Metadata-Version: 2.1
Name: pygeohydro
Version: 0.10.2
Summary: Access geospatial hydrology web services through Python
Home-page: https://github.com/cheginit/pygeohydro
Author: Taher Chegini
Author-email: cheginit@gmail.com
License: MIT
Project-URL: Issues, https://github.com/cheginit/pygeohydro/issues
Project-URL: CI, https://github.com/cheginit/pygeohydro/actions
Project-URL: Changelog, https://github.com/cheginit/pygeohydro/blob/master/HISTORY.rst
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            :target: https://github.com/cheginit/HyRiver
        
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        =========== ==================================================================== ============
        Package     Description                                                          Status
        =========== ==================================================================== ============
        PyGeoHydro_ Access NWIS, NID, HCDN 2009, NLCD, and SSEBop databases              |pygeohydro|
        PyGeoOGC_   Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services    |pygeoogc|
        PyGeoUtils_ Convert responses from PyGeoOGC's supported web services to datasets |pygeoutils|
        PyNHD_      Navigate and subset NHDPlus (MR and HR) using web services           |pynhd|
        Py3DEP_     Access topographic data through National Map's 3DEP web service      |py3dep|
        PyDaymet_   Access Daymet for daily climate data both single pixel and gridded   |pydaymet|
        =========== ==================================================================== ============
        
        .. _PyGeoHydro: https://github.com/cheginit/pygeohydro
        .. _PyGeoOGC: https://github.com/cheginit/pygeoogc
        .. _PyGeoUtils: https://github.com/cheginit/pygeoutils
        .. _PyNHD: https://github.com/cheginit/pynhd
        .. _Py3DEP: https://github.com/cheginit/py3dep
        .. _PyDaymet: https://github.com/cheginit/pydaymet
        
        PyGeoHydro: Retrieve Geospatial Hydrology Data
        ==============================================
        
        .. image:: https://img.shields.io/pypi/v/pygeohydro.svg
            :target: https://pypi.python.org/pypi/pygeohydro
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            :alt: Python Versions
        
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        .. image:: https://pepy.tech/badge/hydrodata
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            :alt: Downloads
        
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        **NOTE**
        
        This software stack was formerly named `hydrodata <https://pypi.org/project/hydrodata>`__.
        Since an `R <https://github.com/mikejohnson51/HydroData>`__ package with the same name
        already exists, we decided to renamed our project to
        `HyRiver <https://github.com/cheginit/HyRiver>`__. The ``hydrodata`` package itself is
        renamed to `pygeohydro <https://pypi.org/project/pygeohydro>`__.
        Installing ``hydrodata`` installs ``pygeohydro`` from now on.
        
        Features
        --------
        
        PyGeoHydro is a part of `HyRiver <https://github.com/cheginit/HyRiver>`__ software stack that
        is designed to aid in watershed analysis through web services. This package provides
        access to some of the public web services that offer geospatial hydrology data. It has three
        main modules: ``pygeohydro``, ``plot``, and ``helpers``.
        
        The ``pygeohydro`` module can pull data from the following web services:
        
        * `NWIS <https://nwis.waterdata.usgs.gov/nwis>`__ for daily mean streamflow observations,
        * `NID <https://nid.sec.usace.army.mil/ords/f?p=105:1::::::>`__ for accessing the National
          Inventory of Dams in the US,
        * `HCDN 2009 <https://www2.usgs.gov/science/cite-view.php?cite=2932>`__ for identifying sites
          where human activity affects the natural flow of the watercourse,
        * `NLCD 2016 <https://www.mrlc.gov/>`__ for land cover/land use, imperviousness, and canopy data,
        * `SSEBop <https://earlywarning.usgs.gov/ssebop/modis/daily>`__ for daily actual
          evapotranspiration, for both single pixel and gridded data.
        
        Also, it has two other functions:
        
        * ``interactive_map``: Interactive map for exploring NWIS stations within a bounding box.
        * ``cover_statistics``: Categorical statistics of land use/land cover data.
        
        The ``plot`` module includes two main functions:
        
        * ``signatures``: Hydrologic signature graphs.
        * ``cover_legends``: Official NLCD land cover legends for plotting a land cover dataset.
        
        The ``helpers`` module includes:
        
        * ``nlcd_helper``: A roughness coefficients lookup table for each land cover type which is
          useful for overland flow routing among other applications.
        * ``nwis_error``: A dataframe for finding information about NWIS requests' errors.
        
        Moreover, requests for additional databases and functionalities can be submitted via
        `issue tracker <https://github.com/cheginit/pygeohydro/issues>`__.
        
