skgpuppy.TaylorPropagation module

class skgpuppy.TaylorPropagation.TaylorPropagation(func, mean, order, dx=0.001)

Bases: skgpuppy.FFNI.PropagateMoments

Class to perform error propagation using Taylor Series

Parameters:
  • func – (n-d) function to approximate
  • mean – approximate around this mean vector
  • order – order of the taylor series
  • dx – step size for the derivatives
estimate(x)

Estimate the value of the approximated function at x

Parameters:x –
Returns:Approximated value of func at x
estimate_many(x_list)

Estimate the value of the approximated function at several x

Parameters:x_list –
Returns:Approximated value of func at the x values
propagate(Sigma_x, skew=False, kurtosis=False)

Propagates a normal distributed uncertainty around self.mean through the deterministic function self.func.

Parameters:
  • Sigma_x – Covariance matrix (assumed to be diagonal)
  • skew – Return the skewness of the resulting distribution
  • kurtosis – Return the kurtosis of the resulting distribution
Returns:

mean, variance, [skewness, [kurtosis]]