perf, build(vector_processing): add optimized version of distance com…

…putation (#455)

* add dependency: Numba
* improve documentation
* comment out the old implementation

Note: distance_to_anomaly, proximity_computation and proximity_to_anomaly all benefit from this optimization

---------

Co-authored-by: Niko Aarnio <[email protected]>

eis_toolkit/vector_processing/distance_computation.py

@@ -4,89 +4,304 @@
 import numpy as np
 from beartype import beartype
 from beartype.typing import Optional, Union
+from numba import njit, prange
 from rasterio import profiles, transform
-from shapely.geometry.base import BaseGeometry, BaseMultipartGeometry
 
-from eis_toolkit.exceptions import EmptyDataFrameException, NonMatchingCrsException, NumericValueSignException
+from eis_toolkit import exceptions
 from eis_toolkit.utilities.checks.raster import check_raster_profile
-from eis_toolkit.utilities.miscellaneous import row_points
 
 
 @beartype
 def distance_computation(
     geodataframe: gpd.GeoDataFrame, raster_profile: Union[profiles.Profile, dict], max_distance: Optional[Number] = None
 ) -> np.ndarray:
-    """Calculate distance from raster cell to nearest geometry.
+    """
+    Calculate distance from each raster cell (centre) to the nearest input geometry.
+
+    Pixels on top of input geometries are assigned distance of 0.
+
+    Uses Numba to perform calculations quickly. The computation time increases (roughly)
+    linearly with the amount of raster pixels defined by given `raster_profile`. Supports
+    Polygon, MultiPolygon, LineString, MultiLineString, Point and MultiPoint geometries.
 
     Args:
         geodataframe: The GeoDataFrame with geometries to determine distance to.
         raster_profile: The raster profile of the raster in which the distances
             to the nearest geometry are determined.
-        max_distance: The maximum distance in the output array.
+        max_distance: The maximum distance in the output array. Pixels beyond this
+            distance will be assigned `max_distance` value.
 
     Returns:
         A 2D numpy array with the distances computed.
 
     Raises:
         NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS.
         EmptyDataFrameException: The input geodataframe is empty.
+        NumericValueSignException: Max distance is defined and is not a positive number.
     """
     if raster_profile.get("crs") != geodataframe.crs:
-        raise NonMatchingCrsException("Expected coordinate systems to match between raster and GeoDataFrame.")
-    if geodataframe.shape[0] == 0:
-        raise EmptyDataFrameException("Expected GeoDataFrame to not be empty.")
+        raise exceptions.NonMatchingCrsException(
+            "Expected coordinate systems to match between raster and GeoDataFrame."
+        )
+    if geodataframe.empty:
+        raise exceptions.EmptyDataFrameException("Expected GeoDataFrame to not be empty.")
     if max_distance is not None and max_distance <= 0:
-        raise NumericValueSignException("Expected max distance to be a positive number.")
+        raise exceptions.NumericValueSignException("Expected max distance to be a positive number.")
 
     check_raster_profile(raster_profile=raster_profile)
 
     raster_width = raster_profile.get("width")
     raster_height = raster_profile.get("height")
     raster_transform = raster_profile.get("transform")
 
-    distance_matrix = _distance_computation(
-        raster_width=raster_width,
-        raster_height=raster_height,
-        raster_transform=raster_transform,
-        geodataframe=geodataframe,
+    # Generate the grid of raster cell center points
+    raster_points = _generate_raster_points(raster_width, raster_height, raster_transform)
+
+    # Initialize lists needed for Numba-compatible calculations
+    segment_coords = []  # These will also contain points coords, if present
+    segment_indices = [0]  # Start index
+    polygon_coords = []
+    polygon_indices = [0]  # Start index
+
+    for geometry in geodataframe.geometry:
+        if geometry.geom_type == "Polygon":
+            coords = list(geometry.exterior.coords)
+            for x, y in coords:
+                polygon_coords.extend([x, y])
+            polygon_indices.append(len(polygon_coords) // 2)
+            segments = [
+                (coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)
+            ]
+
+        elif geometry.geom_type == "MultiPolygon":
+            # For MultiPolygon, iterate over each polygon
+            segments = []
+            for poly in geometry.geoms:
+                coords = list(poly.exterior.coords)
+                for x, y in coords:
+                    polygon_coords.extend([x, y])
+                polygon_indices.append(len(polygon_coords) // 2)
+
+                # Add polygon boundary as segments for distance calculations
+                segments.extend(
+                    [(coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)]
+                )
+
+        elif geometry.geom_type == "LineString":
+            coords = list(geometry.coords)
+            segments = [
+                (coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)
+            ]
+
+        elif geometry.geom_type == "MultiLineString":
+            # For MultiLineString, iterate through each line string component
+            segments = []
+            for line in geometry.geoms:
+                coords = list(line.coords)
+                segments.extend(
+                    [(coords[i][0], coords[i][1], coords[i + 1][0], coords[i + 1][1]) for i in range(len(coords) - 1)]
+                )
+
+        elif geometry.geom_type == "Point":
+            segments = [(geometry.x, geometry.y)]
+
+        elif geometry.geom_type == "MultiPoint":
+            # For MultiPoint, iterate over each point and add as individual (x, y) tuples
+            segments = [(point.x, point.y) for point in geometry.geoms]
+
+        else:
+            raise exceptions.GeometryTypeException(f"Encountered unsupported geometry type: {geometry.geom_type}.")
+
+        segment_coords.extend(segments)
+        segment_indices.append(len(segment_coords))  # End index for this geometry's segments
+
+    # Convert all lists to numpy arrays
+    segment_coords = np.array(segment_coords, dtype=np.float64)
+    segment_indices = np.array(segment_indices, dtype=np.int64)
+    polygon_coords = np.array(polygon_coords, dtype=np.float64)
+    polygon_indices = np.array(polygon_indices, dtype=np.int64)
+
+    distance_matrix = _compute_distances_core(
+        raster_points,
+        segment_coords,
+        segment_indices,
+        polygon_coords,
+        polygon_indices,
+        raster_width,
+        raster_height,
+        max_distance,
     )
-    if max_distance is not None:
-        distance_matrix[distance_matrix > max_distance] = max_distance
 
