import os
import json
import random
from math import cos
import geopandas as gpd
from matplotlib import pyplot as plt
from shapely.ops import linemerge, unary_union, polygonize
from shapely.geometry import Polygon, LineString
from server.consts import LAKE_RELATIONS_PATH
def cut_map_handler(self, cursor, lake_name: str, cell_size_in_km: float = 0.5):
status_code, map_data = cut_map(cursor, lake_name, cell_size_in_km)
# Set headers
self.send_response(status_code)
self.send_header("Content-type", "application/json")
self.end_headers()
# Write the map data to the response
self.wfile.write(json.dumps(map_data).encode('utf-8'))
def cut_map(cursor, lake_name: str, cell_size_in_km: float = 0.5) -> (int, str):
"""
Cuts a map into a grid based on a selected cell size
Parameters:
cursor (cursor): An Sqlite3 cursor object that points to the database
lake_name (str): The name of the lake to be cut
cell_size_in_km (float): The selected cell size in kilometers
"""
try:
# Read relation from GeoJson file and extract all geometry of type Polygon
geo_data = gpd.read_file(LAKE_RELATIONS_PATH + lake_name + ".geojson")
polygon_data = geo_data[geo_data['geometry'].geom_type == 'Polygon']
polygons = [Polygon(polygon.exterior) for polygon in polygon_data['geometry']]
if len(polygons) <= 1:
raise Exception("Failed to convert JSON object to Shapely Polygons")
# Select an arbitrary x and y value from within the polygon
bounds = polygons[0].bounds
start_x, start_y, _, _ = bounds
# Calculate the cell width and height in degrees of latitude and longitude
cell_width = cell_size_in_km / 111.3200
cell_height = cell_width / cos(start_x * 0.01745)
# List to store new GeoJSON feature objects
features = [] # List to store new GeoJSON feature objects
sub_div_id = 0 # Tracker to create unique subdivision ids
divided_map = [] # Object for plotting the tiles
# Process all polygons
for polygon in polygons:
# Generate a grid based on the calculated cell size
lines = create_grid(polygon, cell_width * 2, cell_height)
lines.append(polygon.boundary)
# Merge the grid lines into a single polygonized object
lines = unary_union(lines)
lines = linemerge(lines)
lines = list(polygonize(lines))
# Combine the polygon and the grid to form the subdivisions
for line in lines:
if line.intersects(polygon): # Add the geometry which intersects the gird
cell = (line.intersection(polygon))
divided_map.append(cell)
# Calculate cell center based on bounds, and round down to two decimals
min_x, min_y, max_x, max_y = cell.bounds
center = round(max_y - (max_y - min_y), 6), round(max_x - (max_x - min_x), 6)
rounded_coordinates = []
if isinstance(cell, Polygon):
for coords in cell.exterior.coords:
rounded_coords = (round(coords[0], 4), round(coords[1], 4))
rounded_coordinates.append(rounded_coords)
rounded_tile = Polygon(rounded_coordinates)
geometry = rounded_tile.__geo_interface__
if not geometry['coordinates']:
continue # Skip empty tiles
# Create new feature object
tile_feature = {
'type': 'Feature',
'properties': {
'sub_div_id': str(sub_div_id),
'sub_div_center': center
},
'geometry': geometry
}
# Append new feature object to list, and increment sub_div_id for next iteration
features.append(tile_feature)
sub_div_id += 1
break # NB test break
# Create new GeoJSON object containing all the new feature objects
feature_collection = {
'type': 'FeatureCollection',
'cell_count': sub_div_id, # Add the last subdivision ID as number of tiles
'cell_width': cell_width,
'cell_height': cell_height,
'cell_size_in_km': cell_size_in_km,
'features': features
}
# Check if the name exists in the database
cursor.execute('''
SELECT Name FROM BodyOfWater WHERE Name = ?;
''', (lake_name,))
existing_lake = cursor.fetchone()
# If lake_name doesn't exist, insert it into the database
if existing_lake is None:
cursor.execute('''
INSERT INTO BodyOfWater(Name) VALUES (?);
''', (lake_name,))
# Plot the newly created map and save it to a new file
plot_map(divided_map)
write_json_to_file(lake_name, feature_collection)
# Return OK and the newly divided map
return 200, feature_collection
except FileNotFoundError as e:
print(f"Failed to find the map file: {e}")
return 404, f"Failed to find the map file: {e}"
except Exception as e:
print(f"Error in adding new map: {e}")
return 500, f"Error in adding new map: {e}"
def create_grid(poly: Polygon, cell_width: float, cell_height: float):
"""
Returns a list of vertical and horizontal LineStrings that create a grid.
Parameters:
poly (Polygon): A Shapely Polygon representing a map or part of a map
cell_width (float): The width of the grid cells in degrees
cell_height (float): The height of the grid cells in degrees
Returns:
grid_lines (list): List of LineString objects defining the grid
"""
# Retrieve bounds of the entire polygon
bounds = poly.bounds
min_x, min_y, max_x, max_y = bounds
# List to store all created lines
grid_lines = []
# Create vertical lines while within bounds
x = min_x
while x <= max_x:
line = LineString([(x, min_y), (x, max_y)])
grid_lines.append(line)
x += cell_width
# Create horizontal lines while within bounds
y = min_y
while y <= max_y:
line = LineString([(min_x, y), (max_x, y)])
grid_lines.append(line)
y += cell_height
return grid_lines
def write_json_to_file(lake_name: str, map_data: dict):
"""
Writes a divided map to a JSON file and updates all_lake_names.json
Parameters:
lake_name (str): Name of the lake and file to write to
map_data (dict): List of map polygons converted to a JSON dictionary
"""
# Create and write divided map to new file
print("Writing to file...")
if not os.path.exists(LAKE_RELATIONS_PATH):
raise Exception("Directory from path does not exist")
with open(LAKE_RELATIONS_PATH + '/' + lake_name + '_div.json', 'w') as f:
json.dump(map_data, f)
# Read all_system_lakes.json
with open(LAKE_RELATIONS_PATH + 'all_lake_names.json', 'r', encoding='utf-8') as f:
data = json.load(f)
# Check if the lake name exists in the list
if lake_name not in data:
data.append(lake_name) # Only append to list if it does not already exist
# Update all_lake_names.json with new lake name
with open(LAKE_RELATIONS_PATH + 'all_lake_names.json', 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
# Plotting the map can take a considerable amount of time, especially when creating maps with many
# subdivisions. Removing calls to plot_map will speed up the process, but it is highly recommended
# to plot the map after each division to ensure that the map was divided as intended.
def plot_map(divided_map):
"""
Plots the divisions of a map using matplotlib.
Parameters:
divided_map (list): List of Shapely Polygons to be plotted
"""
print("Plotting... This may take some time...")
# Convert Polygon objects to GeoDataFrames
tiles = [gpd.GeoDataFrame(geometry=[tile]) for tile in divided_map]
# Configure plot settings
fig, ax = plt.subplots()
# Plot each tile
for tile in tiles:
# Give each tile a random color to clearly visualize the grid
random_color = "#{:06x}".format(random.randint(0, 0xFFFFFF))
gpd.GeoSeries(tile.geometry).plot(ax=ax, facecolor=random_color, edgecolor='none')
ax.set_aspect(1.8)
# Display plot
plt.show()