StressModel#

class StressModel(stress, rfunc, name, up=True, settings=None, metadata=None, gain_scale_factor=None)[source]#

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 dict (defined in ps.rcParams[“timeseries”]), or a dict with the settings to apply. 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.
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.

Examples

>>> import pastas as ps
>>> import pandas as pd
>>> sm = ps.StressModel(stress=pd.Series(), rfunc=ps.Gamma(), name="Prec",
>>>                     settings="prec")

Attributes#

nparam

Methods#

`__init__`
`get_nsplit`	Determine in how many time series the contribution can be split.
`get_settings`	Method to obtain the settings of the stresses.
`get_stress`	Returns the stress(es) of the time series object as a pandas DataFrame.
`set_init_parameters`	Set the initial parameters (back) to their default values.
`simulate`	Simulates the head contribution.
`to_dict`	Method to export the StressModel object.
`update_stress`	Method to update the settings of the all stresses in the stress model.