Funcs dict

adresses #449 (closed)

• adds the option to choose a function by passing a dict key to the saqc methods, wherever possible
• the dictionary of available functions is retrieved now from saqc.parsing.environ variables
• adds validation for function selection to the checking library
• moves helper functions from the lib.ts_operators module to saqc.func modules to avoid circular import
• moves the literal checking from funcs.tools to lib.checking to avoid circular imports

saqc/funcs/interpolation.py

@@ -21,12 +21,14 @@ from saqc.lib.checking import (
     isValidChoice,
     validateCallable,
     validateChoice,
+    validateFuncSelection,
     validateMinPeriods,
     validateValueBounds,
     validateWindow,
 )
 from saqc.lib.tools import isflagged
-from saqc.lib.ts_operators import interpolateNANs, shift2Freq
+from saqc.lib.ts_operators import interpolateNANs
+from saqc.parsing.environ import ENV_OPERATORS
 
 if TYPE_CHECKING:
     from saqc import SaQC
@@ -63,6 +65,35 @@ def _resampleOverlapping(data: pd.Series, freq: str, fill_value):
     return data.fillna(fill_value).astype(dtype)
 
 
+def _shift2Freq(
+    data: Union[pd.Series, pd.DataFrame],
+    method: Literal["fshift", "bshift", "nshift"],
+    freq: str,
+    fill_value,
+):
+    """
+    shift timestamps backwards/forwards in order to align them with an equidistant
+    frequency grid. Resulting Nan's are replaced with the fill-value.
+    """
+    validateWindow(freq, "freq", allow_int=False)
+    validateChoice(method, "method", ["fshift", "bshift", "nshift"])
+    methods = {
+        "fshift": lambda freq: ("ffill", pd.Timedelta(freq)),
+        "bshift": lambda freq: ("bfill", pd.Timedelta(freq)),
+        "nshift": lambda freq: ("nearest", pd.Timedelta(freq) / 2),
+    }
+    direction, tolerance = methods[method](freq)
+    target_ind = pd.date_range(
+        start=pd.Timestamp(data.index[0]).floor(freq),
+        end=pd.Timestamp(data.index[-1]).ceil(freq),
+        freq=freq,
+        name=data.index.name,
+    )
+    return data.reindex(
+        target_ind, method=direction, tolerance=tolerance, fill_value=fill_value
+    )
+
+
 class InterpolationMixin:
     @register(
         mask=["field"],
@@ -73,7 +104,7 @@ class InterpolationMixin:
         self: "SaQC",
         field: str,
         window: str | int,
-        func: Callable[[pd.Series], float] = np.median,
+        func: Callable[[pd.Series], float] | str = "median",
         center: bool = True,
         min_periods: int = 0,
         flag: float = UNFLAGGED,
@@ -104,7 +135,9 @@ class InterpolationMixin:
             computed.
         """
         validateWindow(window)
-        validateCallable(func, "func")
+        validateFuncSelection(func, allow_operator_str=True)
+        if isinstance(func, str):
+            func = ENV_OPERATORS[func]
         validateMinPeriods(min_periods)
 
         datcol = self._data[field]
@@ -588,7 +621,7 @@ def _shift(
         return saqc
 
     # do the shift
-    datcol = shift2Freq(datcol, method, freq, fill_value=np.nan)
+    datcol = _shift2Freq(datcol, method, freq, fill_value=np.nan)
 
     # do the shift on the history
     kws = dict(method=method, freq=freq)
@@ -596,7 +629,7 @@ def _shift(
     history = saqc._flags.history[field].apply(
         index=datcol.index,
         func_handle_df=True,
-        func=shift2Freq,
+        func=_shift2Freq,
         func_kws={**kws, "fill_value": np.nan},
     )