class StepModel(StressModelBase):
"""Stressmodel that simulates a step trend.
Parameters
----------
tstart: str or Timestamp
String with the start date of the step, e.g. '2018-01-01'. This
value is fixed by default. Use ml.set_vary("step_tstart", 1) to vary
the start time of the step trend.
name: str
String with the name of the stressmodel.
rfunc: pastas.rfunc.RfuncBase, optional
Pastas response function used to simulate the effect of the step.
Default is rfunc.One, an instant effect.
up: bool, optional
Force a direction of the step. Default is None.
Notes
-----
This step trend is calculated as follows. First, a binary series is
created, with zero values before tstart, and ones after the start. This
series is convoluted with the block response to simulate a step trend.
"""
_name = "StepModel"
def __init__(self, tstart, name, rfunc=One, up=True, cutoff=0.999):
rfunc = rfunc(up=up, cutoff=cutoff, meanstress=1.0)
StressModelBase.__init__(self, name=name, tmin=Timestamp.min,
tmax=Timestamp.max, rfunc=rfunc)
self.tstart = Timestamp(tstart)
self.set_init_parameters()
def set_init_parameters(self):
self.parameters = self.rfunc.get_init_parameters(self.name)
tmin = Timestamp.min.toordinal()
tmax = Timestamp.max.toordinal()
tinit = self.tstart.toordinal()
self.parameters.loc[self.name + "_tstart"] = (tinit, tmin, tmax,
False, self.name)
def simulate(self, p, tmin=None, tmax=None, freq=None, dt=1):
tstart = Timestamp.fromordinal(int(p[-1]), freq="D")
tindex = date_range(tmin, tmax, freq=freq)
h = Series(0, tindex, name=self.name)
h.loc[h.index > tstart] = 1
b = self.get_block(p[:-1], dt, tmin, tmax)
npoints = h.index.size
h = Series(data=fftconvolve(h, b, 'full')[:npoints],
index=h.index, name=self.name, fastpath=True)
return h
def to_dict(self, series=True):
data = {
"stressmodel": self._name,
'tstart': self.tstart,
'name': self.name,
"up": self.rfunc.up,
'rfunc': self.rfunc._name
}
return data
