rapid_models.doe

Basic DOE package for rapid-models based on pyDOE2.

Submodules

Package Contents

Functions

fullfact_with_bounds(LBs, UBs, N_xi)

Return a ND array of corresponding (x_0, ..., x_i) values that span out the inputspace

lhs_with_bounds(nDim, nSamples, LBs, UBs, random_state=None)

Return a 2D array of corresponding (x, y) values that fill the input space

rapid_models.doe.fullfact_with_bounds(LBs, UBs, N_xi)[source]

Return a ND array of corresponding (x_0, …, x_i) values that span out the inputspace between lowerbound and upperbound in a structured (grid-like) way with n_x_i points in the i’th-dimension.

Parameters
  • LBs (list-like, 1D) – lower bounds of the input space. len(LBs) must equal len(UBs)

  • UBs (list-like, 1D) – upper bounds of the input space. len(UBs) must equal len(LBs)

  • N_xi (list-like, 1D) – number of equidistant samples for each xi-dimension

Returns

[[x_0,…,x_i],…,[x_0_n,…,x_i_n]]

Return type

fullfact (ndarray)

rapid_models.doe.lhs_with_bounds(nDim, nSamples, LBs, UBs, random_state=None)[source]

Return a 2D array of corresponding (x, y) values that fill the input space between lowerbound and upperbound with n points using a Latin-hypercube design.

Parameters
  • nDim (int) – Number of dimensions

  • nSamples (int) – Number of total samples

  • LBs (list-like) – 1D, lower bounds of the input space. len(LBs) must equal len(UBs)

  • UBs (list-like) – 1D, upper bounds of the input space. len(UBs) must equal len(LBs)

  • random_state (int, RandomState instance or None, default=None) – Determines random number generation used to initialize the samples. Pass an int for reproducible results across multiple function calls.

Returns

Array of sample points with shape (nSamples, nDim)[[x_0,…,x_i],…,[x_0_n,…,x_i_n]]

Return type

lhs (ndarray)