Inter limit fix

fixed (one-off) error with limit higher than 2 (solving #388 (closed))

saqc/lib/ts_operators.py

@@ -314,11 +314,11 @@ def interpolateNANs(
     :param method:                  String. Method keyword designating interpolation method to use.
     :param order:                   Integer. If your desired interpolation method needs an order to be passed -
                                     here you pass it.
-    :param inter_limit:             Integer. Default = 2. Limit up to which consecutive nan - values in the data get
+    :param inter_limit:             Integer. Default = 2. Number up to which consecutive nan - values in the data get
                                     replaced by interpolation.
                                     Its default value suits an interpolation that only will apply to points of an
                                     inserted frequency grid. (regularization by interpolation)
-                                    Gaps wider than "limit" will NOT be interpolated at all.
+                                    Gaps of size "limit" or greater will NOT be interpolated at all.
     :param downgrade_interpolation:  Boolean. Default False. If True:
                                     If a data chunk not contains enough values for interpolation of the order "order",
                                     the highest order possible will be selected for that chunks interpolation.
@@ -330,11 +330,13 @@ def interpolateNANs(
     if inter_limit is None:
         # if there is actually no limit set to the gaps to-be interpolated, generate a dummy mask for the gaps
         gap_mask = pd.Series(True, index=data.index, name=data.name)
+    elif inter_limit < 2:
+        return data
     else:
         # if there is a limit to the gaps to be interpolated, generate a mask that evaluates to False at the right side
         # of each too-large gap with a rolling.sum combo
         gap_mask = data.isna().rolling(inter_limit, min_periods=0).sum() != inter_limit
-        if inter_limit < 20:
+        if inter_limit == 2:
             # for the common case of inter_limit=2 (default "harmonisation"), we efficiently bag propagate the False
             # value to fill the whole too-large gap by a shift and a conjunction.
             gap_mask &= gap_mask & gap_mask.shift(-1, fill_value=True)
@@ -363,7 +365,7 @@ def interpolateNANs(
         )
 
     else:
-        # if the method that is inerpolated with depends on not only the left and right border points of any gap,
+        # if the method that is interpolated with depends on not only the left and right border points of any gap,
         # but includes more points, it has to be applied on any data chunk seperated by the too-big gaps individually.
         # So we use the gap_mask to group the data into chunks and perform the interpolation on every chunk seperatly
         # with the .transform method of the grouper.