generate_errors¶

Constant error¶

de.generate_errors.constant(ts, offset=1.5)[source]¶

Generate constant errors.

Constant errors are generated by multiplying with either constant positive offset or constant negative offset.

Parameters:	ts ((N,)array_like) – Observed time series offset (float, optional) – Offset multiplied to time series. If greater than 1 positive constant offset and if less than 1 negative constant offset. The default is 1.5.
Returns:	ts_const – Time series with constant error
Return type:	array_like

de.generate_errors.positive_dynamic(ts, prop=0.5)[source]¶

Generate positive dynamic errors (i.e. Overestimate high flows - Underestimate low flows)

High to medium flows are increased by linear decreasing factors. Medium to low flows are decreased by linear decreasing factors.

Parameters:	ts (dataframe) – Dataframe with time series prop (float, optional) – Factor by which time series is tilted.
Returns:	ts_dyn – Time series with positive dynamic error
Return type:	dataframe

de.generate_errors.negative_dynamic(ts, prop=0.5)[source]¶

Generate negative dynamic error (i.e Underestimate high flows - Overestimate low flows)

High to medium flows are decreased by linear increasing factors. Medium to low flows are increased by linear increasing factors.

Parameters:	ts (dataframe) – Observed time series prop (float, optional) – Factor by which time series is tilted.
Returns:	ts_dyn – Time series with negative dynamic error
Return type:	dataframe

de.generate_errors.timing(ts, tshift=3, shuffle=True)[source]¶

Generate timing errors.

Timing errors are generated by either shifting or shuffling.

Parameters:	ts (dataframe) – dataframe with time series tshift (int, optional) – days by which time series is shifted. Both positive and negative time shift are possible. The default is 3 days. shuffle (boolean, optional) – If True, time series is shuffled. The default is shuffling.
Returns:	ts_tim – Time series with timing error
Return type:	dataframe