I’m happy to announce that the first working (and reasonably complete) version of the DataFitting.jl package is available on Github.
The main purpose of
DataFitting is to provide a general purpose data fitting framework for Julia, allowing users to fit observational data against theoretical model (even very complex ones), in a simple and very fast way.
The key points of
- it handles data of any dimensionality;
- the fitting model is built up by individual components, either provided by DataFitting or implemented by the user. All components are combined to evaluate the final model with a standard Julia mathematical expression;
- all components results are cached, so that repeated evaluations with the same parameters do not involve further calculations. This is very important to speed up the fitting process when many components are involved;
- it easily allows to use different minimizers, and compare their results and performances. Currently two minimizers are supported (CMPFit and LsqFit)
- it provides several facilities for data exploration, interactive fitting, and displaying of results.
The development of
DataFitting started a few weeks ago because I wanted to port my software for spectral data fitting (QSFit) in Julia, and it turned out it can be easily adapted to handle similar problems in other research fields.
The package already provides all the basic functionalities, and the first release is foreseen in Feb. 2018.
Any comment, suggestion, bug report, contribution and criticism is very welcome!!