学术文献库

Fitting models to measured data is one of the standard tasks in the natural sciences, typically addressed early on in physics education in the context of laboratory courses, in which statistical methods play a central role in analysing and interpreting experimental results. The increased emphasis placed on such methods in modern school curricula, together with the availability of powerful free and open-source software tools geared towards scientific data analysis, form an excellent premise for the development of new teaching concepts for these methods at the university level. In this article, we present kafe2, a new tool developed at the Faculty of Physics at the Karlsruhe Institute of Technology, which has been used in physics laboratory courses for several years. Written in the {\it Python} programming language and making extensive use of established numerical and optimization libraries, {\it kafe2} provides simple but powerful interfaces for numerically fitting model functions to data. The tools provided allow for fine-grained control over many aspects of the fitting procedure, including the specification of the input data and of arbitrarily complex model functions, the construction of complex uncertainty models, and the visualization of the resulting confidence intervals of the model parameters.