power_law_integrated_distribution

lstchain.mc.mc.power_law_integrated_distribution(emin, emax, tot_num_events, spectral_index, bin_number=30)

For each bin, return the expected number of events for a power-law distribution. bins: numpy.ndarray, e.g. np.logspace(np.log10(emin), np.logspace(emax))

Parameters:

emin: `float` minimum energy

emax: `float` maximum energy

tot_num_events: `int` total number of events simulated

spectral_index: `float` spectral index of the power-law distribution

Returns:

(bins, y):

bins: np.logspace(np.log10(emin), np.log10(emax), bin_number)

tuple of numpy.ndarray, len(y) = len(bins) - 1

TODO: Introduce any spectral form