You're reading the documentation for a development version. For the latest released version, please have a look at v0.1.


Here, we present a (growing) series of working examples of how to use the FitPy framework to perform advanced fitting of models to (experimental) data.

For each of the examples, a full recipe is provided ready to be copied and pasted and run on your local computer.

Creating data

To be independent of actual experimental data, for the time being each of the recipe examples provided here comes with a section at its beginning creating some data that get fitted afterwards. Of course, in real-life applications, rather than first creating the data from a model, adding noise to it, and afterwards fitting (the same) model to the newly created data, you will load your own, experimental data.


To be able to run the example recipes locally, you need to have a working installation of the FitPy package and its dependencies. Have a look at the installation instructions for details.

Furthermore, to be able to run (“cook”) the recipes and get (“serve”) the results, you need to have access to a command line, as running recipes (still) is command-line based using the command serve recipe.yaml.

For some recipes, you will need to have a working LaTeX installation in case you would like to get your reports not only created, but compiled into a well-formatted PDF document as well.