handling empty dit mat case

saqc/funcs/breaks_detection.py

@@ -14,7 +14,7 @@ from saqc.lib.tools import retrieveTrustworthyOriginal, detectDeviants
 @register(masking='all')
 def breaks_flagRegimeAnomaly(data, field, flagger, cluster_field, norm_spread, linkage_method='single',
                      metric=lambda x, y: np.abs(np.nanmean(x) - np.nanmean(y)),
-                     norm_frac=0.5, reset_cluster=False, **kwargs):
+                     norm_frac=0.5, reset_cluster=True, **kwargs):
     """
     A function to flag values belonging to an anomalous regime regarding modelling regimes of field.
 
@@ -49,8 +49,8 @@ def breaks_flagRegimeAnomaly(data, field, flagger, cluster_field, norm_spread, l
     norm_frac : float
         Has to be in [0,1]. Determines the minimum percentage of samples,
         the "normal" group has to comprise to be the normal group actually.
-    reset_cluster : bool, default False
-        If True, all data, considered "normal", gets assigned thee cluster Label "0", the remaining
+    reset_cluster : bool, default True
+        If True, all data, considered "normal", gets assigned the cluster Label "0" and the remaining
         cluster get numbered consecutively.
 
     kwargs

saqc/funcs/modelling.py

@@ -430,9 +430,9 @@ def _reduceCPCluster(stat_arr, thresh_arr, start, end, obj_func, num_val):
 
 
 @register(masking='field')
-def modelling_clusterByChangePoints(data, field, flagger, stat_func, thresh_func, bwd_window, min_periods_bwd,
-                     fwd_window=None, min_periods_fwd=None, closed='both', try_to_jit=True,
-                     reduce_window=None, reduce_func=lambda x, y: x.argmax(), flag_changepoints=False, **kwargs):
+def modelling_changePointCluster(data, field, flagger, stat_func, thresh_func, bwd_window, min_periods_bwd,
+                                 fwd_window=None, min_periods_fwd=None, closed='both', try_to_jit=True,
+                                 reduce_window=None, reduce_func=lambda x, y: x.argmax(), flag_changepoints=False, **kwargs):
     """
     Assigns label to the data, aiming to reflect continous regimes of the processes the data is assumed to be
     generated by.
@@ -502,7 +502,7 @@ def modelling_clusterByChangePoints(data, field, flagger, stat_func, thresh_func
 
     indexer = FreqIndexer()
     indexer.index_array = data_ser.index.to_numpy(int)
-    indexer.win_points = np.array([True]*var_len)
+    indexer.win_points = None
     indexer.window_size = int(pd.Timedelta(bwd_window).total_seconds() * 10 ** 9)
     indexer.forward = False
     indexer.center = False
@@ -540,4 +540,4 @@ def modelling_clusterByChangePoints(data, field, flagger, stat_func, thresh_func
     flagger = flagger.setFlags(field, flag=flagger.UNFLAGGED, force=True, **kwargs)
     if flag_changepoints:
         flagger.setFlags(field, loc=det_index)
-    return data, flagger
\ No newline at end of file
+    return data, flagger

saqc/funcs/proc_functions.py

@@ -998,3 +998,23 @@ def proc_seefoLinearDriftCorrecture(data, field, flagger, x_field, y_field, **kw
     data[field] = datcol
     return data, flagger
 
+
+def proc_correctRegimeAnomaly(data, field, flagger, cluster_field, model):
+    """
+    Function fits the passed model to every regime
+
+    Parameters
+    ----------
+    data
+    field
+    flagger
+    clusterfield
+    model
+
+    Returns
+    -------
+    """
+
+    clusterser = data[cluster_field]
+
+    # fit phase:

saqc/lib/tools.py

@@ -566,6 +566,8 @@ def detectDeviants(data, metric, norm_spread, norm_frac, linkage_method='single'
 
     """
     var_num = len(data.columns)
+    if var_num <= 1:
+        return []
     dist_mat = np.zeros((var_num, var_num))
     combs = list(itertools.combinations(range(0, var_num), 2))
     for i, j in combs: