d1_p

HydroErr.HydroErr.d1_p(simulated_array, observed_array, obs_bar_p=None, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the Legate-McCabe Index of Agreement.

../_images/D1p.png

Range: 0 ≤ d1’ < 1, does not indicate bias, larger is better.

Notes: The obs_bar_p argument represents a seasonal or other selected average.

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.
  • obs_bar_p (float) – Seasonal or other selected average. If None, the mean of the observed array will be used.
  • 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 Legate-McCabe Efficiency index of agreement.
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.d1_p(sim, obs)
0.8434782608695652

References

  • Legates, D.R., McCabe Jr, G.J., 1999. Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation. Water Resources Research 35(1) 233-241. Lehmann, E.L., Casella, G., 1998. Springer Texts in Statistics. Springer-Verlag, New York.