Geographic add-ons for Django Rest Framework.
pip install djangorestframework-gis
pip install https://github.com/openwisp/django-rest-framework-gis/tarball/master
Add rest_framework_gis
in settings.INSTALLED_APPS
, after rest_framework
:
INSTALLED_APPS = [
# ...
'rest_framework',
'rest_framework_gis',
# ...
]
DRF-gis version | DRF version | Django version | Python version |
1.1.x | 3.12 up to 3.15 | 3.2, 4.2 to 5.1 | 3.8 to 3.12 |
1.0.x | 3.10 up to 3.13 | 2.2 to 4.0 | 3.6 to 3.9 |
0.18.x | 3.10 up to 3.13 | 2.2 to 4.0 | 3.6 to 3.9 |
0.17.x | 3.10 up to 3.12 | 2.2 to 3.1 | 3.6 to 3.8 |
0.16.x | 3.10 | 2.2 to 3.1 | 3.6 to 3.8 |
0.15.x | 3.10 | 1.11, 2.2 to 3.0 | 3.5 to 3.8 |
0.14.x | 3.3 to 3.9 | 1.11 to 2.1 | 3.4 to 3.7 |
0.13.x | 3.3 to 3.8 | 1.11 to 2.0 | 2.7 to 3.6 |
0.12.x | 3.1 to 3.7 | 1.11 to 2.0 | 2.7 to 3.6 |
0.11.x | 3.1 to 3.6 | 1.7 to 1.11 | 2.7 to 3.6 |
0.10.x | 3.1 to 3.3 | 1.7 to 1.9 | 2.7 to 3.5 |
0.9.6 | 3.1 to 3.2 | 1.5 to 1.8 | 2.6 to 3.5 |
0.9.5 | 3.1 to 3.2 | 1.5 to 1.8 | 2.6 to 3.4 |
0.9.4 | 3.1 to 3.2 | 1.5 to 1.8 | 2.6 to 3.4 |
0.9.3 | 3.1 | 1.5 to 1.8 | 2.6 to 3.4 |
0.9.2 | 3.1 | 1.5 to 1.8 | 2.6 to 3.4 |
0.9.1 | 3.1 | 1.5 to 1.8 | 2.6 to 3.4 |
0.9 | 3.1 | 1.5 to 1.8 | 2.6, 2.7, 3.3, 3.4 |
0.9 | 3.1 | 1.5 to 1.8 | 2.6, 2.7, 3.3, 3.4 |
0.9 | 3.1 | 1.5 to 1.8 | 2.6, 2.7, 3.3, 3.4 |
0.8.2 | 3.0.4 to 3.1.1 | 1.5 to 1.8 | 2.6, 2.7, 3.3, 3.4 |
0.8.1 | 3.0.4 to 3.1.1 | 1.5 to 1.8 | 2.6, 2.7, 3.3, 3.4 |
0.8 | 3.0.4 | 1.5 to 1.7 | 2.6, 2.7, 3.3, 3.4 |
0.7 | 2.4.3 | 1.5 to 1.7 | 2.6, 2.7, 3.3, 3.4 |
0.6 | 2.4.3 | 1.5 to 1.7 | 2.6, 2.7, 3.3, 3.4 |
0.5 | from 2.3.14 to 2.4.2 | 1.5 to 1.7 | 2.6, 2.7, 3.3, 3.4 |
0.4 | from 2.3.14 to 2.4.2 | 1.5 to 1.7 | 2.6, 2.7, 3.3, 3.4 |
0.3 | from 2.3.14 to 2.4.2 | 1.5, 1.6 | 2.6, 2.7 |
0.2 | from 2.2.2 to 2.3.13 | 1.5, 1.6 | 2.6, 2.7 |
Provides a GeometryField
, which is a subclass of Django Rest Framework
(from now on DRF) WritableField
. This field handles GeoDjango
geometry fields, providing custom to_native
and from_native
methods for GeoJSON input/output.
