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feature: addi error handling

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Adding error handling and loggings. Also refactors the names and locations.

Reviewed-on: https://nextgenerations-cities.encs.concordia.ca/gitea/a_rezaei/district_heating_network_analysis/pulls/4
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Majid Rezaei 2024-07-30 08:59:54 -04:00
parent 97a43a3036
commit b92767a9eb
6 changed files with 331 additions and 286 deletions
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282
DistrictHeatingNetworkCreator.py
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@ -1,282 +0,0 @@
import json
import matplotlib.pyplot as plt
from shapely.geometry import Polygon, Point, LineString
import networkx as nx
from typing import List, Tuple
from rtree import index
import math
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great-circle distance between two points
on the Earth specified by their longitude and latitude.
"""
R = 6371000 # Radius of the Earth in meters
phi1 = math.radians(lat1)
phi2 = math.radians(lat2)
delta_phi = math.radians(lat2 - lat1)
delta_lambda = math.radians(lon2 - lon1)
a = math.sin(delta_phi / 2.0) ** 2 + \
math.cos(phi1) * math.cos(phi2) * \
math.sin(delta_lambda / 2.0) ** 2
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
return R * c # Output distance in meters
class DistrictHeatingNetworkCreator:
def __init__(self, buildings_file: str, roads_file: str):
"""
Initialize the class with paths to the buildings and roads data files.
:param buildings_file: Path to the GeoJSON file containing building data.
:param roads_file: Path to the GeoJSON file containing roads data.
"""
self.buildings_file = buildings_file
self.roads_file = roads_file
def run(self) -> nx.Graph:
"""
Main method to execute the district heating network creation process.
:return: NetworkX graph with nodes and edges representing the network.
"""
self._load_and_process_data()
self._find_nearest_roads()
self._find_nearest_points()
self._break_down_roads()
self._create_graph()
self._create_mst()
self._iteratively_remove_edges()
self._add_centroids_to_mst()
self._convert_edge_weights_to_meters()
return self.final_mst
def _load_and_process_data(self):
"""
Load and process the building and road data.
"""
# Load building data
with open(self.buildings_file, 'r') as file:
city = json.load(file)
self.centroids = []
self.building_ids = []
buildings = city['features']
for building in buildings:
coordinates = building['geometry']['coordinates'][0]
building_polygon = Polygon(coordinates)
centroid = building_polygon.centroid
self.centroids.append(centroid)
self.building_ids.append(building['id'])
# Load road data
with open(self.roads_file, 'r') as file:
roads = json.load(file)
line_features = [feature for feature in roads['features'] if feature['geometry']['type'] == 'LineString']
self.lines = [LineString(feature['geometry']['coordinates']) for feature in line_features]
self.cleaned_lines = [LineString([line.coords[0], line.coords[-1]]) for line in self.lines]
def _find_nearest_roads(self):
"""
Find the nearest road for each building centroid.
"""
self.closest_roads = []
unique_roads_set = set()
# Create spatial index for roads
idx = index.Index()
for pos, line in enumerate(self.cleaned_lines):
idx.insert(pos, line.bounds)
for centroid in self.centroids:
min_distance = float('inf')
closest_road = None
for pos in idx.nearest(centroid.bounds, 10):
road = self.cleaned_lines[pos]
distance = road.distance(centroid)
if distance < min_distance:
min_distance = distance
closest_road = road
if closest_road and closest_road.wkt not in unique_roads_set:
unique_roads_set.add(closest_road.wkt)
self.closest_roads.append(closest_road)
def _find_nearest_points(self):
"""
Find the nearest point on each closest road for each centroid.
"""
def find_nearest_point_on_line(point: Point, line: LineString) -> Point:
return line.interpolate(line.project(point))
self.nearest_points = []
for centroid in self.centroids:
min_distance = float('inf')
closest_road = None
for road in self.closest_roads:
distance = centroid.distance(road)
if distance < min_distance:
min_distance = distance
closest_road = road
if closest_road:
nearest_point = find_nearest_point_on_line(centroid, closest_road)
self.nearest_points.append(nearest_point)
def _break_down_roads(self):
"""
Break down roads into segments connecting nearest points.