     return distance_matrix
 
 
-def _calculate_row_distances(
-    row: int,
-    cols: np.ndarray,
-    raster_transform: transform.Affine,
-    geometries_unary_union: Union[BaseGeometry, BaseMultipartGeometry],
+@njit(parallel=True)
+def _compute_distances_core(
+    raster_points: np.ndarray,
+    segment_coords: np.ndarray,
+    segment_indices: np.ndarray,
+    polygon_coords: np.ndarray,
+    polygon_indices: np.ndarray,
+    width: int,
+    height: int,
+    max_distance: Optional[Number],
 ) -> np.ndarray:
-    row_distances = np.array(
-        [
-            point.distance(geometries_unary_union)
-            for point in row_points(row=row, cols=cols, raster_transform=raster_transform)
-        ]
-    )
-    return row_distances
+    distance_matrix = np.full((height, width), np.inf)
+    for i in prange(len(raster_points)):
+        px, py = raster_points[i]
+        min_dist = np.inf
 
+        # Check if the point is inside any polygon, if polygons are present
+        if len(polygon_indices) > 1 and _point_in_polygon(px, py, polygon_coords, polygon_indices):
+            min_dist = 0  # Set distance to zero if point is inside a polygon
+        else:
+            # Only calculate distance to segments if point is outside all polygons
+            for j in range(len(segment_indices) - 1):
+                for k in range(segment_indices[j], segment_indices[j + 1]):
+                    # Case 1: Point
+                    if len(segment_coords[k]) == 2:
+                        x1, y1 = segment_coords[k]
+                        dist = np.sqrt((px - x1) ** 2 + (py - y1) ** 2)
+                    # Case 2: Line segment
+                    else:
+                        x1, y1, x2, y2 = segment_coords[k]
+                        dist = _point_to_segment_distance(px, py, x1, y1, x2, y2)
+                    if dist < min_dist:
+                        min_dist = dist
 
-def _distance_computation(
-    raster_width: int, raster_height: int, raster_transform: transform.Affine, geodataframe: gpd.GeoDataFrame
-) -> np.ndarray:
+        # Apply max_distance threshold if specified
+        if max_distance is not None:
+            min_dist = min(min_dist, max_distance)
 
-    cols = np.arange(raster_width)
-    rows = np.arange(raster_height)
+        # Update the distance matrix
+        distance_matrix[i // width, i % width] = min_dist
+    return distance_matrix
 
-    geometries_unary_union = geodataframe.geometry.unary_union
 
-    distance_matrix = np.array(
-        [
-            _calculate_row_distances(
-                row=row, cols=cols, raster_transform=raster_transform, geometries_unary_union=geometries_unary_union
-            )
-            for row in rows
-        ]
-    )
+def _generate_raster_points(width: int, height: int, affine_transform: transform.Affine) -> np.ndarray:
+    """Generate a full grid of points from the raster dimensions and affine transform."""
+    cols, rows = np.meshgrid(np.arange(width), np.arange(height))
+    cols = cols.ravel()
+    rows = rows.ravel()
+    xs, ys = transform.xy(affine_transform, rows, cols, offset="center")
+    return np.column_stack([xs, ys])
 
