SNOW-2335536: Add support for Geospatial functions - part 2

CHANGELOG.md

@@ -13,6 +13,18 @@
   - `get_username_password`
   - `get_cloud_provider_token`
 
+- Added support for the following scalar functions in `functions.py`:
+  - `h3_cell_to_boundary`
+  - `h3_cell_to_parent`
+  - `h3_cell_to_point`
+  - `h3_compact_cells`
+  - `h3_compact_cells_strings`
+  - `h3_coverage`
+  - `h3_coverage_strings`
+  - `h3_get_resolution`
+  - `h3_grid_disk`
+  - `h3_grid_distance`
+
 ### Snowpark pandas API Updates
 
 #### New Features

docs/source/snowpark/functions.rst

@@ -229,6 +229,16 @@ Functions
     hex
     hex_encode
     hour
+    h3_cell_to_boundary
+    h3_cell_to_parent
+    h3_cell_to_point
+    h3_compact_cells
+    h3_compact_cells_strings
+    h3_coverage
+    h3_coverage_strings
+    h3_get_resolution
+    h3_grid_disk
+    h3_grid_distance
     iff
     ifnull
     in_

src/snowflake/snowpark/_functions/scalar_functions.py

@@ -13,6 +13,7 @@
 )
 from snowflake.snowpark._functions.general_functions import (
     builtin,
+    lit,
 )
 
 
@@ -321,9 +322,6 @@ def getdate(_emit_ast: bool = True) -> Column:
     """
     Returns the current timestamp for the system in the local time zone.
 
-    Args:
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
-
     Returns:
         A :class:`~snowflake.snowpark.Column` with the current date and time.
 
@@ -342,9 +340,6 @@ def localtime(_emit_ast: bool = True) -> Column:
     """
     Returns the current time for the system.
 
-    Args:
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
-
     Returns:
         A :class:`~snowflake.snowpark.Column` with the current local time.
 
@@ -362,9 +357,6 @@ def systimestamp(_emit_ast: bool = True) -> Column:
     """
     Returns the current timestamp for the system.
 
-    Args:
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
-
     Returns:
         A :class:`~snowflake.snowpark.Column` with the current system timestamp.
 
@@ -383,9 +375,6 @@ def invoker_role(_emit_ast: bool = True) -> Column:
     """
     Returns the name of the role that was active when the current stored procedure or user-defined function was called.
 
-    Args:
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
-
     Returns:
         Column: A Snowflake `Column` object representing the name of the active role.
 
@@ -406,10 +395,6 @@ def invoker_share(_emit_ast: bool = True) -> Column:
     Returns the name of the share that directly accessed the table or view where the INVOKER_SHARE
     function is invoked, otherwise the function returns None.
 
-    Args:
-        _emit_ast (bool, optional): A flag indicating whether to emit the abstract
-                                    syntax tree (AST). Defaults to True.
-
     Returns:
         Column: A Snowflake `Column` object representing the name of the active share.
 
@@ -428,7 +413,6 @@ def is_application_role_in_session(role_name: str, _emit_ast: bool = True) -> Co
 
     Args:
         role_name (str): The name of the application role to check.
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
 
     Returns:
         A :class:`~snowflake.snowpark.Column` indicating whether the specified application role is active in the current session.
@@ -452,7 +436,6 @@ def is_database_role_in_session(
 
     Args:
         role_name (ColumnOrName): The name of the database role to check. Can be a string or a Column.
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
 
     Returns:
         Column: A Snowflake `Column` object representing the result of the check.
@@ -476,7 +459,6 @@ def is_granted_to_invoker_role(role_name: str, _emit_ast: bool = True) -> Column
 
     Args:
         role_name (str): The name of the role to check.
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
 
     Returns:
         Column: A Snowflake `Column` object representing the result of the check.
@@ -498,7 +480,6 @@ def is_role_in_session(role: ColumnOrName, _emit_ast: bool = True) -> Column:
 
     Args:
         role (ColumnOrName): A Column or column name containing the role name to check.
-        _emit_ast (bool, optional): Whether to emit the abstract syntax tree (AST). Defaults to True.
 
     Returns:
         Column: A Snowflake `Column` object representing the result of the check.
@@ -520,8 +501,6 @@ def getvariable(name: str, _emit_ast: bool = True) -> Column:
 
     Args:
         name (str): The name of the session variable to retrieve.
-        _emit_ast (bool, optional): A flag indicating whether to emit the abstract syntax tree (AST).
-                                    Defaults to True.
 
