diff --git a/stac_geoparquet/from_arrow.py b/stac_geoparquet/from_arrow.py index 7cf9d85..f940864 100644 --- a/stac_geoparquet/from_arrow.py +++ b/stac_geoparquet/from_arrow.py @@ -121,20 +121,46 @@ def _convert_bbox_to_array(table: pa.Table) -> pa.Table: new_chunks = [] for chunk in bbox_col.chunks: assert pa.types.is_struct(chunk.type) - xmin = chunk.field(0).to_numpy() - ymin = chunk.field(1).to_numpy() - xmax = chunk.field(2).to_numpy() - ymax = chunk.field(3).to_numpy() - coords = np.column_stack( - [ - xmin, - ymin, - xmax, - ymax, - ] - ) - list_arr = pa.FixedSizeListArray.from_arrays(coords.flatten("C"), 4) + if bbox_col.type.num_fields == 4: + xmin = chunk.field("xmin").to_numpy() + ymin = chunk.field("ymin").to_numpy() + xmax = chunk.field("xmax").to_numpy() + ymax = chunk.field("ymax").to_numpy() + coords = np.column_stack( + [ + xmin, + ymin, + xmax, + ymax, + ] + ) + + list_arr = pa.FixedSizeListArray.from_arrays(coords.flatten("C"), 4) + + elif bbox_col.type.num_fields == 6: + xmin = chunk.field("xmin").to_numpy() + ymin = chunk.field("ymin").to_numpy() + zmin = chunk.field("zmin").to_numpy() + xmax = chunk.field("xmax").to_numpy() + ymax = chunk.field("ymax").to_numpy() + zmax = chunk.field("zmax").to_numpy() + coords = np.column_stack( + [ + xmin, + ymin, + zmin, + xmax, + ymax, + zmax, + ] + ) + + list_arr = pa.FixedSizeListArray.from_arrays(coords.flatten("C"), 6) + + else: + raise ValueError("Expected 4 or 6 fields in bbox struct.") + new_chunks.append(list_arr) return table.set_column(bbox_col_idx, "bbox", new_chunks) diff --git a/stac_geoparquet/to_arrow.py b/stac_geoparquet/to_arrow.py index 7321607..1cc36e7 100644 --- a/stac_geoparquet/to_arrow.py +++ b/stac_geoparquet/to_arrow.py @@ -27,6 +27,7 @@ def parse_stac_items_to_arrow( *, chunk_size: int = 8192, schema: Optional[pa.Schema] = None, + downcast: bool = True, ) -> pa.Table: """Parse a collection of STAC Items to a :class:`pyarrow.Table`. @@ -41,6 +42,7 @@ def parse_stac_items_to_arrow( schema: The schema of the input data. If provided, can improve memory use; otherwise all items need to be parsed into a single array for schema inference. Defaults to None. + downcast: if True, store bbox as float32 for memory and disk saving. Returns: a pyarrow Table with the STAC-GeoParquet representation of items. @@ -53,15 +55,15 @@ def parse_stac_items_to_arrow( for chunk in _chunks(items, chunk_size): batches.append(_stac_items_to_arrow(chunk, schema=schema)) - stac_table = pa.Table.from_batches(batches, schema=schema) + table = pa.Table.from_batches(batches, schema=schema) else: # If schema is _not_ provided, then we must convert to Arrow all at once, or # else it would be possible for a STAC item late in the collection (after the # first chunk) to have a different schema and not match the schema inferred for # the first chunk. - stac_table = pa.Table.from_batches([_stac_items_to_arrow(items)]) + table = pa.Table.from_batches([_stac_items_to_arrow(items)]) - return _process_arrow_table(stac_table) + return _process_arrow_table(table, downcast=downcast) def parse_stac_ndjson_to_arrow( @@ -69,6 +71,7 @@ def parse_stac_ndjson_to_arrow( *, chunk_size: int = 8192, schema: Optional[pa.Schema] = None, + downcast: bool = True, ) -> pa.Table: # Define outside of if/else to make mypy happy items: List[dict] = [] @@ -98,14 +101,14 @@ def parse_stac_ndjson_to_arrow( if len(items) > 0: batches.append(_stac_items_to_arrow(items, schema=schema)) - stac_table = pa.Table.from_batches(batches, schema=schema) - return _process_arrow_table(stac_table) + table = pa.Table.from_batches(batches, schema=schema) + return _process_arrow_table(table, downcast=downcast) -def _process_arrow_table(table: pa.Table) -> pa.Table: +def _process_arrow_table(table: pa.Table, *, downcast: bool = True) -> pa.Table: table = _bring_properties_to_top_level(table) table = _convert_timestamp_columns(table) - table = _convert_bbox_to_struct(table) + table = _convert_bbox_to_struct(table, downcast=downcast) return table @@ -192,11 +195,21 @@ def _convert_timestamp_columns(table: pa.Table) -> pa.Table: except KeyError: continue + field_index = table.schema.get_field_index(column_name) + if