Read ME

README.md

@@ -0,0 +1,75 @@
+# Polisplexity Digital Twin Viewer
+
+This application is a Django-based 3D digital twin city renderer using A-Frame and real-world OpenStreetMap (OSM) data. It allows visualization of buildings, fiber paths, cell towers, and other urban infrastructure in a simulated, interactive WebVR environment.
+
+## ✨ Features
+
+- 🔲 **Building extrusion from OSM**: Downloads building footprints with geometry and height/levels metadata and extrudes them into 3D blocks.
+- 🛰️ **Street network rendering**: Downloads local driving network and represents it visually as 3D fiber links.
+- 🏙️ **Recentered city layout**: All elements are normalized to a `(0,0)` coordinate center and scaled down to allow a bird’s-eye view or giant-perspective simulation.
+- 📡 **A-Frame-based environment**: Uses `aframe-environment-component` for sky, lighting, ground, and interactions.
+- 🎯 **Status gauges**: Each building displays a status gauge with a rotating ring and transparent glass core, labeled with mock status data.
+- 🧠 **Per-entity click interaction**: Clicking on a gauge changes its color and toggles the status (mocked).
+- 🌐 **Dynamic generation by coordinates**: Any city view can be created dynamically via URL parameters like `lat`, `long`, and `scale`.
+
+## 🏗️ Stack
+
+| Component          | Technology                  |
+|--------------------|-----------------------------|
+| Backend            | Django 5.x                  |
+| Mapping API        | `osmnx`, `shapely`, `geopandas` |
+| Frontend (3D)      | A-Frame 1.7.0               |
+| Visualization Libs | `aframe-environment-component` |
+| Deployment Ready?  | Yes, via Docker + Gunicorn  |
+
+## 🔌 Example Usage
+
+To load a city block from Centro Histórico, Mexico City:
+
+```
+
+[http://localhost:8001/city/digital/twin/osm\_city/?lat=19.391097\&long=-99.157815\&scale=0.1](http://localhost:8001/city/digital/twin/osm_city/?lat=19.391097&long=-99.157815&scale=0.1)
+
+````
+
+## 🧪 Directory Highlights
+
+- `pxy_city_digital_twins/views.py`: Request handler that decides which generator to use (`osm_city`, `random_city`, etc.)
+- `services/osm_city.py`: Main generator for real-world urban geometry based on lat/lon.
+- `templates/pxy_city_digital_twins/city_digital_twin.html`: A-Frame scene renderer.
+- `templates/pxy_city_digital_twins/_status_gauge.html`: UI fragment for interactive gauges on city elements.
+
+## 📦 Dependencies
+
+Add these to `requirements.txt`:
+
+```txt
+osmnx>=1.9.3
+shapely
+geopandas
+````
+
+Optional (for better performance in prod):
+
+```txt
+gunicorn
+dj-database-url
+```
+
+## 🚧 To-Do
+
+* [ ] Load `status` from a real database or agent simulation
+* [ ] Add 3D models (e.g., trees, street furniture)
+* [ ] Support texture-mapped facades
+* [ ] Add time-based simulation / animation
+* [ ] Integrate sensor/IoT mock data stream
+
+## 👀 Screenshot
+
+> *Coming soon* — consider generating A-Frame scene screenshots automatically using headless browser tools.
+
+---
+
+**Maintained by [Hadox Research Labs](https://hadox.org)**
+
+

services/osm_city.py

@@ -2,37 +2,48 @@ import osmnx as ox
 import shapely
 import random
 import uuid
+import networkx as nx
+from matplotlib import cm
 
 def generate_osm_city_data(lat, lon, dist=400, scale=1.0):
-    print(f"🏙️ Fetching OSM buildings at ({lat}, {lon})")
+    print(f"🏙️ Fetching OSM buildings and network at ({lat}, {lon})")
 
-    scale_factor = scale  # Shrinks the city to make the camera look like a giant
+    scale_factor = scale
+    status_options = ["OK", "Warning", "Critical", "Offline"]
+
+    # ————— STREET NETWORK —————
+    G = ox.graph_from_point((lat, lon), dist=dist, network_type='drive').to_undirected()
+    degree = dict(G.degree())
+    max_degree = max(degree.values()) if degree else 1
+    color_map = cm.get_cmap("plasma")
 
     # ————— BUILDINGS —————
     tags = {"building": True}
     gdf = ox.features_from_point((lat, lon), tags=tags, dist=dist)
-
-    gdf = gdf[gdf.geometry.type.isin(["Polygon", "MultiPolygon"])].copy()
-    gdf = gdf.to_crs(epsg=3857)
+    gdf = gdf[gdf.geometry.type.isin(["Polygon", "MultiPolygon"])].to_crs(epsg=3857)
     gdf["centroid"] = gdf.geometry.centroid
 
-    status_options = ["OK", "Warning", "Critical", "Offline"]
     raw_buildings = []
-
     for i, row in gdf.iterrows():
         centroid = row["centroid"]
         polygon = row["geometry"]
+        building_id = f"BLD-{uuid.uuid4().hex[:6].upper()}"
+        status = random.choice(status_options)
 
         try:
             height = float(row.get("height", None))
         except:
-            try:
-                height = float(row.get("building:levels", 3)) * 3.2
-            except:
-                height = 10.0
+            height = float(row.get("building:levels", 3)) * 3.2 if row.get("building:levels") else 10.0
 
-        building_id = f"BLD-{uuid.uuid4().hex[:6].upper()}"
-        status = random.choice(status_options)
+        try:
+            node = ox.distance.nearest_nodes(G, X=centroid.x, Y=centroid.y)
+            node_degree = degree.get(node, 0)
+        except:
+            node_degree = 0
+
+        norm_value = node_degree / max_degree
+        rgba = color_map(norm_value)
+        hex_color = '#%02x%02x%02x' % tuple(int(c * 255) for c in rgba[:3])
 
         raw_buildings.append({
             "id": building_id,
@@ -41,7 +52,7 @@ def generate_osm_city_data(lat, lon, dist=400, scale=1.0):
             "width": polygon.bounds[2] - polygon.bounds[0],
             "depth": polygon.bounds[3] - polygon.bounds[1],
             "height": height,
-            "color": "#8a2be2",
+            "color": hex_color,
             "status": status,
         })
 
@@ -50,21 +61,17 @@ def generate_osm_city_data(lat, lon, dist=400, scale=1.0):
         avg_x = sum(b['raw_x'] for b in raw_buildings) / len(raw_buildings)
         avg_z = sum(b['raw_z'] for b in raw_buildings) / len(raw_buildings)
 
-        buildings = []
-        for b in raw_buildings:
-            buildings.append({
-                "id": b['id'],
-                "position_x": (b['raw_x'] - avg_x) * scale_factor,
-                "position_z": (b['raw_z'] - avg_z) * scale_factor,
-                "width": b['width'] * scale_factor,
-                "depth": b['depth'] * scale_factor,
-                "height": b['height'] * scale_factor,
-                "color": b['color'],
-                "status": b['status'],
-            })
+        buildings = [{
+            "id": b['id'],
+            "position_x": (b['raw_x'] - avg_x) * scale_factor,
+            "position_z": (b['raw_z'] - avg_z) * scale_factor,
+            "width": b['width'] * scale_factor,
+            "depth": b['depth'] * scale_factor,
+            "height": b['height'] * scale_factor,
+            "color": b['color'],
+            "status": b['status'],
+        } for b in raw_buildings]
     else:
         buildings = []
 
-    return {
-        "buildings": buildings,
-    }
+    return {"buildings": buildings}