     Returns:
         Column: A Snowflake `Column` object representing the value of the specified session variable.
@@ -531,3 +510,256 @@ def getvariable(name: str, _emit_ast: bool = True) -> Column:
         >>> assert result[0]["RESULT"] is None
     """
     return builtin("getvariable", _emit_ast=_emit_ast)(name)
+
+
+@publicapi
+def h3_cell_to_boundary(cell_id: ColumnOrName, _emit_ast: bool = True) -> Column:
+    """Returns the boundary of an H3 cell as a GeoJSON polygon.
+
+    Args:
+        cell_id (ColumnOrName): The H3 cell IDs.
+
+    Returns:
+        Column: The boundary of the H3 cell as a GeoJSON polygon string.
+
+    Example::
+        >>> df = session.create_dataframe([613036919424548863, 577023702256844799], schema=["cell_id"])
+        >>> result = df.select(h3_cell_to_boundary(df["cell_id"]).alias("boundary")).collect()
+        >>> len(result) == 2
+        True
+    """
+    c = _to_col_if_str(cell_id, "h3_cell_to_boundary")
+    return builtin("h3_cell_to_boundary", _emit_ast=_emit_ast)(c)
+
+
+@publicapi
+def h3_cell_to_parent(
+    cell_id: ColumnOrName, target_resolution: ColumnOrName, _emit_ast: bool = True
+) -> Column:
+    """Returns the parent H3 cell at the specified target resolution.
+
+    Args:
+        cell_id (ColumnOrName): The H3 cell IDs.
+        target_resolution (ColumnOrName) : The target resolution levels.
+
+    Returns:
+        Column: The parent H3 cell at the target resolution.
+
+    Example::
+        >>> from snowflake.snowpark.functions import col
+        >>> df = session.create_dataframe([[613036919424548863, 7], [608533319805566975, 6]], schema=["cell_id", "target_resolution"])
+        >>> df.select(h3_cell_to_parent(col("cell_id"), col("target_resolution")).alias("parent_cell")).collect()
+        [Row(PARENT_CELL=608533319805566975), Row(PARENT_CELL=604029720295636991)]
+    """
+    cell_id_c = _to_col_if_str(cell_id, "h3_cell_to_parent")
+    target_resolution_c = _to_col_if_str(target_resolution, "h3_cell_to_parent")
+    return builtin("h3_cell_to_parent", _emit_ast=_emit_ast)(
+        cell_id_c, target_resolution_c
+    )
+
+
+@publicapi
+def h3_cell_to_point(cell_id: ColumnOrName, _emit_ast: bool = True) -> Column:
+    """
+    Returns the center point of an H3 cell as a GeoJSON Point object.
+
+    Args:
+        cell_id (ColumnOrName): The H3 cell IDs.
+
+    Returns:
+        Column: GeoJSON Point objects representing the center points of the H3 cells.
+
+    Example::
+        >>> import json
+        >>> df = session.create_dataframe([613036919424548863], schema=["cell_id"])
+        >>> result = df.select(h3_cell_to_point(df["cell_id"]).alias("POINT")).collect()
+        >>> assert len(result) == 1
+        >>> point_json = json.loads(result[0]["POINT"])
+        >>> assert point_json["type"] == "Point"
+        >>> assert "coordinates" in point_json
+        >>> assert len(point_json["coordinates"]) == 2
+    """
+    c = _to_col_if_str(cell_id, "h3_cell_to_point")
+    return builtin("h3_cell_to_point", _emit_ast=_emit_ast)(c)
+
+
+@publicapi
+def h3_compact_cells(array_of_cell_ids: ColumnOrName, _emit_ast: bool = True) -> Column:
+    """
+    Returns a compacted array of H3 cell IDs by merging cells at the same resolution into their parent cells when possible.
+
+    Args:
+        array_of_cell_ids (ColumnOrName): An array of H3 cell IDs to be compacted.
+
+    Returns:
+        Column: An array of compacted H3 cell IDs.
+
+    Example::
+        >>> df = session.create_dataframe([
+        ...     [[622236750562230271, 622236750562263039, 622236750562295807, 622236750562328575, 622236750562361343, 622236750562394111, 622236750562426879, 622236750558396415]]
