# read observations fname = 'data/B32D0136001_1.csv' obs = ps.read_dino(fname) # Create the time series model ml = ps.Model(obs) # read climate data fname = 'data/KNMI_Bilt.txt' RH = ps.read_knmi(fname, variables='RH') EV24 = ps.read_knmi(fname, variables='EV24') #rech = RH.series - EV24.series # Create stress #sm = ps.Recharge(RH, EV24, ps.Gamma, ps.Linear, name='recharge') #sm = Recharge(RH, EV24, Gamma, Combination, name='recharge') sm = ps.StressModel2([RH, EV24], ps.Gamma, name='recharge') #sm = ps.StressModel(RH, ps.Gamma, name='precip') #sm1 = ps.StressModel(EV24, ps.Gamma, name='evap') ml.add_stressmodel(sm) #ml.add_tseries(sm1) # Add noise model n = ps.NoiseModel() ml.add_noisemodel(n) # Solve ml.solve(freq="7D") ml.plot()
def test_add_noisemodel():
ml = test_create_model()
ml.add_noisemodel(ps.NoiseModel())
return
def update(self, forcing, oseries, selection):
"""
Update the model with newly observed data
Parameters
----------
forcing: Pandas dataframe
Dataframe that holds the newly observed values of the stresses.
NOTE: column names should correspond to the names of the stresses
in the stressmodels of the Pastas model. The index column should consist
of the dates of observations (Timestamps). The first date should succeed
the last date of the oseries & forcing dataset that are currently
included in the Pastas model.
oseries: Pandas dataframe
Dataframe that holds the newly observed values of the oseries. It
should consist of one column containing the oseries.
NOTE: the column name should equal the column name of the oseries
in the Pastas model. The index should consist of the dates of
observation (Timestamps). The first date should succeed the last date
of the oseries & forcing dataset that are currently included in the
Pastas model.
"""
# Update forcing data
forc = forcing.copy()
initial_data = self.model.get_stress(
self.stressmodelnames[0])[:].to_frame()
initial_data.columns = [self.stressmodelnames[0]]
for i in range(len(list(forcing.columns)) - 1):
initial_data[self.stressmodelnames[i + 1]] = self.model.get_stress(
self.stressmodelnames[i + 1])[:]
forc = initial_data.append(forc[:])
# Update oseries
new_oseries = self.model.oseries.series.to_frame().append(oseries[:])
if selection != 'total':
new_oseries = new_oseries[new_oseries.index[len(new_oseries) - 1] -
timedelta(days=selection):]
# Create new model
new_model = ps.Model(new_oseries, name="forecast")
# Add stressmodels
for i in range(len(self.stressmodelnames)):
# The forecast function automaticly infers the response functions from the stressmodels
if self.stressmodeltypes[i] == 'Gamma':
responsefunc = ps.Gamma
elif self.stressmodeltypes[i] == 'Hantush':
responsefunc = ps.Hantush
elif self.stressmodeltypes[i] == 'Exponential':
responsefunc = ps.Exponential
elif self.stressmodeltypes[i] == 'One':
responsefunc = ps.One
elif self.stressmodeltypes[i] == 'FourParam':
responsefunc = ps.FourParam
elif self.stressmodeltypes[i] == 'DoubleExponential':
responsefunc = ps.DoubleExponential
elif self.stressmodeltypes[i] == 'Polder':
responsefunc = ps.Polder
ts = ps.StressModel(forc[self.stressmodelnames[i]],
responsefunc,
name=self.stressmodelnames[i])
# Add the stressmodels to the new model
new_model.add_stressmodel(ts)
if self.model.constant:
c = ps.Constant()
new_model.add_constant(c)
if self.model.noisemodel:
n = ps.NoiseModel()
new_model.add_noisemodel(n)
if self.model.transform:
tt = ps.ThresholdTransform()
new_model.add_transform(tt)
# Solve the new model and initialise the real-time simulation overnew
new_model.solve()
self.__init__(new_model, self.name)