Remove deprecated lonlat_to_cartesian functions

cpp/src/spatial/sinusoidal_projection.cu

@@ -122,14 +122,4 @@ pair_of_columns sinusoidal_projection(double origin_lon,
     origin_lon, origin_lat, input_lon, input_lat, rmm::cuda_stream_default, mr);
 }
 
-// deprecated
-pair_of_columns lonlat_to_cartesian(double origin_lon,
-                                    double origin_lat,
-                                    cudf::column_view const& input_lon,
-                                    cudf::column_view const& input_lat,
-                                    rmm::mr::device_memory_resource* mr)
-{
-  return sinusoidal_projection(origin_lon, origin_lat, input_lon, input_lat, mr);
-}
-
 }  // namespace cuspatial

docs/source/api_docs/spatial.rst

@@ -40,7 +40,7 @@ Bounding Boxes
 Projection Functions
 ++++++++++++++++++++
 
-.. autofunction:: cuspatial.lonlat_to_cartesian
+.. autofunction:: cuspatial.sinusoidal_projection
 
 Spatial Filtering Functions
 +++++++++++++++++++++++++++

docs/source/user_guide/cuspatial_api_examples.ipynb

@@ -638,36 +638,35 @@
     "cuSpatial provides a simple sinusoidal longitude / latitude to Cartesian coordinate transform.  \n",
     "This function requires an origin point to determine the scaling parameters for the lonlat inputs.  \n",
     "\n",
-    "### [cuspatial.lonlat_to_cartesian](https://docs.rapids.ai/api/cuspatial/nightly/api_docs/spatial.html#cuspatial.lonlat_to_cartesian)\n",
+    "### [cuspatial.sinusoidal_projection](https://docs.rapids.ai/api/cuspatial/nightly/api_docs/spatial.html#cuspatial.sinusoidal_projection)\n",
     "\n",
     "The following cell converts the lonlat coordinates of the country of Afghanistan to Cartesian  \n",
     "coordinates in km, centered around the center of the country, suitable for graphing and display."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "id": "a7a870dd-c0ae-41c1-a66c-cff4bd2db0ec",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            x           y\n",
-      "0  112.173577 -281.589872\n",
-      "1   62.152088 -280.852048\n",
-      "2   -5.573132 -257.390948\n",
-      "3  -33.070528 -243.848851\n",
-      "4  -98.002306 -279.540041\n"
+     "ename": "NameError",
+     "evalue": "name 'geopandas' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m host_dataframe \u001b[39m=\u001b[39m geopandas\u001b[39m.\u001b[39mread_file(geopandas\u001b[39m.\u001b[39mdatasets\u001b[39m.\u001b[39mget_path(\u001b[39m\"\u001b[39m\u001b[39mnaturalearth_lowres\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m      2\u001b[0m gpu_dataframe \u001b[39m=\u001b[39m cuspatial\u001b[39m.\u001b[39mfrom_geopandas(host_dataframe)\n\u001b[1;32m      3\u001b[0m afghanistan \u001b[39m=\u001b[39m gpu_dataframe[\u001b[39m'\u001b[39m\u001b[39mgeometry\u001b[39m\u001b[39m'\u001b[39m][gpu_dataframe[\u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mAfghanistan\u001b[39m\u001b[39m'\u001b[39m]\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'geopandas' is not defined"
      ]
     }
    ],
    "source": [
     "host_dataframe = geopandas.read_file(geopandas.datasets.get_path(\"naturalearth_lowres\"))\n",
     "gpu_dataframe = cuspatial.from_geopandas(host_dataframe)\n",
     "afghanistan = gpu_dataframe['geometry'][gpu_dataframe['name'] == 'Afghanistan']\n",
-    "projected = cuspatial.lonlat_to_cartesian(\n",
+    "projected = cuspatial.sinusoidal_projection(\n",
     "    afghanistan.polygons.x.mean(),\n",
     "    afghanistan.polygons.y.mean(),\n",
     "    afghanistan.polygons.x,\n",
@@ -1362,7 +1361,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.15"
   },
   "vscode": {
    "interpreter": {

java/src/main/native/src/cuSpatialJni.cpp

@@ -290,8 +290,8 @@ JNIEXPORT jlongArray JNICALL Java_ai_rapids_cuspatial_CuSpatial_lonlatToCartesia
     column_view *input_lon_column_view = reinterpret_cast<column_view *>(input_lon_view_handle);
     column_view *input_lat_column_view = reinterpret_cast<column_view *>(input_lat_view_handle);
     std::pair<std::unique_ptr<cudf::column>, std::unique_ptr<cudf::column>> result =
-        cuspatial::lonlat_to_cartesian(origin_lon, origin_lat, *input_lon_column_view,
-                                       *input_lat_column_view);
+        cuspatial::sinusoidal_projection(origin_lon, origin_lat, *input_lon_column_view,
+                                         *input_lat_column_view);
 
