Skip to main content

Letting two great packages work together!

Project description

CI-status-badge version-badge downloads-badge pyversions-badge license-badge

pydantic-shapely

Letting two great packages work together!

pydantic-shapely is a Python package that allows you to use Shapely geometries as Pydantic fields. This package is useful when you want to validate and serialize Shapely geometries using Pydantic models. As an added bonus, you can also use the package to validate and serialize the geometries in GeoJSON format, without the need of any additional code. The GeoJSON serialization is based on the GeoJSON specification.

Installation

You can install the package using pip:

pip install pydantic-shapely

Ofcourse, you can also install the package using poetry as package manager:

poetry add pydantic-shapely

Basic usage

Normally, when you want to use Shapely geometries in Pydantic models, you would have to set arbitrary_types_allowed to True in the Pydantic model. This is because the Shapely geometries are not natively supported by Pydantic.

With pydantic-shapely you can use Shapely geometries as Pydantic fields without having to set arbitrary_types_allowed to True. You only have to add the GeometryField as _additional_ annotation to the field in the Pydantic model.

import typing
from pydantic import BaseModel
from pydantic_shapely import GeometryField
from shapely.geometry import Point

class MyModel(BaseModel):
    point: typing.Annotation[Point, GeometryField(), Field(...)]

model = MyModel(point=Point(0, 0))
print(model.point)  # POINT (0 0)

With the GeometryField allows also to set the following parameters whether the geometry should be 2- or 3-dimensional with the parameter z_values. The following values are allowed:

  • forbidden: the geometry must be strictly 2-dimensional. A ValueError will raised when a shape with z-values is provided.

  • strip: the geometry may have z-values. These values will be stripped from then geometry in the validation process. The resulting shape will be 2-dimensional in all cases.

  • allow (default): both 2- and 3-dimensional values are allowed. During the validation process the data is not altered. This is the default behavior.

  • required: the geometry must be strictly 2-dimensional. A ValueError will raised when a shape without z-values is provided.

GeoJSON serialization

With pydantic-shapely you can also serialize the a Pydantic model with a Shapely geometry to GeoJSON format.

GeoJSON features

In order to add this functionality to your model, you have to inherit from the FeatureBaseModel class. This class is a subclass of the Pydantic BaseModel class and adds the following methods and attributes to the model:

  • GeoJsonDataModel: an attribute that contains the Pydantic GeoJSON model based on the original model. This model is created when the subclass is created.

  • to_geojson_model: a method that returns the GeoJSON model of the model instance. To convert the GeoJSON model back to the original model, you can use the to_feature_model method on the GeoJSON model.

  • model_dump_geojson: a method that serializes the model to GeoJSON format.

Example usage of the GeoJSON serialization:

import typing
from pydantic import BaseModel
from pydantic_shapely import GeometryField, FeatureBaseModel
from shapely.geometry import Point

class MyModel(FeatureBaseModel, geometry_field="point"):
    point: typing.Annotation[Point, GeometryField(), Field(...)]
    a: int = 42
    b: str = "Hello, World!"

model = MyModel(point=Point(0, 0))
print(model.model_dump_geojson())
# {
#     "type": "Feature",
#     "geometry": {
#         "type": "Point",
#         "coordinates": [0.0, 0.0]
#     },
#     "properties": {
#         "a": 42,
#         "b": "Hello, World!"}
# }

The GeoJSON serialization can also be used with FastApi. The following example shows how to create a simple annotated API that returns a GeoJSON representation of a Shapely geometry:

import typing
from fastapi import FastAPI
from pydantic import Field
from pydantic_shapely import FeatureBaseModel, GeometryField
from shapely.geometry import Point

app = FastAPI()

class MyModel(FeatureBaseModel, geometry_field="point"):
    point: typing.Annotated[Point, GeometryField(), Field(...)]

@app.get("/point")
def get_point() -> MyModel.GeoJsonDataModel:
    # Return a GeoJSON representation of a Shapely geometry.
    return MyModel(point=Point(0, 0)).to_geojson_model()

@app.post("/point")
def post_point(model: MyModel.GeoJsonDataModel) -> MyModel:
    # Convert the GeoJSON model back to the original model instance with the
    # `to_feature_model` method. The Shapely geometry will be returned as a
    # WKT-string in this case.
    return model.to_feature_model()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

GeoJSON feature collections

Based on the GeoJsonDataModel, a feature collection can be easily created by using the FeatureCollectionBaseModel class. This class is a subclass of the Pydantic BaseModel class and adds the following methods and attributes to the model:

  • from_feature_models: a class method that creates a feature collection from a list of features. The list of features is validated before the feature collection is created. The validation ensures that all features are of the correct type.

  • to_feature_models: a method that returns a list of feature models from the feature collection.

