Compute statistics for observed and modeled time-series relevant to hydrological modeling

get_stats.py

import scipy.stats as stats
import numpy as np

def get_stats(obs, mod):
    corr, corr_p = stats.pearsonr(obs, mod)
    nse = 1 - (np.sum((obs - mod)**2)/np.sum((obs - np.mean(obs))**2))
    nse1 = 1 - (np.sum(np.abs(obs - mod))/np.sum(np.abs(obs - np.mean(obs))))
    rmse = np.sqrt(np.sum((obs-mod)**2)/len(mod))
    mae = np.sum(np.abs(mod-obs))/len(mod)

    return {'pearson-r': corr, 'pearson-r p-val': corr_p, 'nse': nse, 'nse1': nse1, 'rmse': rmse, 'mae': mae}