     std::vector<std::unique_ptr<cudf::column>> columns;
     columns.emplace_back(std::move(result.first));

python/cuspatial/benchmarks/api/bench_api.py

@@ -78,12 +78,12 @@ def bench_linestring_bounding_boxes(benchmark, sorted_trajectories):
     )
 
 
-def bench_lonlat_to_cartesian(benchmark, gpu_dataframe):
+def bench_sinusoidal_projection(benchmark, gpu_dataframe):
     afghanistan = gpu_dataframe["geometry"][
         gpu_dataframe["name"] == "Afghanistan"
     ]
     benchmark(
-        cuspatial.lonlat_to_cartesian,
+        cuspatial.sinusoidal_projection,
         afghanistan.polygons.y.mean(),
         afghanistan.polygons.x.mean(),
         afghanistan.polygons.y,

python/cuspatial/cuspatial/__init__.py

@@ -8,7 +8,6 @@
     haversine_distance,
     join_quadtree_and_bounding_boxes,
     linestring_bounding_boxes,
-    lonlat_to_cartesian,
     pairwise_linestring_distance,
     pairwise_point_distance,
     pairwise_point_linestring_distance,

python/cuspatial/cuspatial/core/spatial/__init__.py

@@ -17,14 +17,13 @@
     quadtree_point_to_nearest_linestring,
 )
 from .nearest_points import pairwise_point_linestring_nearest_points
-from .projection import lonlat_to_cartesian, sinusoidal_projection
+from .projection import sinusoidal_projection
 
 __all__ = [
     "directed_hausdorff_distance",
     "haversine_distance",
     "join_quadtree_and_bounding_boxes",
     "sinusoidal_projection",
-    "lonlat_to_cartesian",
     "pairwise_point_distance",
     "pairwise_linestring_distance",
     "pairwise_point_linestring_distance",

python/cuspatial/cuspatial/core/spatial/projection.py

@@ -48,44 +48,3 @@ def sinusoidal_projection(origin_lon, origin_lat, input_lon, input_lat):
         origin_lon, origin_lat, input_lon._column, input_lat._column
     )
     return DataFrame({"x": result[0], "y": result[1]})
-
-
-def lonlat_to_cartesian(origin_lon, origin_lat, input_lon, input_lat):
-    """
-    Convert lon/lat to ``x,y`` coordinates with respect to an origin lon/lat
-    point. Results are scaled relative to the size of the Earth in kilometers.
-
-    This API is deprecated. Please use sinusoidal_projection.
-
-    Parameters
-    ----------
-    origin_lon : ``number``
-        longitude offset  (this is subtracted from each input before
-        converting to x,y)
-    origin_lat : ``number``
-        latitude offset (this is subtracted from each input before
-        converting to x,y)
-    input_lon : ``Series`` or ``list``
-        longitude coordinates to convert to x
-    input_lat : ``Series`` or ``list``
-        latitude coordinates to convert to y
-
-    Returns
-    -------
-    result : cudf.DataFrame
-        x : cudf.Series
-            x-coordinate of the input relative to the size of the Earth in
-            kilometers.
-        y : cudf.Series
-            y-coordinate of the input relative to the size of the Earth in
-            kilometers.
-    """
-    warnings.warn(
-        "lonlat_to_cartesian is deprecated. "
-        "Please use sinusoidal_projection.",
-        FutureWarning,
-    )
-    result = cpp_sinusoidal_projection(
-        origin_lon, origin_lat, input_lon._column, input_lat._column
-    )
-    return DataFrame({"x": result[0], "y": result[1]})

python/cuspatial/demos/traj_test_soa_locust.py

@@ -54,7 +54,7 @@ def get_ts_struct(ts):
 
 y, m, d, hh, mm, ss, wd, yd, ms, pid = get_ts_struct(ts_0)
 
-xys = cuspatial.lonlat_to_cartesian(
+xys = cuspatial.sinusoidal_projection(
     cam_lon, cam_lat, lonlats["lon"], lonlats["lat"]
 )
 num_traj, trajectories = cuspatial.derive(xys["x"], xys["y"], ids, ts)