Numba riddance

adresses #425 (closed)

• removes numba dependency/usage from saqc
• adds replacement mechanics to make up for performance loss
• see #425 (closed) for an overview over the loss-trade offs (witch are fairly minimal to non-existant in focus use-cases)

saqc/funcs/changepoints.py

@@ -8,7 +8,7 @@
 from __future__ import annotations
 
 import typing
-from typing import TYPE_CHECKING, Callable, Tuple
+from typing import TYPE_CHECKING, Callable, Tuple, Literal
 
 import numpy as np
 import pandas as pd
@@ -25,7 +25,7 @@ class ChangepointsMixin:
     def flagChangePoints(
         self: "SaQC",
         field: str,
-        stat_func: Callable[[np.ndarray, np.ndarray], float],
+        stat_func: Callable[[np.ndarray, np.ndarray], float] | Literal['mean', 'median', 'std', 'var', 'sum'],
         thresh_func: Callable[[np.ndarray, np.ndarray], float],
         window: str | Tuple[str, str],
         min_periods: int | Tuple[int, int],
@@ -43,9 +43,11 @@ class ChangepointsMixin:
         Parameters
         ----------
         stat_func :
-             A function that assigns a value to every twin window. The backward-facing
-             window content will be passed as the first array, the forward-facing window
-             content as the second.
+             * If callable: A function that assigns a scalar value to every twin window. The backward-facing
+               window content will be passed as the first array, the forward-facing window
+               content as the second.
+             * If string: The respective statistic will be calculated for both the windows and the absolute difference of
+               the results will be returned.
 
         thresh_func :
             A function that determines the value level, exceeding wich qualifies a
@@ -187,7 +189,7 @@ class ChangepointsMixin:
 
 def _getChangePoints(
     data: pd.Series,
-    stat_func: Callable[[np.ndarray, np.ndarray], float],
+    stat_func: Callable[[np.ndarray, np.ndarray], float] | Literal['mean', 'median', 'std', 'var', 'sum'],
     thresh_func: Callable[[np.ndarray, np.ndarray], float],
     window: str | Tuple[str, str],
     min_periods: int | Tuple[int, int],