pa.types.is_timestamp(column.type): continue + + # STAC allows datetimes to be null. If all rows are null, the column type may be + # inferred as null. We cast this to a timestamp column. + elif pa.types.is_null(column.type): + table = table.set_column( + field_index, column_name, column.cast(pa.timestamp("us")) + ) + elif pa.types.is_string(column.type): - table = table.drop(column_name).append_column( - column_name, _convert_timestamp_column(column) + table = table.set_column( + field_index, column_name, _convert_timestamp_column(column) ) else: raise ValueError( @@ -224,7 +237,26 @@ def _convert_timestamp_column(column: pa.ChunkedArray) -> pa.ChunkedArray: return pa.chunked_array(chunks) -def _convert_bbox_to_struct(table: pa.Table, *, downcast: bool = True) -> pa.Table: +def is_bbox_3d(bbox_col: pa.ChunkedArray) -> bool: + """Infer whether the bounding box column represents 2d or 3d bounding boxes.""" + offsets_set = set() + for chunk in bbox_col.chunks: + offsets = chunk.offsets.to_numpy() + offsets_set.update(np.unique(offsets[1:] - offsets[:-1])) + + if len(offsets_set) > 1: + raise ValueError("Mixed 2d-3d bounding boxes not yet supported") + + offset = list(offsets_set)[0] + if offset == 6: + return True + elif offset == 4: + return False + else: + raise ValueError(f"Unexpected bbox offset: {offset=}") + + +def _convert_bbox_to_struct(table: pa.Table, *, downcast: bool) -> pa.Table: """Convert bbox column to a struct representation Since the bbox in JSON is stored as an array, pyarrow automatically converts the @@ -244,6 +276,7 @@ def _convert_bbox_to_struct(table: pa.Table, *, downcast: bool = True) -> pa.Tab """ bbox_col_idx = table.schema.get_field_index("bbox") bbox_col = table.column(bbox_col_idx) + bbox_3d = is_bbox_3d(bbox_col) new_chunks = [] for chunk in bbox_col.chunks: @@ -252,36 +285,80 @@ def _convert_bbox_to_struct(table: pa.Table, *, downcast: bool = True) -> pa.Tab or pa.types.is_large_list(chunk.type) or pa.types.is_fixed_size_list(chunk.type) ) - coords = chunk.flatten().to_numpy().reshape(-1, 4) - xmin = coords[:, 0] - ymin = coords[:, 1] - xmax = coords[:, 2] - ymax = coords[:, 3] + if bbox_3d: + coords = chunk.flatten().to_numpy().reshape(-1, 6) + else: + coords = chunk.flatten().to_numpy().reshape(-1, 4) if downcast: coords = coords.astype(np.float32) - # Round min values down to the next float32 value - # Round max values up to the next float32 value - xmin = np.nextafter(xmin, -np.Infinity) - ymin = np.nextafter(ymin, -np.Infinity) - xmax = np.nextafter(xmax, np.Infinity) - ymax = np.nextafter(ymax, np.Infinity) - - struct_arr = pa.StructArray.from_arrays( - [ - xmin, - ymin, - xmax, - ymax, - ], - names=[ - "xmin", - "ymin", - "xmax", - "ymax", - ], - ) + if bbox_3d: + xmin = coords[:, 0] + ymin = coords[:, 1] + zmin = coords[:, 2] + xmax = coords[:, 3] + ymax = coords[:, 4] + zmax = coords[:, 5] + + if downcast: + # Round min values down to the next float32 value + # Round max values up to the next float32 value + xmin = np.nextafter(xmin, -np.Infinity) + ymin = np.nextafter(ymin, -np.Infinity) + zmin = np.nextafter(zmin, -np.Infinity) + xmax = np.nextafter(xmax, np.Infinity) + ymax = np.nextafter(ymax, np.Infinity) + zmax = np.nextafter(zmax, np.Infinity) + + struct_arr = pa.StructArray.from_arrays( + [ + xmin, + ymin, + zmin, + xmax, + ymax, + zmax, + ], + names=[ + "xmin", + "ymin", + "zmin", + "xmax", + "ymax", + "zmax", + ], + ) + + else: + xmin = coords[:, 0] + ymin = coords[:, 1] + xmax = coords[:, 2] + ymax = coords[:, 3] + + if downcast: + # Round min values down to the next float32 value + # Round max values up to the next float32 value + xmin = np.nextafter(xmin, -np.Infinity) + ymin = np.nextafter(ymin, -np.Infinity) + xmax = np.nextafter(xmax, np.Infinity) + ymax = np.nextafter(ymax, np.Infinity) + + struct_arr = pa.StructArray.from_arrays( + [ + xmin, + ymin, + xmax, + ymax, + ], + names=[ + "xmin", + "ymin", + "xmax", + "ymax", + ], + ) + new_chunks.append(struct_arr) return table.set_column(bbox_col_idx, "bbox", new_chunks) diff --git a/tests/data/3dep-lidar-copc-pc.json b/tests/data/3dep-lidar-copc-pc.json 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open(f"{collection}-pc.json", "w") as