The code is written in Python and only requires standard packages such as numpy, scipy and matplotlib. Also a L A T E Xinstallation is required for the plots. The installation and usages is described in more detail in appendix B. As input the code uses a configuration file were all important parameters can be specified. The main source file then just requires the user to provide the model and its derivative with respect to the independent variable as a Python function which depends on the parameters and the data. Once everything is specified the code internally maximizes equation ( ) with respect to the parameters to find the MLE. At the MLE the Fisher matrix is calculated to define the Gaussian errors (step 1 in figure ). Subsequently a root search is carried out on the function f (C) = 1q(C), where q(C) is given by equation ( ), to find the confidence contours corresponding to q = 0.683, 0.954 , 0.997. Function q(C) contains the Bayes theorem depicted in step 2 in figure , while the root search corresponds to step 3. The results are then used to produce figures such as figure and figure 3. Additionally output files containing the data to produce these figures are generated, given the user the opportunity to self-plot the results if desired.