fix roll method

docs/cookbooks/ResidualOutlierDetection.rst

@@ -323,13 +323,13 @@ function:
 
 .. doctest:: exampleOD
 
-   >>> qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D')
+   >>> qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D', min_periods=27)
 
 .. plot::
    :context: close-figs
    :include-source: False
 
-   qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D')
+   qc = qc.roll(field='incidents_residuals', target='incidents_scores', func=z_score, window='27D', min_periods=27)
 
 Optimization by Decomposition
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

saqc/funcs/rolling.py

@@ -117,6 +117,10 @@ class RollingMixin:
             DeprecationWarning,
         )
 
+        validateFuncSelection(func, allow_operator_str=True)
+        validateWindow(window)
+        validateMinPeriods(min_periods)
+
         # HINT: checking in  _roll
         self._data, self._flags = _roll(
             data=self._data,
@@ -146,70 +150,17 @@ def _roll(
     validateMinPeriods(min_periods)
 
     to_fit = data[field].copy()
+    flags_col = flags[field].copy()
     if to_fit.empty:
         return data, flags
 
-    regular = getFreqDelta(to_fit.index)
-    # starting with the annoying case: finding the rolling interval
-    # centers of not-harmonized input time series:
-    if center and not regular:
-        if isinstance(window, int):
-            raise NotImplementedError(
-                "Integer based window size is not supported for not-harmonized"
-                'sample series when rolling with "center=True".'
-            )
-        # get interval centers
-        centers = np.floor(
-            (
-                to_fit.rolling(
-                    pd.Timedelta(window) / 2, closed="both", min_periods=min_periods
-                ).count()
-            )
-        )
-        centers = centers.drop(centers[centers.isna()].index)
-        centers = centers.astype(int)
-        roller = to_fit.rolling(
-            pd.Timedelta(window), closed="both", min_periods=min_periods
-        )
-        try:
-            means = getattr(roller, func.__name__)()
-        except AttributeError:
-            means = to_fit.rolling(
-                pd.Timedelta(window), closed="both", min_periods=min_periods
-            ).apply(func)
-
-        def center_func(x, y=centers):
-            pos = x.index[int(len(x) - y[x.index[-1]])]
-            return y.index.get_loc(pos)
-
-        centers_iloc = (
-            centers.rolling(window, closed="both")
-            .apply(center_func, raw=False)
-            .astype(int)
-        )
-        temp = means.copy()
-        for k in centers_iloc.iteritems():
-            means.iloc[k[1]] = temp[k[0]]
-        # last values are false, due to structural reasons:
-        means[means.index[centers_iloc[-1]] : means.index[-1]] = np.nan
-
-    # everything is more easy if data[field] is harmonized:
+    d_roller = to_fit.rolling(window, min_periods=min_periods, center=center)
+    if isinstance(func, str):
+        to_fit = getattr(d_roller, func)()
     else:
-        if isinstance(window, str):
-            window = pd.Timedelta(window) // regular
-        if (window % 2 == 0) & center:
-            window = int(window - 1)
-
-        roller = to_fit.rolling(window=window, center=center, closed="both")
-        try:
-            means = getattr(roller, func.__name__)()
-        except AttributeError:
-            means = to_fit.rolling(window=window, center=center, closed="both").apply(
-                func
-            )
-
-    data[field] = means
-    worst = flags[field].rolling(window, center=True, min_periods=min_periods).max()
-    flags[field] = worst
+        to_fit = d_roller.apply(func)
 
+    flags_col = flags_col.rolling(window, min_periods=min_periods, center=center).max()
+    data[field] = to_fit
+    flags[field] = flags_col
     return data, flags