dhn_generator - class, example files and an example main file
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majidr
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vendored
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.gitignore
vendored
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186
DistrictHeatingNetworkCreator.py
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186
DistrictHeatingNetworkCreator.py
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import json
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import geopandas as gpd
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import matplotlib.pyplot as plt
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from shapely.geometry import Polygon, Point, LineString, MultiPoint
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import networkx as nx
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class DistrictHeatingNetworkCreator:
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def __init__(self, buildings_file, roads_file):
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"""
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Initialize the class with paths to the buildings and roads data files.
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:param buildings_file: Path to the GeoJSON file containing building data.
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:param roads_file: Path to the Shapefile containing road data.
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"""
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self.buildings_file = buildings_file
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self.roads_file = roads_file
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def run(self):
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"""
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Main method to execute the district heating network creation process.
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:return: NetworkX graph with nodes and edges representing the network.
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"""
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self._load_and_process_data()
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self._find_nearest_roads()
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self._process_intersections()
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network_graph = self._create_network_graph()
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return network_graph
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def _load_and_process_data(self):
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"""
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Load and process the building and road data.
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"""
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# Load building data
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with open(self.buildings_file, 'r') as file:
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city = json.load(file)
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# Extract centroids from building data
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centroids = []
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buildings = city['features']
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for building in buildings:
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coordinates = building['geometry']['coordinates'][0]
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building_polygon = Polygon(coordinates)
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centroid = building_polygon.centroid
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centroids.append(centroid)
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# Convert centroids to a GeoDataFrame
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self.centroids_gdf = gpd.GeoDataFrame(geometry=[Point(centroid.x, centroid.y) for centroid in centroids],
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crs='EPSG:4326')
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# Load road data
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self.gdf_road = gpd.read_file(self.roads_file)
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# Ensure centroids are in the same CRS as roads
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self.centroids_gdf = self.centroids_gdf.to_crs(self.gdf_road.crs)
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def _find_nearest_roads(self):
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"""
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Find the nearest road for each building centroid.
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"""
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# Process road geometries
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self.gdf_clean = gpd.GeoDataFrame(
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{'geometry': [LineString([coord for coord in line.coords]) for line in self.gdf_road.geometry]})
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# Find the nearest road line and point for each centroid
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self.closest_linestrings = []
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self.nearest_points = []
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for centroid in self.centroids_gdf.geometry:
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closest_road = min(self.gdf_clean.geometry, key=lambda x: x.distance(centroid))
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self.closest_linestrings.append(closest_road)
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nearest_point = closest_road.interpolate(closest_road.project(centroid))
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self.nearest_points.append(nearest_point)
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def _process_intersections(self):
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"""
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Process intersections and create final geometries.
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"""
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# Create additional GeoDataFrames for points and nearest points
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self.gdf_pts = gpd.GeoDataFrame(
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{'geometry': [Point(coord) for line in self.gdf_clean.geometry for coord in line.coords]})
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self.gdf_pts2 = gpd.GeoDataFrame({'geometry': self.nearest_points})
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# Combine nearest points and road points into one GeoDataFrame
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self.gdf_pts3 = gpd.GeoDataFrame({'geometry': self.nearest_points + list(self.gdf_pts.geometry)})
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# Identify intersections and create LineStrings based on intersections
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self.gdf_clean["intersect"] = [
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[y for y in range(len(self.gdf_pts2)) if self.gdf_pts2.geometry[y].distance(geom) <= 1.0] for geom in
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self.gdf_clean.geometry]
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self.gdf_cleaner = self.gdf_clean[self.gdf_clean["intersect"].apply(len).gt(0)].reset_index(drop=True)
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self.test_list = []
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for idx, row in self.gdf_cleaner.iterrows():
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for i in range(len(row["intersect"]) + 1):
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if i == 0:
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self.test_list.append(
