move in power_scale_dataset from arviz-plots

src/arviz_stats/psense.py

@@ -227,6 +227,55 @@ def _diagnose(row):
     return psense_df.round(round_to)
 
 
+def power_scale_dataset(dt, group, alphas, sample_dims):
+    """Resample the dataset with the power scale weights.
+
+    Parameters
+    ----------
+    dt : DataSet
+    group : str
+        Group to resample. Either "prior" or "likelihood"
+    alphas : tuple of float
+        Lower and upper alpha values for power scaling.
+    sample_dims : str or sequence of hashable
+        Dimensions to reduce unless mapped to an aesthetic.
+
+    Returns
+    -------
+    DataSet with resampled data.
+    """
+    lower_w, upper_w = _get_power_scale_weights(dt, alphas, group=group, sample_dims=sample_dims)
+    lower_w = lower_w.values.flatten()
+    upper_w = upper_w.values.flatten()
+    s_size = len(lower_w)
+
+    idxs_to_drop = sample_dims if len(sample_dims) == 1 else ["sample"] + sample_dims
+    idxs_to_drop = set(idxs_to_drop).union(
+        [
+            idx
+            for idx in dt["posterior"].xindexes
+            if any(dim in dt["posterior"][idx].dims for dim in sample_dims)
+        ]
+    )
+    resampled = [
+        extract(
+            dt,
+            group="posterior",
+            sample_dims=sample_dims,
+            num_samples=s_size,
+            weights=weights,
+            random_seed=42,
+            resampling_method="stratified",
+        ).drop_indexes(idxs_to_drop)
+        for weights in (lower_w, upper_w)
+    ]
+    resampled.insert(
+        1, extract(dt, group="posterior", sample_dims=sample_dims).drop_indexes(idxs_to_drop)
+    )
+
+    return xr.concat(resampled, dim="alpha").assign_coords(alpha=[alphas[0], 1, alphas[1]])
+
+
 def _get_power_scale_weights(
     dt, alphas=None, group=None, sample_dims=None, group_var_names=None, group_coords=None
 ):