StressModel

class StressModel(stress, rfunc, name, up=True, settings=None, metadata=None, gain_scale_factor=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 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 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.

Time series 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")

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_parameters	Get parameters and return as array.
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.