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A framework for processing adsorption data for porous materials.

Project description

pyGAPS (Python General Adsorption Processing Suite) is a framework for adsorption data analysis and fitting, written in Python 3.

Features

  • Advanced adsorption data import and manipulation.
  • Routine analysis such as BET/Langmuir surface area, t-plot, alpha-s, Dubinin plots etc.
  • Pore size distribution calculations for mesopores (BJH, Dollimore-Heal).
  • Pore size distribution calculations for micropores (Horvath-Kawazoe).
  • Pore size distribution calculations using DFT kernels
  • Isotherm model fitting (Henry, Langmuir, DS/TS Langmuir, etc..)
  • Isosteric enthalpy of adsorption calculation.
  • IAST calculations for binary and multicomponent adsorption.
  • Parsing to and from multiple formats such as Excel, CSV and JSON.
  • An sqlite database backend for storing and retrieving data.
  • Simple methods for isotherm graphing and comparison.

Documentation

pyGAPS is built with three key mantras in mind:

  • opinionated: There are many places where the code will suggest or default to what the it considers a good practice. As examples: the standard units, pore size distribution methods and BET calculation limits.
  • flexible: While the defaults are there for a reason, you can override pretty much any parameter. Want to pass a custom adsorbate thickness function or use volumetric bases? Can do!
  • transparency: All code is well documented and open source. There are no black boxes.

In-depth explanations, examples and theory can be found in the online documentation. If you are familiar with Python and adsorption theory and want to jump right in, look at the quickstart section. Examples on each of the capabilities specified above can be found in the examples. Most of the pages in the documentation are actually Jupyter Notebooks. You can download them and run them yourself from the /docs/examples folder.

To become well familiarised with the concepts introduced by pyGAPS, such as what is an Isotherm, how units work, what data is required and can be stored etc., a deep dive is available in the manual.

Finally, having a strong grasp of the science of adsorption is recommended, to understand the strengths and shortcomings of various methods. We have done our best to explain the theory and application range of each capability and model. To learn more, look at the reference or simply call help() from a python interpreter (for example help(pygaps.area_BET).

Citing

Please consider citing the related paper we published if you use the program in your research.

Paul Iacomi, Philip L. Llewellyn, Adsorption (2019). pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation. DOI: https://doi.org/10.1007/s10450-019-00168-5

Installation

The easiest way to install pyGAPS is from the command line. Make sure that you have numpy, scipy, pandas and matplotlib, as well as CoolProp already installed.

pip install pygaps

Anaconda/Conda is your best bet since it manages environments for you. First create a new environment and use conda to install the dependencies (or start with one that already has a full instalation). Then use pip inside your environment.

conda create -n myenv python=3 numpy scipy pandas matplotlib
conda activate myenv
pip install pygaps

To install the development branch, clone the repository from GitHub. Then install the package with pip either in regular or developer mode.

git clone https://github.com/pauliacomi/pyGAPS

# then install
pip install pyGAPS/

# or developer mode
pip install -e pyGAPS/

Development

If you want to contribute to pyGAPS or develop your own code from the package, check out the detailed information in CONTRIBUTING.rst.

Bugs or questions?

For any bugs found, please open an issue or, even better, submit a pull request. It’ll make my life easier. This also applies to any features which you think might benefit the project. I’m also more than happy to answer any questions. Shoot an email to mail( at )pauliacomi.com or find me at https://pauliacomi.com or on Twitter.

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