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https://github.com/mroberge/hydrofunctions

A suite of convenience functions for working with hydrology data in an interactive Python session.
https://github.com/mroberge/hydrofunctions

hydrology scientific-visualization water

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A suite of convenience functions for working with hydrology data in an interactive Python session.

Lists

README 

        
===============================

HydroFunctions

===============================



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        :alt: Visit Hydrofunctions on PyPI



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A suite of convenience functions for exploring water data in Python.



Features

--------



* Retrieves stream data from the USGS NWIS service

* Select data using multiple site numbers, by state, county codes, or a boundary box

* Preserves NWIS metadata, including NoData values

* Helpful error messages to help you write valid requests

* Extracts data into a Pandas dataframe, json, or dict

* Plot beautiful graphs in Jupyter Notebooks

   * hydrographs (or time series of any data)

   * flow duration charts

   * cycle plots to illustrate annual or diurnal cycles

   * Interactive map for finding stream gauge ID numbers

* Plotting and manipulation through Pandas dataframes

* Retrieve USGS rating curves, peak discharges, field notes, and site files for gauging stations

* Retrieve USGS daily, monthly, and annual statistics for gauging stations

* Saves data in compact, easy-to-use parquet files instead of requesting the same dataset repeatedly

* **Massive** `Users Guide`_ **that makes Hydrology AND Data Science easy!**



Still in active development! Let me know what features you want!



Read the `Users Guide`_ for more details.



Basic Usage

-----------



First, import hydrofunctions into your project. If you plan to work with Jupyter

notebooks, then go ahead and enable automatic chart display:



.. code-block:: ipython



    In  [1]: import hydrofunctions as hf

             %matplotlib inline



Create an NWIS data object to hold our request and the data we will retrieve.

We will request the instantaneous values ('iv') for site '01585200' for the

past 55 days:



.. code-block:: ipython



    In  [2]: herring = hf.NWIS('01585200', 'iv', period='P55D')

    Requested data from https://waterservices.usgs.gov/nwis/iv/?format=json%2C1.1&sites=01585200&period=P55D



You can check that the request went smoothly:



.. code-block:: ipython



    In  [3]: herring.ok

    Out [3]: True



Find out what data we received:



.. code-block:: ipython



    In  [4]: herring

    Out [4]: USGS:01585200: WEST BRANCH HERRING RUN AT IDLEWYLDE, MD

                 00060: <5 * Minutes>  Discharge, cubic feet per second

                 00065: <5 * Minutes>  Gage height, feet

             Start: 2019-05-25 01:05:00+00:00

             End:   2019-07-19 19:05:00+00:00



This tells us the name of our site and gives a list of the parameters that we

have. For each parameter it lists how frequently the data were collected, and

it shows the common name of the parameter and its units.



Create a dataframe using only our discharge data, and list the first five items:



.. code-block:: ipython



    In  [5]: herring.df('discharge').head()

    Out [5]:



*--a table with our data appears--*



    +------------------------------+---------------------------+

    |          datetimeUTC         | USGS:01585200:00060:00000 |

    +------------------------------+---------------------------+

    |   2019-05-25 01:05:00+00:00  |                1.57       |

    +------------------------------+---------------------------+

    |   2019-05-25 01:10:00+00:00  |                1.57       |

    +------------------------------+---------------------------+

    |   2019-05-25 01:15:00+00:00  |                1.51       |

    +------------------------------+---------------------------+

    |   2019-05-25 01:20:00+00:00  |                1.57       |

    +------------------------------+---------------------------+

    |   2019-05-25 01:25:00+00:00  |                1.57       |

    +------------------------------+---------------------------+



If we're using Jupyter Lab, we can plot a graph of the data using built-in methods from Pandas and mathplotlib:



.. code-block:: ipython



    In  [6]: herring.df('q').plot()

    Out [6]:



*--a stream hydrograph appears--*



.. image:: https://raw.githubusercontent.com/mroberge/hydrofunctions/master/_static/HerringHydrograph.png

   :alt: a stream hydrograph for Herring Run



Learn more:  



* `Users Guide`_



Easy Installation

-----------------



The easiest way to install Hydrofunctions is by typing this from your

command line:



.. code-block:: console



    $ pip install hydrofunctions



Hydrofunctions depends upon Pandas and numerous other scientific packages

for Python. `Anaconda `_

is an easy, safe, open-source method for downloading everything and avoiding

conflicts with other versions of Python that might be running on your

computer.



Visit the `Installation Page `_

in the Users Guide to learn how to install

Anaconda, or if you have problems using the Easy Installation method above.



.. _`Users Guide`:  https://hydrofunctions.readthedocs.io/en/latest