pydantic-geojson 🌍
| GeoJSON Objects | Status |
|---|---|
| Point | ✅ |
| MultiPoint | ✅ |
| LineString | ✅ |
| MultiLineString | ✅ |
| Polygon | ✅ |
| MultiPolygon | ✅ |
| GeometryCollection | ✅ |
| Feature | ✅ |
| FeatureCollection | ✅ |
Installation
pydantic-geojson is compatible with Python 3.7 and up. The recommended way to install is via poetry:
poetry add pydantic_geojson
Using pip to install is also possible.
pip install pydantic_geojson
GEOJSON
GeoJSON is a format for encoding a variety of geographic data structures.
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [125.6, 10.1]
},
"properties": {
"name": "Dinagat Islands"
}
}
GeoJSON supports the following geometry types: Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon. Geometric objects with additional properties are Feature objects. Sets of features are contained by FeatureCollection objects.
Examples of using
Custom properties:
from pydantic import BaseModel
from pydantic_geojson import FeatureModel
class MyPropertiesModel(BaseModel):
name: str
class MyFeatureModel(FeatureModel):
properties: MyPropertiesModel
data = {
"type": "Feature",
"properties": {
"name": "foo name",
},
"geometry": {
"type": "Polygon",
"coordinates": [
[
[-80.724878, 35.265454],
[-80.722646, 35.260338]
]
]
}
}
>>> MyFeatureModel(**data)
>>> type='Feature' geometry=PolygonModel(type='Polygon', coordinates=[[Coordinates(lon=-80.724878, lat=35.265454), Coordinates(lon=-80.722646, lat=35.260338)]]) properties=MyPropertiesModel(name='foo name')
Point
Simple example data:
from pydantic_geojson import PointModel
data = {
"type": "Point",
"coordinates": [-105.01621, 39.57422]
}
>>> PointModel(**data)
>>> type='Point' coordinates=Coordinates(lon=-105.01621, lat=39.57422)
MultiPoint
Simple example data:
from pydantic_geojson import MultiPointModel
data = {
"type": "MultiPoint",
"coordinates": [
[-105.01621, 39.57422],
[-80.666513, 35.053994]
]
}
>>> PointModel(**data)
>>> type='MultiPoint' coordinates=[Coordinates(lon=-105.01621, lat=39.57422), Coordinates(lon=-80.666513, lat=35.053994)]
LineString
Simple example data:
from pydantic_geojson import LineStringModel
data = {
"type": "LineString",
"coordinates": [
[-99.113159, 38.869651],
[-99.0802, 38.85682],
[-98.822021, 38.85682],
[-98.448486, 38.848264]
]
}
>>> LineStringModel(**data)
>>> type='LineString' coordinates=[Coordinates(lon=-99.113159, lat=38.869651), Coordinates(lon=-99.0802, lat=38.85682), Coordinates(lon=-98.822021, lat=38.85682), Coordinates(lon=-98.448486, lat=38.848264)]
MultiLineString
Simple example data:
from pydantic_geojson import MultiLineStringModel
data = {
"type": "MultiLineString",
"coordinates": [
[
[-105.019898, 39.574997],
[-105.019598, 39.574898],
[-105.019061, 39.574782]
],
[
[-105.017173, 39.574402],
[-105.01698, 39.574385],
[-105.016636, 39.574385],
[-105.016508, 39.574402],
[-105.01595, 39.57427]
],
[
[-105.014276, 39.573972],
[-105.014126, 39.574038],
[-105.013825, 39.57417],
[-105.01331, 39.574452]
]
]
}
>>> MultiLineStringModel(**data)
>>> type='MultiLineString' coordinates=[[Coordinates(lon=-105.019898, lat=39.574997), Coordinates(lon=-105.019598, lat=39.574898), Coordinates(lon=-105.019061, lat=39.574782)], [Coordinates(lon=-105.017173, lat=39.574402), Coordinates(lon=-105.01698, lat=39.574385), Coordinates(lon=-105.016636, lat=39.574385), Coordinates(lon=-105.016508, lat=39.574402), Coordinates(lon=-105.01595, lat=39.57427)], [Coordinates(lon=-105.014276, lat=39.573972), Coordinates(lon=-105.014126, lat=39.574038), Coordinates(lon=-105.013825, lat=39.57417), Coordinates(lon=-105.01331, lat=39.574452)]]
Polygon
Simple example data:
from pydantic_geojson import PolygonModel
data = {
"type": "Polygon",
"coordinates": [
[
[100, 0],
[101, 0],
[101, 1],
[100, 1],
[100, 0]
]
]
}
>>> PolygonModel(**data)
>>> type='Polygon' coordinates=[[Coordinates(lon=100.0, lat=0.0), Coordinates(lon=101.0, lat=0.0), Coordinates(lon=101.0, lat=1.0), Coordinates(lon=100.0, lat=1.0), Coordinates(lon=100.0, lat=0.0)]]
MultiPolygon
Simple example data:
from pydantic_geojson import MultiPolygonModel
data = {
"type": "MultiPolygon",
"coordinates": [
[
[
[107, 7],
[108, 7],
[108, 8],
[107, 8],
[107, 7]
]
],
[
[
[100, 0],
[101, 0],
[101, 1],
[100, 1],
[100, 0]
]
]
]
}
>>> MultiPolygonModel(**data)
