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
Attributes#
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Methods#
Determine in how many time series the contribution can be split. |
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Method to obtain the settings of the stresses. |
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Returns the stress(es) of the time series object as a pandas DataFrame. |
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Set the initial parameters (back) to their default values. |
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Simulates the head contribution. |
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Method to export the StressModel object. |
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Method to update the settings of the all stresses in the stress model. |