hydrofunctions 0.2.0

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 and enable automatic chart display:

>>> import hydrofunctions as hf
>>> %matplotlib inline

Create NWIS data object to hold our request and the data we will retrieve. We will request the daily values (‘dv’) for site ‘0158520’ for the past 55 days:

>>> herring = hf.NWIS('01585200', 'dv', period='P55D')
Requested data from https://waterservices.usgs.gov/nwis/iv/?format=json%2C1.1&sites=01585200&period=P55D

Find out what data we received:

>>> herring
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 show the common name of the parameter and its units.

Create a dataframe from our data, and list the first five items:

>>> herring.df().head()

–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

Plot the data using built-in methods from Pandas and mathplotlib:

>>> herring.df().plot()

–a stream hydrograph appears–

Learn more:

• More usage tips

• Introduction to Hydrofunctions, a Jupyter Notebook with a quick tutorial.

Easy Installation

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

$ 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.

Other Projects You Should See

Hydropy, a Python package that builds upon Pandas for enhanced data selection and plotting of hydrology data.

WellApplication a Python package that provides functions for working with dataloggers and USGS well data.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

MIT License

Copyright (c) 2016, Martin Roberge and contributors