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")

See also

pastas.rfunc, pastas.timeseries.TimeSeries

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.