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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 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")

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.