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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

python -m pip install stac-pydantic

# or

python -m pip install stac-pydantic["validation"]

For local development:

python -m pip install -e '.[dev,lint]'

* various beta releases, specs not fully implemented

Development

Install the pre-commit hooks:

pre-commit install

Testing

Ensure you have all Python versions installed that the tests will be run against. If using pyenv, run:

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:

tox

Run a single test case using the standard pytest convention:

python -m pytest -v tests/test_models.py::test_item_extensions

Usage

Loading Models

Load data into models with standard pydantic:

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:

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.

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:

item_dict = item.model_dump()
assert item_dict['properties']['landsat:row'] == item.properties.row == 250

CLI

Usage: stac-pydantic [OPTIONS] COMMAND [ARGS]...

  stac-pydantic cli group

Options:
  --help  Show this message and exit.

Commands:
  validate-item  Validate STAC Item