"""
def break_down_roads(closest_roads: List[LineString], nearest_points_list: List[Point]) -> List[LineString]:
new_segments = []
for road in closest_roads:
coords = list(road.coords)
points_on_road = [point for point in nearest_points_list if road.distance(point) < 0.000000001]
sorted_points = sorted(points_on_road, key=lambda point: road.project(point))
sorted_points.insert(0, Point(coords[0]))
sorted_points.append(Point(coords[-1]))
for i in range(len(sorted_points) - 1):
segment = LineString([sorted_points[i], sorted_points[i + 1]])
new_segments.append(segment)
return new_segments
self.new_segments = break_down_roads(self.closest_roads, self.nearest_points)
self.cleaned_lines = [line for line in self.cleaned_lines if line not in self.closest_roads]
self.cleaned_lines.extend(self.new_segments)
def _create_graph(self):
"""
Create a NetworkX graph from the cleaned lines.
"""
self.G = nx.Graph()
for line in self.cleaned_lines:
coords = list(line.coords)
for i in range(len(coords) - 1):
self.G.add_edge(coords[i], coords[i + 1], weight=Point(coords[i]).distance(Point(coords[i + 1])))
def _create_mst(self):
"""
Create a Minimum Spanning Tree (MST) from the graph.
"""
def find_paths_between_nearest_points(g: nx.Graph, nearest_points: List[Point]) -> List[Tuple]:
edges = []
for i, start_point in enumerate(nearest_points):
start = (start_point.x, start_point.y)
for end_point in nearest_points[i + 1:]:
end = (end_point.x, end_point.y)
if nx.has_path(g, start, end):
path = nx.shortest_path(g, source=start, target=end, weight='weight')
path_edges = list(zip(path[:-1], path[1:]))
edges.extend((u, v, g[u][v]['weight']) for u, v in path_edges)
return edges
edges = find_paths_between_nearest_points(self.G, self.nearest_points)
h = nx.Graph()
h.add_weighted_edges_from(edges)
mst = nx.minimum_spanning_tree(h, weight='weight')
final_edges = []
for u, v in mst.edges():
if nx.has_path(self.G, u, v):
path = nx.shortest_path(self.G, source=u, target=v, weight='weight')
path_edges = list(zip(path[:-1], path[1:]))
final_edges.extend((x, y, self.G[x][y]['weight']) for x, y in path_edges)
self.final_mst = nx.Graph()
self.final_mst.add_weighted_edges_from(final_edges)
def _iteratively_remove_edges(self):
"""
Iteratively remove edges that do not have any nearest points and have one end with only one connection.
Also remove nodes that don't have any connections and street nodes with only one connection.
"""
nearest_points_tuples = [(point.x, point.y) for point in self.nearest_points]
def find_edges_to_remove(graph: nx.Graph) -> List[Tuple]:
edges_to_remove = []
for u, v, d in graph.edges(data=True):
if u not in nearest_points_tuples and v not in nearest_points_tuples:
if graph.degree(u) == 1 or graph.degree(v) == 1:
edges_to_remove.append((u, v, d))
return edges_to_remove
def find_nodes_to_remove(graph: nx.Graph) -> List[Tuple]:
nodes_to_remove = []
for node in graph.nodes():
if graph.degree(node) == 0:
nodes_to_remove.append(node)
return nodes_to_remove
edges_to_remove = find_edges_to_remove(self.final_mst)
self.final_mst_steps = [list(self.final_mst.edges(data=True))]
while edges_to_remove or find_nodes_to_remove(self.final_mst):
self.final_mst.remove_edges_from(edges_to_remove)
nodes_to_remove = find_nodes_to_remove(self.final_mst)
self.final_mst.remove_nodes_from(nodes_to_remove)
edges_to_remove = find_edges_to_remove(self.final_mst)
self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
def find_single_connection_street_nodes(graph: nx.Graph) -> List[Tuple]:
single_connection_street_nodes = []
for node in graph.nodes():
if node not in nearest_points_tuples and graph.degree(node) == 1:
single_connection_street_nodes.append(node)
return single_connection_street_nodes
single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
while single_connection_street_nodes:
for node in single_connection_street_nodes:
neighbors = list(self.final_mst.neighbors(node))
self.final_mst.remove_node(node)
for neighbor in neighbors:
if self.final_mst.degree(neighbor) == 0:
self.final_mst.remove_node(neighbor)
single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
def _add_centroids_to_mst(self):
"""
Add centroids to the final MST graph and connect them to their associated node on the graph.
