Python bindings and utilities for GeoJSON

Overview

geojson

GitHub Actions Codecov Jazzband

This Python library contains:

Table of Contents

Installation

geojson is compatible with Python 3.6, 3.7 and 3.8. The recommended way to install is via pip:

pip install geojson

GeoJSON Objects

This library implements all the GeoJSON Objects described in The GeoJSON Format Specification.

All object keys can also be used as attributes.

The objects contained in GeometryCollection and FeatureCollection can be indexed directly.

Point

>>> from geojson import Point

>>> Point((-115.81, 37.24))  # doctest: +ELLIPSIS
{"coordinates": [-115.8..., 37.2...], "type": "Point"}

Visualize the result of the example above here. General information about Point can be found in Section 3.1.2 and Appendix A: Points within The GeoJSON Format Specification.

MultiPoint

>>> from geojson import MultiPoint

>>> MultiPoint([(-155.52, 19.61), (-156.22, 20.74), (-157.97, 21.46)])  # doctest: +ELLIPSIS
{"coordinates": [[-155.5..., 19.6...], [-156.2..., 20.7...], [-157.9..., 21.4...]], "type": "MultiPoint"}

Visualize the result of the example above here. General information about MultiPoint can be found in Section 3.1.3 and Appendix A: MultiPoints within The GeoJSON Format Specification.

LineString

>>> from geojson import LineString

>>> LineString([(8.919, 44.4074), (8.923, 44.4075)])  # doctest: +ELLIPSIS
{"coordinates": [[8.91..., 44.407...], [8.92..., 44.407...]], "type": "LineString"}

Visualize the result of the example above here. General information about LineString can be found in Section 3.1.4 and Appendix A: LineStrings within The GeoJSON Format Specification.

MultiLineString

>>> from geojson import MultiLineString

>>> MultiLineString([
...     [(3.75, 9.25), (-130.95, 1.52)],
...     [(23.15, -34.25), (-1.35, -4.65), (3.45, 77.95)]
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[3.7..., 9.2...], [-130.9..., 1.52...]], [[23.1..., -34.2...], [-1.3..., -4.6...], [3.4..., 77.9...]]], "type": "MultiLineString"}

Visualize the result of the example above here. General information about MultiLineString can be found in Section 3.1.5 and Appendix A: MultiLineStrings within The GeoJSON Format Specification.

Polygon

>>> from geojson import Polygon

>>> # no hole within polygon
>>> Polygon([[(2.38, 57.322), (23.194, -20.28), (-120.43, 19.15), (2.38, 57.322)]])  # doctest: +ELLIPSIS
{"coordinates": [[[2.3..., 57.32...], [23.19..., -20.2...], [-120.4..., 19.1...]]], "type": "Polygon"}

>>> # hole within polygon
>>> Polygon([
...     [(2.38, 57.322), (23.194, -20.28), (-120.43, 19.15), (2.38, 57.322)],
...     [(-5.21, 23.51), (15.21, -10.81), (-20.51, 1.51), (-5.21, 23.51)]
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[2.3..., 57.32...], [23.19..., -20.2...], [-120.4..., 19.1...]], [[-5.2..., 23.5...], [15.2..., -10.8...], [-20.5..., 1.5...], [-5.2..., 23.5...]]], "type": "Polygon"}

Visualize the results of the example above here. General information about Polygon can be found in Section 3.1.6 and Appendix A: Polygons within The GeoJSON Format Specification.

MultiPolygon

>>> from geojson import MultiPolygon

>>> MultiPolygon([
...     ([(3.78, 9.28), (-130.91, 1.52), (35.12, 72.234), (3.78, 9.28)],),
...     ([(23.18, -34.29), (-1.31, -4.61), (3.41, 77.91), (23.18, -34.29)],)
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[[3.7..., 9.2...], [-130.9..., 1.5...], [35.1..., 72.23...]]], [[[23.1..., -34.2...], [-1.3..., -4.6...], [3.4..., 77.9...]]]], "type": "MultiPolygon"}

Visualize the result of the example above here. General information about MultiPolygon can be found in Section 3.1.7 and Appendix A: MultiPolygons within The GeoJSON Format Specification.

