Merge 88e0593 into 94b8ecb

CHANGES.rst

@@ -6,11 +6,18 @@ v0.9.0 (unreleased)
 -------------------
 Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`), Gabriel Rondeau-Genesse (:user:`RondeauG`), Juliette Lavoie (:user:`juliettelavoie`), Marco Braun (:user:`vindelico`).
 
+New features and enhancements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+* ``xs.reduce_ensemble`` will now call ``xclim.ensembles.create_ensemble`` and ``xclim.ensembles.make_critera`` if required. (:pull:`386`).
+
 Breaking changes
 ^^^^^^^^^^^^^^^^
 * Removed support for the old instances of the `region` argument in ``spatial_mean``, ``extract_dataset``, and ``subset``. (:pull:`367`).
 * Removed ``xscen.extract.clisops_subset``. (:pull:`367`).
 * ``dtr`` (the function) was renamed to ``dtr_from_minmax`` to avoid confusion with the `dtr` variable. (:pull:`372`).
+* The ``xscen.reduce`` module has been abandoned. (:pull:`386`).
+    * ``build_reduction_data`` has been made redundant by ``xclim.ensembles.make_critera`` and will be removed in a future release.
+    * ``xscen.reduce.reduce_ensemble`` has been moved to ``xscen.ensembles.reduce_ensemble``, as a module was no longer necessary.
 
 Internal changes
 ^^^^^^^^^^^^^^^^

docs/notebooks/4_ensembles.ipynb

@@ -55,11 +55,13 @@
     "for d in datasets:\n",
     "    ds = open_dataset(datasets[d]).isel(lon=slice(0, 4), lat=slice(0, 4))\n",
     "    ds = xs.climatological_op(\n",
-    "        ds, op=\"mean\", window=30, periods=[[1981, 2010], [2021, 2050]]\n",
-    "    )\n",
-    "    datasets[d] = xs.compute_deltas(ds, reference_horizon=\"1981-2010\")\n",
-    "    datasets[d].attrs[\"cat:id\"] = d  # Required by build_reduction_data\n",
-    "    datasets[d].attrs[\"cat:xrfreq\"] = \"AS-JAN\""
+    "        ds,\n",
+    "        op=\"mean\",\n",
+    "        window=30,\n",
+    "        periods=[[1981, 2010], [2021, 2050]],\n",
+    "        horizons_as_dim=True,\n",
+    "    ).drop_vars(\"time\")\n",
+    "    datasets[d] = xs.compute_deltas(ds, reference_horizon=\"1981-2010\")"
    ]
   },
   {
@@ -68,47 +70,14 @@
    "source": [
     "### Preparing the data\n",
     "\n",
-    "Ensemble reduction is built upon climate indicators that are relevant to represent the ensemble's variability for a given application. In this case, we'll use the mean temperature delta between 2021-2050 and 1981-2010.\n",
+    "Ensemble reduction is built upon climate indicators that are relevant to represent the ensemble's variability for a given application. In this case, we'll use the mean temperature delta between 2021-2050 and 1981-2010, but monthly or seasonal indicators could also be required. The `horizons_as_dim` argument in `climatological_op` can help combine indicators of multiple frequencies into a single dataset. Alternatively, `xscen.utils.unstack_dates` can also accomplish the same thing if the climatological operations have already been computed.\n",
     "\n",
-    "However, the functions implemented in `xclim.ensembles._reduce` require a very specific 2-D DataArray of dimensions \"realization\" and \"criteria\". That means that all the variables need to be combined and renamed, and that all dimensions need to be stacked together.\n",
+    "The functions implemented in `xclim.ensembles._reduce` require a very specific 2-D DataArray of dimensions \"realization\" and \"criteria\". The first solution is to first create an ensemble using `xclim.ensembles.create_ensemble`, then pass the result to `xclim.ensembles.make_criteria`. Alternatively, the datasets can be passed directly to `xscen.ensembles.reduce_ensemble` and the necessary preliminary steps will be accomplished automatically.\n",
     "\n",
-    "`xs.build_reduction_data` can be used to prepare the data for ensemble reduction. Its arguments are:\n",
+    "In this example, the number of criteria will corresponds to: `indicators x horizons x longitude x latitude`, but criteria that are purely NaN across all realizations will be removed.\n",
     "\n",
-    "- `datasets` (dict, list)\n",
-    "- `xrfreqs` are the unique frequencies of the indicators.\n",
-    "- `horizons` is used to instruct on which horizon(s) to build the data from.\n",
+    "Note that `xs.spatial_mean` could have been used prior to calling that function to remove the spatial dimensions.\n",
     "\n",
-    "Because a simulation could have multiple datasets (in the case of multiple frequencies), an attempt will be made to decipher the ID and frequency from the metadata."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = xs.build_reduction_data(\n",
-    "    datasets=datasets,\n",
-    "    xrfreqs=[\"AS-JAN\"],\n",
-    "    horizons=[\"2021-2050\"],\n",
-    ")\n",
-    "\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The number of criteria corresponds to: `indicators x horizons x longitude x latitude`, but criteria that are purely NaN across all realizations are removed.\n",
-    "\n",
-    "Note that `xs.spatial_mean` could have been used prior to calling that function to remove the spatial dimensions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "### Selecting a reduced ensemble\n",
     "\n",
     "<div class=\"alert alert-info\"> <b>NOTE</b>\n",
@@ -117,9 +86,11 @@
     "</div>\n",
     "\n",
     "Ensemble reduction through `xscen.reduce_ensemble` consists in a simple call to `xclim`. The arguments are:\n",
-    "- `data`, which is the 2D DataArray that is created by using `xs.build_reduction_data`.\n",
+    "- `data`, which is the aforementioned 2D DataArray, or the list/dict of datasets required to build it.\n",
     "- `method` is either `kkz` or `kmeans`. See the link above for further details on each technique.\n",
-    "- `kwargs` is a dictionary of arguments to send to the method chosen."
+    "- `horizons` is used to instruct on which horizon(s) to build the data from, if data needs to be constructed.\n",
+    "- `create_kwargs`, the arguments to pass to `xclim.ensembles.create_ensemble` if data needs to be constructed.\n",
+    "- `kwargs` is a dictionary of arguments to send to the clustering method chosen."
    ]
   },
   {
@@ -129,7 +100,7 @@
    "outputs": [],
    "source": [
     "selected, clusters, fig_data = xs.reduce_ensemble(\n",
-    "    data=data, method=\"kmeans\", kwargs={\"method\": {\"n_clusters\": 3}}\n",
+    "    data=datasets, method=\"kmeans\", horizons=[\"2021-2050\"], max_clusters=3\n",
     ")"
    ]
   },

