Django-Spillway
Django and Django REST Framework integration of raster and feature based geodata.
Spillway builds on the immensely marvelous Django REST Framework by providing facilities for the handling of geospatial formats such as GeoTIFF, GeoJSON, and KML/KMZ.
Specific attention has been paid to speedy serialization of geometries from spatial backends which avoids the cost of unnecessary re-serialization in Python.
Basic Usage
Add vector response formats such as GeoJSON, KML/KMZ, and SVG to your API.
# models.py
from django.contrib.gis.db import models
from spillway.query import GeoQuerySet
class Location(models.Model):
slug = models.SlugField()
geom = models.GeometryField()
objects = GeoQuerySet.as_manager()
# urls.py
from django.conf.urls import url
from spillway import generics
from .models import Location
urlpatterns = [
url(r'^locations/(?P<slug>[\w-]+)/$',
generics.GeoDetailView.as_view(queryset=Location.objects.all()),
name='location'),
url(r'^locations/$',
generics.GeoListView.as_view(queryset=Location.objects.all()),
name='location-list'),
]
Retrieve all locations as GeoJSON:
curl -H 'Accept: application/vnd.geo+json' 127.0.0.1:8000/locations/
Simplify and reproject the geometries to another coordinate system:
curl -H 'Accept: application/vnd.geo+json' '127.0.0.1:8000/locations/?srs=3857&simplify=100'
Any spatial lookup supported by the backend is available to search on. For instance, find the location which intersects a particular point:
curl -g '127.0.0.1:8000/locations/?intersects={"type":"Point","coordinates":[-120,38]}'
Raster data support is provided as well.
# models.py
from spillway.models import AbstractRasterStore
from spillway.query import GeoQuerySet
class RasterStore(AbstractRasterStore):
objects = GeoQuerySet.as_manager()
# urls.py
from django.conf.urls import url
from spillway import generics
from .models import RasterStore
urlpatterns = [
url(r'^rstores/(?P<slug>[\w-]+)/$',
generics.RasterDetailView.as_view(queryset=RasterStore.objects.all()),
name='rasterstore'),
url(r'^rstores/$',
generics.RasterListView.as_view(queryset=RasterStore.objects.all()),
name='rasterstore-list'),
]
Return JSON containing a 2D array of pixel values for a given bounding box:
curl 'http://127.0.0.1:8000/rstores/tasmax/?bbox=-107.74,37.39,-106.95,38.40'
One can crop raster images with a geometry and return a .zip archive of the results:
curl -H 'Accept: application/zip' 'http://127.0.0.1:8000/rstores/?g=-107.74,37.39,-106.95,38.40'
Generic Views
Spillway extends REST framework generic views with GeoJSON and KML/KMZ renderers for geographic data. This includes pagination of features and all available spatial lookups/filters for the spatial backend in use.
ViewSets
View sets exist for geo and raster enabled models following the familiar usage pattern of Django REST Framework. Currently, a writable raster viewset needs to be added and tested though the read-only variety is available.
from spillway import viewsets
from .models import Location, RasterStore
class LocationViewSet(viewsets.GeoModelViewSet):
queryset = Location.objects.all()
class RasterViewSet(viewsets.ReadOnlyRasterModelViewSet):
queryset = RasterStore.objects.all()
Map Tiles
TileView and RasterTileView are available respectively for generating vector or image map tiles. Image tiles require the optional dependency Mapnik, so be sure to have that installed. In this example, GeoJSON or PNG tiles can be requested for the Location geo model, or PNG tiles for RasterStore data sets. The urls presented here use a scheme of "/{z}/{x}/{y}.{format}".
from django.conf.urls import url
from spillway import views, urls
from .models import Location, RasterStore
urlpatterns = [
url(urls.tilepath('^locations/),
views.TileView.as_view(queryset=Location.objects.all()),
name='location-tiles'),
url(urls.tilepath('^tiles/(?P<slug>\d+)/'),
views.RasterTileView.as_view(queryset=RasterStore.objects.all()),
name='map-tiles'),
]
Be sure to cache map tiles through configuration of your web server or Django's cache framework when serving outside of development environments.
Renderers
So far there are renderers for common raster and vector data formats, namely zipped GeoTIFF, JPEG, PNG, and Erdas Imagine, plus GeoJSON, KML/KMZ, and SVG.
Tests
Create a virtualenv with virtualenvwrapper, install dependencies, and run the tests. On Python 2.7, running tests with SpatiaLite requires a build of pysqlite with extension loading enabled. On 3.x, all is well without it.
mkvirtualenv spillway
# Only the following when testing on 2.7, not needed with 3.x.
pip install --global-option=build_ext --global-option='-USQLITE_OMIT_LOAD_EXTENSION' pysqlite
pip install -r requirements.txt Pillow
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