A package for model fitting and evidence calculation.

The BayesicFitting package is a python version of the the fitter classes in Herschel Common Science System (HCSS). The HCSS version was written in JAVA also mostly by me. I encoded features and classes that were requested by my Herschel colleagues or I remembered having used myself while doing data analysis 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. Even now the package is developing in directions that are needed by my work for 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. 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.

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 and bspline (>=0.1.1 at github by Foster and Jeronen) is used in the BSplinesModel.

Download the necessary dependencies. Then download the package and put it in a directory BayesicFitting. Adapt the environment variable PYTHONPATH to include

BayesicFitting/source/
BayesicFitting/source/kernels/
BayesicFitting/examples/

and if you want to run the test harnasses, put

BayesicFitting/test/

also in your PYTHONPATH

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 e.g. to combine into a model.

Almost all classes have a test harnass. 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.

As most functionality is tested in a test harnass, examples on how to use the classes can be found there too.

In BayesicFitting/examples a number of scripts can be found the 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.

Run a jupyter notebook in the 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.

A first draft of a manual can be found in BayesicFitting/documentation/manual.md. It obviously needs more work.

Another hopefully usefull document is troubles.md in the same directory. It lists a number of cases where the fitting went haywire and how it might be solved.

Also in BayesicFitting/documentation are a number of structure diagrams (dia-files and png-files). They show the relationships between the classes in the package. A first version of the Design Document has been written.

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.

More work needs to be done in:

• Documentation, especially the manual.
• Examples, more of them and covering more classes.
• FreeShapeModel needs rethinking.
• Dynamic models for NestedSampler

4 Jan 2018 Do Kester.