GeometryCollection

>>> from geojson import GeometryCollection, Point, LineString

>>> my_point = Point((23.532, -63.12))

>>> my_line = LineString([(-152.62, 51.21), (5.21, 10.69)])

>>> geo_collection = GeometryCollection([my_point, my_line])

>>> geo_collection  # doctest: +ELLIPSIS
{"geometries": [{"coordinates": [23.53..., -63.1...], "type": "Point"}, {"coordinates": [[-152.6..., 51.2...], [5.2..., 10.6...]], "type": "LineString"}], "type": "GeometryCollection"}

>>> geo_collection[1]
{"coordinates": [[-152.62, 51.21], [5.21, 10.69]], "type": "LineString"}

>>> geo_collection[0] == geo_collection.geometries[0]
True

Visualize the result of the example above here. General information about GeometryCollection can be found in Section 3.1.8 and Appendix A: GeometryCollections within The GeoJSON Format Specification.

Feature

>>> from geojson import Feature, Point

>>> my_point = Point((-3.68, 40.41))

>>> Feature(geometry=my_point)  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "properties": {}, "type": "Feature"}

>>> Feature(geometry=my_point, properties={"country": "Spain"})  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "properties": {"country": "Spain"}, "type": "Feature"}

>>> Feature(geometry=my_point, id=27)  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "id": 27, "properties": {}, "type": "Feature"}

Visualize the results of the examples above here. General information about Feature can be found in Section 3.2 within The GeoJSON Format Specification.

FeatureCollection

>>> from geojson import Feature, Point, FeatureCollection

>>> my_feature = Feature(geometry=Point((1.6432, -19.123)))

>>> my_other_feature = Feature(geometry=Point((-80.234, -22.532)))

>>> feature_collection = FeatureCollection([my_feature, my_other_feature])

>>> feature_collection # doctest: +ELLIPSIS
{"features": [{"geometry": {"coordinates": [1.643..., -19.12...], "type": "Point"}, "properties": {}, "type": "Feature"}, {"geometry": {"coordinates": [-80.23..., -22.53...], "type": "Point"}, "properties": {}, "type": "Feature"}], "type": "FeatureCollection"}

>>> feature_collection.errors()
[]

>>> (feature_collection[0] == feature_collection['features'][0], feature_collection[1] == my_other_feature)
(True, True)

Visualize the result of the example above here. General information about FeatureCollection can be found in Section 3.3 within The GeoJSON Format Specification.

GeoJSON encoding/decoding

All of the GeoJSON Objects implemented in this library can be encoded and decoded into raw GeoJSON with the geojson.dump, geojson.dumps, geojson.load, and geojson.loads functions. Note that each of these functions is a wrapper around the core json function with the same name, and will pass through any additional arguments. This allows you to control the JSON formatting or parsing behavior with the underlying core json functions.

>>> import geojson

>>> my_point = geojson.Point((43.24, -1.532))

>>> my_point  # doctest: +ELLIPSIS
{"coordinates": [43.2..., -1.53...], "type": "Point"}

>>> dump = geojson.dumps(my_point, sort_keys=True)

>>> dump  # doctest: +ELLIPSIS
'{"coordinates": [43.2..., -1.53...], "type": "Point"}'

>>> geojson.loads(dump)  # doctest: +ELLIPSIS
{"coordinates": [43.2..., -1.53...], "type": "Point"}

Custom classes

This encoding/decoding functionality shown in the previous can be extended to custom classes using the interface described by the __geo_interface__ Specification.

