def _generate_error_statistics(self):
self._format_swe_data()
self._format_sf_data()
run_error = pd.read_csv(os.path.join(self.err_dir, 'run_error.csv'),
index_col=0)
run_error.columns = run_error.columns.astype(str)
swe_error = pd.read_csv(os.path.join(self.err_dir, 'swe_error.csv'),
index_col=0)
swe_error.columns = swe_error.columns.astype(str)
run_dict = {'run_number': self.run_number}
swe_dict = {'run_number': self.run_number}
for col in self.model_run:
run_dict[str(col)] = kge(self.real_run[col].values,
self.model_run[col].values)
for col in self.model_swe:
swe_dict[str(col)] = kge(self.real_swe[col].values,
self.model_swe[col].values)
run_error = run_error.append(pd.DataFrame(run_dict, index=[1]),
sort=True,
ignore_index=True)
run_error.columns = run_error.columns.astype(str)
swe_error = swe_error.append(pd.DataFrame(swe_dict, index=[1]),
sort=True,
ignore_index=True)
swe_error.columns = swe_error.columns.astype(str)
run_error.to_csv(os.path.join(self.err_dir, 'run_error.csv'))
swe_error.to_csv(os.path.join(self.err_dir, 'swe_error.csv'))
def test_kge_return_all(self):
expected = (-0.92083174734809159, -0.1105109772757096, 0.95721520413458061, -0.56669379018786747)
res = of.kge(self.evaluation, self.simulation, return_all=True)
for exp, actual in zip(expected, res):
self.assertAlmostEqual(actual, exp, self.tolerance)
return {'obs': pend_obs, 'sim': pend_sim, 'correcta': (pend_obs > 0) is (pend_sim > 0)}
algs_spotpy = {
'fast': spotpy.algorithms.fast,
'dream': spotpy.algorithms.dream,
'cm': spotpy.algorithms.mc,
'cmmc': spotpy.algorithms.mcmc,
'epm': spotpy.algorithms.mle,
'mhl': spotpy.algorithms.lhs,
'as': spotpy.algorithms.sa,
'sceua': spotpy.algorithms.sceua,
'erop': spotpy.algorithms.rope,
'caa': spotpy.algorithms.abc,
'fscabc': spotpy.algorithms.fscabc,
'bdd': spotpy.algorithms.dds
}
eval_funcs = {
'ens': lambda o, s, f: spt_f.nashsutcliffe(o, s),
'rcep': lambda o, s, f: -spt_f.rmse(o, s),
'corresp': lambda o, s, f: spt_f.agreementindex(o, s),
'ekg': lambda o, s, f: spt_f.kge(o, s),
'r2': lambda o, s, f: spt_f.rsquared(o, s),
'rcnep': lambda o, s, f: -spt_f.rrmse(o, s),
'log p': lambda o, s, f: spt_f.log_p(o, s),
'verosimil_gaus': lambda o, s, f: spt_l.gaussianLikelihoodMeasErrorOut(o, s),
'tendencia': _anlz_tendencia
}
def test_kge(self):
res = of.kge(self.evaluation, self.simulation)
self.assertAlmostEqual(res, -0.92083174734809159, self.tolerance)