From 890fcf20d34816a714f831f46c142671622c5599 Mon Sep 17 00:00:00 2001 From: msorvoja Date: Mon, 2 Dec 2024 08:59:55 +0200 Subject: [PATCH] feat(IDW): Add search radius parameter --- eis_toolkit/cli.py | 9 +- .../vector_processing/idw_interpolation.py | 45 ++++- notebooks/testing_idw.ipynb | 179 ++++++++---------- .../interpolating/idw_radius_test_data.tif | Bin 0 -> 13186 bytes .../interpolation_test_data_small.gpkg-shm | Bin 32768 -> 0 bytes .../interpolation_test_data_small.gpkg-wal | 0 .../idw_interpolation_test.py | 19 ++ 7 files changed, 143 insertions(+), 109 deletions(-) create mode 100644 tests/data/remote/interpolating/idw_radius_test_data.tif delete mode 100644 tests/data/remote/interpolating/interpolation_test_data_small.gpkg-shm delete mode 100644 tests/data/remote/interpolating/interpolation_test_data_small.gpkg-wal diff --git a/eis_toolkit/cli.py b/eis_toolkit/cli.py index 16929af4..fdef6af0 100644 --- a/eis_toolkit/cli.py +++ b/eis_toolkit/cli.py @@ -1960,6 +1960,7 @@ def idw_interpolation_cli( pixel_size: float = None, extent: Tuple[float, float, float, float] = (None, None, None, None), power: float = 2.0, + search_radius: Optional[float] = None, ): """Apply inverse distance weighting (IDW) interpolation to input vector file.""" from eis_toolkit.exceptions import InvalidParameterValueException @@ -1985,7 +1986,13 @@ def idw_interpolation_cli( with rasterio.open(base_raster) as raster: profile = raster.profile.copy() - out_image = idw(geodataframe=geodataframe, target_column=target_column, raster_profile=profile, power=power) + out_image = idw( + geodataframe=geodataframe, + target_column=target_column, + raster_profile=profile, + power=power, + search_radius=search_radius, + ) typer.echo("Progress: 75%") profile["count"] = 1 diff --git a/eis_toolkit/vector_processing/idw_interpolation.py b/eis_toolkit/vector_processing/idw_interpolation.py index 2f0fb836..6dc7d92f 100644 --- a/eis_toolkit/vector_processing/idw_interpolation.py +++ b/eis_toolkit/vector_processing/idw_interpolation.py @@ -3,7 +3,7 @@ import geopandas as gpd import numpy as np from beartype import beartype -from beartype.typing import Union +from beartype.typing import Optional, Union from rasterio import profiles, transform from eis_toolkit.exceptions import EmptyDataFrameException, InvalidParameterValueException, NonMatchingCrsException @@ -18,6 +18,7 @@ def _idw_interpolation( raster_height: int, raster_transform: transform.Affine, power: Number, + search_radius: Optional[Number], ) -> np.ndarray: points = np.array(geodataframe.geometry.apply(lambda geom: (geom.x, geom.y)).tolist()) @@ -34,26 +35,47 @@ def _idw_interpolation( y = np.linspace(grid_y_min, grid_y_max, raster_height) y = y[::-1].reshape(-1, 1) - interpolated_values = _idw_core(points[:, 0], points[:, 1], values, x, y, power) + interpolated_values = _idw_core(points[:, 0], points[:, 1], values, x, y, power, search_radius) interpolated_values = interpolated_values.reshape(raster_height, raster_width) return interpolated_values # Distance calculations -def _idw_core(x, y, z, xi, yi: np.ndarray, power: Number) -> np.ndarray: +def _idw_core( + x: np.ndarray, + y: np.ndarray, + z: np.ndarray, + xi: np.ndarray, + yi: np.ndarray, + power: Number, + search_radius: Optional[Number], +) -> np.ndarray: over = np.zeros((len(yi), len(xi))) under = np.zeros((len(yi), len(xi))) for n in range(len(x)): dist = np.hypot(xi - x[n], yi - y[n]) - # Add a small epsilon to avoid division by zero - dist = np.where(dist == 0, 1e-12, dist) - dist = dist**power - over += z[n] / dist - under += 1.0 / dist + # Exclude points outside search radius + if search_radius is not None: + mask = dist <= search_radius + if not np.any(mask): + continue + + # Add a small epsilon to avoid division by zero + dist = np.where(dist[mask] == 0, 1e-12, dist[mask]) ** power + + over[mask] += z[n] / dist + under[mask] += 1.0 / dist + + else: + # Add a small epsilon to avoid division by zero + dist = np.where(dist == 0, 1e-12, dist) ** power + + over += z[n] / dist + under += 1.0 / dist - interpolated_values = over / under + interpolated_values = np.divide(over, under, out=np.full_like(over, np.nan), where=under != 0) return interpolated_values @@ -63,6 +85,7 @@ def idw( target_column: str, raster_profile: Union[profiles.Profile, dict], power: Number = 2, + search_radius: Optional[Number] = None, ) -> np.ndarray: """Calculate inverse distance weighted (IDW) interpolation. @@ -73,6 +96,8 @@ def idw( crs, transform, width and height. power: The value for determining the rate at which the weights decrease. As power increases, the weights for distant points decrease rapidly. Defaults to 2. + search_radius: The search radius within which to consider points for interpolation. + If None, all points are used. Returns: Numpy array containing the interpolated values. @@ -97,7 +122,7 @@ def idw( raster_transform = raster_profile.get("transform") interpolated_values = _idw_interpolation( - geodataframe, target_column, raster_width, raster_height, raster_transform, power + geodataframe, target_column, raster_width, raster_height, raster_transform, power, search_radius ) return interpolated_values diff --git a/notebooks/testing_idw.ipynb b/notebooks/testing_idw.ipynb index 89f16100..840c6a32 100644 --- a/notebooks/testing_idw.ipynb +++ b/notebooks/testing_idw.ipynb @@ -5,67 +5,68 @@ "execution_count": 1, "id": "b6eba8d9-924c-488c-bde5-56f91d7e8964", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import sys\n", "sys.path.insert(0, \"..\")\n", "from eis_toolkit.vector_processing.idw_interpolation import idw\n", "\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from shapely.geometry import Point\n", "import geopandas as gpd\n", - "from pyproj import CRS\n", - "import rasterio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "382080ca-3fd1-4f54-bae3-d272bd76d719", - "metadata": {}, - "outputs": [], - "source": [ - "data = {\n", - " 'random_number': [124, 248, 496, 992],\n", - " 'geometry': [Point(24.945831, 60.192059), Point(24.6559, 60.2055),\n", - " Point(25.0378, 60.2934), Point(24.7284, 60.2124)]\n", - " }\n", + "import rasterio\n", + "import time\n", "\n", - "gdf = gpd.GeoDataFrame(data)\n", + "SMALL_RASTER = \"../tests/data/remote/small_raster.tif\"\n", + "POINTS = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n", "\n", - "crs = CRS.from_epsg(4326)\n", - "gdf.crs = crs" + "gdf = gpd.read_file(POINTS)\n", + "\n", + "with rasterio.open(SMALL_RASTER) as src:\n", + " raster_profile = src.profile" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "fe9944d7-daf7-4e5f-a43a-870b1e2ef404", + "execution_count": 2, + "id": "382080ca-3fd1-4f54-bae3-d272bd76d719", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/geoseries.py:643: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", + " result = super().apply(func, convert_dtype=convert_dtype, args=args, **kwargs)\n" + ] + } + ], "source": [ - "interpolated_values, out_meta = idw(\n", + "interpolated_values = idw(\n", " geodataframe=gdf,\n", - " target_column='random_number',\n", - " #resolution=(0.005, 0.005),\n", - " resolution=(0.0049, 0.0047),\n", - " #output_resolution=(0.005, 0.005),\n", - " extent=(24.6558990000000016, 25.0378036000000002, 60.1920590000000004, 60.2934078769999999),\n", - " #extent=None,\n", + " target_column='value',\n", + " raster_profile=raster_profile,\n", " power=2\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "b6e5c929-dc18-4a21-a46f-114f5d21f77f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -87,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "d742b84b-60bd-495f-b0d0-3305a89b2ee2", "metadata": {}, "outputs": [ @@ -116,44 +117,38 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "6b9b4afb-1258-45ec-8896-41b3d4985533", - "metadata": {}, - "outputs": [], - "source": [ - "data = {\n", - " \"value1\": [1, 2, 3, 4, 5],\n", - " \"value2\": [5, 4, 3, 2, 1],\n", - " \"geometry\": [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)],\n", - "}\n", - "simple_gdf = gpd.GeoDataFrame(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c9b8915f-785f-49af-8cbc-c24df8411033", + "execution_count": 5, + "id": "d3f4c60e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mika/.cache/pypoetry/virtualenvs/eis-toolkit-l5cKD1lZ-py3.10/lib/python3.10/site-packages/geopandas/geoseries.py:643: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", + " result = super().apply(func, convert_dtype=convert_dtype, args=args, **kwargs)\n" + ] + } + ], "source": [ - "interpolated_values, out_meta = idw(\n", - " geodataframe=simple_gdf,\n", - " target_column='value1',\n", - " resolution=(1, 1),\n", - " extent=None,\n", - " power=2\n", + "interpolated_values_with_radius = idw(\n", + " geodataframe=gdf,\n", + " target_column='value',\n", + " raster_profile=raster_profile,\n", + " power=2,\n", + " search_radius=50,\n", ")" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "d4180811-d210-4d80-8e0a-55456b01880a", + "execution_count": 6, + "id": "c4c20135", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -163,8 +158,9 @@ } ], "source": [ - "x_min, y_min, x_max, y_max = simple_gdf.geometry.total_bounds\n", - "plt.imshow(interpolated_values, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", + "# Plot the interpolated values\n", + "x_min, y_min, x_max, y_max = gdf.geometry.total_bounds\n", + "plt.imshow(interpolated_values_with_radius, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", "plt.colorbar()\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", @@ -174,50 +170,37 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "9ec231e6-a072-4f95-a32e-a22801a05c5b", - "metadata": {}, - "outputs": [], - "source": [ - "interpolated_values, out_meta = idw(\n", - " geodataframe=simple_gdf,\n", - " target_column='value2',\n", - " resolution=(1, 1),\n", - " extent=None,\n", - " power=2\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "91fe2734-dddf-4049-bd41-1a5efd90ed59", + "execution_count": 7, + "id": "66a365f6", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Interpolated values saved as GeoTIFF: ../tests/data/local/results/idw_radius_test_output.tif\n" + ] } ], "source": [ - "plt.imshow(interpolated_values, cmap='jet', extent=[x_min, x_max, y_min, y_max])\n", - "plt.colorbar()\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')\n", - "plt.title('IDW Interpolation')\n", - "plt.show()" + "# Saves the output of the most recently executed interpolation\n", + "# As a GeoTIFF to the eis_toolkit/notebooks folder.\n", + "output_file = '../tests/data/local/results/idw_radius_test_output.tif'\n", + "\n", + "# Create a rasterio dataset for writing the GeoTIFF\n", + "with rasterio.open(output_file, 'w', driver='GTiff', width=interpolated_values_with_radius.shape[1],\n", + " height=interpolated_values_with_radius.shape[0], count=1, dtype=interpolated_values_with_radius.dtype,\n", + " crs=gdf.crs, transform=rasterio.transform.from_bounds(x_min, y_min, x_max, y_max, interpolated_values_with_radius.shape[1], interpolated_values_with_radius.shape[0])) as dst:\n", + " # Write the interpolated values to the GeoTIFF band\n", + " dst.write(interpolated_values_with_radius, 1)\n", + "\n", + "print(f\"Interpolated values saved as GeoTIFF: {output_file}\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "eis-toolkit-l5cKD1lZ-py3.10", "language": "python", "name": "python3" }, @@ -231,7 +214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tests/data/remote/interpolating/idw_radius_test_data.tif b/tests/data/remote/interpolating/idw_radius_test_data.tif new file mode 100644 index 0000000000000000000000000000000000000000..8f34d6cef6c3072c51fcda73ce29e38e55c9c3e3 GIT binary patch literal 13186 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