This field takes three optional arguments:
precision
: Passes coordinates through Python's builtinround()
function (docs), rounding values to the provided level of precision. E.g. A Point with lat/lng of[51.0486, -114.0708]
passed through aGeometryField(precision=2)
would return a Point with a lat/lng of[51.05, -114.07]
.remove_duplicates
: Remove sequential duplicate coordinates from line and polygon geometries. This is particularly useful when used with theprecision
argument, as the likelihood of duplicate coordinates increase as precision of coordinates are reduced.auto_bbox
: IfTrue
, the GeoJSON object will include a bounding box, which is the smallest possible rectangle enclosing the geometry.
Note: While precision
and remove_duplicates
are designed to reduce the
byte size of the API response, they will also increase the processing time
required to render the response. This will likely be negligible for small GeoJSON
responses but may become an issue for large responses.
New in 0.9.3: there is no need to define this field explicitly in your serializer,
it's mapped automatically during initialization in rest_framework_gis.apps.AppConfig.ready()
.
Provides a GeometrySerializerMethodField
, which is a subclass of DRF
SerializerMethodField
and handles values which are computed with a serializer
method and are used as a geo_field
. See example below.
Deprecated, will be removed in 1.0: Using this serializer is not needed anymore since 0.9.3, if you add
rest_framework_gis
in settings.INSTALLED_APPS
the serialization will work out of the box with DRF.
Refer Issue #156.
Provides a GeoModelSerializer
, which is a subclass of DRF
ModelSerializer
. This serializer updates the field_mapping
dictionary to include field mapping of GeoDjango geometry fields to the
above GeometryField
.
For example, the following model:
class Location(models.Model):
"""
A model which holds information about a particular location
"""
address = models.CharField(max_length=255)
city = models.CharField(max_length=100)
state = models.CharField(max_length=100)
point = models.PointField()
By default, the DRF ModelSerializer ver < 0.9.3 will output:
{
"id": 1,
"address": "742 Evergreen Terrace",
"city": "Springfield",
"state": "Oregon",
"point": "POINT(-123.0208 44.0464)"
}
In contrast, the GeoModelSerializer
will output:
{
"id": 1,
"address": "742 Evergreen Terrace",
"city": "Springfield",
"state": "Oregon",
"point": {
"type": "Point",
"coordinates": [-123.0208, 44.0464],
}
}
Note: For ver>=0.9.3
: The DRF model serializer will give the same output as above, if;
rest_framework_gis
is set insettings.INSTALLED_APPS
or- the field in the serializer is set explicitly as
GeometryField
.
GeoFeatureModelSerializer
is a subclass of rest_framework.ModelSerializer
which will output data in a format that is GeoJSON compatible. Using
the above example, the GeoFeatureModelSerializer
will output:
{
"id": 1,
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-123.0208, 44.0464],
},
"properties": {
"address": "742 Evergreen Terrace",
"city": "Springfield",
"state": "Oregon"
}
}
If you are serializing an object list, GeoFeatureModelSerializer
will create a FeatureCollection
:
{
"type": "FeatureCollection",
"features": [
{
"id": 1
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-123.0208, 44.0464],
},
"properties": {
"address": "742 Evergreen Terrace",
"city": "Springfield",
"state": "Oregon",
}
}
{
"id": 2,
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-123.0208, 44.0489],
},
"properties": {
"address": "744 Evergreen Terrace",
"city": "Springfield",
"state": "Oregon"
}
}
}
GeoFeatureModelSerializer
requires you to define a geo_field
to be serialized as the "geometry". For example:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
class LocationSerializer(GeoFeatureModelSerializer):
""" A class to serialize locations as GeoJSON compatible data """
class Meta:
model = Location
geo_field = "point"
# you can also explicitly declare which fields you want to include
# as with a ModelSerializer.
fields = ('id', 'address', 'city', 'state')
If your model is geometry-less, you can set geo_field
to None
and a null geometry will be produced.
geo_field
may also be an instance of GeometrySerializerMethodField
.
