def cal_model_performance(obsl, siml):
"""Calculate model performance indexes."""
nse = MathClass.nashcoef(obsl, siml)
r2 = MathClass.rsquare(obsl, siml)
rmse = MathClass.rmse(obsl, siml)
pbias = MathClass.pbias(obsl, siml)
rsr = MathClass.rsr(obsl, siml)
print('NSE: %.2f, R$^2$: %.2f, PBIAS: %.2f%%, RMSE: %.2f, RSR: %.2f' %
(nse, r2, pbias, rmse, rsr))
def cal_model_performance(obsl, siml):
"""Calculate model performance indexes."""
nse = MathClass.nashcoef(obsl, siml)
r2 = MathClass.rsquare(obsl, siml)
rmse = MathClass.rmse(obsl, siml)
pbias = MathClass.pbias(obsl, siml)
rsr = MathClass.rsr(obsl, siml)
print('NSE: %.2f, R-square: %.2f, PBIAS: %.2f%%, RMSE: %.2f, RSR: %.2f' %
(nse, r2, pbias, rmse, rsr))
def cal_model_performance(obsl, siml):
nse = MathClass.nashcoef(obsl, siml)
r2 = MathClass.rsquare(obsl, siml)
rmse = MathClass.rmse(obsl, siml)
pbias = MathClass.pbias(obsl, siml)
rsr = MathClass.rsr(obsl, siml)
plt.rcParams['xtick.direction'] = 'out'
plt.rcParams['ytick.direction'] = 'out'
plt.rcParams['font.family'] = 'Times New Roman'
fig, ax = plt.subplots(figsize=(4, 4))
plt.scatter(obsl, siml, marker='.', s=50, color='black')
plt.xlabel('Observation', fontsize=20)
plt.ylabel('Simulation', fontsize=20)
plt.title('\nNSE: %.2f, R$^2$: %.2f, PBIAS: %.2f%%\nRMSE: %.2f, RSR: %.2f' %
(nse, r2, pbias, rmse, rsr), color='red', loc='right')
minv = math.floor(min(min(obsl), min(siml)))
maxv = math.ceil(max(max(obsl), max(siml)))
ax.set_xlim(left=minv, right=maxv)
ax.set_ylim(bottom=minv, top=maxv)
plt.tight_layout()
plt.show()
def calculate_statistics(
sim_obs_dict, # type: Optional[Dict[str, Dict[str, Union[List[datetime], List[float], float]]]]
stime=None, # type: Optional[datetime]
etime=None # type: Optional[datetime]
):
# type: (...) -> Optional[List[str]]
"""Calculate NSE, R-square, RMSE, PBIAS, and RSR.
Args:
sim_obs_dict: {VarName: {'UTCDATETIME': [t1, t2, ..., tn],
'Obs': [o1, o2, ..., on],
'Sim': [s1, s2, ..., sn]
},
...
}
stime: Start time for statistics calculation.
etime: End time for statistics calculation.
Returns:
The dict with the format:
{VarName: {'UTCDATETIME': [t1, t2, ..., tn],
'Obs': [o1, o2, ..., on],
'Sim': [s1, s2, ..., sn]},
'NSE': nse_value,
'R-square': r2_value,
'RMSE': rmse_value,
'PBIAS': pbias_value,
'lnNSE': lnnse_value,
'NSE1': nse1_value,
'NSE3': nse3_value
},
...
}
Return name list of the calculated statistics
"""
if not sim_obs_dict:
return None
for param, values in sim_obs_dict.items():
if stime is None and etime is None:
sidx = 0
eidx = len(values['UTCDATETIME'])
else:
sidx = bisect.bisect_left(values['UTCDATETIME'], stime)
eidx = bisect.bisect_right(values['UTCDATETIME'], etime)
obsl = values['Obs'][sidx:eidx]
siml = values['Sim'][sidx:eidx]
nse_value = MathClass.nashcoef(obsl, siml)
r2_value = MathClass.rsquare(obsl, siml)
rmse_value = MathClass.rmse(obsl, siml)
pbias_value = MathClass.pbias(obsl, siml)
rsr_value = MathClass.rsr(obsl, siml)
lnnse_value = MathClass.nashcoef(obsl, siml, log=True)
nse1_value = MathClass.nashcoef(obsl, siml, expon=1)
nse3_value = MathClass.nashcoef(obsl, siml, expon=3)
values['NSE'] = nse_value
values['R-square'] = r2_value
values['RMSE'] = rmse_value
values['PBIAS'] = pbias_value
values['RSR'] = rsr_value
values['lnNSE'] = lnnse_value
values['NSE1'] = nse1_value
values['NSE3'] = nse3_value
# print('Statistics for %s, NSE: %.3f, R2: %.3f, RMSE: %.3f, PBIAS: %.3f, RSR: %.3f,'
# ' lnNSE: %.3f, NSE1: %.3f, NSE3: %.3f' %
# (param, nse_value, r2_value, rmse_value, pbias_value, rsr_value,
# lnnse_value, nse1_value, nse3_value))
return ['NSE', 'R-square', 'RMSE', 'PBIAS', 'RSR', 'lnNSE', 'NSE1', 'NSE3']