pyproject.toml

@@ -162,7 +162,7 @@ values = [
 [tool.coverage.run]
 relative_files = true
 include = ["xscen/*"]
-omit = ["docs/notebooks/*.ipynb", "tests/*.py"]
+omit = ["docs/notebooks/*.ipynb", "tests/*.py", "xscen/reduce.py"] # FIXME: Remove xscen/reduce.py when it's fully deleted.
 
 [tool.isort]
 append_only = true

templates/1-basic_workflow_with_config/workflow1.py

@@ -417,7 +417,7 @@
                     xs.measure_time(name=f"{cur}", logger=logger),
                 ):
                     # Compute climatological mean
-                    ds_mean = xs.climatological_mean(ds=ds_input)
+                    ds_mean = xs.climatological_op(ds=ds_input)
 
                     # Save to zarr
                     path = f"{CONFIG['paths']['task']}".format(**cur)

tests/test_ensembles.py

@@ -10,6 +10,7 @@
     import xesmf as xe
 except ImportError:
     xe = None
+from xclim.testing import open_dataset
 from xclim.testing.helpers import test_timeseries as timeseries
 
 import xscen as xs
@@ -1119,3 +1120,57 @@ def test_build_partition_data(self, samplecat, tmp_path):
                 subset_kw=dict(name="mtl", method="gridpoint", lat=[45.0], lon=[-74]),
                 indicators_kw=dict(indicators=[xc.atmos.tg_mean, xc.indicators.cf.tg]),
             )
+
+
+class TestReduceEnsemble:
+    def test_with_criteria(self):
+        ds = open_dataset("EnsembleReduce/TestEnsReduceCriteria.nc")
+        selected, clusters, fig_data = xs.reduce_ensemble(
+            ds["data"], method="kmeans", max_clusters=3
+        )
+        assert selected.shape == (3,)
+        np.testing.assert_array_equal(selected, [4, 7, 23])
+        assert len(clusters) == 3
+        assert fig_data is not None
+
+    @pytest.mark.parametrize("horizon", ["1981-2010", "2021-2050"])
+    def test_without_criteria(self, horizon):
+        datasets = {
+            "ACCESS": "EnsembleStats/BCCAQv2+ANUSPLIN300_ACCESS1-0_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
+            "BNU-ESM": "EnsembleStats/BCCAQv2+ANUSPLIN300_BNU-ESM_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
+            "CCSM4-r1": "EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
+            "CCSM4-r2": "EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r2i1p1_1950-2100_tg_mean_YS.nc",
+            "CNRM-CM5": "EnsembleStats/BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_1970-2050_tg_mean_YS.nc",
+        }
+        for d in datasets:
+            ds = open_dataset(datasets[d]).isel(lon=slice(0, 4), lat=slice(0, 4))
+            ds = xs.climatological_op(
+                ds,
+                op="mean",
+                window=30,
+                periods=[["1981", "2010"], ["2021", "2050"]],
+                horizons_as_dim=True,
+            ).drop_vars("time")
+            datasets[d] = xs.compute_deltas(ds, reference_horizon="1981-2010")
+
+        selected, clusters, fig_data = xs.reduce_ensemble(
+            datasets, method="kkz", horizons=[horizon], num_select=4
+        )
+        assert selected.shape == (4,)
+        answer = (
+            ["ACCESS", "BNU-ESM", "CNRM-CM5", "CCSM4-r1"]
+            if horizon == "2021-2050"
+            else ["ACCESS", "BNU-ESM", "CCSM4-r1", "CCSM4-r2"]
+        )
+        np.testing.assert_array_equal(selected, answer)
+        assert clusters == {}
+        assert fig_data == {}
+
+    def test_errors(self):
+        ds = open_dataset("EnsembleReduce/TestEnsReduceCriteria.nc")
+        with pytest.raises(
+            ValueError, match="Data must have a 'horizon' dimension to be subsetted."
+        ):
+            xs.reduce_ensemble(
+                ds["data"], method="kmeans", horizons=["1981-2010"], max_clusters=3
+            )

