Metadata-Version: 2.1
Name: pyEQL
Version: 0.5.2
Summary: A Python library for solution chemistry
Home-page: https://github.com/rkingsbury/pyEQL
Author: Ryan S. Kingsbury
Author-email: RyanSKingsbury@alumni.unc.edu
License: GNU Lesser General Public License v3 (LGPLv3)
Keywords: thermodynamics chemistry chemical equilibrium desalination MinEQL ChemEQL PHREEQC
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: pint
Requires-Dist: scipy

![](pyeql-logo.png)

A Python library for solution chemistry
=======================================

pyEQL is a Python library that provides tools for modeling aqueous electrolyte
solutions. It allows the user to manipulate solutions as Python
objects, providing methods to populate them with solutes, calculate 
species-specific properties (such as activity and diffusion coefficients),
and retreive bulk properties (such as density, conductivity, or volume).

![](pyeql-demo.png)
---

pyEQL is designed to be customizable and easy to integrate into projects 
that require modeling of chemical thermodyanmics of aqueous solutions.
It aspires to provide a flexible, extensible framework for the user, with a 
high level of transparency about data sources and calculation methods. 

pyEQL runs on Python 3.0+ and is licensed under LGPL.

Key Features
------------

- Build accurate solution properties using a minimum of inputs. Just specify
  the identity and quantity of a solute and pyEQL will do the rest.

- "Graceful Decay" from more sophisticated, data-intensive modeling approaches 
  to simpler, less accurate ones depending on the amount of data supplied. 

- Not limited to dilute solutions. pyEQL contains out of the box support for 
  the Pitzer Model and other methods for modeling concentrated solutions.

- Extensible database system that allows one to supplement pyEQL's default
  parameters with project-specific data.

- Units-aware calculations (by means of the [pint](https://github.com/hgrecco/pint) library)

Documentation
-------------
Detailed documentation is available at <https://pyeql.readthedocs.io/>

Dependencies
------------
 - Python 3
 - [pint](https://github.com/hgrecco/pint) - for units-aware calculations
 - [scipy](https://www.scipy.org/) - for certain nonlinear equation solvers


