"""This module contains all the stress models available in Pastas.
Stress models are used to translate an input time series into contribution that
explains (part of) the output series.
Examples
--------
>>> sm = ps.StressModel(stress, rfunc=ps.Gamma(), name="sm1")
>>> ml.add_stressmodel(stressmodel=sm)
See Also
--------
pastas.model.Model.add_stressmodel
"""
from inspect import isclass
from logging import getLogger
# Type Hinting
from typing import List, Optional, Tuple, Union
import numpy as np
from packaging.version import parse as parse_version
from pandas import DataFrame, Series, Timedelta, Timestamp, concat, date_range
from pandas import __version__ as pd_version
from scipy.signal import fftconvolve
from pastas.typing import (
ArrayLike,
Model,
Recharge,
RFunc,
StressSettingsDict,
TimestampType,
)
from .decorators import njit, set_parameter
from .recharge import Linear
from .rfunc import Exponential, HantushWellModel, One
from .timeseries import TimeSeries
from .utils import validate_name
pandas_version = parse_version(pd_version)
logger = getLogger(__name__)
__all__ = [
"StressModel",
"Constant",
"StepModel",
"LinearTrend",
"RechargeModel",
"WellModel",
"TarsoModel",
"ChangeModel",
]
[docs]class StressModelBase:
"""StressModel Base class called by each StressModel object.
Attributes
----------
name: str
Name of this stressmodel object. Used as prefix for the parameters.
parameters: pandas.DataFrame
The DataFrame containing the parameters.
"""
_name = "StressModelBase"
[docs] def __init__(
self,
name: str,
tmin: TimestampType,
tmax: TimestampType,
rfunc: Optional[RFunc] = None,
up: bool = True,
gain_scale_factor: float = 1.0,
) -> None:
self.name = validate_name(name)
self.tmin = tmin
self.tmax = tmax
self.freq = None
if rfunc is not None:
if isclass(rfunc):
raise TypeError(
"the rfunc argument must be an instance of response function, not "
"a class. Please provide an instance, e.g., ps.Exponential()"
)
rfunc.update_rfunc_settings(up=up, gain_scale_factor=gain_scale_factor)
self.rfunc = rfunc
self.parameters = DataFrame(
columns=["initial", "pmin", "pmax", "vary", "name", "dist"]
)
self.stress = []
@property
def nparam(self) -> Tuple[int]:
return self.parameters.index.size
[docs] def set_init_parameters(self) -> None:
"""Set the initial parameters (back) to their default values."""
@set_parameter
def _set_initial(self, name: str, value: float) -> None:
"""Internal method to set the initial parameter value.
Notes
-----
The preferred method for parameter setting is through the model.
"""
self.parameters.at[name, "initial"] = value
@set_parameter
def _set_pmin(self, name: str, value: float) -> None:
"""Internal method to set the lower bound of the parameter value.
Notes
-----
The preferred method for parameter setting is through the model.
"""
self.parameters.at[name, "pmin"] = value
@set_parameter
def _set_pmax(self, name: str, value: float) -> None:
"""Internal method to set the upper bound of the parameter value.
Notes
-----
The preferred method for parameter setting is through the model.
"""
self.parameters.at[name, "pmax"] = value
@set_parameter
def _set_vary(self, name: str, value: float) -> None:
"""Internal method to set if the parameter is varied during optimization.
Notes
-----
The preferred method for parameter setting is through the model.
"""
self.parameters.at[name, "vary"] = bool(value)
@set_parameter
def _set_dist(self, name: str, value: str) -> None:
"""Internal method to set distribution of prior of the parameter.
Notes
-----
The preferred method for parameter setting is through the model.
"""
self.parameters.at[name, "dist"] = str(value)
[docs] def update_stress(
self,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
) -> None:
"""Method to update the settings of the all stresses in the stress model.
Parameters
----------
freq: str, optional
String representing the desired frequency of the time series. Must be one
of the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. "7D".
tmin: str or pandas.Timestamp, optional
String that can be converted to, or a Pandas Timestamp with the minimum
time of the series.
tmax: str or pandas.Timestamp, optional
String that can be converted to, or a Pandas Timestamp with the maximum
time of the series.
Notes
-----
For the individual options for the different settings please refer to the
docstring from the TimeSeries.update_series() method.
See Also
--------
ps.timeseries.TimeSeries.update_series
"""
for stress in self.stress:
stress.update_series(freq=freq, tmin=tmin, tmax=tmax)
if freq:
self.freq = freq
[docs] def get_stress(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
istress: Optional[int] = None,
**kwargs,
) -> DataFrame:
"""Returns the stress(es) of the time series object as a pandas DataFrame.
If the time series object has multiple stresses each column represents a stress.
Returns
-------
stress: pandas.Dataframe
Pandas dataframe of the stress(es)
"""
if tmin is None:
tmin = self.tmin
if tmax is None:
tmax = self.tmax
self.update_stress(tmin=tmin, tmax=tmax, freq=freq)
return self.stress[0].series
[docs] def to_dict(self, **kwargs):
"""Method to export the stress model object."""
[docs] def get_nsplit(self) -> int:
"""Determine in how many time series the contribution can be split."""
if hasattr(self, "nsplit"):
return self.nsplit
else:
return len(self.stress)
def _get_block(
self, p: ArrayLike, dt: float, tmin: TimestampType, tmax: TimestampType
) -> ArrayLike:
"""Internal method to get the block-response function."""
if tmin is not None and tmax is not None:
day = Timedelta(1, "D")
maxtmax = (Timestamp(tmax) - Timestamp(tmin)) / day
else:
maxtmax = None
b = self.rfunc.block(p, dt, maxtmax=maxtmax)
return b
[docs] def get_settings(self) -> dict:
"""Method to obtain the settings of the stresses.
Returns
-------
settings: dict
Notes
-----
To update the settings of the time series, use the `update_stress` method.
"""
if len(self.stress) == 0:
settings = None
else:
settings = {stress.name: stress.settings for stress in self.stress}
return settings
[docs] def get_parameters(self, model=None) -> ArrayLike:
"""Get parameters and return as array.
Parameters
----------
model : pastas.Model, optional
If provided, and the model is solved, return optimal model parameter-values.
Otherwise, return initial parameter-values.
istress : int, optional
if provided, return specific parameter set, else return all parameters.
Returns
-------
p : array_like
An array of the parameters of the stressmodel.
"""
if model is None:
p = self.parameters.initial.values
else:
p = model.get_parameters(self.name)
return p
[docs]class StressModel(StressModelBase):
"""Stress model convoluting a stress with a response function.
