comet_maths.generate_sample.generate_sample.generate_sample_corr#
- comet_maths.generate_sample.generate_sample.generate_sample_corr(MCsteps, param, u_param, corr_param, diff=0.05, dtype=None, pdf_shape='gaussian', pdf_params=None)[source]#
Generate correlated MC sample of input quantity with a given covariance matrix. sample are generated independent and then correlated using Cholesky decomposition.
- Parameters:
MCsteps (int) – number of MC steps
param (array) – values of input quantity (mean of distribution)
cov_param (array) – covariance matrix for input quantity
diff (float, optional) – maximum difference that the error correlation matrix is allowed to be changed by to make it positive definite. Defaults to 0.001
dtype (numpy.dtype, optional) – dtype of the produced sample
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)
- Returns:
generated sample
- Return type:
array