diff --git a/geokde/__init__.py b/geokde/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/geokde/_kernels.py b/geokde/_kernels.py new file mode 100644 index 0000000..ba0a75a --- /dev/null +++ b/geokde/_kernels.py @@ -0,0 +1,22 @@ +import numpy as np + + +@np.vectorize +def quartic(distance: int | float, radius: int | float, weight: int | float) -> float: + """Quartic kernel. + + Parameters + ---------- + distance : int | float + radius : int | float + weight : int | float + + Returns + ------- + value : float + """ + if distance: + value = weight * ((1 - ((distance / radius) ** 2)) ** 2) + else: + value = 0.0 + return value diff --git a/geokde/_utils.py b/geokde/_utils.py new file mode 100644 index 0000000..39319b6 --- /dev/null +++ b/geokde/_utils.py @@ -0,0 +1,112 @@ +import geopandas as gpd +import numpy as np +import rasterio as rio + +from _kernels import quartic + + +def calculate_bounds( + minx: int | float, + miny: int | float, + maxx: int | float, + maxy: int | float, + radius: int | float, +) -> tuple[int | float, int | float, int | float, int | float]: + """Calculate bounds for + + Parameters + ---------- + minx : int | float + miny : int | float + maxx : int | float + radius : int | float + """ + minx -= radius + miny -= radius + maxx += radius + maxy += radius + return minx, miny, maxx, maxy + + +def create_array( + minx: int | float, + miny: int | float, + maxx: int | float, + maxy: int | float, + resolution: int | float, +) -> tuple[np.ndarray, rio.Affine]: + """Generate an array and associated affine transformation from bounding coordinates + and spatial resolution. + + Parameters + ---------- + minx : int | float + miny : int | float + maxx : int | float + resolution : int | float + + Returns + ------- + array : numpy.ndarray + tf : rasterio.Affine + """ + dim_x = round((maxx - minx) / resolution) # round() no? + dim_y = round((maxy - miny) / resolution) + array = np.full((dim_y, dim_x), 0.0) + tf = rio.transform.from_bounds(minx, miny, maxx, maxy, dim_x, dim_y) + return array, tf + + +def get_array_coordinates(gdf: gpd.GeoDataFrame, transform: rio.Affine) -> None: + """ + """ + gdf["arr_idx"] = gdf.geometry.apply( + lambda point: rio.transform.rowcol( + transform, point.x, point.y, op=transform_rowcol_return), + ) + + +def transform_rowcol_return(value: float) -> float: + """Function that returns the value it's passed. Used with `op` kwarg of + `rasterio.transform.rowcol()` to ensure decimal array index return.""" + return value + + +def calculate_kde( + gdf: gpd.GeoDataFrame, + array: np.ndarray, + radius: int | float, + resolution: int | float, +) -> None: + """Iterate over GeoDataFrame records, calculating KDE for a given radius and + resolution and adding the result to a given array. + """ + window = radius / resolution + for point in gdf.to_dict(orient="records"): + add_point_kde(array, point["arr_idx"], window) + + +def add_point_kde( + array: np.ndarray, + point: tuple[int | float, int | float], + window: int | float, + weight: int | float = 1 +) -> None: + """Perform KDE for a given point, window, and weight, adding the result to an array. + + Parameters + ---------- + array : numpy.ndarray + point : tuple[int | float, int | float] + window : int | float + weight: int | float, default = 1 + """ + minx = round(point[1] - window) + miny = round(point[0] - window) + maxx = round(point[1] + window) + maxy = round(point[0] + window) + y_idx, x_idx = np.ogrid[miny + .5:maxy + .5, minx + .5:maxx + .5] + dist_array = np.sqrt((x_idx - point[1]) ** 2 + (y_idx - point[0]) ** 2) + dist_array[dist_array >= window] = 0.0 + kde_array = quartic(dist_array, window, weight) + array[miny:maxy, minx:maxx] += kde_array diff --git a/geokde/geokde.py b/geokde/geokde.py new file mode 100644 index 0000000..a21ff41 --- /dev/null +++ b/geokde/geokde.py @@ -0,0 +1,40 @@ +import geopandas as gpd +import numpy as np +import rasterio as rio + +from _utils import calculate_bounds, calculate_kde, create_array, get_array_coordinates + + +def kernel_density_estimation( + points: gpd.GeoDataFrame, + radius: int | float, + resolution: int | float, + kernel: str = "quartic", +) -> tuple[np.ndarray, rio.Affine]: + """Estimate kernel density with a given radius, resolution, and kernel from point + geometries in a given `GeoDataFrame`. + + Parameters + ---------- + points : geopandas.GeoDataFrame + radius : 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