Parameters
----------
stress: pandas.Series
pandas.Series with pandas.DatetimeIndex containing the stress.
rfunc: pastas.rfunc instance
An instance of the response function used in the convolution with the stress.
name: str
Name of the stress.
up: bool or None, optional
True if response function is positive (default), False if negative. None if
you don't want to define if response is positive or negative.
settings: dict or str, optional
The settings of the stress. This can be a string referring to a predefined
settings dictionary (defined in ps.rcParams["timeseries"]), or a dictionary with
the settings to apply. For more information refer to Time series settings
section below.
metadata: dict, optional
dictionary containing metadata about the stress. This is passed onto the
TimeSeries object.
gain_scale_factor: float, optional
the scale factor is used to set the initial value and the bounds of the gain
parameter, computed as 1 / gain_scale_factor.
Time series settings
--------------------
fill_nan : {"drop", "mean", "interpolate"} or float
Method for filling NaNs.
* `drop`: drop NaNs from time series
* `mean`: fill NaNs with mean value of time series
* `interpolate`: fill NaNs by interpolating between finite values
* `float`: fill NaN with provided value, e.g. 0.0
fill_before : {"mean", "bfill"} or float
Method for extending time series into past.
* `mean`: extend time series into past with mean value of time series
* `bfill`: extend time series into past by back-filling first value
* `float`: extend time series into past with provided value, e.g. 0.0
fill_after : {"mean", "ffill"} or float
Method for extending time series into future.
* `mean`: extend time series into future with mean value of time series
* `ffill`: extend time series into future by forward-filling last value
* `float`: extend time series into future with provided value, e.g. 0.0
sample_up : {"mean", "interpolate", "divide"} or float
Method for up-sampling time series (increasing frequency, e.g. going from weekly
to daily values).
* `bfill` or `backfill`: fill up-sampled time steps by back-filling current
values
* `ffill` or `pad`: fill up-sampled time steps by forward-filling current
values
* `mean`: fill up-sampled time steps with mean of timeseries
* `interpolate`: fill up-sampled time steps by interpolating between current
values
* `divide`: fill up-sampled steps with current value divided by length of
current time steps (i.e. spread value over new time steps).
sample_down : {"mean", "drop", "sum", "min", "max"}
Method for down-sampling time series (decreasing frequency, e.g. going from
daily to weekly values).
* `mean`: resample time series by taking the mean
* `drop`: resample the time series by taking the mean, dropping any
NaN-values
* `sum`: resample time series by summing values
* `max`: resample time series with maximum value
* `min`: resample time series with minimum value
Examples
--------
>>> import pastas as ps
>>> import pandas as pd
>>> sm = ps.StressModel(stress=pd.Series(), rfunc=ps.Gamma(), name="Prec",
>>> settings="prec")
See Also
--------
pastas.rfunc
pastas.timeseries.TimeSeries
"""
_name = "StressModel"
[docs] def __init__(
self,
stress: Series,
rfunc: RFunc,
name: str,
up: bool = True,
settings: Optional[Union[str, StressSettingsDict]] = None,
metadata: Optional[dict] = None,
gain_scale_factor: Optional[float] = None,
) -> None:
stress = TimeSeries(stress, settings=settings, metadata=metadata)
StressModelBase.__init__(
self,
name=name,
tmin=stress.series.index.min(),
tmax=stress.series.index.max(),
rfunc=rfunc,
up=up,
gain_scale_factor=(
stress.series.std() if gain_scale_factor is None else gain_scale_factor
),
)
self.gain_scale_factor = gain_scale_factor
self.freq = stress.settings["freq"]
self.stress.append(stress)
self.set_init_parameters()
[docs] def set_init_parameters(self) -> None:
"""Set the initial parameters (back) to their default values."""
self.parameters = self.rfunc.get_init_parameters(self.name)
[docs] def simulate(
self,
p: ArrayLike,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
) -> Series:
"""Simulates the head contribution.
Parameters
----------
p: array_like
array_like object with the values as floats representing the model
parameters.
tmin: str, optional
tmax: str, optional
freq: str, optional
dt: int, optional
Returns
-------
pandas.Series
The simulated head contribution.
"""
self.update_stress(tmin=tmin, tmax=tmax, freq=freq)
b = self._get_block(p, dt, tmin, tmax)
stress = self.stress[0].series
npoints = stress.index.size
h = Series(
data=fftconvolve(stress, b, "full")[:npoints],
index=stress.index,
name=self.name,
)
return h
[docs] def to_dict(self, series: bool = True) -> dict:
"""Method to export the StressModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the StressModel
object.
Notes
-----
Settings and metadata are exported with the stress.
"""
data = {
"class": self._name,
"rfunc": self.rfunc.to_dict(),
"name": self.name,
"up": self.rfunc.up,
"stress": self.stress[0].to_dict(series=series),
"gain_scale_factor": self.gain_scale_factor,
}
return data
[docs]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_parameter("step_tstart", vary=True) to vary the
start time of the step trend.
name: str
String with the name of the stressmodel.
rfunc: pastas.rfunc instance
Pastas response function used to simulate the effect of the step. Default is
ps.rfunc.One(), an instant effect.
up: bool, optional
Force a direction of the step. Default is None.
Notes
-----
The 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
convolved with the block response to simulate a step trend.
"""
_name = "StepModel"
[docs] def __init__(
self,
tstart: TimestampType,
name: str,
rfunc: Optional[RFunc] = None,
up: bool = None,
) -> None:
if rfunc is None:
rfunc = One()
StressModelBase.__init__(
self,
name=name,
tmin=Timestamp.min,
tmax=Timestamp.max,
rfunc=rfunc,
up=up,
)
self.tstart = Timestamp(tstart)
self.set_init_parameters()
[docs] def set_init_parameters(self) -> None:
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,
"uniform",
)
[docs] def simulate(
self,
p: ArrayLike,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
) -> Series:
tstart = Timestamp.fromordinal(int(p[-1]))
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,
)
return h
[docs] def to_dict(self, **kwargs) -> dict:
"""Method to export the StepModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct object.
"""
data = {
"class": self._name,
"tstart": self.tstart,
"name": self.name,
"rfunc": self.rfunc.to_dict(),
"up": self.rfunc.up,
}
return data
[docs]class LinearTrend(StressModelBase):
"""Stressmodel that simulates a linear trend.
Parameters
----------
start: str
String with a date to start the trend (e.g., "2018-01-01"), will be
transformed to an ordinal number internally.
end: str
String with a date to end the trend (e.g., "2018-01-01"), will be transformed
to an ordinal number internally.
name: str, optional
String with the name of the stress model.
Notes
-----
While possible, it is not recommended to vary the parameters for the start and
end time of the linear trend. These parameters are usually hard or even impossible
to estimate from the data.
"""
_name = "LinearTrend"
[docs] def __init__(
self, start: TimestampType, end: TimestampType, name: str = "trend"
) -> None:
StressModelBase.__init__(
self, name=name, tmin=Timestamp.min, tmax=Timestamp.max
)
self.start = start
self.end = end
self.set_init_parameters()
[docs] def set_init_parameters(self) -> None:
"""Set the initial parameters for the stress model."""
start = Timestamp(self.start).toordinal()
end = Timestamp(self.end).toordinal()
tmin = Timestamp.min.toordinal()
tmax = Timestamp.max.toordinal()
self.parameters.loc[self.name + "_a"] = (
0.0,
-np.inf,
np.inf,
True,
self.name,
"uniform",
)
self.parameters.loc[self.name + "_tstart"] = (
start,
tmin,
tmax,
False,
self.name,
"uniform",
)
self.parameters.loc[self.name + "_tend"] = (
end,
tmin,
tmax,
False,
self.name,
"uniform",
)
[docs] def simulate(
self,
p: ArrayLike,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
) -> Series:
"""Simulate the trend."""
tindex = date_range(tmin, tmax, freq=freq)
if p[1] < tindex[0].toordinal():
tmin = tindex[0]
else:
tmin = Timestamp.fromordinal(int(p[1]))
if p[2] >= tindex[-1].toordinal():
tmax = tindex[-1]
else:
tmax = Timestamp.fromordinal(int(p[2]))
trend = tindex.to_series().diff() / Timedelta(1, "D")
trend.loc[:tmin] = 0
trend.loc[tmax:] = 0
trend = trend.cumsum() * p[0]
return trend.rename(self.name)
[docs] def to_dict(self, **kwargs) -> dict:
"""Method to export a dictionary to reconstruct the stressmodel.
Parameters
----------
kwargs
Returns
-------
data: dict
"""
data = {
"class": self._name,
"start": self.start,
"end": self.end,
"name": self.name,
}
return data
[docs]class Constant(StressModelBase):
"""A constant value that is added to the time series model.
Parameters
----------
name: str, optional
Name of the stressmodel.
initial: float, optional
Initial estimate of the parameter value. For example, the minimum of the
observed series.
"""
_name = "Constant"
[docs] def __init__(self, name: str = "constant", initial: float = 0.0) -> None:
StressModelBase.__init__(
self, name=name, tmin=Timestamp.min, tmax=Timestamp.max
)
self.initial = initial
self.set_init_parameters()
[docs] def set_init_parameters(self):
self.parameters.loc[self.name + "_d"] = (
self.initial,
np.nan,
np.nan,
True,
self.name,
"uniform",
)
[docs] @staticmethod
def simulate(p: Optional[float] = None) -> float:
return p
[docs] def to_dict(self, **kwargs):
"""Method to export the StressModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the StressModel
object.
"""
data = {
"class": self._name,
"name": self.name,
"initial": self.initial,
}
return data
[docs]class WellModel(StressModelBase):
"""Convolution of one or more stresses with a single scaled response function.
Parameters
----------
stress: list
list containing the stresses time series.
name: str
name of the stressmodel.
distances: array_like
array_like of distances between the stresses (wells) and the oseries
(monitoring well), must be in the same order as the stresses. This
distance is used to scale the HantushWellModel response function for
each stress.
rfunc: pastas.rfunc instance, optional
this model only works with the HantushWellModel response function, default is
None which will initialize a HantushWellModel response function.
up: bool, optional
whether a positive stress has an increasing or decreasing effect on the model,
by default False, in which case positive stress lowers e.g., the groundwater
level.
settings: str, list of dict, optional
The settings of the stress. By default this is "well". This can be a string
referring to a predefined settings dictionary (defined in
ps.rcParams["timeseries"]), or a dictionary with the settings to apply. For more
information, refer to Time series settings section below.
sort_wells: bool, optional
sort wells from closest to furthest, by default True.
Time series settings
--------------------
fill_nan : {"drop", "mean", "interpolate"} or float
Method for filling NaNs.
* `drop`: drop NaNs from time series
* `mean`: fill NaNs with mean value of time series
* `interpolate`: fill NaNs by interpolating between finite values
* `float`: fill NaN with provided value, e.g. 0.0
fill_before : {"mean", "bfill"} or float
Method for extending time series into past.
* `mean`: extend time series into past with mean value of time series
* `bfill`: extend time series into past by back-filling first value
* `float`: extend time series into past with provided value, e.g. 0.0
fill_after : {"mean", "ffill"} or float
Method for extending time series into future.
* `mean`: extend time series into future with mean value of time series
* `ffill`: extend time series into future by forward-filling last value
* `float`: extend time series into future with provided value, e.g. 0.0
sample_up : {"mean", "interpolate", "divide"} or float
Method for up-sampling time series (increasing frequency, e.g. going from weekly
to daily values).
* `bfill` or `backfill`: fill up-sampled time steps by back-filling current
values
* `ffill` or `pad`: fill up-sampled time steps by forward-filling current
values
* `mean`: fill up-sampled time steps with mean of timeseries
* `interpolate`: fill up-sampled time steps by interpolating between current
values
* `divide`: fill up-sampled steps with current value divided by length of
current time steps (i.e. spread value over new time steps).
sample_down : {"mean", "drop", "sum", "min", "max"}
Method for down-sampling time series (decreasing frequency, e.g. going from
daily to weekly values).
* `mean`: resample time series by taking the mean
* `drop`: resample the time series by taking the mean, dropping any
NaN-values
* `sum`: resample time series by summing values
* `max`: resample time series with maximum value
* `min`: resample time series with minimum value
Notes
-----
This class implements convolution of multiple series with the same response
function. This can be applied when dealing with multiple wells in a time series
model. The distance(s) from the pumping well(s) to the monitoring well have to be
provided for each stress. See :cite:t:`brakenhoff_application_2022` for more details on the methods for this model.
Only works with the HantushWellModel response function.
"""
_name = "WellModel"
[docs] def __init__(
self,
stress: List[Series],
name: str,
distances: ArrayLike,
rfunc: Optional[RFunc] = None,
up: bool = False,
settings: Union[str, StressSettingsDict] = "well",
sort_wells: bool = True,
metadata: Optional[list] = None,
) -> None:
# check response function
if rfunc is None:
rfunc = HantushWellModel()
elif not isinstance(rfunc, HantushWellModel):
raise NotImplementedError(
"WellModel only supports the rfunc HantushWellModel!"
)
# check if number of stresses and distances match
if len(stress) != len(distances):
msg = (
"The number of stresses does not match the number of distances "
"provided."