xscen/__init__.py

@@ -14,7 +14,6 @@
     extract,
     indicators,
     io,
-    reduce,
     regrid,
     scripting,
     spatial,
@@ -38,7 +37,6 @@
 )
 from .indicators import compute_indicators
 from .io import save_to_netcdf, save_to_table, save_to_zarr
-from .reduce import build_reduction_data, reduce_ensemble
 from .regrid import *
 from .scripting import (
     TimeoutException,

xscen/ensembles.py

@@ -25,6 +25,7 @@
     "build_partition_data",
     "ensemble_stats",
     "generate_weights",
+    "reduce_ensemble",
 ]
 
 
@@ -764,3 +765,76 @@ def build_partition_data(
     ens = ens.rename(rename_dict)
 
     return ens
+
+
+@parse_config
+def reduce_ensemble(
+    data: Union[xr.DataArray, dict, list, xr.Dataset],
+    method: str,
+    *,
+    horizons: Optional[list[str]] = None,
+    create_kwargs: Optional[dict] = None,
+    **kwargs,
+):
+    r"""Reduce an ensemble of simulations using clustering algorithms from xclim.ensembles.
+
+    Parameters
+    ----------
+    data : xr.DataArray
+        Selection criteria data : 2-D xr.DataArray with dimensions 'realization' and 'criteria'.
+        These are the values used for clustering. Realizations represent the individual original
+        ensemble members and criteria the variables/indicators used in the grouping algorithm.
+        This data can be generated using py:func:`xclim.ensembles.make_criteria`.
+        Alternatively, either a xr.Dataset, a list of xr.Dataset or a dictionary of xr.Dataset can be passed,
+        in which case the data will be built using py:func:`xclim.ensembles.create_ensemble` and py:func:`xclim.ensembles.make_criteria`.
+    method : str
+      ['kkz', 'kmeans']. Clustering method.
+    horizons : list of str, optional
+        Subset of horizons on which to create the data. Only used if `data` needs to be built.
+    create_kwargs : dict, optional
+        Arguments to pass to py:func:`xclim.ensembles.create_ensemble` if `data` is not an xr.DataArray.
+    \*\*kwargs : dict
+        Arguments to send to either py:func:`xclim.ensembles.kkz_reduce_ensemble` or py:func:`xclim.ensembles.kmeans_reduce_ensemble`.
+
+    Returns
+    -------
+    selected : xr.DataArray
+        DataArray of dimension 'realization' with the selected simulations.
+    clusters : dict
+        If using kmeans clustering, realizations grouped by cluster.
+    fig_data : dict
+        If using kmeans clustering, data necessary to call py:func:`xclim.ensembles.plot_rsqprofile`.
+
+    Notes
+    -----
+    If building `data` to be constructed by this function, the datasets should already have a climatology computed on them, such that the data
+    has no temporal dimension aside from the "horizon" coordinate (which is optional and might be used to subset the data).
+    If the indicators are a mix of yearly, seasonal, and monthly, they should be stacked on the same time/horizon axis and put in the same dataset.
+    You can use py:func:`xscen.utils.unstack_dates` on seasonal or monthly indicators to this end.
+    """
+    if isinstance(data, (list, dict)):
+        data = ensembles.create_ensemble(datasets=data, **(create_kwargs or {}))
+    if horizons:
+        if "horizon" not in data.dims:
+            raise ValueError("Data must have a 'horizon' dimension to be subsetted.")
+        data = data.sel(horizon=horizons)
+    if "criteria" not in data.dims:
+        data = ensembles.make_criteria(data)
+
+    selected = getattr(ensembles, f"{method}_reduce_ensemble")(data=data, **kwargs)
+
+    clusters = {}
+    fig_data = {}
+    if method == "kmeans":
+        fig_data = selected[2]
+        clusters_tmp = selected[1]
+        selected = selected[0]
+        realization = np.arange(len(clusters_tmp))
+
+        clusters = {
+            g: data.realization.isel(realization=realization[clusters_tmp == g])
+            for g in np.unique(clusters_tmp)
+        }
+    selected = data.realization.isel(realization=selected)
+
+    return selected, clusters, fig_data