Support freq="W" in standardized indices

CHANGELOG.rst

@@ -29,6 +29,7 @@ New features and enhancements
 * ``xclim.indices.run_length.runs_with_holes`` allows to input a condition that must be met for a run to start and a second condition that must be met for the run to stop. (:pull:`1778`).
 * New generic compute function ``xclim.indices.generic.thresholded_events`` that finds events based on a threshold condition and returns basic stats for each. See also ``xclim.indices.run_length.find_events``. (:pull:`1778`).
 * ``xclim.core.units.rate2amount`` and ``xclim.core.units.amount2rate`` can now also accept quantities (pint objects or strings), in which case the ``dim`` argument must be the ``time`` coordinate through which we can find the sampling rate. (:pull:`1778`).
+* ``xclim.indices.stats.standardized_index`` now supports a weekly resampling frequency. Only "standard" calendars using numpy's `datetime64` dtype are supported for this mode. (:issue:`1892`, :pull:`1952`)
 
 Bug fixes
 ^^^^^^^^^

tests/test_indices.py

@@ -623,6 +623,38 @@ class TestStandardizedIndices:
                 [-0.24417774, -0.11404418, 0.64997039, 1.07670517, 0.6462852],
                 2e-2,
             ),
+            (
+                "W",
+                1,
+                "gamma",
+                "APP",
+                [0.64820146, 0.04991201, -1.62956493, 1.08898709, -0.01741762],
+                2e-2,
+            ),
+            (
+                "W",
+                12,
+                "gamma",
+                "APP",
+                [-1.08683311, -0.47230036, -0.7884111, 0.3341876, 0.06282969],
+                2e-2,
+            ),
+            (
+                "W",
+                1,
+                "gamma",
+                "ML",
+                [0.64676962, -0.06904886, -1.60493289, 1.07864037, -0.01415902],
+                2e-2,
+            ),
+            (
+                "W",
+                12,
+                "gamma",
+                "ML",
+                [-1.08627775, -0.46491398, -0.77806462, 0.31759127, 0.03794528],
+                2e-2,
+            ),
         ],
     )
     def test_standardized_precipitation_index(
@@ -636,8 +668,13 @@ def test_standardized_precipitation_index(
             pytest.skip("Skipping SPI/ML/D on older numpy")
         ds = open_dataset("sdba/CanESM2_1950-2100.nc").isel(location=1)
         if freq == "D":
-            ds = ds.convert_calendar("366_day", missing=np.nan)
             # to compare with ``climate_indices``
+            ds = ds.convert_calendar("366_day", missing=np.nan)
+        elif freq == "W":
+            # only standard calendar supported with freq="W"
+            ds = ds.convert_calendar(
+                "standard", missing=np.nan, align_on="year", use_cftime=False
+            )
         pr = ds.pr.sel(time=slice("1998", "2000"))
         pr_cal = ds.pr.sel(time=slice("1950", "1980"))
         fitkwargs = {}

xclim/indices/_agro.py

@@ -1162,6 +1162,8 @@ def standardized_precipitation_index(
     * N-month SPI / N-day SPI is determined by choosing the `window = N` and the appropriate frequency `freq`.
     * Supported statistical distributions are: ["gamma", "fisk"], where "fisk" is scipy's implementation of
        a log-logistic distribution
+    * Supported frequencies are daily ("D"), weekly ("W"), and monthly ("MS").
+      Weekly frequency will only work if the input array has a "standard" (non-cftime) calendar.
     * If `params` is given as input, it overrides the `cal_start`, `cal_end`, `freq` and `window`, `dist` and `method` options.
     * "APP" method only supports two-parameter distributions. Parameter `loc` needs to be fixed to use method `APP`.
     * The standardized index is bounded by ±8.21. 8.21 is the largest standardized index as constrained by the float64 precision in
@@ -1204,7 +1206,7 @@ def standardized_precipitation_index(
     :cite:cts:`mckee_relationship_1993`
     """
     fitkwargs = fitkwargs or {}
-    dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
+    dist_methods = {"gamma": ["ML", "APP"], "fisk": ["ML", "APP"]}
     if dist in dist_methods:
         if method not in dist_methods[dist]:
             raise NotImplementedError(
@@ -1267,10 +1269,8 @@ def standardized_precipitation_evapotranspiration_index(
     dist : {'gamma', 'fisk'}
         Name of the univariate distribution. (see :py:mod:`scipy.stats`).
     method : {'APP', 'ML'}
-        Name of the fitting method, such as `ML` (maximum likelihood), `APP` (approximate), or
-        `PWM` (probability weighted moments).
-        The approximate method uses a deterministic function that doesn't involve any optimization. Available methods
-        vary with the distribution: 'gamma':{'APP', 'ML', 'PWM'}, 'fisk':{'APP', 'ML'}
+        Name of the fitting method, such as `ML` (maximum likelihood), `APP` (approximate). The approximate method
+        uses a deterministic function that doesn't involve any optimization.
     fitkwargs : dict, optional
         Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
     cal_start : DateStr, optional
@@ -1298,7 +1298,7 @@ def standardized_precipitation_evapotranspiration_index(
     """
     fitkwargs = fitkwargs or {}
 
-    dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
+    dist_methods = {"gamma": ["ML", "APP"], "fisk": ["ML", "APP"]}
     if dist in dist_methods:
         if method not in dist_methods[dist]:
             raise NotImplementedError(

xclim/indices/stats.py

@@ -540,6 +540,8 @@ def _fit_start(x, dist: str, **fitkwargs: Any) -> tuple[tuple, dict]:
             loc0 = (x1 * xn - x2**2) / (x1 + xn - 2 * x2)
             loc0 = loc0 if loc0 < x1 else (0.9999 * x1 if x1 > 0 else 1.0001 * x1)
         x_pos = x - loc0
+        # TODO: change this?
+        # not necessary for log-logistic, according to SPEI package
         x_pos = x_pos[x_pos > 0]
         # method of moments:
         # LHS is computed analytically with the two-parameters log-logistic distribution
@@ -675,6 +677,8 @@ def preprocess_standardized_index(
             group = "time.dayofyear"
         elif compare_offsets(final_freq, "==", "MS"):
             group = "time.month"
+        elif compare_offsets(final_freq, "==", "W"):
+            group = "time.week"
         else:
             raise ValueError(
                 f"The input (following resampling if applicable) has a frequency `{final_freq}` "
@@ -775,8 +779,7 @@ def standardized_index_fit_params(
                 "Pass a value for `floc` in `fitkwargs`."
             )
 
-    # "WPM" method doesn't seem to work for gamma or pearson3
-    dist_and_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
+    dist_and_methods = {"gamma": ["ML", "APP"], "fisk": ["ML", "APP"]}
     dist = get_dist(dist)
     if dist.name not in dist_and_methods:
         raise NotImplementedError(f"The distribution `{dist.name}` is not supported.")
@@ -929,6 +932,9 @@ def reindex_time(da, da_ref, group):
         elif group == "time.month":
             da = da.rename(month="time").reindex(time=da_ref.time.dt.month)
             da["time"] = da_ref.time
+        elif group == "time.week":
+            da = da.rename(week="time").reindex(time=da_ref.time.dt.week)
+            da["time"] = da_ref.time
         # I don't think rechunking is necessary here, need to check
         return da if not uses_dask(da) else da.chunk({"time": -1})