def func(y):
# return sp.integrate.quad(interp1d(d,y,kind='cubic'),d1,d2)/(d2-d1)
# 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]
def func2(x):
return metrics.meanabs(np.asarray(x), Obs[oname2].iloc[:,
1])
tmp2 = tmp[j - 1] * simfct[iobs]
# Crop between desired time frame, and account for potential gaps in the obs
sim = [
tmp2[idx + 1] for idx in range(lsim)
if any(obst[obsnames[iobs]] == simt[idx]) == True
]
#if i==21 and j==1:
# tmp24 = [simt[idx] for idx in range(lsim) if simt[idx] in obst[obsnames[iobs]]]
# print
# print tmp24
# Increment cost function
KGE[obsnames[iobs]][j - 1] = metrics.kling_gupta(
sim, obs[obsnames[iobs]], method='2012')
MAE[obsnames[iobs]][j - 1] = metrics.meanabs(
sim, obs[obsnames[iobs]])
RMSE[obsnames[iobs]][j - 1] = metrics.rmse(
sim, obs[obsnames[iobs]])
# A few prints
#if j==1:
#print obsnames[iobs]
#print np.mean(sim), np.mean(np.ma.masked_array(obs[obsnames[iobs]],np.isnan(obs[obsnames[iobs]]))), len(sim), len(obs[obsnames[iobs]])
#print KGE[obsnames[iobs]][0], MAE[obsnames[iobs]][0], RMSE[obsnames[iobs]][0]
if iobs == 0:
# -- Parameters
tmp3 = [tmp_par[j - 1][idx] for idx in range(1, npar + 1)]
with open(
os.getcwd() + '/' + outdir + '/' + MCname +
'_parameters.txt', 'a') as f_out:
