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A set of utilities for manipulating (Geo)JSON and (Geo)TIFF data.

Project description

https://raw.githubusercontent.com/cheginit/HyRiver-examples/main/notebooks/_static/pygeoutils_logo.png

Package

Description

Status

PyGeoHydro

Access NWIS, NID, HCDN 2009, NLCD, and SSEBop databases

Github Actions

PyGeoOGC

Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services

Github Actions

PyGeoUtils

Convert responses from PyGeoOGC’s supported web services to datasets

Github Actions

PyNHD

Navigate and subset NHDPlus (MR and HR) using web services

Github Actions

Py3DEP

Access topographic data through National Map’s 3DEP web service

Github Actions

PyDaymet

Access Daymet for daily climate data both single pixel and gridded

Github Actions

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 watershed analysis through web services. This package provides utilities for manipulating (Geo)JSON and (Geo)TIFF responses from web services. These utilities are:

  • json2geodf: For converting (Geo)JSON objects to GroPandas dataframe.

  • arcgis2geojson: For converting ESRIGeoJSON objects to standard GeoJSON format.

  • gtiff2xarray: For converting (Geo)TIFF objects to xarray datasets.

  • gtiff2file: For saving (Geo)TIFF objects to a raster file.

  • xarray_geomask: For masking a xarray.Dataset or xarray.DataArray using a polygon.

All these functions handle all necessary CRS transformations.

You can find some example notebooks here.

Please note that since this project is in early development stages, while the provided functionalities should be stable, changes in APIs are possible in new releases. But we appreciate it if you give this project a try and provide feedback. Contributions are most welcome.

Moreover, requests for additional functionalities can be submitted via issue tracker.

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

To demonstrate capabilities of PyGeoUtils let’s 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.

import pygeoutils as geoutils
from pygeoogc import WFS, WMS
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],
    ]
)

url_wms = (
    "https://www.fws.gov/wetlands/arcgis/services/Wetlands_Raster/ImageServer/WMSServer"
)
wms = WMS(
    url_wms,
    layers="0",
    outformat="image/tiff",
    crs="epsg:3857",
)
r_dict = wms.getmap_bybox(
    geometry.bounds,
    1e3,
    box_crs="epsg:4326",
)
wetlands = geoutils.gtiff2xarray(r_dict, geometry, "epsg:4326")

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="epsg:4269",
)
r = wfs.getfeature_bybox(geometry.bounds, box_crs="epsg:4326")
flood = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")

We can also save WMS outpus as raster file using gtiff2file:

geoutils.gtiff2file(r_dict, geometry, "epsg:4326", "raster")

Contributing

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

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