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.
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: 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.