Add python API pairwise_point_polygon_distance

docs/source/user_guide/cuspatial_api_examples.ipynb

@@ -889,6 +889,161 @@
     "print(gpu_polygons.head())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "008d320d-ca47-459f-9fff-8769494c8a61",
+   "metadata": {},
+   "source": [
+    "### cuspatial.pairwise_point_polygon_distance\n",
+    "\n",
+    "Using WGS 84 Pseudo-Mercator, distances are in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "258c9a8c-7fe3-4047-80b7-00878d9fb2f1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pop_est</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>name</th>\n",
+       "      <th>iso_a3</th>\n",
+       "      <th>gdp_md_est</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>distance_from</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>889953.0</td>\n",
+       "      <td>Oceania</td>\n",
+       "      <td>Fiji</td>\n",
+       "      <td>FJI</td>\n",
+       "      <td>5496</td>\n",
+       "      <td>MULTIPOLYGON (((20037508.343 -1812498.413, 200...</td>\n",
+       "      <td>Vatican City</td>\n",
+       "      <td>1.969350e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>58005463.0</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>Tanzania</td>\n",
+       "      <td>TZA</td>\n",
+       "      <td>63177</td>\n",
+       "      <td>POLYGON ((3774143.866 -105758.362, 3792946.708...</td>\n",
+       "      <td>San Marino</td>\n",
+       "      <td>5.929777e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>603253.0</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>W. Sahara</td>\n",
+       "      <td>ESH</td>\n",
+       "      <td>907</td>\n",
+       "      <td>POLYGON ((-964649.018 3205725.605, -964597.245...</td>\n",
+       "      <td>Vaduz</td>\n",
+       "      <td>3.421172e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37589262.0</td>\n",
+       "      <td>North America</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>CAN</td>\n",
+       "      <td>1736425</td>\n",
+       "      <td>MULTIPOLYGON (((-13674486.249 6274861.394, -13...</td>\n",
+       "      <td>Lobamba</td>\n",
+       "      <td>1.296059e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>328239523.0</td>\n",
+       "      <td>North America</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>21433226</td>\n",
+       "      <td>MULTIPOLYGON (((-13674486.249 6274861.394, -13...</td>\n",
+       "      <td>Luxembourg</td>\n",
+       "      <td>8.174897e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       pop_est      continent                      name iso_a3  gdp_md_est  \\\n",
+       "0     889953.0        Oceania                      Fiji    FJI        5496   \n",
+       "1   58005463.0         Africa                  Tanzania    TZA       63177   \n",
+       "2     603253.0         Africa                 W. Sahara    ESH         907   \n",
+       "3   37589262.0  North America                    Canada    CAN     1736425   \n",
+       "4  328239523.0  North America  United States of America    USA    21433226   \n",
+       "\n",
+       "                                            geometry distance_from  \\\n",
+       "0  MULTIPOLYGON (((20037508.343 -1812498.413, 200...  Vatican City   \n",
+       "1  POLYGON ((3774143.866 -105758.362, 3792946.708...    San Marino   \n",
+       "2  POLYGON ((-964649.018 3205725.605, -964597.245...         Vaduz   \n",
+       "3  MULTIPOLYGON (((-13674486.249 6274861.394, -13...       Lobamba   \n",
+       "4  MULTIPOLYGON (((-13674486.249 6274861.394, -13...    Luxembourg   \n",
+       "\n",
+       "       distance  \n",
+       "0  1.969350e+07  \n",
+       "1  5.929777e+06  \n",
+       "2  3.421172e+06  \n",
+       "3  1.296059e+07  \n",
+       "4  8.174897e+06  \n",
+       "(GPU)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cities = geopandas.read_file(geopandas.datasets.get_path(\"naturalearth_cities\")).to_crs(3857)\n",
+    "countries = geopandas.read_file(geopandas.datasets.get_path(\"naturalearth_lowres\")).to_crs(3857)\n",
+    "\n",
+    "gpu_cities = cuspatial.from_geopandas(cities)\n",
+    "gpu_countries = cuspatial.from_geopandas(countries)\n",
+    "\n",
+    "dist = cuspatial.pairwise_point_polygon_distance(\n",
+    "    gpu_cities.geometry[:len(gpu_countries)], gpu_countries.geometry\n",
+    ")\n",
+    "\n",
+    "gpu_countries[\"distance_from\"] = cities.name\n",
+    "gpu_countries[\"distance\"] = dist\n",
+    "\n",
+    "gpu_countries.head()"
+   ]
+  },
   {
    "attachments": {
     "351aea0c-f37e-4ab9-bad2-c67bce69b5c3.png": {
@@ -910,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "d1ade9da-c9e2-45c4-9685-dffeda3fd358",
    "metadata": {
     "tags": []
@@ -975,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "cc72a44d-a9bf-4432-9898-de899ac45869",
    "metadata": {
     "tags": []
@@ -993,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "1125fd17-afe1-4b9c-b48d-8842dd3700b3",
    "metadata": {
     "tags": []
@@ -1002,15 +1157,15 @@
     {
      "data": {
       "text/plain": [
-       "<cudf.core.column.numerical.NumericalColumn object at 0x7f7e750a5340>\n",
+       "<cudf.core.column.numerical.NumericalColumn object at 0x7f4a31428840>\n",
        "[\n",
        "  0,\n",
        "  144\n",
        "]\n",
        "dtype: int32"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "b281e3bb-42d4-4d60-9cb2-b7dcc20b4776",
    "metadata": {
     "tags": []
@@ -1046,7 +1201,7 @@
        "Length: 144, dtype: geometry"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1058,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "e19873b9-2614-4242-ad67-caa47f807d04",
    "metadata": {
     "tags": []
@@ -1117,7 +1272,7 @@
        "0  [9, 10, 10, 11, 11, 28, 12, 12, 13, 13, 14, 15...  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1150,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "bf7b2256",
    "metadata": {
     "tags": []
@@ -1167,7 +1322,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1252,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "e3a0a9a3-0bdd-4f05-bcb5-7db4b99a44a3",
    "metadata": {
     "tags": []
@@ -1316,7 +1471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "023bd25a-35be-435d-ab0b-ecbd7a47e147",
    "metadata": {
     "tags": []
@@ -1375,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "784aff8e-c9ed-4a81-aa87-bf301b3b90af",
    "metadata": {
     "tags": []
@@ -1390,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "fea24c78-cf5c-45c6-b860-338238e61323",
    "metadata": {
     "tags": []

