def func4(x):
return metrics.corr(np.asarray(x), Obs[oname2].iloc[:, 1])
# Get sim outpts and apply conversion factor !!
tmp = [
simfct[iobs] * sim[j][x] for x in range(1, simlen + 1)
]
# Crop between desired time frame
sim2 = [
tmp[idx] for idx in range(simlen)
if simdate[idx] >= fitbeg[iobs]
and simdate[idx] <= fitend[iobs]
]
# Metrics
md_nse = metrics.nash_sutcliff(sim2, obs)
md_kge = metrics.kling_gupta(sim2, obs, method='2012')
md_rmse = metrics.rmse(sim2, obs)
#md_bias = metrics.bias(sim2,obs)
md_corr = metrics.corr(sim2, obs)
md_rstd = metrics.rstd(sim2, obs)
# Write
f_out.write(','.join([
str(i + 1),
str(jcomp),
str(md_nse),
str(md_kge),
str(md_rmse),
str(md_corr),
str(md_rstd)
]) + '\n')
# Save the 'coordinates' of this sample (only the first time)
if iobs == 0:
iok.append(i)
jok.append(jcomp)
# return UnivariateSpline(d,y,s=0).integral(d1,d2)/(d2-d1)
return splint(d1, d2,
splrep(d, y, k=min(len(d) - 1, 3),
s=0)) / (d2 - d1)
# return sp.integrate.quad(interp1d.splrep(d,y,k=len(d)-1,s=0),d1,d2)[0]/(d2-d1)
obs = Obs[oname2].iloc[:, 1:].apply(func, axis=1)
#print len(obs)
KGE.loc[js2[j], oname] = metrics.kling_gupta(sim,
obs,
method='2012')
MAE.loc[js2[j], oname] = metrics.meanabs(sim, obs)
RMSE.loc[js2[j], oname] = metrics.rmse(sim, obs)
corr.loc[js2[j], oname] = metrics.corr(sim, obs)
if oname == outnames[0]:
itot += nj
# Clean the metrics dataframe to include only the successful runs common to all obs
# Use MAE or RMSE, because KGE and corr can have NaN only for 'flat' succesful runs
MAE.dropna(inplace=True)
RMSE.dropna(inplace=True)
js3 = MAE.index
KGE = KGE.ix[js3]
corr = corr.ix[js3]
df_par = df_par.loc[:, js3]
KGEcorr[obsnames[iobs]] = [0] * nj
KGEgamma[obsnames[iobs]] = [0] * nj
KGEbeta[obsnames[iobs]] = [0] * nj
for j in range(1, nj + 1):
# -- Cost functions
tmp2 = tmp[j - 1] * simfct[iobs]
# Crop between desired time frame
sim = [
tmp2[idx + 1] for idx in range(lsim)
if simt[idx] >= fitbeg[iobs] and simt[idx] <= fitend[iobs]
]
# Increment cost function
KGEcorr[obsnames[iobs]][j - 1] = metrics.corr(
sim, obs[obsnames[iobs]])
KGEbeta[obsnames[iobs]][j - 1] = metrics.rmu(
sim, obs[obsnames[iobs]])
KGEgamma[obsnames[iobs]][j - 1] = metrics.rstd(
sim, obs[obsnames[iobs]]) / KGEbeta[obsnames[iobs]][j - 1]
KGEmod[obsnames[iobs]][j - 1] = 1 - np.sqrt(
(1 - KGEcorr[obsnames[iobs]][j - 1])**2 +
(1 - KGEgamma[obsnames[iobs]][j - 1])**2 +
(1 - KGEbeta[obsnames[iobs]][j - 1])**2)
# A few prints
if j == 1:
print obsnames[iobs]
print np.mean(sim), np.mean(
np.ma.masked_array(
obs[obsnames[iobs]],