In this case you can compute its value during serialization. For example:
from django.contrib.gis.geos import Point
from rest_framework_gis.serializers import GeoFeatureModelSerializer, GeometrySerializerMethodField
class LocationSerializer(GeoFeatureModelSerializer):
""" A class to serialize locations as GeoJSON compatible data """
# a field which contains a geometry value and can be used as geo_field
other_point = GeometrySerializerMethodField()
def get_other_point(self, obj):
return Point(obj.point.lat / 2, obj.point.lon / 2)
class Meta:
model = Location
geo_field = 'other_point'
Serializer for geo_field
may also return None
value, which will translate to null
value for geojson geometry
field.
The primary key of the model (usually the "id" attribute) is
automatically used as the id
field of each
GeoJSON Feature Object.
The default behaviour follows the GeoJSON RFC,
but it can be disabled by setting id_field
to False
:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
class LocationSerializer(GeoFeatureModelSerializer):
class Meta:
model = Location
geo_field = "point"
id_field = False
fields = ('id', 'address', 'city', 'state')
The id_field
can also be set to use some other unique field in your model, eg: slug
:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
class LocationSerializer(GeoFeatureModelSerializer):
class Meta:
model = Location
geo_field = 'point'
id_field = 'slug'
fields = ('slug', 'address', 'city', 'state')
The GeoJSON specification allows a feature to contain a
boundingbox of a feature.
GeoFeatureModelSerializer
allows two different ways to fill this property. The first
is using the geo_field
to calculate the bounding box of a feature. This only allows
read access for a REST client and can be achieved using auto_bbox
. Example:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
class LocationSerializer(GeoFeatureModelSerializer):
class Meta:
model = Location
geo_field = 'geometry'
auto_bbox = True
The second approach uses the bbox_geo_field
to specify an additional
GeometryField
of the model which will be used to calculate the bounding box. This allows
boundingboxes differ from the exact extent of a features geometry. Additionally this
enables read and write access for the REST client. Bounding boxes send from the client will
be saved as Polygons. Example:
from rest_framework_gis.serializers import GeoFeatureModelSerializer
class LocationSerializer(GeoFeatureModelSerializer):
class Meta:
model = BoxedLocation
geo_field = 'geometry'
bbox_geo_field = 'bbox_geometry'
In GeoJSON each feature can have a properties
member containing the
attributes of the feature. By default this field is filled with the
attributes from your Django model, excluding the id, geometry and bounding
box fields. It's possible to override this behaviour and implement a custom
source for the properties
member.
The following example shows how to use a PostgreSQL HStore field as a source for
the properties
member:
# models.py
class Link(models.Model):
"""
Metadata is stored in a PostgreSQL HStore field, which allows us to
store arbitrary key-value pairs with a link record.
"""
metadata = HStoreField(blank=True, null=True, default=dict)
geo = models.LineStringField()
objects = models.GeoManager()
# serializers.py
class NetworkGeoSerializer(GeoFeatureModelSerializer):
class Meta:
model = models.Link
geo_field = 'geo'
auto_bbox = True
def get_properties(self, instance, fields):
# This is a PostgreSQL HStore field, which django maps to a dict
return instance.metadata
def unformat_geojson(self, feature):
attrs = {
self.Meta.geo_field: feature["geometry"],
"metadata": feature["properties"]
}
if self.Meta.bbox_geo_field and "bbox" in feature:
attrs[self.Meta.bbox_geo_field] = Polygon.from_bbox(feature["bbox"])
return attrs
When the serializer renders GeoJSON, it calls the method
get_properties
for each object in the database. This function
should return a dictionary containing the attributes for the feature. In the
case of a HStore field, this function is easily implemented.
The reverse is also required: mapping a GeoJSON formatted structure to
attributes of your model. This task is done by unformat_geojson
. It should
return a dictionary with your model attributes as keys, and the corresponding
values retrieved from the GeoJSON feature data.
We provide a GeoJsonPagination
class.
Based on rest_framework.pagination.PageNumberPagination
.
