vertiports_df):
#retrieve midway points:
nodes_df = gpd.read_file("center_points.gpkg")
nodes = []
for index, row in nodes_df.iterrows():
nodes.append((list(row.geometry.coords)[0]))
##load distribution centers and vertiport locations + average hourly demand
# Distribution_centers_df = pd.read_csv('Distribution_centers_locations.csv')
# Vertiports_df = pd.read_csv('Vertiport_locations.csv')
df = Vertiports_df
gdf = geopandas.GeoDataFrame(
df, geometry=geopandas.points_from_xy(df.x, df.y, df.demand), crs = 'EPSG:32633')
Vertiports_longlat = gdf.to_crs(crs = 'EPSG:4326', inplace = True)
df = Distribution_centers_df
gdf = geopandas.GeoDataFrame(
df, geometry=geopandas.points_from_xy(df.x, df.y), crs = 'EPSG:32633')
Distribution_centers_longlat = gdf.to_crs(crs = 'EPSG:4326', inplace = True)
#constrained airspace polygon
constrained_airspace_df = geopandas.read_file("updated_constrained_airspace.gpkg")
constrained_airspace_polygon = constrained_airspace_df.geometry[0]
"""
This was for the old constrained airspace:
constrained_airspace_df = pd.read_csv("constrained_airspace.csv")
Corner_list = []
j = 0
for corner in constrained_airspace_df:
if j == 114:
corner = "602136.759781778 5345414.06896959"
corner = corner[:]
corner = corner.split(' ')
corners = [float(i) for i in corner]
Corner_list.append(corners)
j += 1
#print(Corner_list)
constrained_airspace_polygon = Polygon(Corner_list)
#constrained_airspace_polygon_list = list(constrained_airspace_polygon)
"""
#retrieve midway points:
nodes_df = geopandas.read_file("center_points.gpkg")
nodes = []
for index, row in nodes_df.iterrows():
nodes.append((list(row.geometry.coords)[0]))
nodes_df = pd.DataFrame.from_records(nodes)
nodes_df.to_csv('Nodes_center_points.csv', header = False)
#append nodes to distribution centers:
Dcenter_nodelist_x_send_1 = []
Dcenter_nodelist_y_send_1 = []
Dcenter_nodelist_x_send_2 = []
Dcenter_nodelist_y_send_2 = []
Dcenter_nodelist_x_send_3 = []
Dcenter_nodelist_y_send_3 = []
for index, row in Distribution_centers_df.iterrows():
x_port = list(row.geometry.coords)[0][0]
y_port = list(row.geometry.coords)[0][1]
#print(x_port, y_port)
x = row['x']
y = row['y']
if constrained_airspace_polygon.contains(Point(x,y)):
tree = spatial.KDTree(nodes)
index_closest = tree.query([x_port, y_port])[1]
x_node_send_1 = nodes[index_closest][0]
y_node_send_1 = nodes[index_closest][1]
nodes.pop(index_closest)
tree = spatial.KDTree(nodes)
index_closest = tree.query([x_port, y_port])[1]
x_node_send_2 = nodes[index_closest][0]
y_node_send_2 = nodes[index_closest][1]
nodes.pop(index_closest)
tree = spatial.KDTree(nodes)
index_closest = tree.query([x_port, y_port])[1]
x_node_send_3 = nodes[index_closest][0]
y_node_send_3 = nodes[index_closest][1]
nodes.pop(index_closest)
Dcenter_nodelist_x_send_1.append(x_node_send_1)
Dcenter_nodelist_y_send_1.append(y_node_send_1)
Dcenter_nodelist_x_send_2.append(x_node_send_2)
Dcenter_nodelist_y_send_2.append(y_node_send_2)
Dcenter_nodelist_x_send_3.append(x_node_send_3)
Dcenter_nodelist_y_send_3.append(y_node_send_3)
else:
Dcenter_nodelist_x_send_1.append(x_port)
Dcenter_nodelist_y_send_1.append(y_port)
Dcenter_nodelist_x_send_2.append(x_port - 0.000450000000)
Dcenter_nodelist_y_send_2.append(y_port)
Dcenter_nodelist_x_send_3.append(x_port + 0.000450000000)
Dcenter_nodelist_y_send_3.append(y_port)
Distribution_centers_df['node_x_send_1'] = Dcenter_nodelist_x_send_1
Distribution_centers_df['node_y_send_1'] = Dcenter_nodelist_y_send_1