"""
for i, centroid in enumerate(self.centroids):
centroid_tuple = (centroid.x, centroid.y)
building_id = self.building_ids[i]
self.final_mst.add_node(centroid_tuple, type='building', id=building_id)
nearest_point = None
min_distance = float('inf')
for node in self.final_mst.nodes():
if self.final_mst.nodes[node].get('type') != 'building':
node_point = Point(node)
distance = centroid.distance(node_point)
if distance < min_distance:
min_distance = distance
nearest_point = node
if nearest_point:
self.final_mst.add_edge(centroid_tuple, nearest_point, weight=min_distance)
def _convert_edge_weights_to_meters(self):
"""
Convert all edge weights in the final MST graph to meters using the Haversine formula.
"""
for u, v, data in self.final_mst.edges(data=True):
lon1, lat1 = u
lon2, lat2 = v
distance = haversine(lon1, lat1, lon2, lat2)
self.final_mst[u][v]['weight'] = distance
def plot_network_graph(self):
"""
Plot the network graph using matplotlib and networkx.
"""
plt.figure(figsize=(15, 10))
pos = {node: (node[0], node[1]) for node in self.final_mst.nodes()}
nx.draw_networkx_nodes(self.final_mst, pos, node_color='blue', node_size=50)
nx.draw_networkx_edges(self.final_mst, pos, edge_color='gray')
plt.title('District Heating Network Graph')
plt.axis('off')
plt.show()

3
README.md
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@ -23,8 +23,9 @@ To use the `DistrictHeatingNetworkCreator`, follow these steps:
3. Optionally, call the `plot_network_graph` method to visualize the network.
Example:
```python
from DistrictHeatingNetworkCreator import DistrictHeatingNetworkCreator
from scripts.district_heating_network_creator import DistrictHeatingNetworkCreator
# Initialize the class
network_creator = DistrictHeatingNetworkCreator('path/to/buildings.geojson', 'path/to/montreal_roads.shp')

6
main.py
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@ -1,8 +1,8 @@
from DistrictHeatingNetworkCreator import DistrictHeatingNetworkCreator
from Scripts.road_processor import road_processor
from scripts.district_heating_network_creator import DistrictHeatingNetworkCreator
from scripts.road_processor import road_processor
from pathlib import Path
import time
from Scripts.geojson_graph_creator import networkx_to_geojson
from scripts.geojson_graph_creator import networkx_to_geojson
location = [45.51663850312751, -73.59854314961274]
start_time = time.perf_counter()
roads_file = road_processor(location[1], location[0], 0.001)

326
scripts/district_heating_network_creator.py Normal file
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@ -0,0 +1,326 @@
import json
import matplotlib.pyplot as plt
from shapely.geometry import Polygon, Point, LineString
import networkx as nx
from typing import List, Tuple
from rtree import index
import math
import logging
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logging.getLogger('numexpr').setLevel(logging.ERROR)
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great-circle distance between two points
on the Earth specified by their longitude and latitude.
"""
R = 6371000 # Radius of the Earth in meters
phi1 = math.radians(lat1)
phi2 = math.radians(lat2)
delta_phi = math.radians(lat2 - lat1)
delta_lambda = math.radians(lon2 - lon1)
a = math.sin(delta_phi / 2.0) ** 2 + \
math.cos(phi1) * math.cos(phi2) * \
math.sin(delta_lambda / 2.0) ** 2
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
return R * c # Output distance in meters
class DistrictHeatingNetworkCreator:
def __init__(self, buildings_file: str, roads_file: str):
"""
Initialize the class with paths to the buildings and roads data files.
:param buildings_file: Path to the GeoJSON file containing building data.
:param roads_file: Path to the GeoJSON file containing roads data.
"""
self.buildings_file = buildings_file
self.roads_file = roads_file
def run(self) -> nx.Graph:
"""
Main method to execute the district heating network creation process.
:return: NetworkX graph with nodes and edges representing the network.
"""
try:
self._load_and_process_data()
self._find_nearest_roads()
self._find_nearest_points()
self._break_down_roads()
self._create_graph()
self._create_mst()
self._iteratively_remove_edges()
self._add_centroids_to_mst()
self._convert_edge_weights_to_meters()
return self.final_mst
except Exception as e:
logging.error(f"Error during network creation: {e}")
raise
def _load_and_process_data(self):
"""
Load and process the building and road data.