+        ... ], schema=["cell_ids"])
+        >>> df.select(h3_compact_cells(df["cell_ids"]).alias("compacted")).collect()
+        [Row(COMPACTED='[\\n  622236750558396415,\\n  617733150935089151\\n]')]
+    """
+    c = _to_col_if_str(array_of_cell_ids, "h3_compact_cells")
+    return builtin("h3_compact_cells", _emit_ast=_emit_ast)(c)
+
+
+@publicapi
+def h3_compact_cells_strings(
+    array_of_cell_ids: ColumnOrName, _emit_ast: bool = True
+) -> Column:
+    """
+    Returns a compacted array of H3 cell IDs by removing redundant cells that are covered by their parent cells at coarser resolutions.
+
+    Args:
+        array_of_cell_ids (ColumnOrName): An array of H3 cell ID strings to be compacted.
+
+    Returns:
+        Column: The compacted array of H3 cell ID strings.
+
+    Example::
+
+        >>> df = session.create_dataframe([[
+        ...     ['8a2a10705507fff', '8a2a1070550ffff', '8a2a10705517fff', '8a2a1070551ffff',
+        ...      '8a2a10705527fff', '8a2a1070552ffff', '8a2a10705537fff', '8a2a10705cdffff']
+        ... ]], schema=["cell_ids"])
+        >>> df.select(h3_compact_cells_strings("cell_ids").alias("compacted")).collect()
+        [Row(COMPACTED='[\\n  "8a2a10705cdffff",\\n  "892a1070553ffff"\\n]')]
+    """
+    c = _to_col_if_str(array_of_cell_ids, "h3_compact_cells_strings")
+    return builtin("h3_compact_cells_strings", _emit_ast=_emit_ast)(c)
+
+
+@publicapi
+def h3_coverage(
+    geography_expression: ColumnOrName,
+    target_resolution: ColumnOrName,
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Returns an array of H3 cell IDs that cover the given geography at the specified resolution.
+
+    Args:
+        geography_expression (ColumnOrName) : A GEOGRAPHY object.
+        target_resolution (ColumnOrName) : The target H3 resolution (0-15).
+
+    Returns:
+        Column: An array of H3 cell IDs as strings.
+
+    Example::
+        >>> from snowflake.snowpark.functions import to_geography, lit
+        >>> df = session.create_dataframe([
+        ...     ["POLYGON((-122.481889 37.826683,-122.479487 37.808548,-122.474150 37.808904,-122.476510 37.826935,-122.481889 37.826683))"]
+        ... ], schema=["polygon_wkt"])
+        >>> result = df.select(h3_coverage(to_geography(df["polygon_wkt"]), lit(8)).alias("h3_cells")).collect()
+        >>> result
+        [Row(H3_CELLS='[\\n  613196571539931135,\\n  613196571542028287,\\n  613196571548319743,\\n  613196571550416895,\\n  613196571560902655,\\n  613196571598651391\\n]')]
+    """
+    geography_col = _to_col_if_str(geography_expression, "h3_coverage")
+    resolution_col = _to_col_if_str(target_resolution, "h3_coverage")
+    return builtin("h3_coverage", _emit_ast=_emit_ast)(geography_col, resolution_col)
+
+
+@publicapi
+def h3_coverage_strings(
+    geography_expression: ColumnOrName,
+    target_resolution: Union[ColumnOrName, int],
+    _emit_ast: bool = True,
+) -> Column:
+    """
+    Returns an array of H3 cell identifiers as strings that cover the given geography at the specified resolution.
+
+    Args:
+        geography_expression (ColumnOrName): The GEOGRAPHY to cover.
+        target_resolution (ColumnOrName, int): The H3 resolution level (0-15).
+
+    Returns:
+        Column: An array of H3 cell identifiers as strings.
+
+    Example::
+
+        >>> from snowflake.snowpark.functions import to_geography
+        >>> df = session.create_dataframe([
+        ...     "POLYGON((-122.481889 37.826683,-122.479487 37.808548,-122.474150 37.808904,-122.476510 37.826935,-122.481889 37.826683))"
+        ... ], schema=["geo_wkt"])
+        >>> df.select(h3_coverage_strings(to_geography(df["geo_wkt"]), 8).alias("h3_cells")).collect()