>>> import geojson

>>> class MyPoint():
...     def __init__(self, x, y):
...         self.x = x
...         self.y = y
...
...     @property
...     def __geo_interface__(self):
...         return {'type': 'Point', 'coordinates': (self.x, self.y)}

>>> point_instance = MyPoint(52.235, -19.234)

>>> geojson.dumps(point_instance, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [52.23..., -19.23...], "type": "Point"}'

Default and custom precision

GeoJSON Object-based classes in this package have an additional precision attribute which rounds off coordinates to 6 decimal places (roughly 0.1 meters) by default and can be customized per object instance.

>>> from geojson import Point

>>> Point((-115.123412341234, 37.123412341234))  # rounded to 6 decimal places by default
{"coordinates": [-115.123412, 37.123412], "type": "Point"}

>>> Point((-115.12341234, 37.12341234), precision=8)  # rounded to 8 decimal places
{"coordinates": [-115.12341234, 37.12341234], "type": "Point"}

Helpful utilities

coords

geojson.utils.coords yields all coordinate tuples from a geometry or feature object.

>>> import geojson

>>> my_line = LineString([(-152.62, 51.21), (5.21, 10.69)])

>>> my_feature = geojson.Feature(geometry=my_line)

>>> list(geojson.utils.coords(my_feature))  # doctest: +ELLIPSIS
[(-152.62..., 51.21...), (5.21..., 10.69...)]

map_coords

geojson.utils.map_coords maps a function over all coordinate values and returns a geometry of the same type. Useful for scaling a geometry.

>>> import geojson

>>> new_point = geojson.utils.map_coords(lambda x: x/2, geojson.Point((-115.81, 37.24)))

>>> geojson.dumps(new_point, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [-57.905..., 18.62...], "type": "Point"}'

map_tuples

geojson.utils.map_tuples maps a function over all coordinates and returns a geometry of the same type. Useful for changing coordinate order or applying coordinate transforms.

>>> import geojson

>>> new_point = geojson.utils.map_tuples(lambda c: (c[1], c[0]), geojson.Point((-115.81, 37.24)))

>>> geojson.dumps(new_point, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [37.24..., -115.81], "type": "Point"}'

map_geometries

geojson.utils.map_geometries maps a function over each geometry in the input.

>>> import geojson

>>> new_point = geojson.utils.map_geometries(lambda g: geojson.MultiPoint([g["coordinates"]]), geojson.GeometryCollection([geojson.Point((-115.81, 37.24))]))

>>> geojson.dumps(new_point, sort_keys=True)
'{"geometries": [{"coordinates": [[-115.81, 37.24]], "type": "MultiPoint"}], "type": "GeometryCollection"}'

validation

is_valid property provides simple validation of GeoJSON objects.

>>> import geojson

>>> obj = geojson.Point((-3.68,40.41,25.14,10.34))
>>> obj.is_valid
False

errors method provides collection of errors when validation GeoJSON objects.

>>> import geojson

>>> obj = geojson.Point((-3.68,40.41,25.14,10.34))
>>> obj.errors()
'a position must have exactly 2 or 3 values'

generate_random

geojson.utils.generate_random yields a geometry type with random data

>>> import geojson

>>> geojson.utils.generate_random("LineString")  # doctest: +ELLIPSIS
{"coordinates": [...], "type": "LineString"}

>>> geojson.utils.generate_random("Polygon")  # doctest: +ELLIPSIS
{"coordinates": [...], "type": "Polygon"}

Development

To build this project, run python setup.py build. To run the unit tests, run python setup.py test. To run the style checks, run flake8 (install flake8 if needed).

