.. meta:: :description lang=en: kafe2 - a general, Python-based approach to fit a model function to two-dimensional data points with correlated uncertainties in both dimensions :robots: index, follow ####################### **kafe2** documentation ####################### .. image:: _static/img/badge_kafe2.svg.png :width: 128px :height: 128px :alt: kafe2 logo :align: left Welcome to **kafe2**, the *Karlsruhe Fit Environment 2*! *kafe2* is a data fitting framework originally designed for use in undergraduate physics lab courses. It provides a *Python* toolkit for fitting models to data via the maximum likelihood method as well as visualizing the fit results. A quick rundown of why you'd want to use *kafe2* can be found `here `__. The gist of it is that *kafe2* provides a simple, user-friendly interface for state-of-the-art statistical methods. It relies on Python packages such as :py:mod:`numpy` and :py:mod:`matplotlib`, and can use either :py:mod:`scipy` or the minimizer `Minuit` contained in the Python package `iminuit` as the numerical optimization backend. The :ref:`first chapter ` of this documentation gives detailed installation instructions. The :ref:`Beginner's Guide ` explains basic *kafe2* usage to cover simple cases (both Python code and kafe2go). The :ref:`User Guide ` and the :ref:`kafe2go Guide ` describe advanced *kafe2* use with Python code or *kafe2go*. The :ref:`next chapter ` explains the mathematical foundations upon which *kafe* is built. While strictly speaking not required to use *kafe2*, reading the theory chapter is strongly recommended to understand which features to use in a state-of-the-art data analysis (regardless of whether *kafe2* or another data analysis tool is used). The :ref:`Developer Guide ` covers topics that are only relevant if you want to work on *kafe2* as a developer (still very much WIP). Finally, the :ref:`API Documentation ` provides a full description of the user-facing *kafe2* application programming interface. .. toctree:: :name: mastertoc :maxdepth: 2 :includehidden: parts/installation parts/beginners_guide parts/user_guide parts/user_guide_kafe2go parts/mathematical_foundations.rst parts/developer_guide parts/api_documentation/index