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Description:

pygeoutils 0.17.1

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: Utilities for (Geo)JSON and (Geo)TIFF Conversion

Features
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 a geopandas.GeoDataFrame of points.
smooth_linestring: Smooth a shapely.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 to xarray.Dataset.
json2geodf: Create geopandas.GeoDataFrame from (Geo)JSON responses
snap2nearest: Find the nearest points on a line to a set of points.
xarray2geodf: Vectorize a xarray.DataArray to a geopandas.GeoDataFrame.
geodf2xarray: Rasterize a geopandas.GeoDataFrame to a xarray.DataArray.
xarray_geomask: Mask a xarray.Dataset based on a geometry.
query_indices: A wrapper around
geopandas.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 a
geopandas.GeoDataFrame.
multi2poly: For converting a MultiPolygon to a Polygon
in a geopandas.GeoDataFrame.
geometry_reproject: For reprojecting a geometry
(bounding box, list of coordinates, or any shapely.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.

Citation
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}
}

Installation
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

Quick start
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)

Contributing
Contributions are very welcomed. Please read
CONTRIBUTING.rst
file for instructions.