def tslice(self, x, t, tref):
tcommon = intersect1d(t, tref)
nt = len(tcommon)
xs = masked_array(zeros(nt), mask=ones(nt))
for i in range(nt):
tidx = where(t == tcommon[i])[0][0]
xs[i] = x[tidx]
return xs
def tslice(self, x, t, tref):
tcommon = intersect1d(t, tref)
nt = len(tcommon)
xs = masked_array(zeros(nt), mask = ones(nt))
for i in range(nt):
tidx = where(t == tcommon[i])[0][0]
xs[i] = x[tidx]
return xs
def metric(self, sim, obs, time, obsbase=None):
st = time[sim < sim.mean() - sim.std()]
ot = time[obs < obs.mean() - obs.std()]
tc = intersect1d(ot, st)
sim_extreme = self.tslice(sim, time, tc)
obs_extreme = self.tslice(obs, time, tc)
if sim_extreme.size:
return (obs_extreme - sim_extreme).mean()
else:
return masked
def metric(self, sim, obs, time, obsbase = None):
st = time[sim < sim.mean() - sim.std()]
ot = time[obs < obs.mean() - obs.std()]
tc = intersect1d(ot, st)
sim_extreme = self.tslice(sim, time, tc)
obs_extreme = self.tslice(obs, time, tc)
if sim_extreme.size:
return (obs_extreme - sim_extreme).mean()
else:
return masked
def metric(self, sim, obs, time, obsbase=None):
st = time[sim < sim.mean() - sim.std()]
ot = time[obs < obs.mean() - obs.std()]
tc = intersect1d(ot, st)
return double(tc.size) / ot.size if ot.size else masked
def metric(self, sim, obs, time, obsbase = None):
st = time[sim < sim.mean() - sim.std()]
ot = time[obs < obs.mean() - obs.std()]
tc = intersect1d(ot, st)
return double(tc.size) / ot.size if ot.size else masked
