Spei, proper use of loc parameter in scipy dists

CHANGES.rst

@@ -4,12 +4,20 @@ Changelog
 
 v0.49.0 (unreleased)
 --------------------
-Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`).
+Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`), Éric Dupuis (:user:`coxipi`).
 
 Announcements
 ^^^^^^^^^^^^^
 * `xclim` has migrated its development branch name from `master` to `main`. (:issue:`1667`, :pull:`1669`).
 
+New features and enhancements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+* Distribution with negative values are directly fitted without need for an offset for distributions such as `gamma, fisk` in ``xclim.indices.standardized_precipitation_evapotranspiration_index``. (:issue:`1477`  :pull:`1653`).
+
+Breaking changes
+^^^^^^^^^^^^^^^^
+* Argument `offset` is removed  from ``xclim.indices.standardized_precipitation_evapotranspiration_index``. This will slightly change results when using SPEI with default values (:issue:`1477`  :pull:`1653`).
+
 Bug fixes
 ^^^^^^^^^
 * Fixed an bug in sdba's ``map_groups`` that prevented passing DataArrays with cftime coordinates if the ``sdba_encode_cf`` option was True. (:issue:`1673`, :pull:`1674`).
@@ -19,7 +27,6 @@ Internal changes
 ^^^^^^^^^^^^^^^^
 * Added "doymin" and "doymax" to the possible operations of ``generic.stats``. Fixed a warning issue when ``op`` was "integral". (:pull:`1672`).
 
-
 v0.48.2 (2024-02-26)
 --------------------
 Contributors to this version: Juliette Lavoie (:user:`juliettelavoie`).

tests/test_indices.py

@@ -695,14 +695,15 @@ def test_standardized_precipitation_evapotranspiration_index(
             tas = tasmax - 2.5
             tasmin = tasmax - 5
             wb = xci.water_budget(pr, None, tasmin, tasmax, tas)
+            # offset like in climate indices library
+            wb = wb + convert_units_to("1 mm/d", wb, context="hydro")
 
         params = xci.stats.standardized_index_fit_params(
             wb.sel(time=slice("1950", "1980")),
             freq=freq,
             window=window,
             dist=dist,
             method=method,
-            offset="1 mm/d",
         )
         spei = xci.standardized_precipitation_evapotranspiration_index(
             wb.sel(time=slice("1998", "2000")), params=params

xclim/indices/_agro.py

@@ -24,11 +24,7 @@
 )
 from xclim.indices.generic import aggregate_between_dates, get_zones
 from xclim.indices.helpers import _gather_lat, day_lengths
-from xclim.indices.stats import (
-    preprocess_standardized_index,
-    standardized_index,
-    standardized_index_fit_params,
-)
+from xclim.indices.stats import standardized_index
 
 # Frequencies : YS: year start, QS-DEC: seasons starting in december, MS: month start
 # See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
@@ -1094,16 +1090,15 @@ def _get_rain_season(pram):
 
