91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
import osmnx as ox
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import json
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import os
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# 1. Configuration
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PLACE_NAME = "Wisconsin State Capitol, Madison, USA"
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DIST = 2400 # Meters radius around center
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# 2. Download Data
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print(f"Downloading data for {PLACE_NAME}...")
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tags = {"building": True}
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# UPDATED FOR V2.0: Access features module explicitly
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try:
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# Try new v2.0 syntax
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gdf = ox.features.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
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except AttributeError:
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# Fallback for older versions
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gdf = ox.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
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# 3. Project to meters (Local Grid)
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# UPDATED FOR V2.0: Use GeoPandas native estimation
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print("Projecting to local grid...")
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gdf_proj = gdf.to_crs(gdf.estimate_utm_crs())
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# 4. Prepare Data for THREE.js
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buildings = []
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# Calculate center to normalize coordinates to (0,0)
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center_x = gdf_proj.geometry.centroid.x.mean()
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center_y = gdf_proj.geometry.centroid.y.mean()
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print("Processing geometry...")
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for _, row in gdf_proj.iterrows():
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if row.geometry.geom_type == "Polygon":
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# Get dimensions
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minx, miny, maxx, maxy = row.geometry.bounds
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width = maxx - minx
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depth = maxy - miny
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# Get Height (Clean dirty data)
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height = 10 # Default fallback
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# Check for 'height' tag
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if "height" in row and str(row["height"]) != "nan":
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try:
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# Clean strings like "10 m" or "approx 10"
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clean_h = "".join(
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filter(lambda x: x.isdigit() or x == ".", str(row["height"]))
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)
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height = float(clean_h)
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except:
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pass
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# Check for 'building:levels' tag
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elif "building:levels" in row and str(row["building:levels"]) != "nan":
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try:
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clean_l = "".join(
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filter(
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lambda x: x.isdigit() or x == ".", str(row["building:levels"])
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)
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)
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height = float(clean_l) * 3.5 # Approx 3.5m per floor
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except:
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pass
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# Normalize position relative to center
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x = (minx + maxx) / 2 - center_x
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z = center_y - (miny + maxy) / 2 # Invert Y for 3D Z-axis
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# Add to array: [x, z, width, depth, height]
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buildings.append(
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[
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round(x, 1),
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round(z, 1),
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round(width, 1),
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round(depth, 1),
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round(height, 1),
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]
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)
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# 5. Save to Public folder
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output_path = os.path.join(os.path.dirname(__file__), "../public/city_data.json")
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# Ensure directory exists just in case
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(buildings, f)
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print(f"Done! Saved {len(buildings)} buildings to {output_path}")
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