mse

HydroErr.HydroErr.mse(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the mean squared error of the simulated and observed data.

../_images/MSE.png

Range: 0 ≤ MSE < inf, data units squared, smaller is better.

Notes: Random errors do not cancel, highlights larger errors, also referred to as a squared L2-norm.

Parameters:
  • simulated_array (one dimensional ndarray) – An array of simulated data from the time series.
  • observed_array (one dimensional ndarray) – An array of observed data from the time series.
  • replace_nan (float, optional) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • replace_inf (float, optional) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • remove_neg (boolean, optional) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
  • remove_zero (boolean, optional) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
Returns:

The mean squared error value.
Return type:

float

Examples

>>> import HydroErr as he
>>> import numpy as np

>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 6.8])
>>> he.mse(sim, obs)
0.4333333333333333

References

  • Wang, Zhou, and Alan C. Bovik. “Mean Squared Error: Love It or Leave It? A New Look at Signal Fidelity Measures.” IEEE Signal Processing Magazine 26, no. 1 (2009): 98–117.