Distribution_centers_df['node_x_send_2'] = Dcenter_nodelist_x_send_2
Distribution_centers_df['node_y_send_2'] = Dcenter_nodelist_y_send_2
Distribution_centers_df['node_x_send_3'] = Dcenter_nodelist_x_send_3
Distribution_centers_df['node_y_send_3'] = Dcenter_nodelist_y_send_3
#Distribution_centers_df.to_csv('Distribution_centers_locations.csv')
#append nodes to vertiports:
vertiport_nodelist_x_send = []
vertiport_nodelist_y_send = []
vertiport_nodelist_x_recieve = []
vertiport_nodelist_y_recieve = []
for index, row in Vertiports_df.iterrows():
x_port = list(row.geometry.coords)[0][0]
y_port = list(row.geometry.coords)[0][1]
#print(x_port, y_port)
x = row['x']
y = row['y']
if constrained_airspace_polygon.contains(Point(x,y)):
tree = spatial.KDTree(nodes)
index_closest = tree.query([x_port, y_port])[1]
x_node_send = nodes[index_closest][0]
y_node_send = nodes[index_closest][1]
nodes.pop(index_closest)
tree = spatial.KDTree(nodes)
index_closest = tree.query([x_port, y_port])[1]
x_node_recieve = nodes[index_closest][0]
y_node_recieve = nodes[index_closest][1]
nodes.pop(index_closest)
vertiport_nodelist_x_send.append(x_node_send)
vertiport_nodelist_y_send.append(y_node_send)
vertiport_nodelist_x_recieve.append(x_node_recieve)
vertiport_nodelist_y_recieve.append(y_node_recieve)
else:
vertiport_nodelist_x_send.append(x_port)
vertiport_nodelist_y_send.append(y_port)
vertiport_nodelist_x_recieve.append(x_port - 0.000450000000)
vertiport_nodelist_y_recieve.append(y_port)
Vertiports_df['node_x_send'] = vertiport_nodelist_x_send
Vertiports_df['node_y_send'] = vertiport_nodelist_y_send
Vertiports_df['node_x_recieve'] = vertiport_nodelist_x_recieve
Vertiports_df['node_y_recieve'] = vertiport_nodelist_y_recieve
#Vertiports_df.to_csv('Vertiport_locations.csv')
#Create used nodes geopackage for sending and recieving ports seperately
sending_nodes_float_x = []
sending_nodes_float_y = []
for i in range(len(vertiport_nodelist_x_send)):
sending_nodes_float_x.append(vertiport_nodelist_x_send[i])
sending_nodes_float_y.append(vertiport_nodelist_y_send[i])
for i in range(len(Dcenter_nodelist_x_send_1)):
sending_nodes_float_x.append(Dcenter_nodelist_x_send_1[i])
sending_nodes_float_y.append(Dcenter_nodelist_y_send_1[i])
for i in range(len(Dcenter_nodelist_x_send_2)):
sending_nodes_float_x.append(Dcenter_nodelist_x_send_2[i])
sending_nodes_float_y.append(Dcenter_nodelist_y_send_2[i])
for i in range(len(Dcenter_nodelist_x_send_3)):
sending_nodes_float_x.append(Dcenter_nodelist_x_send_3[i])
sending_nodes_float_y.append(Dcenter_nodelist_y_send_3[i])
recieving_nodes_float_x = []
recieving_nodes_float_y = []
for i in range(len(vertiport_nodelist_x_recieve)):
recieving_nodes_float_x.append(vertiport_nodelist_x_recieve[i])
recieving_nodes_float_y.append(vertiport_nodelist_y_recieve[i])
return Distribution_centers_df, Vertiports_df
# df = pd.DataFrame(
# {'x_send': sending_nodes_float_x,
# 'y_send': sending_nodes_float_y})
# gdf = geopandas.GeoDataFrame(
# df, geometry=geopandas.points_from_xy(df.x_send, df.y_send), crs = 'EPSG:4326')
# gdf.to_crs(crs = 'EPSG:32633', inplace = True)
# gdf.to_file("Sending_nodes.gpkg", layer='Sending_nodes', driver="GPKG")
# df = pd.DataFrame(
# {'x_rec': recieving_nodes_float_x,
# 'y_rec': recieving_nodes_float_y})
# gdf = geopandas.GeoDataFrame(
# df, geometry=geopandas.points_from_xy(df.x_rec, df.y_rec), crs = 'EPSG:4326')
# gdf.to_crs(crs = 'EPSG:32633', inplace = True)
# gdf.to_file("Recieving_nodes.gpkg", layer='Recieving_nodes', driver="GPKG")