pastas.stressmodels.StressModel#

class pastas.stressmodels.StressModel(stress: pandas.Series, rfunc: pastas.typing.RFunc, name: str, up: bool = True, settings: str | pastas.typing.StressSettingsDict | None = None, metadata: dict | None = None, gain_scale_factor: float | None = None, max_cache_size: int | None = None)#

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 stressmodel.
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 (Time series) – 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.
max_cache_size (int, optional) – Maximum size of the cache (in number of entries). Only used when cachetools is installed and caching is enabled (see ps.set_use_cache()).
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")

Methods#

`set_stress`(→ None)	Set the stress time series.
`set_init_parameters`(→ None)	Set the initial parameters (back) to their default values.
`simulate`(→ pandas.Series)	Simulate the stress model contribution.
`to_dict`(→ dict)	Export the stress model to a dictionary.