def errfuncFIT(pars, qmeas, mimo, caseName, runId):
q = mimo.run(pars)
res = q - qmeas
# only resturn non-nan values
resnonan = res[~np.isnan(res)]
mimo.log.log(45, "Parameters now: " + str(pars))
pylab.plot(q)
mimo.NS.append(
stats.get_nash_sutcliffe(
qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan
)
)
mimo.BIAS.append(
stats.get_bias(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan)
)
mimo.CORR.append(
stats.get_correlation(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan)
)
mimo.MABSE.append(
stats.get_mean_absolute_error(
qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan
)
)
mimo.log.log(45, "NS: " + str(mimo.NS[-1]))
mimo.log.log(45, "BIAS: " + str(mimo.BIAS[-1]))
mimo.log.log(45, "CORR: " + str(mimo.CORR[-1]))
mimo.log.log(45, "MABSE: " + str(mimo.MABSE[-1]))
pylab.savefig(os.path.join(caseName, runId, str(mimo.ColSim) + "fit.png"))
return resnonan
def errfuncFIT(pars, qmeas, mimo, caseName, runId):
q = mimo.run(pars)
res = q - qmeas
# only resturn non-nan values
resnonan = res[~np.isnan(res)]
mimo.log.log(45, "Parameters now: " + str(pars))
pylab.plot(q)
mimo.NS.append(
stats.get_nash_sutcliffe(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.BIAS.append(
stats.get_bias(qmeas[~np.isnan(res)], q[~np.isnan(res)],
NoData=np.nan))
mimo.CORR.append(
stats.get_correlation(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.MABSE.append(
stats.get_mean_absolute_error(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.log.log(45, "NS: " + str(mimo.NS[-1]))
mimo.log.log(45, "BIAS: " + str(mimo.BIAS[-1]))
mimo.log.log(45, "CORR: " + str(mimo.CORR[-1]))
mimo.log.log(45, "MABSE: " + str(mimo.MABSE[-1]))
pylab.savefig(os.path.join(caseName, runId, str(mimo.ColSim) + "fit.png"))
return resnonan
def bruteforce(mimo, qmeas, caseName, runId):
"""
"""
nowpars = mimo.pars
print nowpars
i = 0
results = []
execstr = "mimo.range[mimo.calibpars[" + str(i) + "]]"
print "========="
for i in range(0, len(mimo.calibpars)):
if i > 0:
execstr = execstr + ", mimo.range[mimo.calibpars[" + str(i) + "]]"
print execstr
for parmult in mimo.range[mimo.calibpars[i]]:
nowpars[i] = parmult
exec "combi = itertools.product(" + execstr + ")"
while 1:
try:
thisparset = combi.next()
print "Parameters:" + str(thisparset)
q = mimo.run(thisparset)
res = q - qmeas
# only resturn non-nan values
resnonan = res[~np.isnan(res)]
mimo.log.log(45, "Parameters now: " + str(thisparset))
pylab.plot(q)
mimo.NS.append(stats.get_nash_sutcliffe(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan))
mimo.BIAS.append(stats.get_bias(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan))
mimo.CORR.append(stats.get_correlation(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan))
mimo.MABSE.append(stats.get_mean_absolute_error(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan))
mimo.log.log(45, "NS: " + str(mimo.NS[-1]))
mimo.log.log(45, "BIAS: " + str(mimo.BIAS[-1]))
mimo.log.log(45, "CORR: " + str(mimo.CORR[-1]))
mimo.log.log(45, "MABSE: " + str(mimo.MABSE[-1]))
results.append(
[
thisparset,
stats.get_nash_sutcliffe(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan),
stats.get_bias(qmeas[~np.isnan(res)], q[~np.isnan(res)], NoData=np.nan),
]
)
pylab.savefig(os.path.join(caseName, runId, str(mimo.ColSim) + "fit.png"))
# zz = errfuncFIT(thisparset,qmeas,mimo,caseName,runId)
except:
print "Unexpected error:", sys.exc_info()[0]
break
return results
def bruteforce(mimo, qmeas, caseName, runId):
"""
"""
nowpars = mimo.pars
print(nowpars)
i = 0
results = []
execstr = "mimo.range[mimo.calibpars[" + str(i) + "]]"
print("=========")
for i in range(0, len(mimo.calibpars)):
if i > 0:
execstr = execstr + ", mimo.range[mimo.calibpars[" + str(i) + "]]"
print(execstr)
for parmult in mimo.range[mimo.calibpars[i]]:
nowpars[i] = parmult
exec("combi = itertools.product(" + execstr + ")")
while 1:
try:
thisparset = next(combi)
print("Parameters:" + str(thisparset))
q = mimo.run(thisparset)
res = q - qmeas
# only resturn non-nan values
resnonan = res[~np.isnan(res)]
mimo.log.log(45, "Parameters now: " + str(thisparset))
pylab.plot(q)
mimo.NS.append(
stats.get_nash_sutcliffe(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.BIAS.append(
stats.get_bias(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.CORR.append(
stats.get_correlation(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.MABSE.append(
stats.get_mean_absolute_error(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan))
mimo.log.log(45, "NS: " + str(mimo.NS[-1]))
mimo.log.log(45, "BIAS: " + str(mimo.BIAS[-1]))
mimo.log.log(45, "CORR: " + str(mimo.CORR[-1]))
mimo.log.log(45, "MABSE: " + str(mimo.MABSE[-1]))
results.append([
thisparset,
stats.get_nash_sutcliffe(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan),
stats.get_bias(qmeas[~np.isnan(res)],
q[~np.isnan(res)],
NoData=np.nan),
])
pylab.savefig(
os.path.join(caseName, runId,
str(mimo.ColSim) + "fit.png"))
# zz = errfuncFIT(thisparset,qmeas,mimo,caseName,runId)
except:
print("Unexpected error:", sys.exc_info()[0])
break
return results