Credits

Constraint-based geometry sketcher for blender

Geometry Sketcher Constraint-based sketcher addon for Blender that allows to create precise 2d shapes by defining a set of geometric constraints like

1.7k Jan 02, 2023
Python library to visualize circular plasmid maps

Plasmidviewer Plasmidviewer is a Python library to visualize plasmid maps from GenBank. This library provides only the function to visualize circular

Mori Hideto 9 Dec 04, 2022
Create Siege configuration files from Cloud Optimized GeoTIFF.

cogeo-siege Documentation: Source Code: https://github.com/developmentseed/cogeo-siege Description Create siege configuration files from Cloud Optimiz

Development Seed 3 Dec 01, 2022
Fiona reads and writes geographic data files

Fiona Fiona reads and writes geographic data files and thereby helps Python programmers integrate geographic information systems with other computer s

987 Jan 04, 2023
Manage your XYZ Hub or HERE Data Hub spaces from Python.

XYZ Spaces for Python Manage your XYZ Hub or HERE Data Hub spaces and Interactive Map Layer from Python. FEATURED IN: Online Python Machine Learning C

HERE Technologies 30 Oct 18, 2022
LEOGPS - Satellite Navigation with GPS on Python!

LEOGPS is an open-source Python software which performs relative satellite navigation between two formation flying satellites, with the objective of high accuracy relative positioning. Specifically,

Samuel Low 50 Dec 13, 2022
Wraps GEOS geometry functions in numpy ufuncs.

PyGEOS PyGEOS is a C/Python library with vectorized geometry functions. The geometry operations are done in the open-source geometry library GEOS. PyG

362 Dec 23, 2022
Django model field that can hold a geoposition, and corresponding widget

django-geoposition A model field that can hold a geoposition (latitude/longitude), and corresponding admin/form widget. Prerequisites Starting with ve

Philipp Bosch 324 Oct 17, 2022
A ninja python package that unifies the Google Earth Engine ecosystem.

A Python package that unifies the Google Earth Engine ecosystem. EarthEngine.jl | rgee | rgee+ | eemont GitHub: https://github.com/r-earthengine/ee_ex

47 Dec 27, 2022
Geocode rows in a SQLite database table

Geocode rows in a SQLite database table

Chris Amico 225 Dec 08, 2022
Get-countries-info - A python code that fetches data of any country

Country-info A python code getting countries information including country's map

CODE 2 Feb 21, 2022
Script that allows to download data with satellite's orbit height and create CSV with their change in time.

Satellite orbit height ◾ Requirements Python = 3.8 Packages listen in reuirements.txt (run pip install -r requirements.txt) Account on Space Track ◾

Alicja Musiał 2 Jan 17, 2022
Blender addons to make the bridge between Blender and geographic data

Blender GIS Blender minimal version : 2.8 Mac users warning : currently the addon does not work on Mac with Blender 2.80 to 2.82. Please do not report

5.9k Jan 02, 2023
This is the antenna performance plotted from tinyGS reception data.

tinyGS-antenna-map This is the antenna performance plotted from tinyGS reception data. See their repository. The code produces a plot that provides Az

Martin J. Levy 14 Aug 21, 2022
Python script to locate mobile number

Python script to locate mobile number How to use this script run the command to install the required libraries pip install -r requirements.txt run the

Shekhar Gupta 8 Oct 10, 2022
A library to access OpenStreetMap related services

OSMPythonTools The python package OSMPythonTools provides easy access to OpenStreetMap (OSM) related services, among them an Overpass endpoint, Nomina

Franz-Benjamin Mocnik 342 Dec 31, 2022
Code and coordinates for Matt's 2021 xmas tree

xmastree2021 Code and coordinates for Matt's 2021 xmas tree This repository contains the code and coordinates used for Matt's 2021 Christmas tree, as

Stand-up Maths 117 Jan 01, 2023
gpdvega is a bridge between GeoPandas and Altair that allows to seamlessly chart geospatial data

gpdvega gpdvega is a bridge between GeoPandas a geospatial extension of Pandas and the declarative statistical visualization library Altair, which all

Ilia Timofeev 49 Jul 25, 2022
Histogram matching plugin for rasterio

rio-hist Histogram matching plugin for rasterio. Provides a CLI and python module for adjusting colors based on histogram matching in a variety of col

Mapbox 75 Sep 23, 2022
Digital Earth Australia notebooks and tools repository

Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and xarray

Geoscience Australia 335 Dec 24, 2022