skrf.network.s_error

skrf.network.s_error(ntwkA, ntwkB, error_function='average_l2_norm')

Compute the error between s-parameters of ntwkA and ntwkB.

Parameters:

• ntwkA (Network object) – The first network.

• ntwkB (Network object) – A second network used to compute the error with ntwkA

• error_function (str) – average_l1_norm, average_l2_norm, maximum_l1_norm, or average_normalized_l1_norm.

Returns:

• error (numpy.ndarray) – The error between ntwkA and ntwkB

• Description

• ———–

• Average L1 Norm. The weighted difference is the average magnitude of the difference between each

• element of the S-parameter matrix.

• .. math:: – delta = frac{1}{N^2} sum_{i=1}^{N} sum_{j=1}^{N} |S_{ij}^A - S_{ij}^B|

• Average L2 Norm. The weighted difference is the average squared magnitude of the difference

• between each element of the S-parameter matrix.

• .. math:: – delta = frac{1}{N^2} sum_{i=1}^{N} sum_{j=1}^{N} |S_{ij}^A - S_{ij}^B|^2

• Maximum L1 Norm. The maximum difference is the magnitude of the maximum difference between each

• element of the S-parameter matrix (the magnitude of the largest difference between any pair of

• entries in the S-parameter matrices).

• .. math:: – delta = max(|S_{ij}^A - S_{ij}^B|)

• Average Normalized L1 Norm. The magnitude of the difference between each element of the

• S-parameter matrix is calculated. Each difference is then normalized by the average magnitude of

• the two matrix elements (one from each set).

• .. math:: – delta = frac{2}{N^2} sum_{i=1}^{N} sum_{j=1}^{N} frac{|S_{ij}^A - S_{ij}^B|}{|S_{ij}^A| + |S_{ij}^B|}

• To express these in decibels, use

• .. math:: – 20log_{10} delta

Return type:

ndarray