Fix regrid.stats for unsorted input coords (#58)

* Add test for unsorted coords in stats routine

* Copy strategy from conservative routines

* Fix type checker, formatting

* Update changelog, bump version

Author: BSchilperoort

CHANGELOG.md

@@ -4,12 +4,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/).
 
-## Unreleased
+## 0.4.1 (2025-04-08)
 
 Fixed:
  - Attributes are now properly preserved when updating coordinates during pre-formatting for regridding ([#54](https://github.com/xarray-contrib/xarray-regrid/pull/54)).
  - Handle datasets with inconsistent chunksizes during pre-formatting ([#57](https://github.com/xarray-contrib/xarray-regrid/pull/57)).
-
+ - `regrid.stats` should properly regrid input data even when coordinates are not monotonic ([#58](https://github.com/xarray-contrib/xarray-regrid/pull/58)).
 
 ## 0.4.0 (2024-09-26)
 

src/xarray_regrid/__init__.py

@@ -9,4 +9,4 @@
     "methods",
 ]
 
-__version__ = "0.4.0"
+__version__ = "0.4.1"

src/xarray_regrid/methods/flox_reduce.py

@@ -72,15 +72,19 @@ def statistic_reduce(
         msg = f"Invalid method. Please choose from '{valid_methods}'."
         raise ValueError(msg)
 
-    coords = utils.common_coords(data, target_ds, remove_coord=time_dim)
-    target_coords = xr.Dataset(target_ds.coords)  # coords target coords for reindexing
-    sorted_target_coords = target_coords.sortby(coords)
+    # Make sure the regridding coordinates are sorted
+    coord_names = utils.common_coords(data, target_ds, remove_coord=time_dim)
+    sorted_target_coords = xr.Dataset(coords=target_ds.coords)
+    for coord_name in coord_names:
+        sorted_target_coords = utils.ensure_monotonic(sorted_target_coords, coord_name)
+        data = utils.ensure_monotonic(data, coord_name)
+    coords = {name: sorted_target_coords[name] for name in coord_names}
 
     bounds = tuple(
         construct_intervals(sorted_target_coords[coord].to_numpy()) for coord in coords
     )
 
-    data = reduce_data_to_new_domain(data, sorted_target_coords, coords)
+    data = reduce_data_to_new_domain(data, sorted_target_coords, coord_names)
 
     result: xr.Dataset = flox.xarray.xarray_reduce(
         data,
@@ -91,8 +95,8 @@ def statistic_reduce(
         fill_value=fill_value,
     )
 
-    result = restore_properties(result, data, target_ds, coords, fill_value)
-    result = result.reindex_like(target_coords, copy=False)
+    result = restore_properties(result, data, target_ds, coord_names, fill_value)
+    result = result.reindex_like(sorted_target_coords, copy=False)
     return result
 
 

tests/test_reduce.py

@@ -233,3 +233,28 @@ def test_var(dummy_lc_data, dummy_target_grid):
         dummy_lc_data["lc"].astype(float).regrid.stat(dummy_target_grid, "var"),
         make_expected_ds(expected_data)["lc"],
     )
+
+
+def test_unsorted_coords(dummy_lc_data, dummy_target_grid):
+    """Should pass if the input data has coords that are not ordered."""
+    expected_data = np.array(
+        [
+            [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+            [1.0, 0.75, 0.75, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [2.25, 0.0, 0.0, 0.0, 0.0, 0.25],
+            [0.0, 1.6875, 2.25, 0.0, 0.25, 0.0],
+        ]
+    )
+    lc_data = dummy_lc_data.copy()
+
+    lc_data["scramble_order"] = lc_data["latitude"] * 0 + np.array(
+        [1, 3, 7, 0, 2, 8, 9, -1, 5, 11, 12]
+    )
+    lc_data = lc_data.sortby("scramble_order")
+
+    xr.testing.assert_equal(
+        lc_data["lc"].astype(float).regrid.stat(dummy_target_grid, "var"),
+        make_expected_ds(expected_data)["lc"],
+    )