)
logger.error(msg)
raise ValueError(msg)
else:
self.distances = Series(
index=[s.squeeze().name for s in stress],
data=distances,
name="distances",
)
# parse settings input
if settings is None or isinstance(settings, str) or isinstance(settings, dict):
settings = len(stress) * [settings]
# if metadata is passed as dict -> convert to list
if metadata is not None and isinstance(metadata, dict):
metadata = [metadata]
# parse stresses input
stress = self._handle_stress(stress, settings, metadata)
# sort wells by distance
self.sort_wells = sort_wells
if self.sort_wells:
stress = [
s for _, s in sorted(zip(distances, stress), key=lambda pair: pair[0])
]
self.distances.sort_values(inplace=True)
# estimate gain_scale_factor w/ max of stresses stdev
gain_scale_factor = np.max([s.series.std() for s in stress])
tmin = np.min([s.series.index.min() for s in stress])
tmax = np.max([s.series.index.max() for s in stress])
StressModelBase.__init__(
self,
name=name,
tmin=tmin,
tmax=tmax,
rfunc=rfunc,
up=up,
gain_scale_factor=gain_scale_factor,
)
self.rfunc.set_distances(self.distances.values)
self.stress = stress
self.freq = self.stress[0].settings["freq"]
self.set_init_parameters()
[docs] def set_init_parameters(self) -> None:
self.parameters = self.rfunc.get_init_parameters(self.name)
[docs] def simulate(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
istress: Optional[int] = None,
**kwargs,
) -> Series:
distances = self.get_distances(istress=istress)
stress_df = self.get_stress(
p=p, tmin=tmin, tmax=tmax, freq=freq, istress=istress, squeeze=False
)
h = Series(data=0, index=self.stress[0].series.index, name=self.name)
for name, r in distances.items():
stress = stress_df.loc[:, name]
npoints = stress.index.size
p_with_r = np.concatenate([p, np.array([r])])
b = self._get_block(p_with_r, dt, tmin, tmax)
c = fftconvolve(stress, b, "full")[:npoints]
h = h.add(Series(c, index=stress.index), fill_value=0.0)
if istress is not None:
if isinstance(istress, list):
h.name = self.name + "_" + "+".join(str(i) for i in istress)
elif self.stress[istress].name is not None:
h.name = self.stress[istress].name
else:
h.name = self.name + "_" + str(istress)
else:
h.name = self.name
return h
@staticmethod
def _handle_stress(stress, settings, metadata):
"""Internal method to handle user provided stress in init.
Parameters
----------
stress: pandas.Series, list or dict
stress or collection of stresses.
settings: dict or iterable
settings dictionary.
metadata : dict or list of dict
metadata dictionaries corresponding to stress
Returns
-------
stress: list
return a list with the stresses transformed to pastas TimeSeries.
"""
data = []
if isinstance(stress, Series):
data.append(TimeSeries(stress, settings=settings, metadata=metadata))
elif isinstance(stress, dict):
for i, (name, value) in enumerate(stress.items()):
if metadata is not None:
imeta = metadata[i]
else:
imeta = None
data.append(
TimeSeries(value, name=name, settings=settings[i], metadata=imeta)
)
elif isinstance(stress, list):
for i, value in enumerate(stress):
if metadata is not None:
imeta = metadata[i]
else:
imeta = None
data.append(TimeSeries(value, settings=settings[i], metadata=imeta))
else:
msg = "Cannot parse 'stress' input. Provide a Series, dict or list."
logger.error(msg)
raise TypeError(msg)
return data
[docs] def get_stress(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
istress: Optional[int] = None,
squeeze: bool = True,
**kwargs,
) -> DataFrame:
if tmin is None:
tmin = self.tmin
if tmax is None:
tmax = self.tmax
self.update_stress(tmin=tmin, tmax=tmax, freq=freq)
if istress is None:
df = DataFrame.from_dict({s.name: s.series for s in self.stress})
if squeeze:
return df.squeeze()
else:
return df
elif isinstance(istress, list):
return DataFrame.from_dict({s.name: s.series for s in self.stress}).iloc[
:, istress
]
else:
if squeeze:
return self.stress[istress].series
else:
return self.stress[istress].series.to_frame()
[docs] def get_distances(self, istress: Optional[int] = None) -> DataFrame:
if istress is None:
return self.distances
elif isinstance(istress, list):
return self.distances.iloc[istress]
else:
return self.distances.iloc[istress : istress + 1]
[docs] def get_parameters(self, model=None, istress: Optional[int] = None) -> ArrayLike:
"""Get parameters including distance to observation point and return as array
(dimensions = (nstresses, 4)).
Parameters
----------
model : pastas.Model, optional
If provided, and the model is solved, return optimal model parameter-values.
Otherwise, return initial parameter-values.
istress : int, optional
if provided, return specific parameter set, else return all parameters.
Returns
-------
p : array_like
parameters for each stress as row of array, if istress is used returns
only one row.
"""
if model is None:
p = self.parameters.initial.values
else:
p = model.get_parameters(self.name)
distances = self.get_distances(istress=istress).values
if distances.size > 1:
p_with_r = np.concatenate(
[np.tile(p, (distances.size, 1)), distances[:, np.newaxis]], axis=1
)
else:
p_with_r = np.r_[p, distances]
return p_with_r
[docs] def dump_stress(self, series: bool = True) -> list:
"""Method to dump all stresses in the stresses list.
Parameters
----------
series: bool, optional
True if time series are to be exported, False if only the name
of the time series are needed. Settings are always exported.
Returns
-------
data: dict
dictionary with the dump of the stresses.
"""
data = []
for stress in self.stress:
stress.name = validate_name(stress.name, raise_error=True)
data.append(stress.to_dict(series=series))
return data
[docs] def to_dict(self, series: bool = True) -> dict:
"""Method to export the WellModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the WellModel
object.
"""
data = {
"class": self._name,
"stress": self.dump_stress(series),
"rfunc": self.rfunc.to_dict(),
"name": self.name,
"distances": self.distances.to_list(),
"up": True if self.rfunc.up else False,
"sort_wells": self.sort_wells,
}
return data
[docs] def variance_gain(
self, model: Model, istress: Optional[int] = None, r: Optional[ArrayLike] = None
) -> float:
"""Calculate variance of the gain for WellModel.
Variance of the gain is calculated based on propagation of uncertainty using
optimal parameter values and the estimated variances of A and b and the
covariance between A and b.
Parameters
----------
model : pastas.Model
optimized model
istress : int or list of int, optional
index of stress(es) for which to calculate variance of gain
r : array_like, optional
radial distance(s) at which to calculate variance of the gain,
only considered if istress is None
Returns
-------
var_gain : float
variance of the gain calculated from model results for parameters A and b.
