def extractTimeSeries(self,variable,lats=None,lons=None,alt_vars=[],initial_time=-1e20,final_time=1e20,output_unit="",expression=None):
"""Extracts a time series of the given variable from the model.
Parameters
----------
variable : str
name of the variable to extract
alt_vars : list of str, optional
alternate variables to search for if *variable* is not found
initial_time : float, optional
include model results occurring after this time
final_time : float, optional
include model results occurring before this time
output_unit : str, optional
if specified, will try to convert the units of the variable
extract to these units given.
lats : numpy.ndarray, optional
a 1D array of latitude locations at which to extract information
lons : numpy.ndarray, optional
a 1D array of longitude locations at which to extract information
expression : str, optional
an algebraic expression describing how to combine model outputs
Returns
-------
var : ILAMB.Variable.Variable
the extracted variable
"""
# prepend the target variable to the list of possible variables
altvars = list(alt_vars)
altvars.insert(0,variable)
# checks on input consistency
if lats is not None: assert lons is not None
if lons is not None: assert lats is not None
if lats is not None: assert lats.shape == lons.shape
# create a list of datafiles which have a non-null intersection
# over the desired time range
V = []
tmin = 1e20
tmax = -1e20
for v in altvars:
if not self.variables.has_key(v): continue
for pathName in self.variables[v]:
var = Variable(filename = pathName,
variable_name = variable,
alternate_vars = altvars[1:],
area = self.land_areas,
t0 = initial_time - self.shift,
tf = final_time - self.shift)
tmin = min(tmin,var.time_bnds.min())
tmax = max(tmax,var.time_bnds.max())
if ((var.time_bnds.max() < initial_time - self.shift) or
(var.time_bnds.min() > final_time - self.shift)): continue
if lats is not None and var.ndata:
r = np.sqrt((lats[:,np.newaxis]-var.lat)**2 +
(lons[:,np.newaxis]-var.lon)**2)
imin = r.argmin(axis=1)
rmin = r. min(axis=1)
imin = imin[np.where(rmin<1.0)]
if imin.size == 0:
logger.debug("[%s] Could not find [%s] at the input sites in the model results" % (self.name,",".join(altvars)))
raise il.VarNotInModel()
var.lat = var.lat [ imin]
var.lon = var.lon [ imin]
var.data = var.data[:,imin]
var.ndata = var.data.shape[1]
if lats is not None and var.spatial: var = var.extractDatasites(lats,lons)
var.time += self.shift
var.time_bnds += self.shift
V.append(var)
if len(V) > 0: break
# If we didn't find any files, try to put together the
# variable from a given expression
if len(V) == 0:
if expression is not None:
v = self.derivedVariable(variable,
expression,
lats = lats,
lons = lons,
initial_time = initial_time,
final_time = final_time)
else:
tstr = ""
if tmin < tmax: tstr = " in the given time frame, tinput = [%.1f,%.1f], tmodel = [%.1f,%.1f]" % (initial_time,final_time,tmin+self.shift,tmax+self.shift)
logger.debug("[%s] Could not find [%s] in the model results%s" % (self.name,",".join(altvars),tstr))
raise il.VarNotInModel()
else:
v = il.CombineVariables(V)
return v
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