PyGeoUtils: Utilities for (Geo)JSON and (Geo)TIFF Conversion ------------------------------------------------------------ .. image:: https://img.shields.io/pypi/v/pygeoutils.svg :target: https://pypi.python.org/pypi/pygeoutils :alt: PyPi .. image:: https://img.shields.io/conda/vn/conda-forge/pygeoutils.svg :target: https://anaconda.org/conda-forge/pygeoutils :alt: Conda Version .. image:: https://codecov.io/gh/hyriver/pygeoutils/branch/main/graph/badge.svg :target: https://codecov.io/gh/hyriver/pygeoutils :alt: CodeCov .. image:: https://img.shields.io/pypi/pyversions/pygeoutils.svg :target: https://pypi.python.org/pypi/pygeoutils :alt: Python Versions .. image:: https://static.pepy.tech/badge/pygeoutils :target: https://pepy.tech/project/pygeoutils :alt: Downloads | .. image:: https://www.codefactor.io/repository/github/hyriver/pygeoutils/badge :target: https://www.codefactor.io/repository/github/hyriver/pygeoutils :alt: CodeFactor .. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json :target: https://github.com/astral-sh/ruff :alt: Ruff .. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white :target: https://github.com/pre-commit/pre-commit :alt: pre-commit .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/hyriver/HyRiver-examples/main?urlpath=lab/tree/notebooks :alt: Binder | 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. 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)