fixed python bindings and tests

python/mosaic/api/functions.py

@@ -622,6 +622,7 @@ def st_triangulate(
     lines_array: ColumnOrName,
     merge_tolerance: ColumnOrName,
     snap_tolerance: ColumnOrName,
+    split_point_finder: str = "NONENCROACHING",
 ) -> Column:
     """
     Generate a triangulated surface mesh from the set of points `points_array` and constraint lines `lines_array`.
@@ -641,6 +642,9 @@ def st_triangulate(
         A tolerance used to coalesce points in close proximity to each other before performing triangulation.
     snap_tolerance : Column
         A snapping tolerance used to relate created points to their corresponding lines for elevation interpolation.
+    split_point_finder : String
+        (Optional) The split point finding algorithm used to incorporate `lines_array` into the triangulation.
+        Default is "NONENCROACHING", alternative is "MIDPOINT".
 
     Returns
     -------
@@ -652,6 +656,7 @@ def st_triangulate(
         pyspark_to_java_column(lines_array),
         pyspark_to_java_column(merge_tolerance),
         pyspark_to_java_column(snap_tolerance),
+        pyspark_to_java_column(lit(split_point_finder)),
     )
 
 
@@ -665,6 +670,7 @@ def st_interpolateelevation(
     y_width: ColumnOrName,
     x_size: ColumnOrName,
     y_size: ColumnOrName,
+    split_point_finder: str = "NONENCROACHING",
 ) -> Column:
     """
     Compute interpolated elevations across a grid of points described by
@@ -701,6 +707,9 @@ def st_interpolateelevation(
     y_size : Column
         The spacing between each point on the grid's y-axis
         (in meters or degrees depending on the projection of `points_array`)
+    split_point_finder : String
+        (Optional) The split point finding algorithm used to incorporate `lines_array` into the triangulation.
+        Default is "NONENCROACHING", alternative is "MIDPOINT".
 
     Returns
     -------
@@ -712,6 +721,7 @@ def st_interpolateelevation(
         pyspark_to_java_column(lines_array),
         pyspark_to_java_column(merge_tolerance),
         pyspark_to_java_column(snap_tolerance),
+        pyspark_to_java_column(lit(split_point_finder)),
         pyspark_to_java_column(origin),
         pyspark_to_java_column(x_width),
         pyspark_to_java_column(y_width),

python/mosaic/api/raster.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from typing import Any
 
 from pyspark.sql import Column
@@ -267,6 +269,8 @@ def rst_dtmfromgeoms(
     y_width: ColumnOrName,
     x_size: ColumnOrName,
     y_size: ColumnOrName,
+    split_point_finder: str = "NONENCROACHING",
+    no_data_value: float = np.nan,
 ) -> Column:
     """
     Generate a raster with interpolated elevations across a grid of points described by
@@ -303,6 +307,11 @@ def rst_dtmfromgeoms(
     y_size : Column
         The spacing between each point on the grid's y-axis
         (in meters or degrees depending on the projection of `points_array`)
+    split_point_finder : String
+        (Optional) The split point finding algorithm used to incorporate `lines_array` into the triangulation.
+        Default is "NONENCROACHING", alternative is "MIDPOINT".
+    no_data_value : Float
+        (Optional) The nodata value to assign to the output raster. Must be a Double Type value. Default is NaN.
 
     Returns
     -------
@@ -315,11 +324,13 @@ def rst_dtmfromgeoms(
         pyspark_to_java_column(lines_array),
         pyspark_to_java_column(merge_tolerance),
         pyspark_to_java_column(snap_tolerance),
+        pyspark_to_java_column(lit(split_point_finder)),
         pyspark_to_java_column(origin),
         pyspark_to_java_column(x_width),
         pyspark_to_java_column(y_width),
         pyspark_to_java_column(x_size),
         pyspark_to_java_column(y_size),
+        pyspark_to_java_column(lit(no_data_value)),
     )
 
 

python/test/test_raster_functions.py

@@ -115,7 +115,9 @@ def test_raster_scalar_functions(self):
                 "rst_worldtorastercoord",
                 api.rst_worldtorastercoord("tile", lit(0.0), lit(0.0)),
             )
-            .withColumn("rst_write", api.rst_write("tile", lit("/mnt/mosaic_tmp/write-tile")))
+            .withColumn(
+                "rst_write", api.rst_write("tile", lit("/mnt/mosaic_tmp/write-tile"))
+            )
         )
         result.write.format("noop").mode("overwrite").save()
         self.assertEqual(result.count(), 1)
@@ -300,6 +302,8 @@ def test_dtmfromgeoms(self):
                     "grid_size_y",
                     "pixel_size_x",
                     "pixel_size_y",
+                    "NONENCROACHING",
+                    -9999.0,
                 ),
             )
             .drop(

python/test/test_vector_functions.py

@@ -325,7 +325,9 @@ def test_triangulate_interpolate(self):
 
         triangulation_df = df.withColumn(
             "triangles",
-            api.st_triangulate("masspoints", "breaklines", lit(0.0), lit(0.01)),
+            api.st_triangulate(
+                "masspoints", "breaklines", lit(0.0), lit(0.01), lit("NONENCROACHING")
+            ),
         )
         triangulation_df.cache()
         self.assertEqual(triangulation_df.count(), 2)
@@ -338,7 +340,7 @@ def test_triangulate_interpolate(self):
         )
 
         interpolation_df = (
-            df.withColumn("origin", api.st_geomfromwkt(lit("POINT (0.6 1.8)")))
+            df.withColumn("origin", api.st_geomfromwkt(lit("POINT (0.55 1.75)")))
             .withColumn("xWidth", lit(12))
             .withColumn("yWidth", lit(6))
             .withColumn("xSize", lit(0.1))
@@ -355,13 +357,14 @@ def test_triangulate_interpolate(self):
                     "yWidth",
                     "xSize",
                     "ySize",
+                    lit("NONENCROACHING"),
                 ),
             )
         )
 
         interpolation_df.cache()
         self.assertEqual(interpolation_df.count(), 12 * 6)
         self.assertIn(
-            "POINT Z(0.6 2 1.8)",
+            "POINT Z(1.6 1.8 1.2)",
             [r["interpolated"] for r in interpolation_df.collect()],
         )