diff --git a/saqc/funcs/functions.py b/saqc/funcs/functions.py index 1821c7db53520cf96de85eae6b4eb0db0612acde..66ea41e38e34b3ee8015f7f836e5388db5af4a20 100644 --- a/saqc/funcs/functions.py +++ b/saqc/funcs/functions.py @@ -196,7 +196,7 @@ def flagManual(data, field, flagger, mdata, mflag: Any = 1, method='plain', **kw It also determine the column in mdata if its of type pd.Dataframe or dios.DictOfSeries. flagger : flagger - +range_dict.keys() mdata : {pd.Series, pd.Dataframe, DictOfSeries, str} The manual data diff --git a/saqc/funcs/harm_functions.py b/saqc/funcs/harm_functions.py index 88e1fe533da9292bd5fd314257d3aafa92d826b2..1eff59ebbdb955cd6cc8555399547fd850ad9407 100644 --- a/saqc/funcs/harm_functions.py +++ b/saqc/funcs/harm_functions.py @@ -13,42 +13,41 @@ logger = logging.getLogger("SaQC") @register -def harm_shift2Grid(data, field, flagger, freq, method="nshift", drop_flags=None, empty_intervals_flag=None, **kwargs): +def harm_shift2Grid(data, field, flagger, freq, method="nshift", drop_flags=None, **kwargs): data, flagger = proc_fork(data, field, flagger) data, flagger = proc_shift(data, field, flagger, freq, method, drop_flags=drop_flags, - empty_intervals_flag=empty_intervals_flag, **kwargs) + empty_intervals_flag=flagger.UNFLAGGED, **kwargs) return data, flagger @register def harm_aggregate2Grid( - data, field, flagger, freq, value_func, flag_func=np.nanmax, method="nagg", drop_flags=None, - empty_intervals_flag=None, **kwargs + data, field, flagger, freq, value_func, flag_func=np.nanmax, method="nagg", drop_flags=None, **kwargs ): data, flagger = proc_fork(data, field, flagger) data, flagger = proc_resample(data, field, flagger, freq, agg_func=value_func, flag_agg_func=flag_func, - method=method, empty_intervals_flag=empty_intervals_flag, drop_flags=drop_flags, + method=method, empty_intervals_flag=flagger.UNFLAGGED, drop_flags=drop_flags, all_na_2_empty=True, **kwargs) return data, flagger @register -def harm_linear2Grid(data, field, flagger, freq, drop_flags=None, empty_intervals_flag=None, **kwargs): +def harm_linear2Grid(data, field, flagger, freq, drop_flags=None, **kwargs): data, flagger = proc_fork(data, field, flagger) data, flagger = proc_interpolateGrid(data, field, flagger, freq, 'time', - drop_flags=drop_flags, empty_intervals_flag=empty_intervals_flag, **kwargs) + drop_flags=drop_flags, empty_intervals_flag=flagger.UNFLAGGED, **kwargs) return data, flagger @register def harm_interpolate2Grid( - data, field, flagger, freq, method, order=1, drop_flags=None, empty_intervals_flag=None, **kwargs, + data, field, flagger, freq, method, order=1, drop_flags=None, **kwargs, ): data, flagger = proc_fork(data, field, flagger) data, flagger = proc_interpolateGrid(data, field, flagger, freq, method=method, inter_order=order, - drop_flags=drop_flags, empty_intervals_flag=empty_intervals_flag, + drop_flags=drop_flags, empty_intervals_flag=flagger.UNFLAGGED, **kwargs) return data, flagger diff --git a/saqc/funcs/spikes_detection.py b/saqc/funcs/spikes_detection.py index 0f1f3a6177851f470896f1a94906d8b00992fb1e..3f5f714a5d09ed0d54bd34be226c1f15e7897dc3 100644 --- a/saqc/funcs/spikes_detection.py +++ b/saqc/funcs/spikes_detection.py @@ -742,7 +742,5 @@ def spikes_flagGrubbs(data, field, flagger, winsz, alpha=0.05, min_periods=8, ** if partition.shape[0] > min_periods: to_flag = smirnov_grubbs.two_sided_test_indices(partition['data'].values, alpha=alpha) to_flag = partition['ts'].iloc[to_flag] - if not to_flag.empty: - print(to_flag) flagger = flagger.setFlags(field, loc=to_flag, **kwargs) return data, flagger