dev

Author: davidhassell

cf/data/data.py

@@ -2383,6 +2383,8 @@ def _regrid(
 
             {{max_masked: `int`, optional}}
 
+                .. versionadded:: NEXVERSION
+
         :Returns:
 
             `Data`

cf/docstring/docstring.py

@@ -411,8 +411,8 @@
                 The maximum allow number of masked source cells which
                 are allowed to be ignored when calculating a
                 non-masked destination cell. When masked source cells
-                are ignored, the weights of non-masked source cells i
-                are adjusted so that they sum to 1.
+                are ignored, the weights of the non-masked source
+                cells are adjusted so that they sum to 1.
 
                 Define ``w_ji`` as the multiplicative weight that
                 defines how much of ``Vs_i`` (the value in source grid
@@ -424,9 +424,7 @@
                 i is masked and ``w_ji >= min_weight``. If set to
                 ``N``, then destination grid cell j will be masked if
                 more than ``N`` source cells i are masked with ``w_ji
-                >= min_weight``.
-
-                .. versionadded:: NEXTVERSION""",
+                >= min_weight``.""",
     # weights_file
     "{{weights_file: `str` or `None`, optional}}": """weights_file: `str` or `None`, optional
                 Provide a netCDF file that contains, or will contain,

cf/regrid/regrid.py

@@ -399,6 +399,12 @@ def regrid(
     """
     debug = is_log_level_debug(logger)
 
+    if not isinstance(max_masked, int) or max_masked < 0:
+        raise ValueError(
+            "The max_masked keyword must be a non-negative integer. "
+            f"Got: {max_masked!r}"
+        )
+
     if not inplace:
         src = src.copy()
 

cf/test/test_regrid.py

@@ -914,18 +914,24 @@ def test_regrids_max_masked(self):
             )
         )
 
-        x = s.regrids(d, method="linear", use_dst_mask=False, max_masked=3)
-        self.assertTrue(
-            np.array_equal(
-                x.data.mask,
-                [
-                    [False, False, True, True, True, True, True, True],
-                    [False, False, False, True, True, True, True, True],
-                    [False, False, False, False, True, True, True, True],
-                    [False, False, False, False, False, True, True, True],
-                ],
+        for n in (3, 4, 5):
+            x = s.regrids(d, method="linear", use_dst_mask=False, max_masked=n)
+            self.assertTrue(
+                np.array_equal(
+                    x.data.mask,
+                    [
+                        [False, False, True, True, True, True, True, True],
+                        [False, False, False, True, True, True, True, True],
+                        [False, False, False, False, True, True, True, True],
+                        [False, False, False, False, False, True, True, True],
+                    ],
+                )
             )
-        )
+
+        # Check bad values of max_masked
+        for n in (-1, 3.14, "string", None):
+            with self.assertRaises(ValueError):
+                x = s.regrids(d, method="linear", max_masked=n)
 
     def test_regridc_max_masked(self):
         """Test max_masked keyword to regridc."""
@@ -1011,20 +1017,21 @@ def test_regridc_max_masked(self):
             )
         )
 
-        x = s.regridc(
-            d, axes=axes, method="linear", use_dst_mask=False, max_masked=3
-        )
-        self.assertTrue(
-            np.array_equal(
-                x.data.mask,
-                [
-                    [True, False, True, True, True, True, True, True],
-                    [True, False, False, True, True, True, True, True],
-                    [True, False, False, False, True, True, True, True],
-                    [True, False, False, False, False, True, True, True],
-                ],
+        for n in (3, 4, 5):
+            x = s.regridc(
+                d, axes=axes, method="linear", use_dst_mask=False, max_masked=n
+            )
+            self.assertTrue(
+                np.array_equal(
+                    x.data.mask,
+                    [
+                        [True, False, True, True, True, True, True, True],
+                        [True, False, False, True, True, True, True, True],
+                        [True, False, False, False, True, True, True, True],
+                        [True, False, False, False, False, True, True, True],
+                    ],
+                )
             )
-        )
 
 
 if __name__ == "__main__":