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gameplay mechanics

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This commit is contained in:
Evan Scamehorn
2025-12-02 14:40:18 -06:00
parent a3e057ef37
commit 68ca45f58e
8 changed files with 316 additions and 140 deletions
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86
data-pipeline/generate_city.py
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@@ -3,10 +3,10 @@ import json
import os
import networkx as nx
from shapely.ops import unary_union
from shapely.geometry import Polygon, MultiPolygon
from scipy.spatial import cKDTree
import re
import numpy as np
import pandas as pd
# ==========================================
# 1. Configuration
@@ -41,7 +41,6 @@ JOB_DENSITY_FACTOR = 0.08 # Jobs per m3 (Commercial)
# 2. Helpers
# ==========================================
def get_height(row):
"""Estimates building height."""
h = 8.0
if "height" in row and str(row["height"]).lower() != "nan":
try:
@@ -63,7 +62,6 @@ def get_height(row):
def estimate_road_width(row):
"""Estimates width with US-unit safety checks."""
for key in ["width", "width:carriageway", "est_width"]:
if key in row and str(row[key]) != "nan":
val_str = str(row[key]).lower()
@@ -96,10 +94,6 @@ def estimate_road_width(row):
def classify_building(row, height, area):
"""
Classifies a building as Residential (Pop) or Commercial (Jobs)
and estimates the count based on volume.
"""
b_type = str(row.get("building", "yes")).lower()
amenity = str(row.get("amenity", "")).lower()
office = str(row.get("office", "")).lower()
@@ -107,7 +101,6 @@ def classify_building(row, height, area):
volume = area * height
# Lists of tags
residential_tags = [
"apartments",
"residential",
@@ -136,9 +129,7 @@ def classify_building(row, height, area):
or (shop != "nan" and shop != "")
)
# Default logic if generic "yes"
if not is_res and not is_com:
# Small buildings likely houses, big generic likely commercial in city center
if volume > 5000:
is_com = True
else:
@@ -152,11 +143,11 @@ def classify_building(row, height, area):
if is_res:
pop = round(volume * POP_DENSITY_FACTOR)
category = "residential"
density_score = min(1.0, pop / 500) # Normalize for color (0-1)
density_score = min(1.0, pop / 500)
elif is_com:
jobs = round(volume * JOB_DENSITY_FACTOR)
category = "commercial"
density_score = min(1.0, jobs / 1000) # Normalize for color (0-1)
density_score = min(1.0, jobs / 1000)
return category, density_score, pop, jobs
@@ -201,12 +192,32 @@ def parse_line_points(geom, center_x, center_y):
# ==========================================
print(f"1. Downloading Data for: {PLACE_NAME}...")
# Define valid tags lists for filtering later
PARK_TAGS_LEISURE = [
"park",
"garden",
"playground",
"golf_course",
"pitch",
"recreation_ground",
]
PARK_TAGS_LANDUSE = [
"grass",
"forest",
"park",
"meadow",
"village_green",
"recreation_ground",
"orchard",
]
NATURAL_TAGS = ["water", "bay", "coastline"]
tags_visual = {
"building": True,
"natural": ["water", "bay"],
"leisure": ["park", "garden"],
"landuse": ["grass", "forest", "park"],
"amenity": True, # Fetch amenities to help classify jobs
"natural": NATURAL_TAGS,
"leisure": PARK_TAGS_LEISURE,
"landuse": PARK_TAGS_LANDUSE,
"amenity": True, # Needed for zoning, but MUST be filtered out of geometry
"office": True,
"shop": True,
}
@@ -228,23 +239,26 @@ center_y = gdf_visual.geometry.centroid.y.mean()
output_visual = {"buildings": [], "water": [], "parks": [], "roads": []}
output_routing = {"nodes": {}, "edges": []}
# We will store building data to map it to graph nodes later
building_data_points = [] # (x, y, pop, jobs)
building_data_points = []
print("3. Processing Visual Layers & Census Simulation...")
for idx, row in gdf_visual.iterrows():
# Only process polygons
if row.geometry.geom_type not in ["Polygon", "MultiPolygon"]:
continue
polygons = parse_geometry(row.geometry, center_x, center_y)
for poly_data in polygons:
# 1. Buildings (With Zoning Logic)
# -----------------------------
# 1. BUILDINGS
# -----------------------------
if "building" in row and str(row["building"]) != "nan":
height = get_height(row)
area = row.geometry.area
# Zoning / Census Simulation
cat, score, pop, jobs = classify_building(row, height, area)
# Store centroid for graph mapping
cx = row.geometry.centroid.x - center_x
cy = row.geometry.centroid.y - center_y
building_data_points.append([cx, cy, pop, jobs])
@@ -257,16 +271,29 @@ for idx, row in gdf_visual.iterrows():
}
)
# 2. Water
elif ("natural" in row and str(row["natural"]) != "nan") or (
# -----------------------------
# 2. WATER
# -----------------------------
elif ("natural" in row and str(row["natural"]) in NATURAL_TAGS) or (
"water" in row and str(row["water"]) != "nan"
):
output_visual["water"].append({"shape": poly_data})
# 3. Parks
else:
# -----------------------------
# 3. PARKS (STRICT FILTER)
# -----------------------------
elif ("leisure" in row and str(row["leisure"]) in PARK_TAGS_LEISURE) or (
"landuse" in row and str(row["landuse"]) in PARK_TAGS_LANDUSE
):
output_visual["parks"].append({"shape": poly_data})
# -----------------------------
# 4. IGNORE EVERYTHING ELSE
# -----------------------------
# Amenities, parking lots, and landuse=commercial fall here and are NOT drawn.
else:
pass
print(" Buffering roads...")
road_polys = []
for idx, row in gdf_edges.iterrows():
@@ -281,21 +308,18 @@ if road_polys:
output_visual["roads"].append({"shape": shape})
print("4. Mapping Census Data to Graph Nodes...")
# Create a KDTree of building centroids
if building_data_points:
b_coords = np.array([[b[0], b[1]] for b in building_data_points])
b_data = np.array([[b[2], b[3]] for b in building_data_points]) # pop, jobs
b_data = np.array([[b[2], b[3]] for b in building_data_points])
tree = cKDTree(b_coords)
for node_id, row in gdf_nodes.iterrows():
nx = row.geometry.x - center_x
ny = row.geometry.y - center_y
# Find all buildings within 100m of this node
if building_data_points:
indices = tree.query_ball_point([nx, ny], r=100)
if indices:
# Sum pop/jobs of nearby buildings
nearby_stats = np.sum(b_data[indices], axis=0)
node_pop = int(nearby_stats[0])
node_jobs = int(nearby_stats[1])
@@ -307,7 +331,7 @@ for node_id, row in gdf_nodes.iterrows():
output_routing["nodes"][int(node_id)] = {
"x": round(nx, 2),
"y": round(ny, 2),
"pop": node_pop, # Store for gameplay later
"pop": node_pop,
"jobs": node_jobs,
}