See Also
--------
pastas.HantushWellModel.variance_gain
"""
if model.solver is None:
raise AttributeError("Model not optimized! Run solve() first!")
if self.rfunc._name != "HantushWellModel":
raise ValueError("Response function must be HantushWellModel!")
if model.solver.pcov.isna().all(axis=None):
model.logger.warning("Covariance matrix contains only NaNs!")
# get parameters and (co)variances
A = model.parameters.at[self.name + "_A", "optimal"]
b = model.parameters.at[self.name + "_b", "optimal"]
var_A = model.solver.pcov.at[self.name + "_A", self.name + "_A"]
var_b = model.solver.pcov.at[self.name + "_b", self.name + "_b"]
cov_Ab = model.solver.pcov.at[self.name + "_A", self.name + "_b"]
if istress is None and r is None:
r = np.asarray(self.distances)
elif isinstance(istress, int) or isinstance(istress, list):
if r is not None:
logger.warning("kwarg 'r' is only used if istress is None!")
r = self.distances.iloc[istress]
elif istress is not None and r is None:
raise ValueError("Parameter 'istress' must be None, list or int!")
return self.rfunc.variance_gain(A, b, var_A, var_b, cov_Ab, r=r)
[docs]class RechargeModel(StressModelBase):
"""Stressmodel simulating the effect of groundwater recharge on the head.
Parameters
----------
prec: pandas.Series
pandas.Series with pandas.DatetimeIndex containing the precipitation series.
The precipitation series should be provided in mm/day when a nonlinear model is
used.
evap: pandas.Series
pandas.Series with pandas.DatetimeIndex containing the potential evaporation
series. The evaporation series should be provided in mm/day when a nonlinear
model is used.
rfunc: pastas.rfunc instance, optional
Instance of the response function used in the convolution with the stress.
Default is ps.Exponential().
name: str, optional
Name of the stress. Default is "recharge".
recharge: pastas.recharge instance, optional
Instance of a recharge model. Options are: Linear, FlexModel and Berendrecht.
These can be accessed through ps.rch. Default is ps.rch.Linear().
temp: pandas.Series, optional
pandas.Series with pandas.DatetimeIndex containing the temperature series.
It depends on the recharge model if this argument is required or not. The
temperature series should be provided in degrees Celsius.
settings: list of dicts or str, optional
The settings of the precipitation, evaporation and optionally temperature time
series, in this order. By default ("prec", "evap", "evap"). This can be a string
referring to a predefined settings dict (defined in ps.rcParams["timeseries"]),
or a dict with the settings to apply. For more information refer to Time Series
Settings section below for more information.
metadata: tuple of dicts or list of dicts, optional
dictionary containing metadata about the stress. This is passed onto the
TimeSeries object.
Examples
--------
>>> sm = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(),
>>> recharge=ps.rch.FlexModel(), name="rch")
>>> ml.add_stressmodel(sm)
Time series settings
--------------------
fill_nan : {"drop", "mean", "interpolate"} or float
Method for filling NaNs.
* `drop`: drop NaNs from time series
* `mean`: fill NaNs with mean value of time series
* `interpolate`: fill NaNs by interpolating between finite values
* `float`: fill NaN with provided value, e.g. 0.0
fill_before : {"mean", "bfill"} or float
Method for extending time series into past.
* `mean`: extend time series into past with mean value of time series
* `bfill`: extend time series into past by back-filling first value
* `float`: extend time series into past with provided value, e.g. 0.0
fill_after : {"mean", "ffill"} or float
Method for extending time series into future.
* `mean`: extend time series into future with mean value of time series
* `ffill`: extend time series into future by forward-filling last value
* `float`: extend time series into future with provided value, e.g. 0.0
sample_up : {"mean", "interpolate", "divide"} or float
Method for up-sampling time series (increasing frequency, e.g. going from weekly
to daily values).
* `bfill` or `backfill`: fill up-sampled time steps by back-filling current
values
* `ffill` or `pad`: fill up-sampled time steps by forward-filling current
values
* `mean`: fill up-sampled time steps with mean of timeseries
* `interpolate`: fill up-sampled time steps by interpolating between current
values
* `divide`: fill up-sampled steps with current value divided by length of
current time steps (i.e. spread value over new time steps).
sample_down : {"mean", "drop", "sum", "min", "max"}
Method for down-sampling time series (decreasing frequency, e.g. going from
daily to weekly values).
* `mean`: resample time series by taking the mean
* `drop`: resample the time series by taking the mean, dropping any
NaN-values
* `sum`: resample time series by summing values
* `max`: resample time series with maximum value
* `min`: resample time series with minimum value
Notes
-----
This stress model computes the contribution of precipitation and potential
evaporation in two steps. In the first step a recharge flux is computed by a
model determined by the input argument `recharge`. In the second step this
recharge flux is convolved with a response function to obtain the contribution
of recharge to the groundwater levels. If a nonlinear recharge model is used, the
precipitation should be in mm/d.
Warnings
--------
We recommend not to store a RechargeModel is a variable named `rm`. This name is
already reserved in IPython to remove files and will cause problems later.
Raises
------
A warning if the the maximum annual precipitation is smaller than 12 and a
nonlinear recharge model is applied. This is likely an indication that the units of
the precipitation series are in m/d instead of mm/d. Please check the units of the
precipitation series.
See Also
--------
pastas.rfunc
pastas.timeseries.TimeSeries
pastas.recharge
"""
_name = "RechargeModel"
[docs] def __init__(
self,
prec: Series,
evap: Series,
rfunc: Optional[RFunc] = None,
name: str = "recharge",
recharge: Optional[Recharge] = None,
temp: Optional[Series] = None,
settings: Tuple[
Union[str, StressSettingsDict],
Union[str, StressSettingsDict],
Union[str, StressSettingsDict],
] = (
"prec",
"evap",
"evap",
),
metadata: Optional[Tuple[dict, dict, dict]] = (None, None, None),
) -> None:
if rfunc is None:
rfunc = Exponential()
if recharge is None:
recharge = Linear()
# Store the precipitation and evaporation time series
self.prec = TimeSeries(prec, settings=settings[0], metadata=metadata[0])
self.evap = TimeSeries(evap, settings=settings[1], metadata=metadata[1])
# Store recharge object
self.recharge = recharge
# Store a temperature time series if provided/needed or set to None
if self.recharge.snow is True and temp is None:
msg = (
"Recharge model requires a temperature series. No temperature series "
"were provided."
)
raise TypeError(msg)
if temp is not None:
if len(settings) < 3 or len(metadata) < 3:
msg = "Number of values for the settings and/or metadata is incorrect."
raise TypeError(msg)
else:
self.temp = TimeSeries(temp, settings=settings[2], metadata=metadata[2])
else:
self.temp = None
# Select indices from validated stress where both series are available.
index = self.prec.series.index.intersection(self.evap.series.index)
if index.empty:
msg = (
"The stresses that were provided have no overlapping time indices. "
"Please make sure the indices of the time series overlap."
