removed frequency restriction from mad

docs/FunctionDescriptions.md

@@ -11,7 +11,7 @@ Main documentation of the implemented functions, their purpose and parameters an
  - [clear](#clear)
  - [force](#force)
  - [sliding_outlier](#sliding_outlier)
- - [mad](#mad)
+ - [spikes_simpleMad](#spikes_simpleMad)
  - [spikes_Basic](#spikes_basic)
  - [Spikes_SpektrumBased](#spikes_spektrumbased)
  - [constant](#constant)
@@ -188,22 +188,19 @@ with $` r, M, mad, z `$: data, data median, data median absolute deviation, `z`.
 See also:
 [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
 
-## mad
+## spikes_simpleMad
 
 Flag outlier by simple median absolute deviation test.
 
 ```
-mad(length, z=3.5, freq=None)
+spikes_simpleMad(length, z=3.5)
 ```
 
-| parameter  | data type     | default value | description |
-| ---------  | -----------   | ----          | ----------- |
-| length     | offset-string | `"1h"`        | size of the sliding window, where the modified Z-score is applied on |
-| z          | float         | `3.5`         | z-parameter the modified Z-score |
-| freq       |               | `None`        | The frequency the data have |
+| parameter | data type            | default value | description                                                          |
+| --------- | -----------          | ----          | -----------                                                          |
+| winsz     | offset-string or int | `"1h"`        | size of the sliding window, where the modified Z-score is applied on |
+| z         | float                | `3.5`         | z-parameter the modified Z-score                                     |
 
-Parameter note: If freq is omitted, it is tried to infer the correct frequency. This is not fail save (!), because
-if no frequency can be found a error is thrown, but even worse, also a wrong frequency could be assumed. 
 
 The *modified Z-score* [1] is used to detect outlier. 
 All values are flagged as outlier, if in any slice of thw sliding window, a value fulfill:
@@ -997,4 +994,4 @@ Interpolation of an inserted equidistant frequency grid of sampling rate `freq`.
                 , and the result gets assigned to the next grid point.
     * `"nearest_agg"`: all flags in the range (+/- freq/2) of a grid point get 
                        aggregated with the function passed to `agg_func` and assigned to it.
-          
+          
\ No newline at end of file

saqc/funcs/spike_detection.py

@@ -144,7 +144,7 @@ def flagSpikes_slidingZscore(
 
 
 @register("spikes_simpleMad")
-def flagSpikes_simpleMad(data, field, flagger, length, z=3.5, freq=None, **kwargs):
+def flagSpikes_simpleMad(data, field, flagger, winsz, z=3.5, **kwargs):
     """ The function represents an implementation of the modyfied Z-score outlier detection method, as introduced here:
 
     [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
@@ -156,21 +156,14 @@ def flagSpikes_simpleMad(data, field, flagger, length, z=3.5, freq=None, **kwarg
                         time raster with seconds precision.
     :param field:       Fieldname of the Soil moisture measurements field in data.
     :param flagger:     A flagger - object. (saqc.flagger.X)
-    :param length:      Offset String. Denoting the windows size that that th "Z-scored" values have to lie in.
+    :param winsz:      Offset String. Denoting the windows size that that th "Z-scored" values have to lie in.
     :param z:           Float. The value the Z-score is tested against. Defaulting to 3.5 (Recommendation of [1])
-    :param freq:        Frequencie.
     """
 
     d = data[field].copy()
-    freq = inferFrequency(d) if freq is None else freq
-    if freq is None:
-        raise ValueError(
-            "freqency cannot inferred, provide `freq` as a param to mad()."
-        )
-    winsz = int(pd.to_timedelta(length) / freq)
-    median = d.rolling(window=winsz, center=True, closed="both").median()
+    median = d.rolling(window=winsz, closed="both").median()
     diff = abs(d - median)
-    mad = diff.rolling(window=winsz, center=True, closed="both").median()
+    mad = diff.rolling(window=winsz, closed="both").median()
     mask = (mad > 0) & (0.6745 * diff > z * mad)
 
     flagger = flagger.setFlags(field, mask, **kwargs)