stac-pydantic Pydantic models for STAC Catalogs, Collections, Items, and the STAC API spec. Initially developed by arturo-ai. The main purpose of this library is to provide reusable request/response models for tools such as fastapi. For more comprehensive schema validation and robust extension support, use pystac. Installation ```shell python -m pip install stac-pydantic or python -m pip install stac-pydantic["validation"] ``` For local development: shell python -m pip install -e '.[dev,lint]' | stac-pydantic | STAC Version | STAC API Version | Pydantic Version | |--------------|---------------|------------------|-----------------| | 1.2.x | 1.0.0-beta.1 | <1 | ^1.6 | | 1.3.x | 1.0.0-beta.2 | <1 | ^1.6 | | 2.0.x | 1.0.0 | <1* | ^1.6 | | 3.0.x | 1.0.0 | 1.0.0 | ^2.4 | | 3.1.x | 1.0.0 | 1.0.0 | ^2.4 | * various beta releases, specs not fully implemented Development Install the pre-commit hooks: shell pre-commit install Testing Ensure you have all Python versions installed that the tests will be run against. If using pyenv, run: shell pyenv install 3.8.18 pyenv install 3.9.18 pyenv install 3.10.13 pyenv install 3.11.5 pyenv local 3.8.18 3.9.18 3.10.13 3.11.5 Run the entire test suite: shell tox Run a single test case using the standard pytest convention: shell python -m pytest -v tests/test_models.py::test_item_extensions Usage Loading Models Load data into models with standard pydantic: ```python from stac_pydantic import Catalog stac_catalog = { "type": "Catalog", "stac_version": "1.0.0", "id": "sample", "description": "This is a very basic sample catalog.", "links": [ { "href": "item.json", "rel": "item" } ] } catalog = Catalog(**stac_catalog) assert catalog.id == "sample" assert catalog.links[0].href == "item.json" ``` Extensions STAC defines many extensions which let the user customize the data in their catalog. stac-pydantic.extensions.validate_extensions gets the JSON schemas from the URLs provided in the stac_extensions property (caching the last fetched ones), and will validate a dict, Item, Collection or Catalog against those fetched schemas: ```python from stac_pydantic import Item from stac_pydantic.extensions import validate_extensions stac_item = { "id": "12345", "type": "Feature", "stac_extensions": [ "https://stac-extensions.github.io/eo/v1.0.0/schema.json" ], "geometry": { "type": "Point", "coordinates": [0, 0] }, "bbox": [0.0, 0.0, 0.0, 0.0], "properties": { "datetime": "2020-03-09T14:53:23.262208+00:00", "eo:cloud_cover": 25, }, "links": [], "assets": {}, } model = Item(**stac_item) validate_extensions(model, reraise_exception=True) assert getattr(model.properties, "eo:cloud_cover") == 25 ``` The complete list of current STAC Extensions can be found here. Vendor Extensions The same procedure described above works for any STAC Extension schema as long as it can be loaded from a public url. STAC API The STAC API Specs extent the core STAC specification for implementing dynamic catalogs. STAC Objects used in an API context should always import models from the api subpackage. This package extends Catalog, Collection, and Item models with additional fields and validation rules and introduces Collections and ItemCollections models and Pagination/ Search Links. It also implements models for defining ItemSeach queries. ```python from stac_pydantic.api import Item, ItemCollection stac_item = Item(**{ "id": "12345", "type": "Feature", "stac_extensions": [], "geometry": { "type": "Point", "coordinates": [0, 0] }, "bbox": [0.0, 0.0, 0.0, 0.0], "properties": { "datetime": "2020-03-09T14:53:23.262208+00:00", }, "collection": "CS3", "links": [ { "rel": "self", "href": "http://stac.example.com/catalog/collections/CS3-20160503_132130_04/items/CS3-20160503_132130_04.json" }, { "rel": "collection", "href": "http://stac.example.com/catalog/CS3-20160503_132130_04/catalog.json" }, { "rel": "root", "href": "http://stac.example.com/catalog" }], "assets": {}, }) stac_item_collection = ItemCollection(**{ "type": "FeatureCollection", "features": [stac_item], "links": [ { "rel": "self", "href": "http://stac.example.com/catalog/search?collection=CS3", "type": "application/geo+json" }, { "rel": "root", "href": "http://stac.example.com/catalog", "type": "application/json" }], }) ``` Exporting Models Most STAC extensions are namespaced with a colon (ex eo:gsd) to keep them distinct from other extensions. Because Python doesn't support the use of colons in variable names, we use Pydantic aliasing to add the namespace upon model export. This requires exporting the model with the by_alias = True parameter. Export methods (model_dump() and model_dump_json()) for models in this library have by_alias and exclude_unset st to True by default: python item_dict = item.model_dump() assert item_dict['properties']['landsat:row'] == item.properties.row == 250 CLI ```text Usage: stac-pydantic [OPTIONS] COMMAND [ARGS]... stac-pydantic cli group Options: --help Show this message and exit. Commands: validate-item Validate STAC Item ```
Latest version: 3.2.0 Released: 2025-03-20