[docs]defmin_impute(incomp_data,params=None):""" Impute NaN values with the minimum value of the time series. Parameters ---------- incomp_data : numpy.ndarray The input time series with contamination (missing values represented as NaNs). params : dict, optional Optional parameters for the algorithm. If None, the minimum value from the contamination is used (default is None). Returns ------- numpy.ndarray The imputed matrix where NaN values have been replaced with the minimum value from the time series. Notes ----- This function finds the minimum non-NaN value in the time series and replaces all NaN values with this minimum value. It is a simple imputation technique for filling missing data points in a dataset. Example ------- >>> incomp_data = np.array([[1, 2, np.nan], [4, np.nan, 6]]) >>> recov_data = min_impute(incomp_data) >>> print(recov_data) array([[1., 2., 1.], [4., 1., 6.]]) """# logicmin_value=np.nanmin(incomp_data)# Imputationrecov_data=np.nan_to_num(incomp_data,nan=min_value)returnrecov_data