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Readme.

BayesicFitting is a package for model fitting and Bayesian evidence calculation.

In case you are wondering what that is about take a quick look at the examples in the side bars.

We have a paper out in "Astronomy and Computing" about BayesicFitting. Kester and Mueller (2021).

Citation index for the BayesicFitting package: DOI: 10.5281/zenodo.2597200

What's new.

20 Oct 2025 version 3.2.5<br>
- Removed unused import statements from source and test (pull request #1)
13 Aug 2025 version 3.2.4<br>
- Lauch new web site https://dokester.github.io/BayesicFitting/
  - Almost all docstrings adapted for the new site.
- GalileanEngine
  - add a pertubation attribute: wiggle=0.2. See Dimensions
  - find edge by repeated quadratic interpolation before mirroring
- NestedSampler
  - add new attribute avoid=0.1. See Dimensions
- PhantomSampler now uses all phantoms to do the evidence integral
  - Adaptions in Engines, Explorer, WalkerList, NestedSampler.
  - PhantomCollection has one WalkerList also in Dynamic cases.
- Removed Deprecations, Checks and/or Warnings.
  - Model, ErrorDistributions, Problem
  - Formatter
  - Sample

1. History

The BayesicFitting package is a python version of the the fitter classes in Herschel Common Science System (HCSS). HCSS was the all encompassing software system for the operations and analysis of the ESA satelite Herschel. The HCSS version of the fitting software was written in JAVA, mostly by me. I encoded features and classes that were requested by my Herschel colleagues or that I remembered having used myself during my lifelong career as data analyst for earlier satellites as IRAS, ISO and AKARI. So most of the stuff in here was needed and used at a certain moment in time. Later, the package was developing in directions that were needed by my work for the James Webb Space Telescope (JWST).

The HCSS system is in the public domain under GPL3. It was used by the 3 instrument groups of the Herschel satellite to write calibration and analysis software. Since the end of the mission HCSS is not being maintained

I used a customized version of java2python (j2py on github) to translate the JAVA classes to python. However, the actual code needed serious pythonization. Every line has been inspected. Every construct has been revised.

The documentation got most profit from the automated conversion. Also the structure into classes, the inheritance, methods and dependencies are largely the same as in the original HCSS.

2. Setup

The package is written in python3 although I am not aware of using any specific python3 features. It uses numpy (>= 1.9) for its array structure, scipy (>=1.0) for linear algebra and other stuff and astropy (>=2.0) for units. Matplotlib (>=2.0) is used for plotting.

Download and unpack the BayesicFitting zip file from github. Move into the BayesicFitting-master directory and run:

python setup.py install

where python is python3. Or install it as :

pip install BayesicFitting

3. Structure

source

The BayesicFitting package consists of over 100 classes, each class in its own file. These classes can be divided into 3 broad categories: models, fitters and nested sampling. About 50 models, 10 fitters and the remainder is needed to run the nested sampling algorithm. All these classes are in a directory BayesicFitting/source. A special type of functions are found in BayesicFitting/source/kernels. They can be used to construct a model.

examples

In BayesicFitting/examples a number of scripts can be found to exercise the classes. They are in the form of jupyter notebooks. Some are using real data; others have synthetic data specially constructed to make some point.

All examples can be inspected by clicking on them. They will fold out in the browser.

To actually exercise the examples and maybe adapt then, start a jupyter notebook in your examples directory.

jupyter notebook

The program will open a list in your webbrowser where you can select a notebook file (.ipynb), which can be run.

documentation

In the documenation directory a number of documents can be found.

Manual: The manual for the package.
Classes: Detailed documentation on all classes and their methods. Taken from the Python docstrings.
Troubles: A list of troublesome situations and what to do about it.
Glossary: A list of the terms used throughout this package, with explanations.
Design: An architectural design document, displaying the relationships between the classes.
Style: A few notes on my style of code and documenation.
References: A list of external references for BayesicFitting.

test

Almost all classes have a test harness. These are located in BayesicFitting/test. They can be execised as:

python -m unittest <file>

where python refers to python3 and file refers to one of the files in BayesicFitting/test.<br> As most functionality is tested in a test harness, examples on how to use the classes can be found there too.

4. Status

A package like this is never finished. Always more classes and/or functionalities can be added. I present it now as it is in the hope it will be usefull and it will generate feedback.

According to Wikipedia -> "Software release life cycle" it is called "Perpetual Beta". It continues to be in a beta-release because new classes and features can be added.

Some of the newer additions will be indicated as having and "Alpha" status and keep that until they matured somewhat further. These classes are more prone to change in their interfaces, methods etc.

More work needs to be done in:

Introduction of more Classes: NeuralNetModel, Evolving Models, Filters (maybe) ...

5. Versions

4 Jan 2018 version 0.9.0

Initial upload to github.

26 Jan 2018 version 1.0.0
5 Mar 2018 version 1.0.1

Package on pypi.com.
Restructured all import statement to comply with PYPI package.

14 Mar 2018 version 1.0.2
- Added Dynamic Models
- Added piping of models
23 Mar 2018 version 1.0.3
- Some issues with ErrorDistributions and map fitting
- 2-d fitting examples added
- All examples revisited
- Links in README.md updated
28 May 2018 version 1.0.4
- New classes: CircularUniformPrior, PseudoVoigtModel
- VoigtModel uses scipy.special.wozf() and has partials now,
- Refactoring Priors to the BaseModel
- Restructuring Dynamic
- Threading optional in NestedSampler.
- New classes: UniformErrorDistribution, FreeShapeModel and kernels/Tophat
- added to testharnesses and examples
27 June 2018 version 1.0.5
- New classes: RadialVelocityModel and MixedErrorDistribution
- testharnesses and examples
- documentation updates
28 June 2018 version 1.0.6
- longdescription set to markdown (Still not OK on pypi.org)
28 July 2018 version 1.0.7
- small compilation error in 1.0.6
11 October 2018 version 1.0.8
- refactoring the setting of attributes in Models
- documentation (manual, design, etc.) updated.
28 December 2018 version 2.0.0
- Introduction of Problem Classes:
  - Problem. <br> Base class for problems to be handled by NestedSampler.
  - ClassicProblem. <br> Common class for everything that was possible in version 1. ClassicProblem is transparant as all interfaces to NestedSampler have remained the same as they were in version 1.0, even though behind the scenes a ClassicProblem has been invoked.
  - ErrorsInXandYProblem. <br> Problem that have errors in the xdata and in the ydata.
  - ... more to come.
- Introduction of Walker and WalkerList to represent the internal ensemble in NestedSampler.
- Adaptations in NestedSampler, ErrorDistributions, Engines, Sample, SampleList.
- Better separation of responsibilities of ErrorDistribution and Problem. <br> Consequently ErrorDistribution has a new initialisation, which is incompatible with previous versions. In most cases this has no effect on the calling sequences of NestedSampler.
- Rename GenGaussErrorDistribution into ExponentialErrorDistribution.
- New testharnesses and examples.
- Adaptations of documentation: manual and design.
16 Jan 2019 version 2.1.0
- MultipleOutputProblem. Problems with more dimensional outputs
- StellarOrbitModel. A 2 dim output model to calculate the orbit of a double star
- Keppler2ndLaw. To calcul

BayesicFitting

Install / Use

README