pygeoutils 0.19.5
Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data.
License: MIT
Requires Python: >=3.9

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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 <https://github.com/hyriver/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 <https://github.com/hyriver/HyRiver-examples>`__.

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 <https://github.com/hyriver/pygeoutils/issues>`__.

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 <https://docs.conda.io/en/latest/>`__:

.. 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 <https://github.com/hyriver/pygeoogc>`__ to access
`National Wetlands Inventory <https://www.fws.gov/wetlands/>`__ from WMS, and
`FEMA National Flood Hazard <https://www.fema.gov/national-flood-hazard-layer-nfhl>`__
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 <https://github.com/hyriver/pygeoogc/blob/main/CONTRIBUTING.rst>`__
file for instructions.