Code example:
from rest_framework_gis.pagination import GeoJsonPagination
# --- other omitted imports --- #
class GeojsonLocationList(generics.ListCreateAPIView):
# -- other omitted view attributes --- #
pagination_class = GeoJsonPagination
Example result response (cut to one element only instead of 10):
{
"type": "FeatureCollection",
"count": 25,
"next": "http://localhost:8000/geojson/?page=2",
"previous": null,
"features": [
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
42.0,
50.0
]
},
"properties": {
"name": "test"
}
}
]
}
note: this feature has been tested up to django-filter 1.0.
We provide a GeometryFilter
field as well as a GeoFilterSet
for usage with django_filter
. You simply provide, in the query
string, one of the textual types supported by GEOSGeometry
. By
default, this includes WKT, HEXEWKB, WKB (in a buffer), and GeoJSON.
from rest_framework_gis.filterset import GeoFilterSet
from rest_framework_gis.filters import GeometryFilter
from django_filters import filters
class RegionFilter(GeoFilterSet):
slug = filters.CharFilter(name='slug', lookup_expr='istartswith')
contains_geom = GeometryFilter(name='geom', lookup_expr='contains')
class Meta:
model = Region
We can then filter in the URL, using GeoJSON, and we will perform a
__contains
geometry lookup, e.g.
/region/?contains_geom={ "type": "Point", "coordinates": [ -123.26436996459961, 44.564178042345375 ] }
.
The GeoFilterSet
provides a django_filter
compatible
FilterSet
that will automatically create GeometryFilters
for
GeometryFields
.
Provides a InBBoxFilter
, which is a subclass of DRF
BaseFilterBackend
. Filters a queryset to only those instances within
a certain bounding box.
views.py:
from rest_framework_gis.filters import InBBoxFilter
class LocationList(ListAPIView):
queryset = models.Location.objects.all()
serializer_class = serializers.LocationSerializer
bbox_filter_field = 'point'
filter_backends = (InBBoxFilter,)
bbox_filter_include_overlapping = True # Optional
We can then filter in the URL, using Bounding Box format (min Lon, min
Lat, max Lon, max Lat), and we can search for instances within the
bounding box, e.g.:
/location/?in_bbox=-90,29,-89,35
.
By default, InBBoxFilter will only return those instances entirely
within the stated bounding box. To include those instances which overlap
the bounding box, include bbox_filter_include_overlapping = True
in your view.
Note that if you are using other filters, you'll want to include your other filter backend in your view. For example:
filter_backends = (InBBoxFilter, DjangoFilterBackend,)
Provides a TMSTileFilter
, which is a subclass of InBBoxFilter
.
Filters a queryset to only those instances within a bounding box defined
by a TMS tile address.
views.py:
from rest_framework_gis.filters import TMSTileFilter
class LocationList(ListAPIView):
queryset = models.Location.objects.all()
serializer_class = serializers.LocationSerializer
bbox_filter_field = 'point'
filter_backends = (TMSTileFilter,)
bbox_filter_include_overlapping = True # Optional
We can then filter in the URL, using TMS tile addresses in the zoom/x/y format,
eg:.
/location/?tile=8/100/200
which is equivalent to filtering on the bbox (-39.37500,-71.07406,-37.96875,-70.61261).
For more information on configuration options see InBBoxFilter.
Note that the tile address start in the upper left, not the lower left origin used by some implementations.
Provides a DistanceToPointFilter
, which is a subclass of DRF
BaseFilterBackend
. Filters a queryset to only those instances within
a certain distance of a given point.
views.py:
from rest_framework_gis.filters import DistanceToPointFilter
class LocationList(ListAPIView):
queryset = models.Location.objects.all()
serializer_class = serializers.LocationSerializer
distance_filter_field = 'geometry'
filter_backends = (DistanceToPointFilter,)
We can then filter in the URL, using a distance and a point in (lon, lat) format. The distance can be given in meters or in degrees.
eg:.
