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punpy.mc.mc_propagation.MCPropagation.generate_MC_sample

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]

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