"""
try:
# Load building data
with open(self.buildings_file, 'r') as file:
city = json.load(file)
self.centroids = []
self.building_ids = []
buildings = city['features']
for building in buildings:
coordinates = building['geometry']['coordinates'][0]
building_polygon = Polygon(coordinates)
centroid = building_polygon.centroid
self.centroids.append(centroid)
self.building_ids.append(building['id'])
# Load road data
with open(self.roads_file, 'r') as file:
roads = json.load(file)
line_features = [feature for feature in roads['features'] if feature['geometry']['type'] == 'LineString']
self.lines = [LineString(feature['geometry']['coordinates']) for feature in line_features]
self.cleaned_lines = [LineString([line.coords[0], line.coords[-1]]) for line in self.lines]
except Exception as e:
logging.error(f"Error loading and processing data: {e}")
raise
def _find_nearest_roads(self):
"""
Find the nearest road for each building centroid.
"""
try:
self.closest_roads = []
unique_roads_set = set()
# Create spatial index for roads
idx = index.Index()
for pos, line in enumerate(self.cleaned_lines):
idx.insert(pos, line.bounds)
for centroid in self.centroids:
min_distance = float('inf')
closest_road = None
for pos in idx.nearest(centroid.bounds, 10):
road = self.cleaned_lines[pos]
distance = road.distance(centroid)
if distance < min_distance:
min_distance = distance
closest_road = road
if closest_road and closest_road.wkt not in unique_roads_set:
unique_roads_set.add(closest_road.wkt)
self.closest_roads.append(closest_road)
except Exception as e:
logging.error(f"Error finding nearest roads: {e}")
raise
def _find_nearest_points(self):
"""
Find the nearest point on each closest road for each centroid.
"""
def find_nearest_point_on_line(point: Point, line: LineString) -> Point:
return line.interpolate(line.project(point))
try:
self.nearest_points = []
for centroid in self.centroids:
min_distance = float('inf')
closest_road = None
for road in self.closest_roads:
distance = centroid.distance(road)
if distance < min_distance:
min_distance = distance
closest_road = road
if closest_road:
nearest_point = find_nearest_point_on_line(centroid, closest_road)
self.nearest_points.append(nearest_point)
except Exception as e:
logging.error(f"Error finding nearest points: {e}")
raise
def _break_down_roads(self):
"""
Break down roads into segments connecting nearest points.
"""
def break_down_roads(closest_roads: List[LineString], nearest_points_list: List[Point]) -> List[LineString]:
new_segments = []
for road in closest_roads:
coords = list(road.coords)
points_on_road = [point for point in nearest_points_list if road.distance(point) < 0.000000001]
sorted_points = sorted(points_on_road, key=lambda point: road.project(point))
sorted_points.insert(0, Point(coords[0]))
sorted_points.append(Point(coords[-1]))
for i in range(len(sorted_points) - 1):
segment = LineString([sorted_points[i], sorted_points[i + 1]])
new_segments.append(segment)
return new_segments
try:
self.new_segments = break_down_roads(self.closest_roads, self.nearest_points)
self.cleaned_lines = [line for line in self.cleaned_lines if line not in self.closest_roads]
self.cleaned_lines.extend(self.new_segments)
except Exception as e:
logging.error(f"Error breaking down roads: {e}")
raise
def _create_graph(self):
"""
Create a NetworkX graph from the cleaned lines.
"""
try:
self.G = nx.Graph()
for line in self.cleaned_lines:
coords = list(line.coords)
for i in range(len(coords) - 1):
self.G.add_edge(coords[i], coords[i + 1], weight=Point(coords[i]).distance(Point(coords[i + 1])))
except Exception as e:
logging.error(f"Error creating graph: {e}")
raise
def _create_mst(self):
"""
Create a Minimum Spanning Tree (MST) from the graph.
"""
def find_paths_between_nearest_points(g: nx.Graph, nearest_points: List[Point]) -> List[Tuple]:
edges = []
for i, start_point in enumerate(nearest_points):
start = (start_point.x, start_point.y)
for end_point in nearest_points[i + 1:]:
end = (end_point.x, end_point.y)
if nx.has_path(g, start, end):
path = nx.shortest_path(g, source=start, target=end, weight='weight')
path_edges = list(zip(path[:-1], path[1:]))
edges.extend((u, v, g[u][v]['weight']) for u, v in path_edges)
return edges
try:
edges = find_paths_between_nearest_points(self.G, self.nearest_points)
h = nx.Graph()
h.add_weighted_edges_from(edges)
mst = nx.minimum_spanning_tree(h, weight='weight')
final_edges = []
for u, v in mst.edges():
if nx.has_path(self.G, u, v):
path = nx.shortest_path(self.G, source=u, target=v, weight='weight')
path_edges = list(zip(path[:-1], path[1:]))
final_edges.extend((x, y, self.G[x][y]['weight']) for x, y in path_edges)
self.final_mst = nx.Graph()
self.final_mst.add_weighted_edges_from(final_edges)
except Exception as e:
logging.error(f"Error creating MST: {e}")
raise
def _iteratively_remove_edges(self):
"""
Iteratively remove edges that do not have any nearest points and have one end with only one connection.