Example usage of the feature collection:

import typing
from shapely import Point

from pydantic_shapely import FeatureBaseModel, GeometryField
from pydantic_shapely.geojson import GeoJsonFeatureCollectionBaseModel


class TestModel(FeatureBaseModel):
    """Test class for a feature which supports GeoJSON serialization."""

    geometry: typing.Annotated[Point, GeometryField()]
    name: str = "Hello World"
    answer: int = 42


TestFeatureCollection = GeoJsonFeatureCollectionBaseModel[TestModel.GeoJsonDataModel]

# Method 1: Create a feature collection from a list of features.
test = TestFeatureCollection(
    features=[
        TestModel(geometry=Point(0, 0)).to_geojson_model(),
        TestModel(geometry=Point(1, 1)).to_geojson_model(),
    ]
)
# Method 2: Create a feature collection from a list features using the `from_feature_models`
# class method.
test = TestFeatureCollection.from_feature_models(
    [
        TestModel(geometry=Point(0, 0)),
        TestModel(geometry=Point(1, 1)),
    ]
)

# Print the resluting GeoJSON feature collection.
print(test.model_dump_json(indent=2))
# RESULT:
# {
#   "type": "FeatureCollection",
#   "features": [
#     {
#       "type": "Feature",
#       "geometry": {
#         "type": "Point",
#         "coordinates": [
#           0.0,
#           0.0
#         ]
#       },
#       "properties": {
#         "name": "Hello World",
#         "answer": 42
#       }
#     },
#     {
#       "type": "Feature",
#       "geometry": {
#         "type": "Point",
#         "coordinates": [
#           1.0,
#           1.0
#         ]
#       },
#       "properties": {
#         "name": "Hello World",
#         "answer": 42
#       }
#     }
#   ]
# }

The GeoJSON serialization can also be used with FastApi. The following example shows how to create a simple annotated API that returns a GeoJSON Feature Collection:

import typing
from fastapi import FastAPI
from pydantic import Field
from pydantic_shapely import FeatureBaseModel, GeometryField
from pydantic_shapely.geojson import GeoJsonFeatureCollectionBaseModel
from shapely.geometry import Point

app = FastAPI()

class MyModel(FeatureBaseModel, geometry_field="point"):
    point: typing.Annotated[Point, GeometryField(), Field(...)]
    name: str = "Hello World"
    answer: int = 42


# NOTE: Sub-classing the GeoJsonFeatureCollectionBaseModel gives a cleaner description
# in the API documentation.
class MyModelFeatureCollection(GeoJsonFeatureCollectionBaseModel[MyModel.GeoJsonDataModel]):
    ...


@app.get("/points")
def get_points() -> MyModelFeatureCollection:
    # Return a GeoJSON representation of a Shapely geometry.
    return MyModelFeatureCollection.from_feature_models(
        [
            MyModel(point=Point(0, 0)).to_geojson_model(),
            MyModel(point=Point(1, 1)).to_geojson_model(),
        ]
    )

@app.post("/points")
def post_points(model: MyModelFeatureCollection) -> typing.List[MyModel]:
    # Convert the GeoJSON model back to the original model instance with the
    # `to_feature_model` method. The Shapely geometry will be returned as a
    # WKT-string in this case.
    return model.to_feature_models()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Work in progress

This package is still in development. The following features are planned for the future:

  • Adding more options for the GeometryField annotation. For example, the ability to set a bounding box for the geometry.

  • Adding the CRS to the both GeometryField and the GeoJSON serialization. This functionality will automatically transform the geometries to the specified CRS.

Allthough the package is still in development, the current features are tested and ready for use. The signature of the methods and classes will not change in the future. If you have any suggestions or questions, feel free to open an issue on the GitHub repository.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_shapely-1.0.0a3.tar.gz (17.8 kB view details)

Uploaded Source

Built Distribution

pydantic_shapely-1.0.0a3-py3-none-any.whl (21.7 kB view details)

Uploaded Python 3

File details

Details for the file pydantic_shapely-1.0.0a3.tar.gz.

File metadata

  • Download URL: pydantic_shapely-1.0.0a3.tar.gz
  • Upload date:
  • Size: 17.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for pydantic_shapely-1.0.0a3.tar.gz
Algorithm Hash digest
SHA256 f50a0c108b4791ca1064d88f1a84c0c8b5aabfe8d53725a897e42e9eeb97e16a
MD5 8d23e7c61c25e6d80b7c598e819a724d
BLAKE2b-256 cffd77deeb3978164956ad985bb5a777d023fd7c3372e49f5ba0733422f6297e

See more details on using hashes here.

File details

Details for the file pydantic_shapely-1.0.0a3-py3-none-any.whl.

File metadata

File hashes

Hashes for pydantic_shapely-1.0.0a3-py3-none-any.whl
Algorithm Hash digest
SHA256 c8b50a5b4604223c44a21089d0c6c42184a0c85f5bc6ee2b4bfb2fb6b17ed550
MD5 b89a5716c8538ee84ed173c5b0acfb63
BLAKE2b-256 eac6d8ae3e5fb9d7a39c9975a22e84c81355685a6715a34df575527a55d2cc1e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page