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

Project description

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 part of a software stack for retrieving and processing hydrology and climatology dataset. 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.

Project details


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