        You can find some example notebooks `here <https://github.com/cheginit/HyRiver-examples>`__.
        
        You can also try using PyGeoHydro without installing
        it on you system by clicking on the binder badge below the PyGeoHydro banner. A Jupyter notebook
        instance with the stack pre-installed will be launched in your web browser
        and you can start coding!
        
        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 <https://github.com/cheginit/pygeohydro/issues>`__.
        
        Installation
        ------------
        
        You can install PyGeoHydro using ``pip`` after installing ``libgdal`` on your system
        (for example, in Ubuntu run ``sudo apt install libgdal-dev``):
        
        .. code-block:: console
        
            $ pip install pygeohydro
        
        Alternatively, PyGeoHydro can be installed from the ``conda-forge`` repository
        using `Conda <https://docs.conda.io/en/latest/>`__:
        
        .. code-block:: console
        
            $ conda install -c conda-forge pygeohydro
        
        Quick start
        -----------
        
        We can explore the available NWIS stations within a bounding box using ``interactive_map``
        function. It returns an interactive map and by clicking on an station some of the most
        important properties of stations are shown.
        
        .. code-block:: python
        
            import pygeohydro as gh
        
            bbox = (-69.5, 45, -69, 45.5)
            gh.interactive_map(bbox)
        
        .. image:: https://raw.githubusercontent.com/cheginit/HyRiver-examples/main/notebooks/_static/interactive_map.png
            :target: https://github.com/cheginit/HyRiver-examples/blob/main/notebooks/nwis.ipynb
            :width: 400
            :alt: Interactive Map
        
        We can select all the stations within this boundary box that have daily mean streamflow data from
        2000-01-01 to 2010-12-31:
        
        .. code-block:: python
        
            from pygeohydro import NWIS
        
            nwis = NWIS()
            info_box = nwis.get_info(nwis.query_bybox(bbox))
            dates = ("2000-01-01", "2010-12-31")
            stations = info_box[
                (info_box.begin_date <= dates[0]) & (info_box.end_date >= dates[1])
            ].site_no.tolist()
        
        Then, we can get the streamflow data in mm/day (by default the data are in cms) and plot them:
        
        .. code-block:: python
        
            from pygeohydro import plot
        
            qobs = nwis.get_streamflow(stations, dates, mmd=True)
            plot.signatures(qobs)
        
        Moreover, we can get land use/land cove data using ``nlcd`` function, percentages of
        land cover types using ``cover_statistics``, and actual ET with ``ssebopeta_bygeom``:
        
        .. code-block:: python
        
            from pynhd import NLDI
        
            geometry = NLDI().get_basins("01031500").geometry[0]
            lulc = gh.nlcd(geometry, 100, years={"impervious": None, "cover": 2016, "canopy": None})
            stats = gh.cover_statistics(lulc.cover)
            eta = gh.ssebopeta_bygeom(geometry, dates=("2005-10-01", "2005-10-05"))
        
        .. image:: https://raw.githubusercontent.com/cheginit/HyRiver-examples/main/notebooks/_static/lulc.png
            :target: https://github.com/cheginit/HyRiver-examples/blob/main/notebooks/nlcd.ipynb
            :width: 200
            :alt: Land Use/Land Cover
        
        .. image:: https://raw.githubusercontent.com/cheginit/HyRiver-examples/main/notebooks/_static/eta.png
            :target: https://github.com/cheginit/HyRiver-examples/blob/main/notebooks/ssebop.ipynb
            :width: 200
            :alt: Actual ET
        
        Additionally, we can pull all the US dams data using ``get_nid`` and ``get_nid_codes``:
        
        .. code-block:: python
        
            nid = gh.get_nid()
            codes = gh.get_nid_codes()
        
        .. image:: https://raw.githubusercontent.com/cheginit/HyRiver-examples/main/notebooks/_static/dams.png
            :target: https://github.com/cheginit/HyRiver-examples/blob/main/notebooks/nid.ipynb
            :width: 400
            :alt: Dams
        
        Contributing
        ------------
        
        PyGeoHydro offers some limited analysis tools. It could be more useful for
        the watershed modeling community to integrate more data exploratory and analysis
        capabilities to the package. Additionally, adding support for more databases such
        as water quality, phenology, and water level, are very welcome. If you are interested
        please get in touch. You can find more information about contributing to PyGeoHydro at our
        `Contributing <https://pygeohydro.readthedocs.io/en/latest/contributing.html>`__ webpage.
        
        Credits
        -------
        
        This package was created based on the `audreyr/cookiecutter-pypackage`__ project template.
        
        __ https://github.com/audreyr/cookiecutter-pypackage
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: test
