Rm callable types from flagUniLof

saqc/funcs/noise.py

@@ -8,6 +8,7 @@
 from __future__ import annotations
 
 import operator
+import warnings
 from typing import TYPE_CHECKING, Callable, Literal
 
 import numpy as np
@@ -17,7 +18,7 @@ from scipy.stats import median_abs_deviation
 from saqc.constants import BAD
 from saqc.core.register import flagging
 from saqc.lib.checking import (
-    validateCallable,
+    isCallable,
     validateChoice,
     validateMinPeriods,
     validateWindow,
@@ -31,13 +32,79 @@ if TYPE_CHECKING:
 
 
 class NoiseMixin:
+    def flagByStatLowPass(
+        self: "SaQC",
+        field: str,
+        window: str | pd.Timedelta,
+        thresh: float,
+        func: Literal["std", "var", "mad"]
+        | Callable[[np.ndarray, pd.Series], float] = "std",
+        sub_window: str | pd.Timedelta | None = None,
+        sub_thresh: float | None = None,
+        min_periods: int | None = None,
+        flag: float = BAD,
+        **kwargs,
+    ) -> "SaQC":
+        """
+        Flag data chunks of length ``window`` dependent on the data deviation.
+
+        Flag data chunks of length ``window`` if
+
+        1. they excexceed ``thresh`` with regard to ``func`` and
+        2. all (maybe overlapping) sub-chunks of the data chunks with length ``sub_window``,
+           exceed ``sub_thresh`` with regard to ``func``
+
+        Parameters
+        ----------
+        func :
+            Either a String value, determining the aggregation function applied on every chunk.
+            * 'std': standard deviation
+            * 'var': variance
+            * 'mad': median absolute deviation
+            Or a Callable function mapping 1 dimensional arraylikes onto scalars.
+
+        window :
+            Window (i.e. chunk) size.
+
+        thresh :
+            Threshold. A given chunk is flagged, if the return value of ``func`` excceeds ``thresh``.
+
+        sub_window :
+            Window size of sub chunks, that are additionally tested for exceeding ``sub_thresh``
+            with respect to ``func``.
+
+        sub_thresh :
+            Threshold. A given sub chunk is flagged, if the return value of ``func` excceeds ``sub_thresh``.
+
+        min_periods :
+            Minimum number of values needed in a chunk to perfom the test.
+            Ignored if ``window`` is an integer.
+        """
+        warnings.warn(
+            "function 'flagByStatLowPass' is deprecated and will be removed in a future release, "
+            "use 'flagByScatterLowPass' instead.",
+            DeprecationWarning,
+        )
+
+        return self.flagByScatterLowPass(
+            field=field,
+            window=window,
+            thresh=thresh,
+            func=func,
+            sub_window=sub_window,
+            sub_thresh=sub_thresh,
+            min_periods=min_periods,
+            flag=flag,
+        )
+
     @flagging()
     def flagByScatterLowPass(
         self: "SaQC",
         field: str,
         window: str | pd.Timedelta,
         thresh: float,
-        func: Literal["std", "var", "mad"] = "std",
+        func: Literal["std", "var", "mad"]
+        | Callable[[np.ndarray, pd.Series], float] = "std",
         sub_window: str | pd.Timedelta | None = None,
         sub_thresh: float | None = None,
         min_periods: int | None = None,
@@ -56,10 +123,11 @@ class NoiseMixin:
         Parameters
         ----------
         func :
-            String value determining the aggregation function applied on every chunk.
+            Either a String value, determining the aggregation function applied on every chunk.
             * 'std': standard deviation
             * 'var': variance
             * 'mad': median absolute deviation
+            Or a Callable function mapping 1 dimensional arraylikes onto scalars.
 
         window :
             Window (i.e. chunk) size.
@@ -78,14 +146,19 @@ class NoiseMixin:
             Minimum number of values needed in a chunk to perfom the test.
             Ignored if ``window`` is an integer.
         """
-        validateChoice(func, "func", ["std", "var", "mad"])
+        if (not isCallable(func)) and (func not in ["std", "var", "mad"]):
+            raise TypeError(
+                f"Parameter 'func' must either be of type 'Callable' or one out of ['std', 'var', 'mad']. Got {func}."
+            )
+
         validateWindow(window, allow_int=False)
         validateMinPeriods(min_periods)
         if sub_window is not None:
             validateWindow(sub_window, "sub_window", allow_int=False)
             sub_window = pd.Timedelta(sub_window)
 
-        func = STATS_DICT[func]
+        if not isCallable(func):
+            func = STATS_DICT[func]
 
         to_set = statPass(
             datcol=self._data[field],