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LineString([row['geometry'].coords[0], self.gdf_pts3.geometry[row['intersect'][i]]]))
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elif i < len(row['intersect']):
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self.test_list.append(LineString(
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[self.gdf_pts3.geometry[row['intersect'][i - 1]], self.gdf_pts3.geometry[row['intersect'][i]]]))
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else:
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self.test_list.append(
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LineString([self.gdf_pts3.geometry[row['intersect'][i - 1]], row['geometry'].coords[-1]]))
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self.gdf_cleanest = gpd.GeoDataFrame({'geometry': self.test_list})
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points = [coord for geom in self.gdf_cleanest.geometry for coord in geom.coords]
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gdf_pts_cnt = self.gdf_pts.copy()
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gdf_pts_cnt["count"] = gdf_pts_cnt.geometry.apply(lambda x: points.count(x.coords[0]))
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gdf_pts_reset = gdf_pts_cnt[gdf_pts_cnt["count"] > 1].reset_index(drop=True)
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gdf_pts_drop = gdf_pts_cnt[gdf_pts_cnt["count"] <= 1].reset_index(drop=True)
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# Remove unnecessary geometries from gdf_cleanest
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for idx, geom in self.gdf_cleanest.iterrows():
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for coord in geom.geometry.coords:
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if coord in [pt.coords[0] for pt in gdf_pts_drop.geometry]:
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self.gdf_cleanest = self.gdf_cleanest.drop(idx)
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self.gdf_cleanest.reset_index(drop=True, inplace=True)
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def _create_network_graph(self):
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"""
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Create a NetworkX graph from the processed geospatial data.
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:return: A NetworkX graph representing the district heating network.
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"""
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G = nx.Graph()
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# Add centroids and nearest points as nodes
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for centroid in self.centroids_gdf.geometry:
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G.add_node((centroid.x, centroid.y), type='centroid')
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for point in self.nearest_points:
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G.add_node((point.x, point.y), type='nearest_point')
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# Add edges with lengths as weights for the road network
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for line in self.gdf_cleanest.geometry:
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length = line.length
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if isinstance(line.boundary, MultiPoint):
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# Handle MultiPoint boundary
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points = list(line.boundary.geoms)
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for i in range(len(points) - 1):
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start_point = points[i]
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end_point = points[i + 1]
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G.add_edge((start_point.x, start_point.y), (end_point.x, end_point.y), weight=length)
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else:
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# Handle typical case with two endpoints
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start_point, end_point = line.boundary
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G.add_edge((start_point.x, start_point.y), (end_point.x, end_point.y), weight=length)
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# Add edges connecting nearest points to their centroids
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for point, centroid in zip(self.nearest_points, self.centroids_gdf.geometry):
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distance = point.distance(centroid)
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G.add_edge((point.x, point.y), (centroid.x, centroid.y), weight=distance)
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return G
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def plot_network_graph(self, network_graph):
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"""
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Plot the network graph using matplotlib and networkx.
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:param network_graph: The NetworkX graph to be plotted.
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"""
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plt.figure(figsize=(12, 12))
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pos = {node: (node[0], node[1]) for node in network_graph.nodes()}
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# Draw nodes with type 'centroid'
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centroids = [node for node, attr in network_graph.nodes(data=True) if attr.get('type') == 'centroid']
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nx.draw_networkx_nodes(network_graph, pos, nodelist=centroids, node_color='blue', node_size=50,
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label='Centroids')
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# Draw nodes with type 'nearest_point'
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nearest_points = [node for node, attr in network_graph.nodes(data=True) if attr.get('type') == 'nearest_point']
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nx.draw_networkx_nodes(network_graph, pos, nodelist=nearest_points, node_color='green', node_size=30,
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label='Nearest Points')
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# Draw other nodes
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other_nodes = [node for node, attr in network_graph.nodes(data=True) if 'type' not in attr]
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nx.draw_networkx_nodes(network_graph, pos, nodelist=other_nodes, node_color='red', node_size=20,
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label='Other Nodes')
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# Draw edges
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nx.draw_networkx_edges(network_graph, pos, edge_color='gray')
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plt.legend()
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plt.title('District Heating Network Graph')
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plt.axis('off')
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plt.show()
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76
README.md
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README.md
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# District Heating Network Creator
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## Introduction
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The `DistrictHeatingNetworkCreator` class is designed to process geospatial data for district heating networks. It takes building and road data as input, identifies the nearest road to each building centroid, processes intersections, and creates a network graph representing the district heating system.
