Fix window attrs in SPI/SPEI (#1554)

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### Pull Request Checklist:
- [x] This PR addresses an already opened issue (for bug fixes /
features)
    - This PR fixes #xyz
- [ ] Tests for the changes have been added (for bug fixes / features)
- [ ] (If applicable) Documentation has been added / updated (for bug
fixes / features)
- [x] CHANGES.rst has been updated (with summary of main changes)
- [ ] Link to issue (:issue:`number`) and pull request (:pull:`number`)
has been added

### What kind of change does this PR introduce?

* If `params` is not given, we pre-process dataset (resampling with
`freq` rolling with a general `window`) before doing anything, then
compute `params` with `freq=None, window=1`. But we must adjust attrs to
reflect the rolling `window` done before computing the params.
* I added a missing reference for the approximation of gamma parameters

### Does this PR introduce a breaking change?
No

### Other information:
Yet another spi fix attrs ... oups

CHANGES.rst

@@ -22,7 +22,8 @@ Breaking changes
 
 Bug fixes
 ^^^^^^^^^
-* Fixed passing ``missing=0`` to ``xclim.core.calendar.convert_calendar`` (:issue:`1562`, :pull:`1563`).
+* Fixed passing ``missing=0`` to ``xclim.core.calendar.convert_calendar``. (:issue:`1562`, :pull:`1563`).
+* Fix wrong `window` attributes in ``xclim.indices.standardized_precipitation_index``, ``xclim.indices.standardized_precipitation_evapotranspiration_index``. (:issue:`1552`  :pull:`1554`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^

docs/references.bib

@@ -2103,3 +2103,18 @@ @article{Lafferty2023
    url = {https://www.nature.com/articles/s41612-023-00486-0},
    year = {2023},
 }
+
+@article{thom_1958,
+  title = {A Note on the Gamma Distribution},
+  author = {Thom, H. C. S.},
+  year = {1958},
+  journal = {Monthly Weather Review},
+  volume = {86},
+  number = {4},
+  pages = {117--122},
+  publisher = {{American Meteorological Society}},
+  issn = {1520-0493, 0027-0644},
+  doi = {10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2},
+  abstract = {Abstract The general properties of the gamma distribution, which has several applications in meteorology, are discussed. A short review of the general properties of good statistical estimators is given. This is applied to the gamma distribution to show that the maximum likelihood estimators are jointly sufficient. A new, simple approximation of the likelihood solutions is given, and the efficiency of the fitting procedure is computed.},
+  chapter = {Monthly Weather Review},
+}

xclim/indices/_agro.py

@@ -1239,6 +1239,7 @@ def standardized_precipitation_index(
     spi = standardized_index(pr, params)
     spi.attrs = params.attrs
     spi.attrs["freq"] = freq or xarray.infer_freq(spi.time)
+    spi.attrs["window"] = window
     spi.attrs["units"] = ""
     return spi
 

xclim/indices/stats.py

@@ -476,7 +476,7 @@ def _fit_start(x, dist: str, **fitkwargs: Any) -> tuple[tuple, dict]:
 
     References
     ----------
-    :cite:cts:`coles_introduction_2001,cohen_parameter_2019`
+    :cite:cts:`coles_introduction_2001,cohen_parameter_2019, thom_1958`
 
     """
     x = np.asarray(x)