h6_rmshe

HydroErr.HydroErr.h6_rmshe(simulated_array, observed_array, k=1, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the H6 root mean square error.

../_images/H6.png ../_images/RMSHE.png

Range:

Notes:

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.
  • k (int or float) – If given, sets the value of k. If None, k=1.
  • 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 root mean square H6 error.
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, 7])
>>> he.h6_rmshe(sim, obs)
0.13147667616722278

References

  • Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured? The American Statistician 43-46.