pearson_r¶
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HydroErr.HydroErr.
pearson_r
(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)¶ Compute the pearson correlation coefficient.
Range: -1 ≤ R (Pearson) ≤ 1. 1 indicates perfect postive correlation, 0 indicates complete randomness, -1 indicate perfect negative correlation.
Notes: The pearson r coefficient measures linear correlation. It is sensitive to outliers.
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 Pearson correlation coefficient.
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.pearson_r(sim, obs) 0.9610793632835262
References
- Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240-242.