 @declare_units(
     pr="[precipitation]",
-    pr_cal="[precipitation]",
     params="[]",
 )
 def standardized_precipitation_index(
     pr: xarray.DataArray,
-    pr_cal: Quantified | None = None,
     freq: str | None = "MS",
     window: int = 1,
     dist: str = "gamma",
     method: str = "APP",
+    fitkwargs: dict = {},
     cal_start: DateStr | None = None,
     cal_end: DateStr | None = None,
     params: Quantified | None = None,
@@ -1115,9 +1110,6 @@ def standardized_precipitation_index(
     ----------
     pr : xarray.DataArray
         Daily precipitation.
-    pr_cal : xarray.DataArray, optional
-        Daily precipitation used for calibration. Usually this is a temporal subset of `pr` over some reference period.
-        This option will be removed in xclim >=0.47.0. Two behaviour will be possible (see below)
     freq : str, optional
         Resampling frequency. A monthly or daily frequency is expected. Option `None` assumes that desired resampling
         has already been applied input dataset and will skip the resampling step.
@@ -1129,6 +1121,8 @@ def standardized_precipitation_index(
     method : {'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
+        Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
     cal_start : DateStr, optional
         Start date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
         Default option `None` means that the calibration period begins at the start of the input dataset.
@@ -1189,75 +1183,33 @@ def standardized_precipitation_index(
     ----------
     :cite:cts:`mckee_relationship_1993`
     """
-    if params is not None and pr_cal is None:
-        freq, window, indexer = (
-            params.attrs[s] for s in ["freq", "window", "time_indexer"]
-        )
-        # Unpack attrs to None and {} if needed
-        freq = None if freq == "" else freq
-        indexer = {} if indexer[0] == "" else {indexer[0]: indexer[1:]}
-        if cal_start or cal_end:
-            warnings.warn(
-                "Expected either `cal_{start|end}` or `params`, got both. The `params` input overrides other inputs."
-                "If `cal_start`, `cal_end`, `freq`, `window`, and/or `dist` were given as input, they will be ignored."
+    dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
+    if dist in dist_methods.keys():
+        if method not in dist_methods[dist]:
+            raise NotImplementedError(
+                f"{method} method is not implemented for {dist} distribution"
             )
-
-    if pr_cal is not None:
-        warnings.warn(
-            "Inputting a calibration array will be deprecated in xclim>=0.47.0. "
-            "For example, if `pr_cal` is a subset of `pr`, then instead of say:\n"
-            "`standardized_precipitation_index(pr=pr,pr_cal=pr.sel(time=slice(t0,t1)),...)`,\n"
-            "one can call:\n"
-            "`standardized_precipitation_index(pr=pr,cal_range=(t0,t1),...).\n"
-            "If for some reason `pr_cal` is not a subset of `pr`, then the following approach will still be possible:\n"
-            "`params = standardized_index_fit_params(da=pr_cal, freq=freq, window=window, dist=dist, method=method)`.\n"
-            "`spi = standardized_precipitation_index(pr=pr, params=params)`.\n"
-            "This approach can be used in both scenarios to break up the computations in two,"
-            "i.e. get params, then compute standardized indices."
-        )
-        params = standardized_index_fit_params(
-            pr_cal, freq=freq, window=window, dist=dist, method=method, **indexer
-        )
-
-    pr, _ = preprocess_standardized_index(pr, freq=freq, window=window, **indexer)
-    if params is None:
-        params = standardized_index_fit_params(
-            pr.sel(time=slice(cal_start, cal_end)),
-            freq=None,
-            window=1,
-            dist=dist,
-            method=method,
-        )
-
-    # If params only contains a subset of main dataset time grouping
-    # (e.g. 8/12 months, etc.), it needs to be broadcasted
-    template = pr.groupby(params.attrs["group"]).first()
-    paramsd = {k: v for k, v in params.sizes.items() if k != "dparams"}
-    if paramsd != template.sizes:
-        params = params.broadcast_like(template)
-
-    spi = standardized_index(pr, params)
-    spi.attrs = params.attrs
-    spi.attrs["freq"] = (freq or xarray.infer_freq(spi.time)) or "undefined"
-    spi.attrs["window"] = window
-    spi.attrs["units"] = ""
+    else:
+        raise NotImplementedError(f"{dist} distribution is not implemented yet")
+    spi = standardized_index(
+        pr, freq, window, dist, method, fitkwargs, cal_start, cal_end, params, **indexer
+    )
     return spi
 
