def func1(x):
return metrics.kling_gupta(x,
Obs[oname2].iloc[:, 1],
method='2012')
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
# -- Cost functions
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(
sim = np.genfromtxt(f_in, delimiter=',', skip_header=1)
for j in range(sim.shape[0]):
jcomp = int(sim[j][0])
# 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)
j += 1
continue
tmp = np.genfromtxt(f_in,
delimiter='\t',
skip_header=nts + 3,
unpack=True)[1]
#print i ,j ,len(tmp)
if len(tmp) < lobs:
j += 1
continue
# Crop between desired time frame (based on the length of this time frame)
sim = [tmp[idx] for idx in range(len(tmp) - ltf, len(tmp))]
# Metrics
md_nse = metrics.nash_sutcliff(sim, obs['Streamflow'])
md_kge = metrics.kling_gupta(sim,
obs['Streamflow'],
method='2012')
md_rmse = metrics.rmse(sim, obs['Streamflow'])
md_bias = metrics.bias(sim, obs['Streamflow'])
# Write
f_out.write(','.join([
str(i + 1),
str(j + 1),
str(md_nse),
str(md_kge),
str(md_rmse),
str(md_bias)
]) + '\n')
# Save the 'coordinates' of this sample
iok.append(i)
jok.append(j)