modefit
Fit your data !
The concept of this API is to be able to fit your data using minuit or scipy as will as to run Markov Chain Mote Carlo using the same object.
Once a fitter object is loaded: (see below) simply:
 use
fit()
to fit the data. See the results infitvalues
 use
run_mcmc()
to run the markov chain monte carlo. Best values inmcmc.derived_values
The fitter objects also have a show()
method in which you can choose to show the best fit or the mcmc samples on top on the data.
Current Models
Currently, the following models are implemented:

binormal step (
stepfit
). It has 4 parameters: mean_a, mean_b, sigma_a, sigma_b, which are the mean and the dispersion (sigma) of the normal distributions a and b, respectively. Each datapoint can have a probabilityproba
(1proba
) to belong to the group "a" ("b"). 
PolynomeFit (
get_polyfit
). Fit any degree polynome (legendre or simple) to your dataset (x, y, dy). 
NormPolynomeFit (
get_normpolyfit
). Fit any number of gaussian on top of a polynome of any degree (legendre or simple) to your dataset (x, y, dy).
Dependencies
 iminuit (>1.1)
 propobject (>=0.1)
 emcee (>=2.0)