f: + json.dump(items, f, indent=2) + + +def main(): + download("3dep-lidar-copc") + download("3dep-lidar-dsm") + download("cop-dem-glo-30") + download("io-lulc-annual-v02") + download("io-lulc") + download("landsat-c2-l1") + download("landsat-c2-l2") + download("naip") + download("planet-nicfi-analytic") + download("sentinel-1-rtc") + download("sentinel-2-l2a") + download("us-census") + + +if __name__ == "__main__": + main() diff --git a/tests/data/io-lulc-annual-v02-pc.json b/tests/data/io-lulc-annual-v02-pc.json new file mode 100644 index 0000000..1b60ed6 --- /dev/null +++ b/tests/data/io-lulc-annual-v02-pc.json @@ -0,0 +1,2174 @@ +[ + { + "id": "60W-2023", + "bbox": [ + -180.0, + 63.969110597182826, + 180.0, + 72.02291285284377 + ], + "type": "Feature", + "links": [ + { + "rel": "collection", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02" + }, + { + "rel": "parent", + 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"description": "Area (Water)" + }, + { + "name": "INTPTLAT", + "type": "double", + "description": "Internal Point (Latitude)" + }, + { + "name": "INTPTLON", + "type": "double", + "description": "Internal Point (Longitude)" + }, + { + "name": "geometry", + "type": "byte_array", + "description": "Census block geometry" + }, + { + "name": "GEOID", + "type": "byte_array", + "description": "Geographic record identifier" + } + ] + }, + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "stac_version": "1.0.0" + }, + { + "id": "2020-cb_2020_us_vtd_500k", + "bbox": [ + -179.148909, + 17.88328, + 179.77847, + 71.365162 + ], + "type": "Feature", + "links": [ + { + "rel": "collection", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census" + }, + { + "rel": "parent", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census" + }, + { + "rel": "root", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/" + }, + { + "rel": "self", + "type": "application/geo+json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/items/2020-cb_2020_us_vtd_500k" + } + ], + "assets": { + "data": { + "href": "abfs://us-census/2020/cb_2020_us_vtd_500k.parquet", + "type": "application/x-parquet", + "roles": [ + "data" + ], + "title": "Dataset root", + "table:storage_options": { + "account_name": "ai4edataeuwest" + } + } + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + 179.77847, + 17.88328 + ], + [ + 179.77847, + 71.365162 + ], + [ + -179.148909, + 71.365162 + ], + [ + -179.148909, + 17.88328 + ], + [ + 179.77847, + 17.88328 + ] + ] + ] + }, + "collection": "us-census", + "properties": { + "datetime": "2021-08-01T00:00:00Z", + "proj:epsg": 4269, + "table:columns": [ + { + "name": "STATEFP20", + "type": "byte_array", + "description": "State FIPS code" + }, + { + "name": "COUNTYFP20", + "type": "byte_array", + "description": "County FIPS code" + }, + { + "name": "VTDST20", + "type": "byte_array", + "description": "Voting district code" + }, + { + "name": "AFFGEOID20", + "type": "byte_array" + }, + { + "name": "GEOID20", + "type": "byte_array" + }, + { + "name": "VTDI20", + "type": "byte_array", + "description": "Voting district indicator" + }, + { + "name": "NAME20", + "type": "byte_array", + "description": "Voting district name" + }, + { + "name": "NAMELSAD20", + "type": "byte_array", + "description": "Voting district name and legal/statistical division" + }, + { + "name": "LSAD20", + "type": "byte_array", + "description": "Legal/statistical classification" + }, + { + "name": "ALAND20", + "type": "int64", + "description": "Current land area" + }, + { + "name": "AWATER20", + "type": "int64", + "description": "Current water area" + }, + { + "name": "geometry", + "type": "byte_array", + "description": "coordinates for Voting District polygons" + } + ], + "table:row_count": 158320 + }, + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/projection/v1.0.0/schema.json" + ], + "stac_version": "1.0.0" + }, + { + "id": "2020-cb_2020_us_unsd_500k", + "bbox": [ + -179.148909, + -14.548699, + 179.77847, + 71.365162 + ], + "type": "Feature", + "links": [ + { + "rel": "collection", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census" + }, + { + "rel": "parent", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census" + }, + { + "rel": "root", + "type": "application/json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/" + }, + { + "rel": "self", + "type": "application/geo+json", + "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/items/2020-cb_2020_us_unsd_500k" + } + ], + "assets": { + "data": { + "href": "abfs://us-census/2020/cb_2020_us_unsd_500k.parquet", + "type": "application/x-parquet", + "roles": [ + "data" + ], + "title": "Dataset root", + "table:storage_options": { + "account_name": "ai4edataeuwest" + } + } + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + 179.77847, + -14.548699 + ], + [ + 179.77847, + 71.365162 + ], + [ + -179.148909, + 71.365162 + ], + [ + -179.148909, + -14.548699 + ], + [ + 179.77847, + -14.548699 + ] + ] + ] + }, + "collection": "us-census", + "properties": { + "datetime": "2021-08-01T00:00:00Z", + "proj:epsg": 4269, + "table:columns": [ + { + "name": "STATEFP", + "type": "byte_array", + "description": "State FIPS code" + }, + { + "name": "UNSDLEA", + "type": "int64", + "description": "Unified School District local education agency code" + }, + { + "name": "AFFGEOID", + "type": "byte_array", + "description": "American FactFinder summary level code + geovariant code + \"00US\" + GEOID" + }, + { + "name": "GEOID", + "type": "byte_array", + "description": "Concatenation of State FIPS code and UNSDLEA code" + }, + { + "name": "NAME", + "type": "byte_array", + "description": "Unified School District name" + }, + { + "name": "STUSPS", + "type": "byte_array", + "description": "FIPS Postal code" + }, + { + "name": "STATE_NAME", + "type": "byte_array", + "description": "State name" + }, + { + "name": "LSAD", + "type": "byte_array", + "description": "Legal/statistical classification" + }, + { + "name": "ALAND", + "type": "int64", + "description": "Current land area" + }, + { + "name": "AWATER", + "type": "int64", + "description": "Current water area" + }, + { + "name": "geometry", + "type": "byte_array", + "description": "Coordinates for Unified School District polygons" + } + ], + "table:row_count": 10867 + }, + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/projection/v1.0.0/schema.json" + ], + "stac_version": "1.0.0" + } +] \ No newline at end of file diff --git a/tests/test_arrow.py b/tests/test_arrow.py new file mode 100644 index 0000000..8daac78 --- /dev/null +++ b/tests/test_arrow.py @@ -0,0 +1,211 @@ +import json +import math +from pathlib import Path +from typing import Any, Dict, Sequence, Union + +import pytest +from ciso8601 import parse_rfc3339 + +from stac_geoparquet.from_arrow import stac_table_to_items +from stac_geoparquet.to_arrow import parse_stac_items_to_arrow + +HERE = Path(__file__).parent + +JsonValue = Union[list, tuple, int, float, dict, str, bool, None] + + +def assert_json_value_equal( + result: JsonValue, + expected: JsonValue, + *, + key_name: str = "root", + precision: float = 0.0001, +) -> None: + """Assert that the JSON value in `result` and `expected` are equal for our purposes. + + We allow these variations between result and expected: + + - We allow numbers to vary up to `precision`. + - We consider `key: None` and a missing key to be equivalent. + - We allow RFC3339 date strings with varying precision levels, as long as they + represent the same parsed datetime. + + Args: + result: The result to assert against. + expected: The expected item to compare against. + key_name: The key name of the current path in the JSON. Used for error messages. + precision: The precision to use for comparing integers and floats. + + Raises: + AssertionError: If the two values are not equal + """ + if isinstance(result, list) and isinstance(expected, list): + assert_sequence_equal(result, expected, key_name=key_name, precision=precision) + + elif isinstance(result, tuple) and isinstance(expected, tuple): + assert_sequence_equal(result, expected, key_name=key_name, precision=precision) + + elif isinstance(result, (int, float)) and isinstance(expected, (int, float)): + assert_number_equal(result, expected, key_name=key_name, precision=precision) + + elif isinstance(result, dict) and