Also remove nodes that don't have any connections and street nodes with only one connection.
"""
nearest_points_tuples = [(point.x, point.y) for point in self.nearest_points]
def find_edges_to_remove(graph: nx.Graph) -> List[Tuple]:
edges_to_remove = []
for u, v, d in graph.edges(data=True):
if u not in nearest_points_tuples and v not in nearest_points_tuples:
if graph.degree(u) == 1 or graph.degree(v) == 1:
edges_to_remove.append((u, v, d))
return edges_to_remove
def find_nodes_to_remove(graph: nx.Graph) -> List[Tuple]:
nodes_to_remove = []
for node in graph.nodes():
if graph.degree(node) == 0:
nodes_to_remove.append(node)
return nodes_to_remove
try:
edges_to_remove = find_edges_to_remove(self.final_mst)
self.final_mst_steps = [list(self.final_mst.edges(data=True))]
while edges_to_remove or find_nodes_to_remove(self.final_mst):
self.final_mst.remove_edges_from(edges_to_remove)
nodes_to_remove = find_nodes_to_remove(self.final_mst)
self.final_mst.remove_nodes_from(nodes_to_remove)
edges_to_remove = find_edges_to_remove(self.final_mst)
self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
def find_single_connection_street_nodes(graph: nx.Graph) -> List[Tuple]:
single_connection_street_nodes = []
for node in graph.nodes():
if node not in nearest_points_tuples and graph.degree(node) == 1:
single_connection_street_nodes.append(node)
return single_connection_street_nodes
single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
while single_connection_street_nodes:
for node in single_connection_street_nodes:
neighbors = list(self.final_mst.neighbors(node))
self.final_mst.remove_node(node)
for neighbor in neighbors:
if self.final_mst.degree(neighbor) == 0:
self.final_mst.remove_node(neighbor)
single_connection_street_nodes = find_single_connection_street_nodes(self.final_mst)
self.final_mst_steps.append(list(self.final_mst.edges(data=True)))
except Exception as e:
logging.error(f"Error iteratively removing edges: {e}")
raise
def _add_centroids_to_mst(self):
"""
Add centroids to the final MST graph and connect them to their associated node on the graph.
"""
try:
for i, centroid in enumerate(self.centroids):
centroid_tuple = (centroid.x, centroid.y)
building_id = self.building_ids[i]
self.final_mst.add_node(centroid_tuple, type='building', id=building_id)
nearest_point = None
min_distance = float('inf')
for node in self.final_mst.nodes():
if self.final_mst.nodes[node].get('type') != 'building':
node_point = Point(node)
distance = centroid.distance(node_point)
if distance < min_distance:
min_distance = distance
nearest_point = node
if nearest_point:
self.final_mst.add_edge(centroid_tuple, nearest_point, weight=min_distance)
except Exception as e:
logging.error(f"Error adding centroids to MST: {e}")
raise
def _convert_edge_weights_to_meters(self):
"""
Convert all edge weights in the final MST graph to meters using the Haversine formula.
"""
try:
for u, v, data in self.final_mst.edges(data=True):
lon1, lat1 = u
lon2, lat2 = v
distance = haversine(lon1, lat1, lon2, lat2)
self.final_mst[u][v]['weight'] = distance
except Exception as e:
logging.error(f"Error converting edge weights to meters: {e}")
raise
def plot_network_graph(self):
"""
Plot the network graph using matplotlib and networkx.
"""
plt.figure(figsize=(15, 10))
pos = {node: (node[0], node[1]) for node in self.final_mst.nodes()}
nx.draw_networkx_nodes(self.final_mst, pos, node_color='blue', node_size=50)
nx.draw_networkx_edges(self.final_mst, pos, edge_color='gray')
plt.title('District Heating Network Graph')
plt.axis('off')
plt.show()

0
Scripts/geojson_graph_creator.py → scripts/geojson_graph_creator.py
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0
Scripts/road_processor.py → scripts/road_processor.py
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