Calculate P-Value via ToyMC

[JosephJHowlett]

I'm in the process of incorporating this package into bbf, and realized my use case might be somewhat common. I have a histogrammed model (ER template) and histogrammed data (Rn220 data), and want the p-value of the fit. With this PR you can do (if e.g. these are both multihists):

from GOFevaluation import binned_poisson_chi2_gof
gof_object = binned_poisson_chi2_gof.from_binned(datahist.histogram, mchist.histogram)
gof_object.get_pvalue(n_mc=1000)

If you want to plot the distribution of GoF's, you can do:

gofs = gof_object.sample_gofs()
hist, bin_edges = np.histogram(gofs, bins=1000)
cumulative_density = 1.0 - np.cumsum(hist)/np.sum(hist)
plt.plot(np.mean(np.vstack([bin_edges[0:-1], bin_edges[1:]]), axis=0), cumulative_density)
plt.axvline(gof_object.calculate_gof())
plt.ylabel('P(less than GoF)')
plt.xlabel('GoF')
plt.show()

to get:

[WolfXeHD]

Hi @jhowl01 !
It is great to see that you are such an early adopter and try to incorporate the package in bbf.
From a first quick look, this seems to be a great and useful feature. I also added @hammannr as a reviewer since he is also actively developing the package at the moment. I think he might also have some ideas about what to do and how to incorporate your idea.

[hammannr]

I think this is a really cool and handy feature to have in the package so that one can directly get a proper understanding of how well the fit performed and for comparing different gof measures! If we add get_pvalue() to all gof tests we might want to outsource the calculations into a dedicated class.