ut_heatmap_generator.py¶

Overview¶

This file contains 9 documented elements.

Classes¶

UTHeatmapGenerator¶

Generates heatmaps for archaeological potential and risk analysis.

Supports three interpolation methods: - KDE (Kernel Density Estimation): Best for density visualization - IDW (Inverse Distance Weighting): Best for value interpolation - Grid: Best for simple aggregation visualization

Methods¶

init(self, output_dir, crs)¶

Initialize the heatmap generator.

Args: output_dir: Directory for output files (default: temp dir) crs: Coordinate Reference System (QgsCoordinateReferenceSystem)

log_message(self, message, level)¶

Log message to QGIS if available.

generate_heatmap(self, points, values, method, cell_size, bandwidth, power, search_radius, extent, map_type)¶

Generate a heatmap using the specified method.

Args: points: List of (x, y) coordinate tuples values: List of values corresponding to each point method: 'kde', 'idw', or 'grid' cell_size: Grid cell size in map units bandwidth: KDE bandwidth (or 'auto') power: IDW power parameter search_radius: Maximum search radius for IDW extent: (xmin, ymin, xmax, ymax) or QgsRectangle map_type: 'potential' or 'risk' (for color styling)

Returns: Dictionary with: - raster_path: Path to output GeoTIFF - layer: QgsRasterLayer (if QGIS available) - stats: Statistics dictionary - method: Method used - timestamp: Generation timestamp

generate_vector_grid(self, points, values, cell_size, extent, value_field, map_type)¶

Generate a vector grid layer with aggregated values per cell.

Args: points: List of (x, y) coordinate tuples values: List of values corresponding to each point cell_size: Grid cell size in map units extent: (xmin, ymin, xmax, ymax) or None to auto-calculate value_field: Name of the value field map_type: 'potential' or 'risk'

Returns: Dictionary with: - layer: QgsVectorLayer (memory layer) - stats: Statistics dictionary - cell_count: Number of cells with data

generate_masked_heatmap(self, points, values, mask_polygon, method, cell_size, classification_scheme, map_type)¶

Generate a heatmap masked to an irregular polygon boundary (MOPR).

This method is designed for GNA export, generating heatmaps that are clipped to the project area polygon and optionally classified into VRP/VRD categories.

Args: points: List of (x, y) coordinate tuples values: List of values (potential_score or risk_score) mask_polygon: QgsGeometry of the project boundary (MOPR) method: 'kde', 'idw', or 'grid' cell_size: Grid cell size in map units (default 50m) classification_scheme: Dict for VRP/VRD classification: {(min, max): {'code': 'XX', 'label': '...', 'color': '#XXXXXX'}} map_type: 'potential' or 'risk' for styling

Returns: Dictionary with: - raster_path: Path to masked GeoTIFF - vector_layer: Classified multipolygon layer (if scheme provided) - stats: Statistics dictionary - method: Method used

generate_gna_layers(self, ut_records, mask_polygon, method, cell_size, vrp_scheme, vrd_scheme)¶

Generate complete GNA VRP and VRD layers from UT records.

Convenience method that generates both potential and risk heatmaps masked to the project polygon.

Args: ut_records: List of UT records with potential_score and risk_score mask_polygon: QgsGeometry of project boundary method: Interpolation method cell_size: Grid cell size vrp_scheme: VRP classification scheme (or use default) vrd_scheme: VRD classification scheme (or use default)

Returns: Dictionary with vrp_layer, vrd_layer, vrp_raster, vrd_raster

Functions¶

get_cell_polygon(iy, ix)¶

Create polygon for a single cell.

Parameters: - iy - ix