+        [Row(H3_CELLS='[\\n  "8828308701fffff",\\n  "8828308703fffff",\\n  "8828308709fffff",\\n  "882830870bfffff",\\n  "8828308715fffff",\\n  "8828308739fffff"\\n]')]
+    """
+    geo_col = _to_col_if_str(geography_expression, "h3_coverage_strings")
+    res_col = (
+        target_resolution
+        if isinstance(target_resolution, Column)
+        else lit(target_resolution)
+    )
+    return builtin("h3_coverage_strings", _emit_ast=_emit_ast)(geo_col, res_col)
+
+
+@publicapi
+def h3_get_resolution(cell_id: ColumnOrName, _emit_ast: bool = True) -> Column:
+    """
+        Returns the resolution of an H3 cell ID.
+
+    Args:
+        cell_id (ColumnOrName): The H3 cell ID.
+
+    Returns:
+        Column: The resolution of the H3 cell ID.
+
+    Example::
+
+        >>> df = session.create_dataframe([617540519050084351, 617540519050084352], schema=["cell_id"])
+        >>> df.select(h3_get_resolution(df["cell_id"]).alias("resolution")).collect()
+        [Row(RESOLUTION=9), Row(RESOLUTION=9)]
+    """
+    c = _to_col_if_str(cell_id, "h3_get_resolution")
+    return builtin("h3_get_resolution", _emit_ast=_emit_ast)(c)
+
+
+@publicapi
+def h3_grid_disk(
+    cell_id: ColumnOrName, k_value: ColumnOrName, _emit_ast: bool = True
+) -> Column:
+    """
+    Returns an array of H3 cell IDs within k distance of the origin cell.
+
+    Args:
+        cell_id (ColumnOrName): The H3 cell ID as the center of the disk.
+        k_value (ColumnOrName): The distance (number of rings) from the center cell.
+
+    Returns:
+        Column: An array of H3 cell IDs within the specified distance.
+
+    Example::
+
+        >>> df = session.create_dataframe([[617540519050084351, 1], [617540519050084351, 2]], schema=["cell_id", "k_value"])
+        >>> df.select(h3_grid_disk("cell_id", "k_value").alias("grid_disk")).collect()
+        [Row(GRID_DISK='[\\n  617540519050084351,\\n  617540519051657215,\\n  617540519050608639,\\n  617540519050870783,\\n  617540519050346495,\\n  617540519051395071,\\n  617540519051132927\\n]'), Row(GRID_DISK='[\\n  617540519050084351,\\n  617540519051657215,\\n  617540519050608639,\\n  617540519050870783,\\n  617540519050346495,\\n  617540519051395071,\\n  617540519051132927,\\n  617540519048249343,\\n  617540519048773631,\\n  617540519089143807,\\n  617540519088095231,\\n  617540519107756031,\\n  617540519108018175,\\n  617540519104086015,\\n  617540519103561727,\\n  617540519046414335,\\n  617540519047462911,\\n  617540519044579327,\\n  617540519044317183\\n]')]
+    """
+    cell_id_col = _to_col_if_str(cell_id, "h3_grid_disk")
+    k_value_col = _to_col_if_str(k_value, "h3_grid_disk")
+    return builtin("h3_grid_disk", _emit_ast=_emit_ast)(cell_id_col, k_value_col)
+
+
+@publicapi
+def h3_grid_distance(
+    cell_id_1: ColumnOrName, cell_id_2: ColumnOrName, _emit_ast: bool = True
+) -> Column:
+    """
+        Returns the grid distance between two H3 cell IDs.
+
+    Args:
+        cell_id_1 (ColumnOrName): The first H3 cell ID column or value.
+        cell_id_2 (ColumnOrName): The second H3 cell ID column or value.
+
+    Returns:
+        Column: The grid distance between the two H3 cells.
+
+    Example::
+        >>> df = session.create_dataframe([[617540519103561727, 617540519052967935]], schema=["cell_id_1", "cell_id_2"])
+        >>> df.select(h3_grid_distance(df["cell_id_1"], df["cell_id_2"]).alias("distance")).collect()
+        [Row(DISTANCE=5)]
+    """
+    cell_id_1 = _to_col_if_str(cell_id_1, "h3_grid_distance")
+    cell_id_2 = _to_col_if_str(cell_id_2, "h3_grid_distance")
+    return builtin("h3_grid_distance", _emit_ast=_emit_ast)(cell_id_1, cell_id_2)