-    return distance_matrix
+
+@njit
+def _point_to_segment_distance(px: Number, py: Number, x1: Number, y1: Number, x2: Number, y2: Number) -> np.ndarray:
+    """Calculate the minimum distance from a point to a line segment."""
+    dx, dy = x2 - x1, y2 - y1
+    if dx == 0 and dy == 0:
+        # Segment is a point (Should not happen)
+        return np.sqrt((px - x1) ** 2 + (py - y1) ** 2)
+    t = max(0, min(1, ((px - x1) * dx + (py - y1) * dy) / (dx * dx + dy * dy)))
+    nearest_x, nearest_y = x1 + t * dx, y1 + t * dy
+    return np.sqrt((px - nearest_x) ** 2 + (py - nearest_y) ** 2)
+
+
+@njit
+def _point_in_polygon(px: Number, py: Number, polygon_coords: np.ndarray, polygon_indices: np.ndarray) -> bool:
+    """Determine if a point is inside any polygon using the ray-casting algorithm."""
+    for p_start, p_end in zip(polygon_indices[:-1], polygon_indices[1:]):
+        inside = False
+        xints = 0.0
+        n = p_end - p_start
+        p1x, p1y = polygon_coords[2 * p_start], polygon_coords[2 * p_start + 1]
+        for i in range(n + 1):
+            p2x, p2y = polygon_coords[2 * (p_start + i % n)], polygon_coords[2 * (p_start + i % n) + 1]
+            if py > min(p1y, p2y):
+                if py <= max(p1y, p2y):
+                    if px <= max(p1x, p2x):
+                        if p1y != p2y:
+                            xints = (py - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
+                        if p1x == p2x or px <= xints:
+                            inside = not inside
+            p1x, p1y = p2x, p2y
+        if inside:
+            return True
+    return False
+
+
+# @beartype
+# def distance_computation(
+#     geodataframe: gpd.GeoDataFrame,
+#     raster_profile: Union[profiles.Profile, dict],
+#     max_distance: Optional[Number] = None
+# ) -> np.ndarray:
+#     """Calculate distance from raster cell to nearest geometry.
+
+#     Args:
+#         geodataframe: The GeoDataFrame with geometries to determine distance to.
+#         raster_profile: The raster profile of the raster in which the distances
+#             to the nearest geometry are determined.
+#         max_distance: The maximum distance in the output array.
+
+#     Returns:
+#         A 2D numpy array with the distances computed.
+
+#     Raises:
+#         NonMatchingCrsException: The input raster profile and geodataframe have mismatching CRS.
+#         EmptyDataFrameException: The input geodataframe is empty.
+#         NumericValueSignException: Max distance is defined and is not a positive number.
+#     """
+#     if raster_profile.get("crs") != geodataframe.crs:
+#         raise exceptions.NonMatchingCrsException(
+#             "Expected coordinate systems to match between raster and GeoDataFrame."
+#         )
+#     if geodataframe.shape[0] == 0:
+#         raise exceptions.EmptyDataFrameException("Expected GeoDataFrame to not be empty.")
+#     if max_distance is not None and max_distance <= 0:
+#         raise exceptions.NumericValueSignException("Expected max distance to be a positive number.")
+
+#     check_raster_profile(raster_profile=raster_profile)
+
+#     raster_width = raster_profile.get("width")
+#     raster_height = raster_profile.get("height")
+#     raster_transform = raster_profile.get("transform")
+
+#     distance_matrix = _distance_computation(
+#         raster_width=raster_width,
+#         raster_height=raster_height,
+#         raster_transform=raster_transform,
+#         geodataframe=geodataframe,
+#     )
+#     if max_distance is not None:
+#         distance_matrix[distance_matrix > max_distance] = max_distance
+
+#     return distance_matrix
+
+
+# def _calculate_row_distances(
+#     row: int,
+#     cols: np.ndarray,
+#     raster_transform: transform.Affine,
+#     geometries_unary_union: Union[BaseGeometry, BaseMultipartGeometry],
+# ) -> np.ndarray:
+#     row_distances = np.array(
+#         [
+#             point.distance(geometries_unary_union)
+#             for point in row_points(row=row, cols=cols, raster_transform=raster_transform)
+#         ]
+#     )
+#     return row_distances
+
+
+# def _distance_computation(
+#     raster_width: int, raster_height: int, raster_transform: transform.Affine, geodataframe: gpd.GeoDataFrame
+# ) -> np.ndarray:
+
+#     cols = np.arange(raster_width)
+#     rows = np.arange(raster_height)
+
+#     geometries_unary_union = geodataframe.geometry.unary_union
+
+#     distance_matrix = np.array(
+#         [
+#             _calculate_row_distances(
+#                 row=row, cols=cols, raster_transform=raster_transform, geometries_unary_union=geometries_unary_union
+#             )
+#             for row in rows
+#         ]
+#     )
+
+#     return distance_matrix

environment.yml

@@ -22,5 +22,6 @@ dependencies:
   - imbalanced-learn >= 0.11.0
   - mapclassify >= 2.6.1
   - esda >= 2.5.1
+  - numba >= 0.60.0
   # Dependencies for testing
   - pytest >=7.2.1