)
logger.error(msg)
raise Exception(msg)
# Calculate initial recharge estimation for initial rfunc parameters
p = self.recharge.get_init_parameters().initial.values
gain_scale_factor = self.get_stress(
p=p, tmin=index.min(), tmax=index.max(), freq=self.prec.settings["freq"]
).std()
StressModelBase.__init__(
self,
name=name,
tmin=index.min(),
tmax=index.max(),
rfunc=rfunc,
up=True,
gain_scale_factor=gain_scale_factor,
)
self.stress = [self.prec, self.evap]
if self.temp:
self.stress.append(self.temp)
self.freq = self.prec.settings["freq"]
self.set_init_parameters()
if isinstance(self.recharge, Linear):
self.nsplit = 2
else:
self.nsplit = 1
# Check if precipitation is likely in mm/d and not m/d. If the maximum
# value of the annual sums is smaller than 12 (m/d), the highest annual
# precipitation in the world, then the precipitation is very likely in m/d
# and not in mm/d. In this case a warning is given for nonlinear models.
freq_offset = "YE" if pandas_version >= parse_version("2.2.0") else "A"
if self.prec.series.resample(freq_offset).sum().max() < 12:
msg = (
"The maximum annual precipitation is smaller than 12 m/d. Please "
"double-check if the stresses are in mm/d and not in m/d."
)
logger.warning(msg)
[docs] def set_init_parameters(self) -> None:
"""Internal method to set the initial parameters."""
self.parameters = concat(
[
self.rfunc.get_init_parameters(self.name),
self.recharge.get_init_parameters(self.name),
]
)
[docs] def update_stress(
self,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
) -> None:
"""Method to update the settings of the all stresses in the stress model.
Parameters
----------
freq: str, optional
String representing the desired frequency of the time series. Must be one
of the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. "7D".
tmin: str or pandas.Timestamp, optional
String that can be converted to, or a Pandas Timestamp with the minimum
time of the series.
tmax: str or pandas.Timestamp, optional
String that can be converted to, or a Pandas Timestamp with the maximum
time of the series.
Notes
-----
For the individual options for the different settings please refer to the
docstring from the TimeSeries.update_series() method.
See Also
--------
ps.timeseries.TimeSeries.update_series
"""
self.prec.update_series(freq=freq, tmin=tmin, tmax=tmax)
self.evap.update_series(freq=freq, tmin=tmin, tmax=tmax)
if self.temp is not None:
self.temp.update_series(freq=freq, tmin=tmin, tmax=tmax)
if freq:
self.freq = freq
[docs] def simulate(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
istress: Optional[int] = None,
**kwargs,
) -> Series:
"""Method to simulate the contribution of recharge to the head.
Parameters
----------
p: array_like, optional
array_like object with the values as floats representing the model
parameters.
tmin: string, optional
tmax: string, optional
freq: string, optional
dt: float, optional
Time step to use in the recharge calculation.
istress: int, optional
This only works for the Linear model!
Returns
-------
pandas.Series
"""
if p is None:
p = self.parameters.initial.values
b = self._get_block(p[: self.rfunc.nparam], dt, tmin, tmax)
stress = self.get_stress(
p=p, tmin=tmin, tmax=tmax, freq=freq, istress=istress
).values
name = self.name
if istress is not None:
if istress == 1 and self.nsplit > 1:
# only happen when Linear is used as the recharge model
stress = stress * p[-1]
if self.stress[istress].name is not None:
name = f"{self.name} ({self.stress[istress].name})"
return Series(
data=fftconvolve(stress, b, "full")[: stress.size],
index=self.prec.series.index,
name=name,
)
[docs] def get_stress(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
istress: Optional[int] = None,
**kwargs,
) -> Series:
"""Method to obtain the recharge stress calculated by the model.
Parameters
----------
p: array_like, optional
array_like object with the values as floats representing the model
parameters.
tmin: string, optional
tmax: string, optional
freq: string, optional
istress: int, optional
Return one of the stresses used for the recharge calculation. 0 for
precipitation, 1 for evaporation and 2 for temperature.
kwargs
Returns
-------
stress: pandas.Series
When no istress is selected, this return the estimated recharge flux that
is convolved with a response function on the simulate method.
"""
if tmin is None:
tmin = self.tmin
if tmax is None:
tmax = self.tmax
self.update_stress(tmin=tmin, tmax=tmax, freq=freq)
if istress is None:
prec = self.prec.series.values
evap = self.evap.series.values
temp = None
if self.temp is not None:
temp = self.temp.series.values
if p is None:
p = self.parameters.initial.values
stress = self.recharge.simulate(
prec=prec, evap=evap, p=p[-self.recharge.nparam :], **{"temp": temp}
)
return Series(
data=stress,
index=self.prec.series.index,
name="recharge",
)
elif istress == 0:
return self.prec.series
elif istress == 1:
return self.evap.series
else:
return self.temp.series
[docs] def get_water_balance(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
) -> DataFrame:
"""Method to obtain the water balance components.
Parameters
----------
p: array_like, optional
array_like object with the values as floats representing the model
parameters.
tmin: string, optional
tmax: string, optional
freq: string, optional
Returns
-------
wb: pandas.DataFrame
Dataframe with the water balance components, both fluxes and states.
Notes
-----
This method return a data frame with all water balance components, fluxes and
states. All ingoing fluxes have a positive sign (e.g., precipitation) and all
outgoing fluxes have negative sign (e.g., recharge).
Warnings
--------
This is an experimental method and may change in the future.
Examples
--------
>>> sm = ps.RechargeModel(prec, evap, ps.Gamma(), ps.rch.FlexModel(),
>>> name="rch")
>>> ml.add_stressmodel(sm)
>>> ml.solve()
>>> wb = sm.get_water_balance(ml.get_parameters("rch"))
>>> wb.plot(subplots=True)
"""
if p is None:
p = self.parameters.initial.values
prec = self.get_stress(tmin=tmin, tmax=tmax, freq=freq, istress=0).values
evap = self.get_stress(tmin=tmin, tmax=tmax, freq=freq, istress=1).values
if self.temp is not None:
temp = self.get_stress(tmin=tmin, tmax=tmax, freq=freq, istress=2).values
else:
temp = None
df = self.recharge.get_water_balance(
prec=prec, evap=evap, temp=temp, p=p[-self.recharge.nparam :]
)
df.index = self.prec.series.index
return df
[docs] def get_parameters(self, model=None, istress: Optional[int] = None) -> ArrayLike:
"""Get parameters and return as array.
