Rolling statistics

CHANGES.rst

@@ -4,7 +4,7 @@ Changelog
 
 v0.48 (unreleased)
 ------------------
-Contributors to this version: Juliette Lavoie (:user:`juliettelavoie`), Pascal Bourgault (:user:`aulemahal`), Trevor James Smith (:user:`Zeitsperre`), David Huard (:user:`huard`), Éric Dupuis (:user:`coxipi`), Dante Castro (:user:`profesorpaiche`).
+Contributors to this version: Juliette Lavoie (:user:`juliettelavoie`), Pascal Bourgault (:user:`aulemahal`), Trevor James Smith (:user:`Zeitsperre`), David Huard (:user:`huard`), Éric Dupuis (:user:`coxipi`), Dante Castro (:user:`profesorpaiche`), Gabriel Rondeau-Genesse (:user:`RondeauG`).
 
 Announcements
 ^^^^^^^^^^^^^
@@ -22,10 +22,10 @@ New features and enhancements
 * New ``xclim.core.calendar.stack_periods`` and ``unstack_periods`` for performing ``rolling(time=...).construct(..., stride=...)`` but with non-uniform temporal periods like years or months. They replace ``xclim.sdba.processing.construct_moving_yearly_window`` and ``unpack_moving_yearly_window`` which are deprecated and will be removed in a future release.
 * New ``as_dataset`` options for ``xclim.set_options``. When True, indicators will output Datasets instead of DataArrays. (:issue:`1257`, :pull:`1625`).
 * Added new option for UTCI calculation to cap low wind velocities to a minimum of 0.5 m/s following Bröde (2012) guidelines. (:issue:`1634`, :pull:`1635`).
-
 * Added option ``never_reached`` to ``degree_days_exceedance_date`` to assign a custom value when the sum threshold is never reached. (:issue:`1459`, :pull:`1647`).
 * Added option ``min_members`` to ensemble statistics to mask elements when the number of valid members is under a threshold. (:issue:`1459`, :pull:`1647`).
 * Distribution instances can now be passed to the ``dist`` argument of most statistical indices. (:pull:`1644`).
+* Added a new `xclim.indices.generic.select_rolling_resample_op` function to allow for computing rolling statistics. (:issue:`1480`, :pull:`1643`).
 
 Breaking changes
 ^^^^^^^^^^^^^^^^

tests/test_generic.py

@@ -31,6 +31,47 @@ def test_season(self, q_series):
         assert o[0] == 31 + 29
 
 
+class TestSelectRollingResampleOp:
+    def test_rollingmax(self, q_series):
+        q = q_series(np.arange(1, 366 + 365 + 365 + 1))  # 1st year is leap
+        o = generic.select_rolling_resample_op(
+            q, "max", window=14, window_center=False, window_op="mean"
+        )
+        np.testing.assert_array_equal(
+            [
+                np.mean(np.arange(353, 366 + 1)),
+                np.mean(np.arange(353 + 365, 366 + 365 + 1)),
+                np.mean(np.arange(353 + 365 * 2, 366 + 365 * 2 + 1)),
+            ],
+            o.values,
+        )
+        assert o.attrs["units"] == "m3 s-1"
+
+    def test_rollingmaxindexer(self, q_series):
+        q = q_series(np.arange(1, 366 + 365 + 365 + 1))  # 1st year is leap
+        o = generic.select_rolling_resample_op(
+            q, "min", window=14, window_center=False, window_op="max", season="DJF"
+        )
+        np.testing.assert_array_equal(
+            [14, 367, 367 + 365], o.values
+        )  # 14th day for 1st year, then Jan 1st for the next two
+        assert o.attrs["units"] == "m3 s-1"
+
+    def test_freq(self, q_series):
+        q = q_series(np.arange(1, 366 + 365 + 365 + 1))  # 1st year is leap
+        o = generic.select_rolling_resample_op(
+            q, "max", window=3, window_center=True, window_op="integral", freq="MS"
+        )
+        np.testing.assert_array_equal(
+            [
+                np.sum([30, 31, 32]) * 86400,
+                np.sum([30 + 29, 31 + 29, 32 + 29]) * 86400,
+            ],  # m3 s-1 being summed by the frequency of the data
+            o.isel(time=slice(0, 2)).values,
+        )
+        assert o.attrs["units"] == "m3"
+
+
 class TestThresholdCount:
     def test_simple(self, tas_series):
         ts = tas_series(np.arange(365))

xclim/indices/generic.py

@@ -104,6 +104,52 @@ def select_resample_op(
     return to_agg_units(out, da, op)
 
 
+def select_rolling_resample_op(
+    da: xr.DataArray,
+    op: str,
+    window: int,
+    window_center: bool = True,
+    window_op: str = "mean",
+    freq: str = "YS",
+    out_units=None,
+    **indexer,
+) -> xr.DataArray:
+    """Apply operation over each period that is part of the index selection, using a rolling window before the operation.
+
+    Parameters
+    ----------
+    da : xr.DataArray
+        Input data.
+    op : str {'min', 'max', 'mean', 'std', 'var', 'count', 'sum', 'integral', 'argmax', 'argmin'} or func
+        Reduce operation. Can either be a DataArray method or a function that can be applied to a DataArray.
+    window : int
+        Size of the rolling window (centered).
+    window_center : bool
+        If True, the window is centered on the date. If False, the window is right-aligned.
+    window_op : str {'min', 'max', 'mean', 'std', 'var', 'count', 'sum', 'integral'}
+        Operation to apply to the rolling window. Default: 'mean'.
+    freq : str
+        Resampling frequency defining the periods as defined in :ref:`timeseries.resampling`. Applied after the rolling window.
+    out_units : str, optional
+        Output units to assign. Only necessary if `op` is function not supported by :py:func:`xclim.core.units.to_agg_units`.
+    indexer : {dim: indexer, }, optional
+        Time attribute and values over which to subset the array. For example, use season='DJF' to select winter values,
+        month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are
+        considered.
+
+    Returns
+    -------
+    xr.DataArray
+        The array for which the operation has been applied over each period.
+    """
+    rolled = getattr(
+        da.rolling(time=window, center=window_center),
+        window_op.replace("integral", "sum"),
+    )()
+    rolled = to_agg_units(rolled, da, window_op)
+    return select_resample_op(rolled, op=op, freq=freq, out_units=out_units, **indexer)
+
+
 def doymax(da: xr.DataArray) -> xr.DataArray:
     """Return the day of year of the maximum value."""
     i = da.argmax(dim="time")