pygeoutils 0.13.6

Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data.

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, HCDN 2009, NLCD, CAMELS, and SSEBop databases
PyDaymet	Access daily, monthly, and annual climate data via Daymet
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:

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

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

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

• xarray2geodf: For converting xarray.DataArray to a geopandas.GeoDataFrame, i.e., vectorization.

• 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.

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

To demonstrate the 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, 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 = "epsg: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 = geoutils.gtiff2xarray(r_dict, geometry, crs)

mask = canopy > 60
canopy_gdf = geoutils.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="epsg:4269",
)
r = wfs.getfeature_bybox(geometry.bounds, box_crs=crs)
flood = geoutils.json2geodf(r.json(), "epsg:4269", crs)

Contributing

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