punpy.mc.mc_propagation.MCPropagation.generate_MC_sample#
- MCPropagation.generate_MC_sample(x, u_x, corr_x, corr_between=None, pdf_shape='gaussian', pdf_params=None, comp_list=False)[source]#
function to generate MC sample for input quantities
- Parameters:
x (list[array]) – list of input quantities (usually numpy arrays)
u_x (list[array]) – list of systematic uncertainties on input quantities (usually numpy arrays)
corr_x (list[array]) – list of correlation matrices (n,n) along non-repeating axis. Can be set to “rand” (diagonal correlation matrix), “syst” (correlation matrix of ones) or a custom correlation matrix.
corr_between (array, optional) – correlation matrix (n,n) between input quantities, defaults to None
pdf_shape (str, optional) – string identifier of the probability density function shape, defaults to gaussian
pdf_params (dict, optional) – dictionaries defining optional additional parameters that define the probability density function, Defaults to None (gaussian does not require additional parameters)
comp_list (bool, optional) – boolean to define whether u_x and corr_x are given as a list or individual uncertainty components. Defaults to False, in which case a single combined uncertainty component is given per input quantity.
- Returns:
MC sample for input quantities
- Return type:
list[array]