def stageData(self, m):
r"""Extracts model data which is comparable to the observations.
The observational data measure the anomaly in terrestrial
water storage, 'twsa' in terms of [kg m-2]. We convert this
unit to [cm] using the density of water. The models are
expected to provide the terrestrial water storage variable,
'tws', and we need to find the anomaly. First, the model
result is trimmed to match the temporal extents of the
observational data. Then, to get the anomaly, we subtract off
the temporal mean,
.. math:: \mathit{twsa}(t,\mathbf{x}) = tws(t,\mathbf{x}) - \frac{1}{t_f-t_0}\int_{t_0}^{t_f} tws(t,\mathbf{x})\ dt
We do this for the model 'tws' variable, and optionally for
the observation, treating its 'twsa' variable as 'tws' in the
above expression. This is because the observational data can
have a small mean even though it represents only the anomaly.
Parameters
----------
m : ILAMB.ModelResult.ModelResult
the model result context
Returns
-------
obs : ILAMB.Variable.Variable
the variable context associated with the observational dataset
mod : ILAMB.Variable.Variable
the variable context associated with the model result
"""
# get the observational data
obs = Variable(filename=self.source,
variable_name=self.variable,
alternate_vars=self.alternate_vars).convert("cm")
# get the model data, in the units of the obseravtions
mod = m.extractTimeSeries(
self.variable, alt_vars=self.alternate_vars).convert(obs.unit)
obs, mod = il.MakeComparable(obs, mod, clip_ref=True)
# subtract off the mean
mean = obs.integrateInTime(mean=True)
obs.data -= mean.data
mean = mod.integrateInTime(mean=True)
mod.data -= mean.data
return obs, mod
def stageData(self,m):
r"""Extracts model data which is comparable to the observations.
The observational data measure the anomaly in terrestrial
water storage, 'twsa' in terms of [kg m-2]. We convert this
unit to [cm] using the density of water. The models are
expected to provide the terrestrial water storage variable,
'tws', and we need to find the anomaly. First, the model
result is trimmed to match the temporal extents of the
observational data. Then, to get the anomaly, we subtract off
the temporal mean,
.. math:: \mathit{twsa}(t,\mathbf{x}) = tws(t,\mathbf{x}) - \frac{1}{t_f-t_0}\int_{t_0}^{t_f} tws(t,\mathbf{x})\ dt
We do this for the model 'tws' variable, and optionally for
the observation, treating its 'twsa' variable as 'tws' in the
above expression. This is because the observational data can
have a small mean even though it represents only the anomaly.
Parameters
----------
m : ILAMB.ModelResult.ModelResult
the model result context
Returns
-------
obs : ILAMB.Variable.Variable
the variable context associated with the observational dataset
mod : ILAMB.Variable.Variable
the variable context associated with the model result
"""
# get the observational data
obs = Variable(filename = self.source,
variable_name = self.variable,
alternate_vars = self.alternate_vars).convert("cm")
# get the model data, in the units of the obseravtions
mod = m.extractTimeSeries(self.variable,
alt_vars = self.alternate_vars,
expression = self.derived,
initial_time = obs.time_bnds[ 0,0],
final_time = obs.time_bnds[-1,1])
# if the derived expression is used, then we get a mass flux
# rate and need to accumulate
try:
mod.convert(obs.unit)
except:
mod = mod.accumulateInTime()
mod.name = obs.name
obs,mod = il.MakeComparable(obs,mod,clip_ref=True)
# subtract off the mean
mean = obs.integrateInTime(mean=True)
obs.data -= mean.data
mean = mod.integrateInTime(mean=True)
mod.data -= mean.data
# compute mean values over each basin
odata = np.ma.zeros((obs.time.size,len(self.basins)))
mdata = np.ma.zeros((mod.time.size,len(self.basins)))
for i,basin in enumerate(self.basins):
odata[:,i] = obs.integrateInSpace(region=basin,mean=True).data
mdata[:,i] = mod.integrateInSpace(region=basin,mean=True).data
obs.data = odata; obs.ndata = odata.shape[1]; obs.spatial = False
mod.data = mdata; mod.ndata = mdata.shape[1]; mod.spatial = False
mod.data.mask = obs.data.mask
return obs,mod
