Package | Description | Status |
---|---|---|
Hydrodata | Access NWIS, HCDN 2009, NLCD, and SSEBop databases | |
PyGeoOGC | Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services | |
PyGeoUtils | Convert responses from PyGeoOGC's supported web services to datasets | |
PyNHD | Navigate and subset NHDPlus (MR and HR) using web services | |
Py3DEP | Access topographic data through National Map's 3DEP web service | |
PyDaymet | Access Daymet for daily climate data both single pixel and gridded |
🚨 This package is under heavy development and breaking changes are likely to happen. 🚨
PyGeoOGC is a part of Hydrodata software stack and provides interfaces to web services that are based on ArcGIS RESTful, WMS, and WFS. It is noted that although all these web service have limits on the number of objects per requests (e.g., 1000 objectIDs for RESTful and 8 million pixels for WMS), PyGeoOGC divides the requests into smaller chunks under-the-hood and then merges the returned responses.
There is also an inventory of URLs for some of these web services in form of a class called ServiceURL
. These URLs are in three categories: ServiceURL().restful
, ServiceURL().wms
, and ServiceURL().wfs
. These URLs provide you with some examples of the services that PyGeoOGC supports. All the URLs are read from a YAML file located here. If you had success using PyGeoOGC with a web service please consider adding its URL to this YAML file which is located at pygeoogc/static/urls.yml
.
There are three main classes:
ArcGISRESTful
: This class can be instantiated by providing the target layer URL. For example, for getting Watershed Boundary Data we can useServiceURL().restful.wbd
. By looking at the web service website (https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer) we see that there are 9 layers; 1 for 2-digit HU (Region), 6 for 12-digit HU (Subregion), and so on. We can either pass the base URL or concatenate the target layer number like sof"{ServiceURL().restful.wbd}/6"
.If you want to change the layer you can simply set the
layer
property of the class. Afterward, we can request for the data in two steps. First, get the object IDs usingoids_bygeom
(within a geometry),oids_byfield
(specific field IDs), oroids_bysql
(any valid SQL 92 WHERE clause) class methods. Second, get the actual data usingget_features
class method. The returned response can be converted into a GeoDataFrame usingjson2geodf
function from PyGeoOGC package.WMS
: Instantiation of this class requires at least 3 arguments: service URL, layer(s) name(s), and output format. Additionally, target CRS and the web service version can be provided. Upon instantiation, we could usegetmap_bybox
method class to get the raster data within a bounding box. The box can be any valid CRS and if it is different from the default EPSG:4326, it should be passed to the function usingbox_crs
argumnet. The service response can be converted into axarray.Dataset
usinggtiff2xarray
function from PyGeoOGC package.WFS
: Instantiation of this class is similar toWMS
and the only difference is that only one layer name can be passed. Upon instantiation there are three ways to get the data:getfeature_bybox
: Get all the features within a bounding box in any valid CRS.getfeature_byid
: Get all the features based on the IDs. Note that two arguments should be provided:featurename
, andfeatureids
. You can get a list of valid feature names usingget_validnames
class method.getfeature_byfilter
: Get the data based on a valid CQL filter.
You can convert the returned response to a GeoDataFrame using
json2geodf
function from PyGeoOGC package.
You can try using PyGeoOGC without installing it on you system by clicking on the binder badge below the PyGeoOGC banner. A Jupyter notebook instance with the Hydrodata software stack pre-installed will be launched in your web browser and you can start coding!
Moreover, requests for additional functionalities can be submitted via issue tracker.
You can install PyGeoOGC using pip
:
$ pip install pygeoogc
Alternatively, PyGeoOGC can be installed from the conda-forge
repository using Conda:
$ conda install -c conda-forge pygeoogc
We can access NHDPlus HR via RESTful service, National Wetlands Inventory from WMS, and FEMA National Flood Hazard via WFS. The output for these functions are of type requests.Response
that can be converted to GeoDataFrame
or xarray.Dataset
using PyGeoOGC.
Let's start the National Map's NHDPlus HR web service. We can query the flowlines that are within a geometry as follows:
from pygeoogc import ArcGISRESTful, WFS, WMS, ServiceURL
import pygeoutils as geoutils
from pynhd import NLDI
basin_geom = NLDI().getfeature_byid(
"nwissite",
"USGS-11092450",
basin=True
).geometry[0]
hr = ArcGISRESTful(ServiceURL().restful.nhdplushr, outformat="json")
hr.layer = 2
hr.oids_bygeom(basin_geom, "epsg:4326")
resp = hr.get_features()
flowlines = geoutils.json2geodf(resp)
Note oids_bygeom
has an additional argument for passing any valid SQL WHERE clause to further filter the data on the server side.
We can also submit a query based on IDs of any valid field in the database. If the measure property is desired you can pass return_m
as True
to the get_features
class method:
hr.oids_byfield("NHDPLUSID", [5000500013223, 5000400039708, 5000500004825])
resp = hr.get_features(return_m=True)
flowlines = geoutils.json2geodf(resp)
Additionally, any valid SQL 92 WHERE clause can be used. For more details look here.
hr.oids_bysql("NHDPLUSID IN (5000500013223, 5000400039708, 5000500004825)")
resp = hr.get_features()
flowlines = geoutils.json2geodf(resp)
A WMS-based example is shown below:
wms = WMS(
ServiceURL().wms.fws,
layers="0",
outformat="image/tiff",
crs="epsg:3857",
)
r_dict = wms.getmap_bybox(
basin_geom.bounds,
1e3,
box_crs="epsg:4326",
)
wetlands = geoutils.gtiff2xarray(r_dict, basin_geom, "epsg:4326")
Query from a WFS-based web service can be done either within a bounding box or using any valid CQL filter.
wfs = WFS(
ServiceURL().wfs.fema,
layer="public_NFHL:Base_Flood_Elevations",
outformat="esrigeojson",
crs="epsg:4269",
)
r = wfs.getfeature_bybox(basin_geom.bounds, box_crs="epsg:4326")
flood = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")
layer = "wmadata:huc08"
wfs = WFS(
ServiceURL().wfs.waterdata,
layer=layer,
outformat="application/json",
version="2.0.0",
crs="epsg:4269",
)
r = wfs.getfeature_byfilter(f"huc8 LIKE '13030%'")
huc8 = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")
Contributions are appreciated and very welcomed. Please read CONTRIBUTING.rst for instructions.