https://github.com/tillbiskup/fitpy

A Python package for fitting models to (spectroscopic) data.
https://github.com/tillbiskup/fitpy

electron-paramagnetic-resonance fitting reproducible-research reproducible-science simulation spectral-analysis spectroscopy

Last synced: about 1 month ago
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A Python package for fitting models to (spectroscopic) data.

• Host: GitHub
• URL: https://github.com/tillbiskup/fitpy
• Owner: tillbiskup
• License: bsd-2-clause
• Created: 2019-09-26T05:03:49.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-12-01T19:51:07.000Z (11 months ago)
• Last Synced: 2025-08-23T08:09:15.988Z (about 2 months ago)
• Topics: electron-paramagnetic-resonance, fitting, reproducible-research, reproducible-science, simulation, spectral-analysis, spectroscopy
• Language: Python
• Size: 367 KB
• Stars: 3
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE
  • Citation: CITATION.cff
  • Roadmap: docs/roadmap.rst

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README

          
FitPy

=====

FitPy is a **framework** for the **advanced fitting of models to spectroscopic data** focussing on **reproducibility**. Supported are semi-stochastic sampling of starting conditions, global fitting of several datasets at once, and fitting several concurrent models to one dataset. FitPy builds upon and extends the `ASpecD framework `_. At the same time, it relies on the `SciPy software stack `_ and on `lmfit `_ for its fitting capabilities.

Making use of the concept of **recipe-driven data analysis**, actual fitting **no longer requires programming skills**, but is as simple as writing a text file defining both, the model and the fitting parameters in an organised way. Curious? Have a look at the following example::

    format:

      type: ASpecD recipe

      version: '0.2'

    datasets:

      - /path/to/dataset

    tasks:

      - kind: model

        type: Gaussian

        properties:

          parameters:

            position: 1.5

            width: 0.5

        from_dataset: /path/to/dataset

        output: model

        result: gaussian_model

      - kind: fitpy.singleanalysis

        type: SimpleFit

        properties:

          model: gaussian_model

          parameters:

            fit:

              amplitude:

                start: 5

                range: [3, 7]

        result: fitted_gaussian

For more general information on the FitPy framework see its `homepage `_, and for how to use it, its `documentation `_.

Features

--------

A list of features, planned for the first public release:

* Framework for the advanced fitting of models to spectroscopic data focussing on reproducibility.

* Simple user interface requiring no programming skills.

* Semi-stochastic sampling of starting conditions (Latin hypercube sampling, LHS)

* Global fitting of several datasets at once

* Fitting of several concurrent models (*i.e.*, "species") to one dataset

.. warning::

  FitPy is currently under active development and still considered in Alpha development state. Therefore, expect frequent changes in features and public APIs that may break your own code. Nevertheless, feedback as well as feature requests are highly welcome.

Installation

------------

Install the package by running::

    pip install fitpy

Related projects

----------------

There is a number of related packages that are based on the ASpecD framework and each focus on one particular type of spectroscopy. The most mature packages available to date are:

* `ASpecD `_

  A Python framework for the analysis of spectroscopic data focussing on reproducibility and good scientific practice. The framework the cwepr package is based on, developed by T. Biskup.

* `trepr `_

  Package for processing and analysing time-resolved electron paramagnetic resonance (TREPR) data, developed by J. Popp and maintained by T. Biskup.

* `cwepr `_

  Package for processing and analysing continuous-wave electron paramagnetic resonance (cw-EPR) data, originally implemented by P. Kirchner, currently developed and maintained by M. Schröder and T. Biskup.

You may as well be interested in the `LabInform project `_ focussing on the necessary more global infrastructure in a laboratory/scientific workgroup interested in more `reproducible research `_. In short, LabInform is "The Open-Source Laboratory Information System".

Finally, don't forget to check out the website on `reproducible research `_ covering in more general terms aspects of reproducible research and good scientific practice.

License

-------

This program is free software: you can redistribute it and/or modify it under the terms of the **BSD License**.