vibespatial.io.geoparquet¶

Classes¶

`GeoParquetScanPlan`
`GeoParquetChunkPlan`
`GeoParquetEnginePlan`
`GeoParquetEngineBenchmark`

Functions¶

`plan_geoparquet_scan`(→ GeoParquetScanPlan)
`plan_geoparquet_engine`(→ GeoParquetEnginePlan)
`read_geoparquet_owned`(...)
`write_geoparquet`(→ None)
`read_geoparquet`(path, *[, columns, storage_options, ...])	Read a GeoParquet file into a GeoDataFrame.
`benchmark_geoparquet_scan_engine`(...)

Module Contents¶

class vibespatial.io.geoparquet.GeoParquetScanPlan¶

selected_path: vibespatial.io.support.IOPathKind¶

canonical_gpu: bool¶

uses_pylibcudf: bool¶

bbox_requested: bool¶

metadata_summary_available: bool¶

metadata_source: str | None¶

uses_covering_bbox: bool¶

uses_point_encoding_pushdown: bool¶

row_group_pushdown: bool¶

planner_strategy: str¶

available_row_groups: int | None¶

selected_row_groups: tuple[int, Ellipsis] | None¶

decoded_row_fraction_estimate: float | None¶

pruned_row_group_fraction: float | None¶

reason: str¶

class vibespatial.io.geoparquet.GeoParquetChunkPlan¶

chunk_index: int¶

row_groups: tuple[int, Ellipsis]¶

estimated_rows: int¶

class vibespatial.io.geoparquet.GeoParquetEnginePlan¶

selected_path: vibespatial.io.support.IOPathKind¶

backend: str¶

geometry_encoding: str | None¶

chunk_count: int¶

target_chunk_rows: int | None¶

uses_row_group_pruning: bool¶

reason: str¶

class vibespatial.io.geoparquet.GeoParquetEngineBenchmark¶

backend: str¶

geometry_encoding: str¶

rows: int¶

chunk_rows: int | None¶

chunk_count: int¶

elapsed_seconds: float¶

rows_per_second: float¶

planning_elapsed_seconds: float = 0.0¶

scan_elapsed_seconds: float = 0.0¶

decode_elapsed_seconds: float = 0.0¶

concat_elapsed_seconds: float = 0.0¶

vibespatial.io.geoparquet.plan_geoparquet_scan(*, bbox: tuple[float, float, float, float] | None = None, geo_metadata: dict[str, Any] | None = None, metadata_summary: vibespatial.io.geoparquet_planner.GeoParquetMetadataSummary | None = None, planner_strategy: str = 'auto') → GeoParquetScanPlan¶

vibespatial.io.geoparquet.plan_geoparquet_engine(*, geo_metadata: dict[str, Any] | None, scan_plan: GeoParquetScanPlan, chunk_plans: tuple[GeoParquetChunkPlan, Ellipsis], target_chunk_rows: int | None) → GeoParquetEnginePlan¶

vibespatial.io.geoparquet.read_geoparquet_owned(path, *, columns=None, storage_options=None, bbox=None, chunk_rows: int | None = None, backend: str = 'auto', **kwargs) → vibespatial.geometry.owned.OwnedGeometryArray¶

vibespatial.io.geoparquet.write_geoparquet(df, path, *, index: bool | None = None, compression: str = 'snappy', geometry_encoding: str = 'WKB', schema_version: str | None = None, write_covering_bbox: bool = False, **kwargs) → None¶

vibespatial.io.geoparquet.read_geoparquet(path, *, columns=None, storage_options=None, bbox=None, to_pandas_kwargs=None, **kwargs)¶

Read a GeoParquet file into a GeoDataFrame.

When PyArrow is available the reader plans row-group selection from spatial metadata, decodes WKB geometry on GPU when possible, and produces device-resident OwnedGeometryArray without host round-trips.

Aliased as vibespatial.read_parquet().

Parameters¶

pathstr or Path: Path to the GeoParquet file.
columnslist of str, optional: Subset of columns to read.
storage_optionsdict, optional: Storage options for fsspec-compatible filesystems.
bboxtuple of (minx, miny, maxx, maxy), optional: Spatial filter bounding box for row-group pruning.
to_pandas_kwargsdict, optional: Extra keyword arguments passed to pyarrow.Table.to_pandas().
**kwargs: Passed through to the underlying Parquet reader.

Returns¶

GeoDataFrame

vibespatial.io.geoparquet.benchmark_geoparquet_scan_engine(*, geometry_type: str = 'point', rows: int = 100000, geometry_encoding: str = 'geoarrow', chunk_rows: int | None = None, backend: str = 'cpu', repeat: int = 5, seed: int = 0) → GeoParquetEngineBenchmark¶