The default behavior for adding and solving with noisemodels has changed from Pastas 1.5. Find more information here

Source code for pastas.stressmodels

"""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 pandas import DataFrame, Series, Timedelta, Timestamp, concat, date_range
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

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.loc[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.loc[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.loc[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.loc[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.loc[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. 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.loc[self.name + "_A", "optimal"] b = model.parameters.loc[self.name + "_b", "optimal"] var_A = model.solver.pcov.loc[self.name + "_A", self.name + "_A"] var_b = model.solver.pcov.loc[self.name + "_b", self.name + "_b"] cov_Ab = model.solver.pcov.loc[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. if self.prec.series.resample("A").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