set up python data pipeline

.gitignore

@@ -4,4 +4,4 @@ __pycache__
 .DS_Store
 dist
 *.log
-data-pipeline/.cache
+data-pipeline/cache

data-pipeline/generate_city.py

@@ -0,0 +1,90 @@
+import osmnx as ox
+import json
+import os
+
+# 1. Configuration
+PLACE_NAME = "Empire State Building, New York, USA"
+DIST = 500  # Meters radius around center
+
+# 2. Download Data
+print(f"Downloading data for {PLACE_NAME}...")
+tags = {"building": True}
+
+# UPDATED FOR V2.0: Access features module explicitly
+try:
+    # Try new v2.0 syntax
+    gdf = ox.features.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
+except AttributeError:
+    # Fallback for older versions
+    gdf = ox.features_from_address(PLACE_NAME, tags=tags, dist=DIST)
+
+# 3. Project to meters (Local Grid)
+# UPDATED FOR V2.0: Use GeoPandas native estimation
+print("Projecting to local grid...")
+gdf_proj = gdf.to_crs(gdf.estimate_utm_crs())
+
+# 4. Prepare Data for THREE.js
+buildings = []
+
+# Calculate center to normalize coordinates to (0,0)
+center_x = gdf_proj.geometry.centroid.x.mean()
+center_y = gdf_proj.geometry.centroid.y.mean()
+
+print("Processing geometry...")
+for _, row in gdf_proj.iterrows():
+    if row.geometry.geom_type == "Polygon":
+        # Get dimensions
+        minx, miny, maxx, maxy = row.geometry.bounds
+        width = maxx - minx
+        depth = maxy - miny
+
+        # Get Height (Clean dirty data)
+        height = 10  # Default fallback
+
+        # Check for 'height' tag
+        if "height" in row and str(row["height"]) != "nan":
+            try:
+                # Clean strings like "10 m" or "approx 10"
+                clean_h = "".join(
+                    filter(lambda x: x.isdigit() or x == ".", str(row["height"]))
+                )
+                height = float(clean_h)
+            except:
+                pass
+        # Check for 'building:levels' tag
+        elif "building:levels" in row and str(row["building:levels"]) != "nan":
+            try:
+                clean_l = "".join(
+                    filter(
+                        lambda x: x.isdigit() or x == ".", str(row["building:levels"])
+                    )
+                )
+                height = float(clean_l) * 3.5  # Approx 3.5m per floor
+            except:
+                pass
+
+        # Normalize position relative to center
+        x = (minx + maxx) / 2 - center_x
+        z = center_y - (miny + maxy) / 2  # Invert Y for 3D Z-axis
+
+        # Add to array: [x, z, width, depth, height]
+        buildings.append(
+            [
+                round(x, 1),
+                round(z, 1),
+                round(width, 1),
+                round(depth, 1),
+                round(height, 1),
+            ]
+        )
+
+# 5. Save to Public folder
+output_path = os.path.join(os.path.dirname(__file__), "../public/city_data.json")
+
+# Ensure directory exists just in case
+os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+with open(output_path, "w") as f:
+    json.dump(buildings, f)
+
+print(f"Done! Saved {len(buildings)} buildings to {output_path}")