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ChangeModel#

class ChangeModel(stress, rfunc1, rfunc2, name, tchange, up=True, settings=None, metadata=None)[source]#

Model where the response function changes from one to another over time.

Parameters
  • stress (pandas.Series) – pandas Series object containing the stress.

  • rfunc1 (pastas.rfunc instance) – The instance of the response function used in the convolution with the stress.

  • rfunc2 (pastas.rfunc instance) – The instance of the response function used in the convolution with the stress.

  • name (str) – name of the stress.

  • tchange (str) – string with the approximate date of the change.

  • 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. For more information, 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.

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

Notes

This model is based on Obergfell et al. [2019].

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

Internal method to set the initial parameters.

simulate

to_dict

Method to export the ChangeModel object.

update_stress

Method to update the settings of the all stresses in the stress model.