stac-utils/stac-table

stac-table

This library generates STAC objects for tabular dataset. It uses the table STAC extension.

Installation

stac-table is only available through GitHub right now:

python -m pip install git+https://github.com/TomAugspurger/stac-table

Examples

Generate a STAC item from a Parquet Dataset.

>>> import datetime, geopandas, pystac, stac_table
>>> # generate the sample data
>>> gdf = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))
>>> gdf.to_parquet("data.parquet")
>>> # Create the template Item
>>> item = pystac.Item(
...     "naturalearth_lowres", geometry=None, bbox=None, datetime=datetime.datetime(2021, 1, 1), properties={}
... )
>>> result = stac_table.generate("data.parquet", item)
>>> result
<Item id=naturalearth_lowres>

The new item is updated to include the table STAC extension

>>> result.stac_extensions
['https://stac-extensions.github.io/table/v1.0.0/schema.json',
 'https://stac-extensions.github.io/projection/v1.0.0/schema.json']

The updated fields are available under properties.

>>> result.properties
{'table:columns': [{'name': 'pop_est', 'type': 'int64'},
  {'name': 'continent', 'type': 'byte_array'},
  {'name': 'name', 'type': 'byte_array'},
  {'name': 'iso_a3', 'type': 'byte_array'},
  {'name': 'gdp_md_est', 'type': 'double'},
  {'name': 'geometry', 'type': 'byte_array'}],
 'proj:epsg': 4326}

Finally, an Asset is added with a link to the the dataset,

>>> result.assets["data"].to_dict()
{'href': 'data.parquet',
 'type': 'application/x-parquet',
 'title': 'Dataset root',
 'roles': ['data']}

stac_table will optionally fill in some additional values in your STAC item if you pass the appropriate keywords.

• infer_bbox: Sets the item's bbox to the bounding box of the union of the geometry column's values. Relies on spatial partitions.
• infer_geometry: Sets the item's geometry to the union of the geometry column's values.
• infer_datetime: Sets the item's properties.datetime or properties.start_datetime and properties.end_daetime based on the values in the datetime_column column.