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## Installation
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Before using `DistrictHeatingNetworkCreator`, ensure that the required libraries are installed. You can install them using pip:
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```bash
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pip install geopandas networkx matplotlib shapely
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```
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## Usage
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To use the `DistrictHeatingNetworkCreator`, follow these steps:
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1. Initialize the class with paths to your GeoJSON file containing building data and Shapefile containing road data.
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Example files:
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- `buildings.geojson`: A GeoJSON file containing building data.
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- `montreal_roads.shp`: A Shapefile containing road data for Montreal.
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2. Call the `run` method to process the data and create the network graph.
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3. Optionally, call the `plot_network_graph` method to visualize the network.
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Example:
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```python
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from DistrictHeatingNetworkCreator import DistrictHeatingNetworkCreator
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# Initialize the class
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network_creator = DistrictHeatingNetworkCreator('path/to/buildings.geojson', 'path/to/montreal_roads.shp')
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# Create the network graph
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network_graph = network_creator.run()
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# Plot the network graph (optional)
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network_creator.plot_network_graph(network_graph)
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```
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## Class Methods
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### `__init__(self, buildings_file, roads_file)`
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Initializes the class with paths to the building and road data files.
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- `buildings_file`: Path to the GeoJSON file containing building data.
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- `roads_file`: Path to the Shapefile containing road data.
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### `run(self)`
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Executes the district heating network creation process. Returns a NetworkX graph representing the network.
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### `_load_and_process_data(self)`
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Loads and processes the building and road data from the specified files.
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### `_find_nearest_roads(self)`
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Identifies the nearest road for each building centroid and calculates the nearest point on these roads.
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### `_process_intersections(self)`
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Processes intersections and creates final geometries for the network graph.
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### `_create_network_graph(self)`
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Creates a NetworkX graph from the processed geospatial data, representing the district heating network.
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### `plot_network_graph(self, network_graph)`
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Plots the network graph using matplotlib and networkx.
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- `network_graph`: The NetworkX graph to be plotted.
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515
input_files/buildings.geojson
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input_files/buildings.geojson
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{
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"type": "FeatureCollection",
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"features": [
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{
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"type": "Feature",
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"geometry": {
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"type": "Polygon",
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"coordinates": [
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[
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"year_of_construction": 1986
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||||
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||||
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||||
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||||
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|
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||||
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"year_of_construction": 1986
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}
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"type": "Feature",
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}
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}
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]
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||||
}
|
||||
1
input_files/roads/geobase_mtl.cpg
Normal file
1
input_files/roads/geobase_mtl.cpg
Normal file
|
|
@ -0,0 +1 @@
|
|||
UTF-8
|
||||
BIN
input_files/roads/geobase_mtl.dbf
Normal file
BIN
input_files/roads/geobase_mtl.dbf
Normal file
Binary file not shown.
1
input_files/roads/geobase_mtl.prj
Normal file
1
input_files/roads/geobase_mtl.prj
Normal file
|
|
@ -0,0 +1 @@
|
|||
PROJCS["NAD_1983_MTM_8",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",304800.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-73.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
|
||||
BIN
input_files/roads/geobase_mtl.shp
Normal file
BIN
input_files/roads/geobase_mtl.shp
Normal file
Binary file not shown.
BIN
input_files/roads/geobase_mtl.shx
Normal file
BIN
input_files/roads/geobase_mtl.shx
Normal file
Binary file not shown.
13
main.py
Normal file
13
main.py
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
from DistrictHeatingNetworkCreator import DistrictHeatingNetworkCreator
|
||||
|
||||
# Initialize the class
|
||||
network_creator = DistrictHeatingNetworkCreator(
|
||||
'./input_files/buildings.geojson',
|
||||
'./input_files/roads/geobase_mtl.shp'
|
||||
)
|
||||
|
||||
# Create the network graph
|
||||
network_graph = network_creator.run()
|
||||
|
||||
# Plot the network graph (optional)
|
||||
network_creator.plot_network_graph(network_graph)
|
||||
Loading…
Reference in New Issue
Block a user