 
 @declare_units(
     wb="[precipitation]",
-    wb_cal="[precipitation]",
     offset="[precipitation]",
     params="[]",
 )
 def standardized_precipitation_evapotranspiration_index(
     wb: xarray.DataArray,
-    wb_cal: Quantified | None = None,
     freq: str | None = "MS",
     window: int = 1,
     dist: str = "gamma",
     method: str = "APP",
-    offset: Quantified = "1.000 mm/d",
+    fitkwargs: dict = {},
+    offset: Quantified = "",
     cal_start: DateStr | None = None,
     cal_end: DateStr | None = None,
     params: Quantified | None = None,
@@ -1273,9 +1225,6 @@ def standardized_precipitation_evapotranspiration_index(
     ----------
     wb : xarray.DataArray
         Daily water budget (pr - pet).
-    wb_cal : xarray.DataArray, optional
-        Daily water budget used for calibration. Usually this is a temporal subset of `wb` over some reference period.
-        This option will be removed in xclim >=0.47.0. Two behaviours will be possible (see below).
     freq : str, optional
         Resampling frequency. A monthly or daily frequency is expected. Option `None` assumes that desired resampling
         has already been applied input dataset and will skip the resampling step.
@@ -1289,6 +1238,12 @@ def standardized_precipitation_evapotranspiration_index(
         `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'}
+    fitkwargs : dict
+        Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
+    offset : Quantified
+        For distributions bounded by zero (e.g. "gamma", "fisk"), the two-parameters distributions only accept positive
+        values. An offset can be added to make sure this is the case. This option will be removed in xclim >=0.49.0, ``xclim``
+        will rely on three-parameters distributions instead.
     cal_start : DateStr, optional
         Start date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
         Default option `None` means that the calibration period begins at the start of the input dataset.
@@ -1299,11 +1254,6 @@ def standardized_precipitation_evapotranspiration_index(
         Fit parameters.
         The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
         The output can be given here as input, and it overrides other options.
-    offset : Quantified
-        For distributions bounded by zero (e.g. "gamma", "fisk"), an offset must be added to the water budget
-        to make sure there are no negative values.
-        Keep the offset as small as possible to minimize its influence on the results.
-        This can be given as a precipitation flux or a rate.
     \*\*indexer
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
@@ -1323,7 +1273,9 @@ def standardized_precipitation_evapotranspiration_index(
 
     See Standardized Precipitation Index (SPI) for more details on usage.
     """
-    uses_default_offset = offset == "1.000 mm/d"
+    uses_default_offset = offset != ""
+    if uses_default_offset is False:
+        warnings.warn("Inputting an offset will be deprecated in xclim>=0.49.0. ")
     if params is not None:
         params_offset = params.attrs["offset"]
         if uses_default_offset is False and offset != params_offset:
@@ -1337,20 +1289,26 @@ def standardized_precipitation_evapotranspiration_index(
     if dist in ["gamma", "fisk"] and offset <= 0:
         raise ValueError(
             "The water budget must be shifted towards positive values to be used with `gamma` and `fisk` "
-            "distributions which are bounded by zero. A positive offset is required. Current value: "
-            f"{offset}{wb.attrs['units']}."
+            "distributions which are bounded by zero (only when using two-parameters distributions: in xclim>=0.49.0,"
+            "three-parameters distributions are used to accommodate negative values). Only positive offsets are accepted."
         )
+
     # Note that the default behaviour would imply an offset for any distribution, even those distributions
     # that can accommodate negative values of the water budget. This needs to be changed in future versions
     # of the index.
     if offset != 0:
         with xarray.set_options(keep_attrs=True):
             wb = wb + offset
-            if wb_cal is not None:
-                wb_cal = wb_cal + offset
-
-    spei = standardized_precipitation_index(
-        wb, wb_cal, freq, window, dist, method, cal_start, cal_end, params, **indexer
+    dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
+    if dist in dist_methods.keys():
+        if method not in dist_methods[dist]:
+            raise NotImplementedError(
+                f"{method} method is not implemented for {dist} distribution"
+            )
+    else:
+        raise NotImplementedError(f"{dist} distribution is not implemented yet")
+    spei = standardized_index(
+        wb, freq, window, dist, method, fitkwargs, cal_start, cal_end, params, **indexer
     )
 
     return spei