StressModel#

class StressModel(stress, rfunc, name, up=True, cutoff=0.999, settings=None, metadata=None, meanstress=None)[source]#

Time series model consisting of the convolution of one stress with one response function.

Parameters

stress (pandas.Series) – pandas Series object containing the stress.
rfunc (pastas.rfunc instance (class is deprecated)) – 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.
cutoff (float, optional) – float between 0 and 1 to determine how long the response is (default is 99% of the actual response time). Used to reduce computation times.
settings (dict or str, optional) – The settings of the stress. This can be a string referring to a predefined settings dict, or a dict with the settings to apply. Refer to the docstring of pastas.Timeseries for further information.
metadata (dict, optional) – dictionary containing metadata about the stress. This is passed onto the TimeSeries object.
meanstress (float, optional) – The mean stress determines the initial parameters of rfunc. The initial parameters are chosen in such a way that the gain of meanstress is 1.

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__`
`dump_stress`	Method to dump all stresses in the stresses list.
`get_nsplit`	Determine in how many timeseries the contribution can be split.
`get_stress`	Returns the stress or stresses 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 individual TimeSeries.