![https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/pygeoutils_logo.png](https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/pygeoutils_logo.png)
Package | Description | Status |
---|---|---|
PyNHD | Navigate and subset NHDPlus (MR and HR) using web services | |
Py3DEP | Access topographic data through National Map's 3DEP web service | |
PyGeoHydro | Access NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databases | |
PyDaymet | Access daily, monthly, and annual climate data via Daymet | |
PyGridMET | Access daily climate data via GridMET | |
PyNLDAS2 | Access hourly NLDAS-2 data via web services | |
HydroSignatures | A collection of tools for computing hydrological signatures | |
AsyncRetriever | High-level API for asynchronous requests with persistent caching | |
PyGeoOGC | Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services | |
PyGeoUtils | Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data |
PyGeoUtils is a part of HyRiver software stack that is designed to aid in hydroclimate analysis through web services. This package provides utilities for manipulating (Geo)JSON and (Geo)TIFF responses from web services. These utilities are:
Coordinates
: Generate validated and normalized coordinates in WGS84.GeoBSpline
: Create B-spline from ageopandas.GeoDataFrame
of points.smooth_linestring
: Smooth ashapely.geometry.LineString
using B-spline.bspline_curvature
: Compute tangent angles, curvature, and radius of curvature of a B-Spline at any points along the curve.arcgis2geojson
: Convert ESRIGeoJSON format to GeoJSON.break_lines
: Break lines at specified points in a given direction.gtiff2xarray
: Convert (Geo)Tiff byte responses toxarray.Dataset
.json2geodf
: Creategeopandas.GeoDataFrame
from (Geo)JSON responsessnap2nearest
: Find the nearest points on a line to a set of points.xarray2geodf
: Vectorize axarray.DataArray
to ageopandas.GeoDataFrame
.geodf2xarray
: Rasterize ageopandas.GeoDataFrame
to axarray.DataArray
.xarray_geomask
: Mask axarray.Dataset
based on a geometry.query_indices
: A wrapper aroundgeopandas.sindex.query_bulk
. However, instead of returning an array of positional indices, it returns a dictionary of indices where keys are the indices of the input geometry and values are a list of indices of the tree geometries that intersect with the input geometry.nested_polygons
: Determining nested (multi)polygons in ageopandas.GeoDataFrame
.multi2poly
: For converting aMultiPolygon
to aPolygon
in ageopandas.GeoDataFrame
.geometry_reproject
: For reprojecting a geometry (bounding box, list of coordinates, or anyshapely.geometry
) to a new CRS.gtiff2vrt
: For converting a list of GeoTIFF files to a VRT file.
You can find some example notebooks here.
You can also try using PyGeoUtils without installing it on your system by clicking on the binder badge. A Jupyter Lab instance with the HyRiver 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.
If you use any of HyRiver packages in your research, we appreciate citations:
@article{Chegini_2021,
author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},
doi = {10.21105/joss.03175},
journal = {Journal of Open Source Software},
month = {10},
number = {66},
pages = {1--3},
title = {{HyRiver: Hydroclimate Data Retriever}},
volume = {6},
year = {2021}
}
You can install PyGeoUtils using pip
after installing libgdal
on your system
(for example, in Ubuntu run sudo apt install libgdal-dev
).
$ pip install pygeoutils
Alternatively, PyGeoUtils can be installed from the conda-forge
repository
using Conda:
$ conda install -c conda-forge pygeoutils
We start by smoothing a shapely.geometry.LineString
using B-spline:
import pygeoutils as pgu
from shapely import LineString
line = LineString(
[
(-97.06138, 32.837),
(-97.06133, 32.836),
(-97.06124, 32.834),
(-97.06127, 32.832),
]
)
line = pgu.geometry_reproject(line, 4326, 5070)
sp = pgu.smooth_linestring(line, 5070, 5)
line_sp = pgu.geometry_reproject(sp.line, 5070, 4326)
Next, we use
PyGeoOGC to access
National Wetlands Inventory from WMS, and
FEMA National Flood Hazard
via WFS, then convert the output to xarray.Dataset
and GeoDataFrame
, respectively.
from pygeoogc import WFS, WMS, ServiceURL
from shapely.geometry import Polygon
geometry = Polygon(
[
[-118.72, 34.118],
[-118.31, 34.118],
[-118.31, 34.518],
[-118.72, 34.518],
[-118.72, 34.118],
]
)
crs = 4326
wms = WMS(
ServiceURL().wms.mrlc,
layers="NLCD_2011_Tree_Canopy_L48",
outformat="image/geotiff",
crs=crs,
)
r_dict = wms.getmap_bybox(
geometry.bounds,
1e3,
box_crs=crs,
)
canopy = pgu.gtiff2xarray(r_dict, geometry, crs)
mask = canopy > 60
canopy_gdf = pgu.xarray2geodf(canopy, "float32", mask)
url_wfs = "https://hazards.fema.gov/gis/nfhl/services/public/NFHL/MapServer/WFSServer"
wfs = WFS(
url_wfs,
layer="public_NFHL:Base_Flood_Elevations",
outformat="esrigeojson",
crs=4269,
)
r = wfs.getfeature_bybox(geometry.bounds, box_crs=crs)
flood = pgu.json2geodf(r.json(), 4269, crs)
Contributions are very welcomed. Please read CONTRIBUTING.rst file for instructions.