Parameters
----------
model : pastas.Model, optional
If provided, and the model is solved, return optimal model parameter-values.
Otherwise, return initial parameter-values.
istress : int, optional
if provided, return specific parameter set, else return all parameters.
Returns
-------
p : array_like
An array of the parameters of the stressmodel.
"""
if model is None:
p = self.parameters.initial.values
else:
p = model.get_parameters(self.name)
if istress is not None and isinstance(self.recharge, Linear):
if istress == 0:
p = p[:-1]
elif istress == 1:
p[0] *= p[-1]
p = p[:-1]
return p
[docs] def to_dict(self, series: bool = True) -> dict:
"""Method to export the RechargeModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the object.
Notes
-----
Settings and metadata are exported with the stress.
"""
data = {
"class": self._name,
"prec": self.prec.to_dict(series=series),
"evap": self.evap.to_dict(series=series),
"rfunc": self.rfunc.to_dict(),
"name": self.name,
"recharge": self.recharge.to_dict(),
"temp": self.temp.to_dict() if self.temp else None,
}
return data
[docs]class TarsoModel(RechargeModel):
"""Stressmodel simulating the effect of recharge using the Tarso method.
Parameters
----------
prec: pandas.Series
pandas.Series with pandas.DatetimeIndex containing the precipitation series.
evap: pandas.Series
pandas.Series with pandas.DatetimeIndex containing the potential evaporation
series.
oseries: pandas.Series, optional
A pandas.Series with pandas.DatetimeIndex of observations to which the model
will be calibrated. It is used to determine the initial values of the
drainage levels and the boundaries of the upper drainage level. Specify
either oseries or dmin and dmax.
dmin: float, optional
The minimum drainage level. It is used to determine the initial values of the
drainage levels and the lower boundary of the upper drainage level. Specify
either oseries or dmin and dmax.
dmax : float, optional
The maximum drainage level. It is used to determine the initial values of the
drainage levels and the upper boundary of the upper drainage level. Specify
either oseries or dmin and dmax.
rfunc: pastas.rfunc instance
this model only works with the Exponential response function.
See Also
--------
pastas.recharge
Notes
-----
The Threshold autoregressive self-exciting open-loop (Tarso) model
:cite:t:`knotters_tarso_1999` is nonlinear in structure because it incorporates
two regimes which are separated by a threshold. This model method can be used to
simulate a groundwater system where the groundwater head reaches the surface or
drainage level in wet conditions. TarsoModel uses two drainage levels, with two
exponential response functions. When the simulation reaches the second drainage
level, the second response function becomes active. Because of its structure,
TarsoModel cannot be combined with other stress models, a constant or a transform.
TarsoModel inherits from RechargeModel. Only parameters specific to the child
class are named above.
"""
_name = "TarsoModel"
[docs] def __init__(
self,
prec: Series,
evap: Series,
oseries: Optional[Series] = None,
dmin: Optional[float] = None,
dmax: Optional[float] = None,
rfunc: Optional[RFunc] = None,
**kwargs,
) -> None:
if oseries is not None:
if dmin is not None or dmax is not None:
msg = "Please specify either oseries or dmin and dmax"
raise (Exception(msg))
dmin = oseries.min()
dmax = oseries.max()
elif dmin is None or dmax is None:
msg = "Please specify either oseries or dmin and dmax"
raise (Exception(msg))
if rfunc is None:
rfunc = Exponential()
if not isinstance(rfunc, Exponential):
raise NotImplementedError("TarsoModel only supports rfunc Exponential!")
self.dmin = dmin
self.dmax = dmax
super().__init__(prec=prec, evap=evap, rfunc=rfunc, **kwargs)
self.nsplit = 1
[docs] def set_init_parameters(self) -> None:
# parameters for the first drainage level
p0 = self.rfunc.get_init_parameters(self.name)
initial = self.dmin + 0.5 * (self.dmax - self.dmin)
pd0 = Series(
{
"initial": initial,
"pmin": np.nan,
"pmax": np.nan,
"vary": True,
"name": self.name,
"dist": "uniform",
}
)
p0.loc[f"{self.name}_d"] = pd0
p0.index = [f"{x}0" for x in p0.index]
# parameters for the second drainage level
p1 = self.rfunc.get_init_parameters(self.name)
initial = self.dmin + 0.75 * (self.dmax - self.dmin)
pd1 = Series(
{
"initial": initial,
"pmin": self.dmin,
"pmax": self.dmax,
"vary": True,
"name": self.name,
"dist": "uniform",
}
)
p1.loc[f"{self.name}_d"] = pd1
p1.index = [f"{x}1" for x in p1.index]
# parameters for the recharge-method
pr = self.recharge.get_init_parameters(self.name)
# combine all parameters
self.parameters = concat([p0, p1, pr])
[docs] def simulate(
self,
p: Optional[ArrayLike] = None,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq=None,
dt: float = 1.0,
) -> Series:
stress = self.get_stress(p=p, tmin=tmin, tmax=tmax, freq=freq)
h = self.tarso(p[: -self.recharge.nparam], stress.values, dt)
sim = Series(h, name=self.name, index=stress.index)
return sim
[docs] def to_dict(self, series: bool = True) -> dict:
"""Method to export the TarsoModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the object.
Notes
-----
Settings and metadata are exported with the stress.
"""
data = super().to_dict(series)
data["dmin"] = self.dmin
data["dmax"] = self.dmax
return data
@staticmethod
def _check_stressmodel_compatibility(ml: Model) -> None:
"""Internal method to check if no other stressmodels, a constants or a transform
is used."""
msg = (
"A TarsoModel cannot be combined with %s. Either remove the TarsoModel or "
"the %s."
)
if len(ml.stressmodels) > 1:
logger.warning(msg, "other stressmodels", "stressmodels")
if ml.constant is not None:
logger.warning(msg, "a constant", "constant")
if ml.transform is not None:
logger.warning(msg, "a transform", "transform")
[docs] @staticmethod
@njit
def tarso(p: ArrayLike, r: ArrayLike, dt: float) -> ArrayLike:
"""Calculates the head based on exponential decay of the previous timestep and
recharge, using two thresholds."""
