.. 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