https://github.com/pauliacomi/pygaps
A framework for processing adsorption data and isotherm fitting
https://github.com/pauliacomi/pygaps
adsorption data-processing materials-science
Last synced: 3 months ago
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A framework for processing adsorption data and isotherm fitting
- Host: GitHub
- URL: https://github.com/pauliacomi/pygaps
- Owner: pauliacomi
- License: mit
- Created: 2017-07-25T21:10:59.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-03-04T01:49:13.000Z (11 months ago)
- Last Synced: 2025-10-21T19:58:09.965Z (3 months ago)
- Topics: adsorption, data-processing, materials-science
- Language: Python
- Homepage:
- Size: 26.4 MB
- Stars: 77
- Watchers: 5
- Forks: 26
- Open Issues: 17
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Citation: CITATION.cff
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
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.. image:: https://raw.githubusercontent.com/pauliacomi/pyGAPS/master/docs/logo.svg
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========
Overview
========
pyGAPS (Python General Adsorption Processing Suite) is a framework for
adsorption data analysis and fitting, written in Python 3.
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:alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.
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:target: https://github.com/pauliacomi/pygaps/compare/master...develop
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:alt: GHA-CI Build Status
:target: https://github.com/pauliacomi/pyGAPS/actions
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:alt: Requirements Status
:target: https://requires.io/github/pauliacomi/pyGAPS/requirements/?branch=master
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:alt: Coverage Status
:target: https://codecov.io/gh/pauliacomi/pyGAPS
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:target: https://pypi.org/project/pygaps
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Features
========
- Advanced adsorption data import and manipulation.
- A fully feature thermodynamic backend allowing conversions between
pressures, temperatures, loadings, etc.
- Routine analysis such as BET/Langmuir surface area, t-plots, alpha-s
plots, 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 kernels (DFT, QSDFT, ...)
- Isotherm fitting with various models (Henry, Langmuir, DS/TS Langmuir,
etc..)
- Enthalpy of adsorption calculations.
- IAST predictions for binary and multicomponent adsorption.
- Parsing to and from multiple formats such as AIF, Excel, CSV and JSON.
- Simple methods for isotherm graphing and comparison.
- An database backend for storing and retrieving data.
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!
- **Transparent**: 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 and want to jump right in, look at the `quickstart
section `__.
Examples for each of the capabilities specified above can be found documented
`here `__. Most of
the pages 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.PointIsotherm)``.
Support and sponsorship
=======================
This project is graciously sponsored by
`Surface Measurement Systems `__, by
employing Paul Iacomi, the core maintainer. The work would not be possible
without their contribution, keeping this open source project alive.
.. image:: https://raw.githubusercontent.com/pauliacomi/pyGAPS/master/docs/figures/SMS-Logo.jpg
:width: 200px
:align: left
If you are interested in implementing a particular feature,
or obtaining professional level support, contact us here
`Bugs or questions?`_.
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. You can use ``pip``:
.. code:: bash
pip install pygaps
or `Anaconda/Conda `__:
.. code:: bash
conda install -c conda-forge pygaps
If you are just starting out, `Anaconda/Conda `__ is
a good bet since it manages virtual environments for you. Check out
`Installation `__ for
more details.
Development
===========
To install the development branch, clone the repository from GitHub.
Then install the package with pip either in regular or developer mode.
.. code:: bash
git clone https://github.com/pauliacomi/pyGAPS
# then install editable/develop mode
# adding [dev] will install the development dependencies
pip install -e ./pyGAPS[dev]
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 `__.