pastas.typing.types.StressSettingsDict

class pastas.typing.types.StressSettingsDict

Stress time series settings dictionary defining logic for filling and up- or downsampling time series.

Parameters:

• sample_up ({"mean", "interpolate", "divide", "bfill", "ffill"}) – Method for up-sampling time series (increasing frequency, e.g. weekly to daily). - “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. - “bfill”: Back-fill up-sampled time steps with current values. - “ffill”: Forward-fill up-sampled time steps with current values.

• sample_down ({"mean", "drop", "sum", "min", "max"}) – Method for down-sampling time series (decreasing frequency, e.g. daily to weekly). - “mean”: Resample time series by taking the mean. - “drop”: Resample by taking the mean, dropping any NaN-values. - “sum”: Resample by summing values. - “max”: Resample with maximum value. - “min”: Resample with minimum value.

• 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 the past. - “mean”: Extend into past with mean value of time series. - “bfill”: Back-fill into past with first value. - float: Extend into past with provided value, e.g. 0.0.

• fill_after ({"mean", "ffill"} or float) – Method for extending time series into the future. - “mean”: Extend into future with mean value of time series. - “ffill”: Forward-fill into future with last value. - float: Extend into future with provided value, e.g. 0.0.