python/cuspatial/cuspatial/__init__.py

@@ -10,6 +10,7 @@
     pairwise_point_distance,
     pairwise_point_linestring_distance,
     pairwise_point_linestring_nearest_points,
+    pairwise_point_polygon_distance,
     point_in_polygon,
     points_in_spatial_window,
     polygon_bounding_boxes,

python/cuspatial/cuspatial/_lib/cpp/distance/point_polygon_distance.pxd

@@ -0,0 +1,17 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+
+from cuspatial._lib.cpp.column.geometry_column_view cimport (
+    geometry_column_view,
+)
+
+
+cdef extern from "cuspatial/distance/point_polygon_distance.hpp" \
+        namespace "cuspatial" nogil:
+    cdef unique_ptr[column] pairwise_point_polygon_distance(
+        const geometry_column_view & multipoints,
+        const geometry_column_view & multipolygons
+    ) except +

python/cuspatial/cuspatial/_lib/distance.pyx

@@ -1,12 +1,15 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
-from libcpp.memory cimport unique_ptr
+from libcpp.memory cimport make_shared, shared_ptr, unique_ptr
 from libcpp.utility cimport move
 
 from cudf._lib.column cimport Column
 from cudf._lib.cpp.column.column cimport column
 from cudf._lib.cpp.column.column_view cimport column_view
 
+from cuspatial._lib.cpp.column.geometry_column_view cimport (
+    geometry_column_view,
+)
 from cuspatial._lib.cpp.distance.linestring_distance cimport (
     pairwise_linestring_distance as c_pairwise_linestring_distance,
 )
@@ -16,7 +19,12 @@ from cuspatial._lib.cpp.distance.point_distance cimport (
 from cuspatial._lib.cpp.distance.point_linestring_distance cimport (
     pairwise_point_linestring_distance as c_pairwise_point_linestring_distance,
 )
+from cuspatial._lib.cpp.distance.point_polygon_distance cimport (
+    pairwise_point_polygon_distance as c_pairwise_point_polygon_distance,
+)
 from cuspatial._lib.cpp.optional cimport optional
+from cuspatial._lib.cpp.types cimport collection_type_id, geometry_type_id
+from cuspatial._lib.types cimport collection_type_py_to_c
 from cuspatial._lib.utils cimport unwrap_pyoptcol
 
 
@@ -102,3 +110,35 @@ def pairwise_point_linestring_distance(
             c_linestring_points_xy,
         ))
     return Column.from_unique_ptr(move(c_result))
+
+
+def pairwise_point_polygon_distance(
+    point_collection_type,
+    Column multipoints,
+    Column multipolygons
+):
+
+    cdef collection_type_id point_multi_type = collection_type_py_to_c(
+        point_collection_type
+    )
+
+    cdef shared_ptr[geometry_column_view] c_multipoints = \
+        make_shared[geometry_column_view](
+            multipoints.view(),
+            point_multi_type,
+            geometry_type_id.POINT)
+
+    cdef shared_ptr[geometry_column_view] c_multipolygons = \
+        make_shared[geometry_column_view](
+            multipolygons.view(),
+            collection_type_id.MULTI,
+            geometry_type_id.POLYGON)
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(c_pairwise_point_polygon_distance(
+            c_multipoints.get()[0], c_multipolygons.get()[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))