A0, a0, d0, A1, a1, d1 = p
# calculate physical meaning of these parameters
S0 = a0 / A0
c0 = A0
S1 = a1 / A1
c1 = A1
# calculate effective parameters for the top level
c_e = 1 / ((1 / c0) + (1 / c1))
d_e = (c1 / (c0 + c1)) * d0 + (c0 / (c0 + c1)) * d1
a_e = S1 * c_e
h = np.full(len(r), np.nan)
for i in range(len(r)):
if i == 0:
h0 = (d0 + d1) / 2
high = h0 > d1
if high:
S, a, c, d = S1, a_e, c_e, d_e
else:
S, a, c, d = S0, a0, c0, d0
else:
h0 = h[i - 1]
exp_a = np.exp(-dt / a)
h[i] = (h0 - d) * exp_a + r[i] * c * (1 - exp_a) + d
newhigh = h[i] > d1
if high != newhigh:
# calculate time until d1 is reached
dtdr = -S * c * np.log((d1 - d - r[i] * c) / (h0 - d - r[i] * c))
if dtdr > dt:
raise (Exception())
# change parameters
high = newhigh
if high:
S, a, c, d = S1, a_e, c_e, d_e
else:
S, a, c, d = S0, a0, c0, d0
# calculate new level after reaching d1
exp_a = np.exp(-(dt - dtdr) / a)
h[i] = (d1 - d) * exp_a + r[i] * c * (1 - exp_a) + d
return h
[docs]class ChangeModel(StressModelBase):
"""Model where the response function changes from one to another over time.
Parameters
----------
stress: pandas.Series
pandas Series object containing the stress.
rfunc1: pastas.rfunc instance
The instance of the response function used in the convolution with the stress.
rfunc2: pastas.rfunc instance
The instance of the response function used in the convolution with the stress.
name: str
name of the stress.
tchange: str
string with the approximate date of the change.
up: bool or None, optional
True if response function is positive (default), False if negative. None if
you don't want to define if response is positive or negative.
settings: dict or str, optional
The settings of the stress. This can be a string referring to a predefined
settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
settings to apply. For more information, refer to the docs of pastas.Timeseries
for further information.
metadata: dict, optional
dictionary containing metadata about the stress. This is passed onto the
TimeSeries object.
Time series settings
--------------------
fill_nan : {"drop", "mean", "interpolate"} or float
Method for filling NaNs.
* `drop`: drop NaNs from time series
* `mean`: fill NaNs with mean value of time series
* `interpolate`: fill NaNs by interpolating between finite values
* `float`: fill NaN with provided value, e.g. 0.0
fill_before : {"mean", "bfill"} or float
Method for extending time series into past.
* `mean`: extend time series into past with mean value of time series
* `bfill`: extend time series into past by back-filling first value
* `float`: extend time series into past with provided value, e.g. 0.0
fill_after : {"mean", "ffill"} or float
Method for extending time series into future.
* `mean`: extend time series into future with mean value of time series
* `ffill`: extend time series into future by forward-filling last value
* `float`: extend time series into future with provided value, e.g. 0.0
sample_up : {"mean", "interpolate", "divide"} or float
Method for up-sampling time series (increasing frequency, e.g. going from weekly
to daily values).
* `bfill` or `backfill`: fill up-sampled time steps by back-filling current
values
* `ffill` or `pad`: fill up-sampled time steps by forward-filling current
values
* `mean`: fill up-sampled time steps with mean of timeseries
* `interpolate`: fill up-sampled time steps by interpolating between current
values
* `divide`: fill up-sampled steps with current value divided by length of
current time steps (i.e. spread value over new time steps).
sample_down : {"mean", "drop", "sum", "min", "max"}
Method for down-sampling time series (decreasing frequency, e.g. going from
daily to weekly values).
* `mean`: resample time series by taking the mean
* `drop`: resample the time series by taking the mean, dropping any
NaN-values
* `sum`: resample time series by summing values
* `max`: resample time series with maximum value
* `min`: resample time series with minimum value
Notes
-----
This model is based on :cite:t:`obergfell_identification_2019`.
"""
_name = "ChangeModel"
[docs] def __init__(
self,
stress: Series,
rfunc1: RFunc,
rfunc2: RFunc,
name: str,
tchange: Union[str, TimestampType],
up: bool = True,
settings: Optional[Union[str, StressSettingsDict]] = None,
metadata: Optional[dict] = None,
) -> None:
stress = TimeSeries(stress, settings=settings, metadata=metadata)
StressModelBase.__init__(
self,
name=name,
rfunc=None,
tmin=stress.series.index.min(),
tmax=stress.series.index.max(),
)
rfunc1.update_rfunc_settings(up=up)
self.rfunc1 = rfunc1
rfunc2.update_rfunc_settings(up=up)
self.rfunc2 = rfunc2
self.tchange = Timestamp(tchange)
self.freq = stress.settings["freq"]
self.stress.append(stress)
self.set_init_parameters()
[docs] def set_init_parameters(self) -> None:
"""Internal method to set the initial parameters."""
self.parameters = concat(
[
self.rfunc1.get_init_parameters("{}_1".format(self.name)),
self.rfunc2.get_init_parameters("{}_2".format(self.name)),
]
)
tmin = Timestamp.min.toordinal()
tmax = Timestamp.max.toordinal()
tchange = self.tchange.toordinal()
self.parameters.loc[self.name + "_beta"] = (
0.0,
-np.inf,
np.inf,
True,
self.name,
"uniform",
)
self.parameters.loc[self.name + "_tchange"] = (
tchange,
tmin,
tmax,
False,
self.name,
"uniform",
)
self.parameters.name = self.name
[docs] def simulate(
self,
p: ArrayLike,
tmin: Optional[TimestampType] = None,
tmax: Optional[TimestampType] = None,
freq: Optional[str] = None,
dt: float = 1.0,
) -> Series:
self.update_stress(tmin=tmin, tmax=tmax, freq=freq)
rfunc1 = self.rfunc1.block(p[: self.rfunc1.nparam])
rfunc2 = self.rfunc2.block(
p[self.rfunc1.nparam : self.rfunc1.nparam + self.rfunc2.nparam]
)
stress = self.stress[0].series
npoints = stress.index.size
t = np.linspace(0, 1, npoints)
beta = p[-2]
sigma = stress.index.get_loc(Timestamp.fromordinal(int(p[-1]))) / npoints
omega = 1 / (np.exp(beta * (t - sigma)) + 1)
h1 = Series(
data=fftconvolve(stress, rfunc1, "full")[:npoints],
index=stress.index,
name="1",
)
h2 = Series(
data=fftconvolve(stress, rfunc2, "full")[:npoints],
index=stress.index,
name="1",
)
h = omega * h1 + (1 - omega) * h2
return h
[docs] def to_dict(self, series: bool = True):
"""Method to export the ChangeModel object.
Returns
-------
data: dict
dictionary with all necessary information to reconstruct the object.
Notes
-----
Settings and metadata are exported with the stress.
"""
data = {
"stress": self.stress[0].to_dict(series=series),
"rfunc1": self.rfunc1.to_dict(),
"rfunc2": self.rfunc2.to_dict(),
"name": self.name,
"tchange": self.tchange,
"up": self.rfunc1.up,
}
return data
