Home

Awesome

DOI Build Status PyPI version codecov Binder Coolness

<img src="http://www.exchangenetwork.net/wp-content/themes/exchange-network/images/exchange-network-logo.jpg"> Developed with funding from the <a href="http://www.exchangenetwork.net/">U.S. EPA Exchange Network</a>

Well Application

Set of tools for groundwater level and water chemistry analysis. Allows for rapid download and graphing of data from the USGS NWIS database and the Water Quality Portal.

Installation

Wellapplication should be compatible with both Python 2.7 and 3.5. It has been tested most rigously on Python 2.7. It should work on both 32 and 64-bit platforms. I have used it on Linux and Windows machines.

To install the most recent version, use <a href='https://pypi.python.org/pypi/pip'>pip</a>.

pip install wellapplication

Modules

transport

This module:

This class has functions used to import transducer data and condition it for analysis.

The most important function in this library is new_xle_imp, which uses the path and filename of an xle file, commonly produced by pressure transducers, to convert that file into a <a href=http://pandas.pydata.org/>Pandas</a> DataFrame.

A <a href=http://jupyter.org/> Jupyter Notebook</a> using some of the transport functions can be found <a href = http://nbviewer.jupyter.org/github/utah-geological-survey/WellApplication/blob/master/docs/UMAR_WL_Data.ipynb>here</a>.

usgs

This module has functions used to apply the USGS's rest-based api to download USGS data by leveraging <a href = http://docs.python-requests.org/en/master/>`requests`</a> package and <a href=http://pandas.pydata.org/>Pandas</a>.

The most powerful class in this module is nwis. It is called by nwis(service, location value, location type). The main <a href='https://waterservices.usgs.gov/rest/'>USGS services</a> are dv for daily values, iv for instantaneous values, gwlevels for groundwater levels, and site for site information. The nwis class allows for rapid download of NWIS data.

>>> import wellapplication as wa
>>> discharge = wa.nwis('dv','10109000','sites')
>>> site_data = discharge.sites
>>> flow_data = discharge.data

A <a href=http://jupyter.org/> Jupyter Notebook</a> using some of the usgs functions can be found <a href=https://nbviewer.jupyter.org/github/utah-geological-survey/WellApplication/blob/master/docs/USGS.ipynb> here</a>.