GeoModeler 0.1.dev0

GeoModeler is a Python project based on pydantic, designed to model and validate geojson data structures such as points, lines, and polygons. It provides a set of tools and validators for working with GeoJSON data.

GeoJSON Models

This project provides a set of Python models for GeoJSON objects conforming to RFC 7946. It includes models for all the basic GeoJSON types:

• Point
• MultiPoint
• LineString
• MultiLineString
• Polygon
• MultiPolygon
• GeometryCollection
• Feature
• FeatureCollection

These models are implemented using the Pydantic library, which provides runtime data validation and settings management using Python type annotations.

Installation

This project requires Python 3.10 or higher. You can install it using pip:

pip install geo-modeler

Usage

Here is an example of how to create a Point object:

from src import Point

point = Point(type='Point', coordinates=[1.0, 2.0])

You can also validate a GeoJSON string:

point = Point.validate_json('{"type":"Point","coordinates":[1.0,2.0]}')

And convert a model to a GeoJSON string:

json_string = point.model_dump_json()

Initializing and Validating

To initialize a FeatureCollection or a Point using their corresponding JSON representations, you can use the validate_json method provided by the geojson_models library. This method will take care of all subtypes and validate the structure of the GeoJSON.

If the GeoJSON does not follow the right-hand rule, a warning will be issued, but it won't break the execution.

When dumping the model to a GeoJSON string using the model_dump_json method, you can use the unset option to exclude empty defaults.

Here is an example of how to use these features:

from src import FeatureCollection, Point

# Initialize a FeatureCollection with data Point from a JSON string.
feature_collection_json = '{"type":"FeatureCollection","features":[{"type":"Feature","properties":{"id":"1","name":"Litter Bin","description":"Litter Bin","type":"Litter Bin","colour":"Green","location":"Leeds","location_type":"Street","location_subtype":"Road","location_name":"Leeds","latitude":"53.71583","longitude":"-1.74448","easting":"429000","northing":"433000","northing":"433000","postcode_sector":"LS1","postcode_district":"LS","postcode_area":"LS","uprn":"100335","organisation":"Leeds City Council","organisation_uri":"http://opendatacommunities.org/id/leeds-city-council","organisation_label":"Leeds City Council","uri":"http://opendatacommunities.org/id/litter-bin/leeds/1","label":"Litter Bin","notation":"1","notation_uri":"http://opendatacommunities.org/id/litter-bin/leeds/1","notation_label":"1","notation_type":"http://opendatacommunities.org/def/litter-bin/leeds/notation","notation_type_label":"Notation"},"geometry":{"type":"Point","coordinates":[-1.74448,53.71583]}}]}'
feature_collection = FeatureCollection.model_validate_json(feature_collection_json)

# Initialize a single Point with a JSON string
point_json = '{"type":"Point","coordinates":[1.0,2.0]}'
point = Point.model_validate_json(point_json)


# Dump the models to GeoJSON strings, excluding empty defaults
print(feature_collection.model_dump_json(exclude_unset=True))
# {"type":"FeatureCollection","features":[{"type":"Feature","properties":{"id":"1","name":"Litter Bin","description":"Litter Bin","type":"Litter Bin","colour":"Green","location":"Leeds","location_type":"Street","location_subtype":"Road","location_name":"Leeds","latitude":"53.71583","longitude":"-1.74448","easting":"429000","northing":"433000","postcode_sector":"LS1","postcode_district":"LS","postcode_area":"LS","uprn":"100335","organisation":"Leeds City Council","organisation_uri":"http://opendatacommunities.org/id/leeds-city-council","organisation_label":"Leeds City Council","uri":"http://opendatacommunities.org/id/litter-bin/leeds/1","label":"Litter Bin","notation":"1","notation_uri":"http://opendatacommunities.org/id/litter-bin/leeds/1","notation_label":"1","notation_type":"http://opendatacommunities.org/def/litter-bin/leeds/notation","notation_type_label":"Notation"},"geometry":{"type":"Point","coordinates":[-1.74448,53.71583]}}]}
print(point.model_dump_json())
# {"type":"Point","coordinates":[1.0,2.0]}

This will output valid GeoJSON, although it may contain warnings for direction. The model_validate_json method also validates lengths and required fields according to the GeoJSON specification.

Testing

This project includes a suite of tests that you can run using pytest:

pytest

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

This project is licensed under the terms of the MIT license.