def get_time_indices(self,trange):
"""
Find the time indices
"""
if not self.multifile:
self.time = gettime(self._nc,self.timecoord)
else:
if self.MF==None:
self.MF = MFncdap(self._ncfiles,timevar=self.timecoord)
self.time = self.MF.time
# Time
if trange==None:
self.t1=0
self.t2 = self.time.shape[0]
else:
self.t1 = othertime.findNearest(trange[0],self.time)
self.t2 = othertime.findNearest(trange[1],self.time)
if self.t1==self.t2:
self.t2+=1
self.nt = self.t2 - self.t1
print trange
def get_time_indices(self,trange):
"""
Find the time indices
"""
if not self.multifile:
self.time = gettime(self._nc,self.timecoord)
else:
if self.MF==None:
self.MF = MFncdap(self._ncfiles,timevar=self.timecoord)
self.time = self.MF.time
# Time
if trange==None:
self.t1=0
self.t2 = self.time.shape[0]
else:
self.t1 = othertime.findNearest(trange[0],self.time)
self.t2 = othertime.findNearest(trange[1],self.time)
if self.t1==self.t2:
self.t2+=1
self.nt = self.t2 - self.t1
print(trange)
def __init__(self,tmod,ymod,tobs,yobs, interpmodel=True, **kwargs):
"""
Inputs:
tmod,tobs - vector of datetime object
ymod,yobs - vector of values
interpmodel - [default: True] interp the model onto the observations
Keywords:
long_name: string containing variable's name (used for plotting)
units: string containing variable's units (used for plotting)
Note that tmod and tobs don't need to be the same length. yobs is
linearly interpolated onto tmod.
"""
self.__dict__.update(**kwargs)
# Set the range inclusive of both observation and model result
if isinstance(tmod,list):
time0 = max(tmod[0],tobs[0])
time1 = min(tmod[-1],tobs[-1])
elif isinstance(tmod[0], np.datetime64):
time0 = max(tmod[0],tobs[0])
time1 = min(tmod[-1],tobs[-1])
if time1 < time0:
print 'Error - the two datasets have no overlapping period.'
return None
# Clip both the model and observation to this daterange
t0m = othertime.findNearest(time0,tmod)
t1m = othertime.findNearest(time1,tmod)
TSmod = timeseries(tmod[t0m:t1m],ymod[...,t0m:t1m], **kwargs)
t0 = othertime.findNearest(time0,tobs)
t1 = othertime.findNearest(time1,tobs)
TSobs = timeseries(tobs[t0:t1],yobs[...,t0:t1], **kwargs)
# Interpolate the observed value onto the model step
#tobs_i, yobs_i = TSobs.interp(tmod[t0:t1],axis=0)
#self.TSobs = timeseries(tobs_i, yobs_i)
# Interpolate the modeled value onto the observation time step
if interpmodel:
tmod_i, ymod_i = TSmod.interp(tobs[t0:t1],axis=-1,method='nearestmask')
#self.TSmod = timeseries(tmod_i,ymod_i, **kwargs)
self.TSmod = timeseries(tobs[t0:t1], ymod_i, **kwargs)
self.TSobs = TSobs
else:
tobs_i, yobs_i = TSobs.interp(tmod[t0m:t1m],axis=-1,method='nearestmask')
#self.TSobs = timeseries(tobs_i,yobs_i, **kwargs)
self.TSobs = timeseries(tmod[t0m:t1m], yobs_i, **kwargs)
self.TSmod = TSmod
self.N = self.TSmod.t.shape[0]
if self.N==0:
print 'Error - zero model points detected'
return None
# Compute the error
self.error = self.TSmod.y - self.TSobs.y
self.calcStats()
# Calculate the data limits
self._calc_data_lims()
def __init__(self, tmod, ymod, tobs, yobs, interpmodel=True, **kwargs):
"""
Inputs:
tmod,tobs - vector of datetime object
ymod,yobs - vector of values
interpmodel - [default: True] interp the model onto the observations
Keywords:
long_name: string containing variable's name (used for plotting)
units: string containing variable's units (used for plotting)
Note that tmod and tobs don't need to be the same length. yobs is
linearly interpolated onto tmod.
"""
self.__dict__.update(**kwargs)
# Set the range inclusive of both observation and model result
#if isinstance(tmod,list):
# time0 = max(tmod[0],tobs[0])
# time1 = min(tmod[-1],tobs[-1])
#elif isinstance(tmod[0], np.datetime64):
# time0 = max(tmod[0],tobs[0])
# time1 = min(tmod[-1],tobs[-1])
time0 = max(tmod[0], tobs[0])
time1 = min(tmod[-1], tobs[-1])
if time1 < time0:
print('Error - the two datasets have no overlapping period.')
return None
if not (tmod.shape[0] == tobs.shape[0]) and\
not (tmod[0] == tobs[0]) and not (tmod[-1] == tobs[-1]) :
# Clip both the model and observation to this daterange
t0m = othertime.findNearest(time0, tmod)
t1m = othertime.findNearest(time1, tmod)
TSmod = timeseries(tmod[t0m:t1m], ymod[..., t0m:t1m], **kwargs)
t0 = othertime.findNearest(time0, tobs)
t1 = othertime.findNearest(time1, tobs)
TSobs = timeseries(tobs[t0:t1], yobs[..., t0:t1], **kwargs)
# Interpolate the observed value onto the model step
#tobs_i, yobs_i = TSobs.interp(tmod[t0:t1],axis=0)
#self.TSobs = timeseries(tobs_i, yobs_i)
## Don't interpolate if datasets are the same
#if np.all(tobs==tmod):
# self.TSobs = TSobs
# self.TSmod = TSmod
# Interpolate the modeled value onto the observation time step
if interpmodel:
tmod_i, ymod_i = TSmod.interp(tobs[t0:t1],
axis=-1,
method='nearestmask')
#self.TSmod = timeseries(tmod_i,ymod_i, **kwargs)
self.TSmod = timeseries(tobs[t0:t1], ymod_i, **kwargs)
self.TSobs = TSobs
else:
tobs_i, yobs_i = TSobs.interp(tmod[t0m:t1m],
axis=-1,
method='nearestmask')
#self.TSobs = timeseries(tobs_i,yobs_i, **kwargs)
self.TSobs = timeseries(tmod[t0m:t1m], yobs_i, **kwargs)
self.TSmod = TSmod
else:
self.TSmod = timeseries(tmod, ymod, **kwargs)
self.TSobs = timeseries(tobs, yobs, **kwargs)
### Check the dimension sizes
#print self.TSmod.y.shape, self.TSobs.y.shape
#print self.TSmod.t.shape[0], self.TSobs.t.shape[0]
assert self.TSmod.t.shape[0] == self.TSobs.t.shape[0],\
'Number of time records not equal'
assert self.TSmod.y.shape == self.TSobs.y.shape,\
'Dimensions sizes not equal'
self.N = self.TSmod.t.shape[0]
if self.N == 0:
print('Error - zero model points detected')
return None
# Compute the error
self.error = self.TSmod.y - self.TSobs.y
self.calcStats()
# Calculate the data limits
self._calc_data_lims()
