GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:573-671 (handler)

  The primary handler function implementing the 'distance_band_weights' tool. It loads point data from a shapefile, extracts coordinates, creates a libpysal DistanceBand weights object based on the given threshold and binary option, computes statistics and previews, ensures JSON serialization compatibility by converting numpy types, and returns structured results including weights_info for test compatibility.
  def distance_band_weights( data_path: str, threshold: float, binary: bool = True, id_field: Optional[str] = None ) -> Dict[str, Any]: """ Create a distance-based spatial weights (W) object from point data. - data_path: path to point shapefile or GeoPackage - threshold: distance threshold for neighbors (in CRS units, e.g., meters) - binary: True for binary weights, False for inverse distance weights - id_field: optional attribute name to use as observation IDs """ try: if not os.path.exists(data_path): return {"status": "error", "message": f"Data file not found: {data_path}"} gdf = gpd.read_file(data_path) if gdf.empty: return {"status": "error", "message": "Input file contains no features"} # Extract coordinates coords = [(geom.x, geom.y) for geom in gdf.geometry] # Create DistanceBand weights import libpysal if id_field and id_field in gdf.columns: ids = gdf[id_field].tolist() w = libpysal.weights.DistanceBand(coords, threshold=threshold, binary=binary, ids=ids) else: w = libpysal.weights.DistanceBand(coords, threshold=threshold, binary=binary) ids = w.id_order neighbor_counts = [w.cardinalities[i] for i in ids] islands = list(w.islands) if hasattr(w, "islands") else [] # Previews - convert to native Python types immediately preview_ids = ids[:5] neighbors_preview = {} weights_preview = {} for i in preview_ids: # Convert neighbor IDs and weights to native Python types neighbors = w.neighbors.get(i, []) weights_list = w.weights.get(i, []) neighbors_preview[i] = [int(n) if isinstance(n, (np.integer, np.int32, np.int64)) else n for n in neighbors] weights_preview[i] = [float(w_val) if isinstance(w_val, (np.floating, np.float32, np.float64)) else (int(w_val) if isinstance(w_val, (np.integer, np.int32, np.int64)) else w_val) for w_val in weights_list] result = { "n": int(w.n), "id_count": int(len(ids)), "threshold": float(threshold), "binary": bool(binary), "id_field": id_field, "neighbors_stats": { "min": int(min(neighbor_counts)) if neighbor_counts else 0, "max": int(max(neighbor_counts)) if neighbor_counts else 0, "mean": float(np.mean(neighbor_counts)) if neighbor_counts else 0.0, }, "islands": [int(i) if isinstance(i, (np.integer, np.int32, np.int64)) else i for i in islands], "neighbors_preview": neighbors_preview, "weights_preview": weights_preview, } # Convert numpy types to native Python types for serialization (recursive) def convert_numpy_types(obj): """Recursively convert numpy types to native Python types.""" if obj is None: return None if isinstance(obj, dict): return {k: convert_numpy_types(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return [convert_numpy_types(item) for item in obj] elif isinstance(obj, (np.integer, np.int32, np.int64, np.int8, np.int16)): return int(obj) elif isinstance(obj, (np.floating, np.float32, np.float64, np.float16)): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes)): # Handle other iterable types return [convert_numpy_types(item) for item in obj] else: return obj result = convert_numpy_types(result) return { "status": "success", "message": "DistanceBand spatial weights constructed successfully", "result": result, "weights_info": result, # Also include as weights_info for test compatibility } except Exception as e: logger.error(f"Error creating DistanceBand weights: {str(e)}") return {"status": "error", "message": f"Failed to create DistanceBand weights: {str(e)}"}