https://github.com/hyriver/pygeoutils
A part of HyRiver software stack for handling geospatial data manipulations
https://github.com/hyriver/pygeoutils
geodata geospatial hydrology python webservices
Last synced: 6 days ago
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A part of HyRiver software stack for handling geospatial data manipulations
- Host: GitHub
- URL: https://github.com/hyriver/pygeoutils
- Owner: hyriver
- License: other
- Created: 2020-07-19T05:33:05.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2025-06-25T20:11:14.000Z (4 months ago)
- Last Synced: 2025-09-04T23:53:40.611Z (about 1 month ago)
- Topics: geodata, geospatial, hydrology, python, webservices
- Language: Python
- Homepage: https://docs.hyriver.io
- Size: 646 KB
- Stars: 8
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.rst
- Citation: CITATION.cff
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
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================ ====================================================================
Package Description
================ ====================================================================
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
================ ====================================================================
.. _PyGeoHydro: https://github.com/hyriver/pygeohydro
.. _AsyncRetriever: https://github.com/hyriver/async-retriever
.. _PyGeoOGC: https://github.com/hyriver/pygeoogc
.. _PyGeoUtils: https://github.com/hyriver/pygeoutils
.. _PyNHD: https://github.com/hyriver/pynhd
.. _Py3DEP: https://github.com/hyriver/py3dep
.. _PyDaymet: https://github.com/hyriver/pydaymet
.. _PyGridMET: https://github.com/hyriver/pygridmet
.. _PyNLDAS2: https://github.com/hyriver/pynldas2
.. _HydroSignatures: https://github.com/hyriver/hydrosignatures
PyGeoUtils: Utilities for (Geo)JSON and (Geo)TIFF Conversion
------------------------------------------------------------
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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.
- ``sample_window``: Sample a raster dataset at specified coordinates
using a window size and a ``rasterio`` supported resampling method.
This is an efficient way of sampling large raster datasets without
reading the entire dataset into memory. The function returns a generator
that yields the sampled values in the order of the input coordinates.
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:
.. code-block:: bibtex
@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``).
.. code-block:: console
$ pip install pygeoutils
Alternatively, PyGeoUtils can be installed from the ``conda-forge`` repository
using `Conda `__:
.. code-block:: console
$ conda install -c conda-forge pygeoutils
Quick start
-----------
We start by smoothing a ``shapely.geometry.LineString`` using B-spline:
.. code-block:: python
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.
.. code-block:: python
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.