isinstance(expected, dict): + assert_dict_equal(result, expected, key_name=key_name, precision=precision) + + elif isinstance(result, str) and isinstance(expected, str): + assert_string_equal(result, expected, key_name=key_name) + + elif isinstance(result, bool) and isinstance(expected, bool): + assert_bool_equal(result, expected, key_name=key_name) + + elif result is None and expected is None: + pass + + else: + raise AssertionError( + f"Mismatched types at {key_name}. {type(result)=}, {type(expected)=}" + ) + + +def assert_sequence_equal( + result: Sequence, expected: Sequence, *, key_name: str, precision: float +) -> None: + """Compare two JSON arrays, recursively""" + assert len(result) == len(expected), ( + f"List at {key_name} has different lengths." f"{len(result)=}, {len(expected)=}" + ) + + for i in range(len(result)): + assert_json_value_equal( + result[i], expected[i], key_name=f"{key_name}.[{i}]", precision=precision + ) + + +def assert_number_equal( + result: Union[int, float], + expected: Union[int, float], + *, + precision: float, + key_name: str, +) -> None: + """Compare two JSON numbers""" + # Allow NaN equality + if math.isnan(result) and math.isnan(expected): + return + + assert abs(result - expected) <= precision, ( + f"Number at {key_name} not within precision. " + f"{result=}, {expected=}, {precision=}." + ) + + +def assert_string_equal( + result: str, + expected: str, + *, + key_name: str, +) -> None: + """Compare two JSON strings. + + We attempt to parse each string to a datetime. If this succeeds, then we compare the + datetime.datetime representations instead of the bare strings. + """ + + # Check if both strings are dates, then assert the parsed datetimes are equal + try: + result_datetime = parse_rfc3339(result) + expected_datetime = parse_rfc3339(expected) + + assert result_datetime == expected_datetime, ( + f"Date string at {key_name} not equal. " + f"{result=}, {expected=}." + f"{result_datetime=}, {expected_datetime=}." + ) + + except ValueError: + assert ( + result == expected + ), f"String at {key_name} not equal. {result=}, {expected=}." + + +def assert_bool_equal( + result: bool, + expected: bool, + *, + key_name: str, +) -> None: + """Compare two JSON booleans.""" + assert result == expected, f"Bool at {key_name} not equal. {result=}, {expected=}." + + +def assert_dict_equal( + result: Dict[str, Any], + expected: Dict[str, Any], + *, + key_name: str, + precision: float, +) -> None: + """ + Assert that two JSON dicts are equal, recursively, allowing missing keys to equal + None. + """ + result_keys = set(result.keys()) + expected_keys = set(expected.keys()) + + # For any keys that exist in result but not expected, assert that the result value + # is None + for key in result_keys - expected_keys: + assert ( + result[key] is None + ), f"Expected key at {key_name} to be None in result. Got {result['key']}" + + # And vice versa + for key in expected_keys - result_keys: + assert ( + expected[key] is None + ), f"Expected key at {key_name} to be None in expected. Got {expected['key']}" + + # For any overlapping keys, assert that their values are equal + for key in result_keys & expected_keys: + assert_json_value_equal( + result[key], + expected[key], + key_name=f"{key_name}.{key}", + precision=precision, + ) + + +TEST_COLLECTIONS = [ + "3dep-lidar-copc", + "3dep-lidar-dsm", + "cop-dem-glo-30", + "io-lulc-annual-v02", + "io-lulc", + "landsat-c2-l1", + "landsat-c2-l2", + "naip", + "planet-nicfi-analytic", + "sentinel-1-rtc", + "sentinel-2-l2a", + "us-census", +] + + +@pytest.mark.parametrize( + "collection_id", + TEST_COLLECTIONS, +) +def test_round_trip(collection_id: str): + with open(HERE / "data" / f"{collection_id}-pc.json") as f: + items = json.load(f) + + table = parse_stac_items_to_arrow(items, downcast=True) + items_result = list(stac_table_to_items(table)) + + for result, expected in zip(items_result, items): + assert_json_value_equal(result, expected, precision=0.001) + + table = parse_stac_items_to_arrow(items, downcast=False) + items_result = list(stac_table_to_items(table)) + + for result, expected in zip(items_result, items): + assert_json_value_equal(result, expected, precision=0)