>>> type='MultiPolygon' coordinates=[[[Coordinates(lon=107.0, lat=7.0), Coordinates(lon=108.0, lat=7.0), Coordinates(lon=108.0, lat=8.0), Coordinates(lon=107.0, lat=8.0), Coordinates(lon=107.0, lat=7.0)]], [[Coordinates(lon=100.0, lat=0.0), Coordinates(lon=101.0, lat=0.0), Coordinates(lon=101.0, lat=1.0), Coordinates(lon=100.0, lat=1.0), Coordinates(lon=100.0, lat=0.0)]]]
GeometryCollection
Simple example data:
from pydantic_geojson import GeometryCollectionModel
data = {
"type": "GeometryCollection",
"geometries": [
{
"type": "Point",
"coordinates": [-80.660805, 35.049392]
},
{
"type": "Polygon",
"coordinates": [
[
[-80.664582, 35.044965],
[-80.663874, 35.04428],
[-80.662586, 35.04558],
[-80.663444, 35.046036],
[-80.664582, 35.044965]
]
]
},
{
"type": "LineString",
"coordinates": [
[-80.662372, 35.059509],
[-80.662693, 35.059263],
[-80.662844, 35.05893]
]
}
]
}
>>> GeometryCollectionModel(**data)
>>> type='GeometryCollection' geometries=[PointModel(type='Point', coordinates=Coordinates(lon=-80.660805, lat=35.049392)), PolygonModel(type='Polygon', coordinates=[[Coordinates(lon=-80.664582, lat=35.044965), Coordinates(lon=-80.663874, lat=35.04428), Coordinates(lon=-80.662586, lat=35.04558), Coordinates(lon=-80.663444, lat=35.046036), Coordinates(lon=-80.664582, lat=35.044965)]]), LineStringModel(type='LineString', coordinates=[Coordinates(lon=-80.662372, lat=35.059509), Coordinates(lon=-80.662693, lat=35.059263), Coordinates(lon=-80.662844, lat=35.05893)])]
Feature
Simple example data:
from pydantic_geojson import FeatureModel
data = {
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[-80.724878, 35.265454],
[-80.722646, 35.260338],
[-80.720329, 35.260618],
[-80.71681, 35.255361],
[-80.704793, 35.268397],
[-82.715179, 35.267696],
[-80.721359, 35.267276],
[-80.724878, 35.265454]
]
]
}
}
>>> FeatureModel(**data)
>>> type='Feature' geometry=PolygonModel(type='Polygon', coordinates=[[Coordinates(lon=-80.724878, lat=35.265454), Coordinates(lon=-80.722646, lat=35.260338), Coordinates(lon=-80.720329, lat=35.260618), Coordinates(lon=-80.71681, lat=35.255361), Coordinates(lon=-80.704793, lat=35.268397), Coordinates(lon=-82.715179, lat=35.267696), Coordinates(lon=-80.721359, lat=35.267276), Coordinates(lon=-80.724878, lat=35.265454)]])
FeatureCollection
Simple example data:
from pydantic_geojson import FeatureCollectionModel
data = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-80.870885, 35.215151]
}
},
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[-80.724878, 35.265454],
[-80.722646, 35.260338],
[-80.720329, 35.260618],
[-80.704793, 35.268397],
[-80.724878, 35.265454]
]
]
}
}
]
}
>>> FeatureCollectionModel(**data)
>>> type='FeatureCollection' features=[FeatureModel(type='Feature', geometry=PointModel(type='Point', coordinates=Coordinates(lon=-80.870885, lat=35.215151))), FeatureModel(type='Feature', geometry=PolygonModel(type='Polygon', coordinates=[[Coordinates(lon=-80.724878, lat=35.265454), Coordinates(lon=-80.722646, lat=35.260338), Coordinates(lon=-80.720329, lat=35.260618), Coordinates(lon=-80.704793, lat=35.268397), Coordinates(lon=-80.724878, lat=35.265454)]]))]
Metadata
Release files for pydantic-geojson 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| pydantic_geojson-0.1.1.tar.gz | 7.8 kB | Without provenance | Details for pydantic_geojson-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| pydantic_geojson-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for pydantic_geojson-0.1.1-py3-none-any.whl |
Total release size: 16.8 kB
Release files / pydantic_geojson-0.1.1.tar.gz
| Download URL | pydantic_geojson-0.1.1.tar.gz |
|---|---|
| Size | 7.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bcb5d6a0ef553d754e7883af2fa2d233bf97a84a1e7b9393ceaa3acabeccd402
|
|
BLAKE2b-256 checksum How to use checksums |
79f7be6bd14a8fbf22e25a15af2ec0b1f1bb5fdec87a402133962b82713a8be8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.5.1 CPython/3.10.0 Darwin/21.6.0
|
Release files / pydantic_geojson-0.1.1-py3-none-any.whl
| Download URL | pydantic_geojson-0.1.1-py3-none-any.whl |
|---|---|
| Size | 9.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b9ee9455818c413f664a8cf6e8bf0d75154a429fc6a641e24998de62337933aa
|
|
BLAKE2b-256 checksum How to use checksums |
846ea4b7afff9f4da18632490b6873e88c17ace97bb2f39c9ff127927ccced9c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.5.1 CPython/3.10.0 Darwin/21.6.0
|
Security for pydantic-geojson 0.1.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.