/location/?dist=4000&point=-122.4862,37.7694&format=json
which is equivalent to filtering within 4000 meters of the point (-122.4862, 37.7694).
By default, DistanceToPointFilter will pass the 'distance' in the URL directly to the database for the search. The effect depends on the srid of the database in use. If geo data is indexed in meters (srid 3875, aka 900913), a distance in meters can be passed in directly without conversion. For lat-lon databases such as srid 4326, which is indexed in degrees, the 'distance' will be interpreted as degrees. Set the flag, 'distance_filter_convert_meters' to 'True' in order to convert an input distance in meters to degrees. This conversion is approximate, and the errors at latitudes > 60 degrees are > 25%.
Provides a DistanceToPointOrderingFilter
, available on Django >= 3.0, which is a subclass of DistanceToPointFilter
.
Orders a queryset by distance to a given point, from the nearest to the most distant point.
views.py:
from rest_framework_gis.filters import DistanceToPointOrderingFilter
class LocationList(ListAPIView):
queryset = models.Location.objects.all()
serializer_class = serializers.LocationSerializer
distance_ordering_filter_field = 'geometry'
filter_backends = (DistanceToPointOrderingFilter,)
We can then order the results by passing a point in (lon, lat) format in the URL.
eg:.
/location/?point=-122.4862,37.7694&format=json
will order the results by the distance to the point (-122.4862, 37.7694).
We can also reverse the order of the results by passing order=desc
:
/location/?point=-122.4862,37.7694&order=desc&format=json
Note: Schema generation support is available only for DRF >= 3.12.
Simplest Approach would be, change DEFAULT_SCHEMA_CLASS
to rest_framework_gis.schema.GeoFeatureAutoSchema
:
REST_FRAMEWORK = {
...
'DEFAULT_SCHEMA_CLASS': 'rest_framework_gis.schema.GeoFeatureAutoSchema',
...
}
If you do not want to change default schema generator class:
- You can pass this class as an argument to
get_schema_view
function [Ref]. - You can pass this class as an argument to the
generateschema
command [Ref].
You need one of the Spatial Database servers supported by GeoDjango, and create a database for the tests.
The following can be used with PostgreSQL:
createdb django_restframework_gis
psql -U postgres -d django_restframework_gis -c "CREATE EXTENSION postgis"
You might need to tweak the DB settings according to your DB
configuration. You can copy the file local_settings.example.py
to
local_settings.py
and change the DATABASES
and/or
INSTALLED_APPS
directives there.
This should allow you to run the tests already.
For reference, the following steps will setup a development environment for contributing to the project:
- create a spatial database named "django_restframework_gis"
- create
local_settings.py
, eg:cp local_settings.example.py local_settings.py
- tweak the
DATABASES
configuration directive according to your DB settings - uncomment
INSTALLED_APPS
- run
python manage.py syncdb
- run
python manage.py collectstatic
- run
python manage.py runserver
The recommended way to run the tests is by using tox, which can be installed using pip install tox.
You can use tox -l
to list the available environments, and then e.g. use
the following to run all tests with Python 3.6 and Django 1.11:
tox -e py36-django111
By default Django's test runner is used, but there is a variation of tox's
envlist to use pytest (using the -pytest
suffix).
You can pass optional arguments to the test runner like this:
tox -e py36-django111-pytest -- -k test_foo
Please refer to the tox.ini
file for reference/help in case you want to run
tests manually / without tox.
To run tests in docker use
docker-compose build
docker-compose run --rm test
Install the test requirements:
pip install -r requirements-test.txt
Reformat the code according to our coding style conventions with:
openwisp-qa-format
Run the QA checks by using
./run-qa-checks
In docker testing, QA checks are executed automatically.
- Announce your intentions in the
Github Discussions Forum
- Follow the PEP8 Style Guide for Python Code
- Fork this repo
- Write code
- Write tests for your code
- Ensure all tests pass
- Ensure test coverage is not under 90%
- Document your changes
- Send pull request