BUG : avoid maximum fill value of datetime and timedelta return NaT in masked array (numpy#27643)

Fill datetime/timedelta with the correct minimum value for the masked maximum operations.
The lowest integer value is NaT, so the actual smallest value is `-2**63+1`.

---------

Co-authored-by: fengluo <[email protected]>
Co-authored-by: Sebastian Berg <[email protected]>

Author: 3 people

numpy/ma/core.py

@@ -193,7 +193,7 @@ class MaskError(MAError):
 
     if scalar_dtype.kind in "Mm":
         info = np.iinfo(np.int64)
-        min_val, max_val = info.min, info.max
+        min_val, max_val = info.min + 1, info.max
     elif np.issubdtype(scalar_dtype, np.integer):
         info = np.iinfo(sctype)
         min_val, max_val = info.min, info.max
@@ -5979,7 +5979,7 @@ def min(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
                 result = masked
             return result
         # Explicit output
-        result = self.filled(fill_value).min(axis=axis, out=out, **kwargs)
+        self.filled(fill_value).min(axis=axis, out=out, **kwargs)
         if isinstance(out, MaskedArray):
             outmask = getmask(out)
             if outmask is nomask:
@@ -6084,7 +6084,7 @@ def max(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
                 result = masked
             return result
         # Explicit output
-        result = self.filled(fill_value).max(axis=axis, out=out, **kwargs)
+        self.filled(fill_value).max(axis=axis, out=out, **kwargs)
         if isinstance(out, MaskedArray):
             outmask = getmask(out)
             if outmask is nomask:

numpy/ma/tests/test_core.py

@@ -1351,6 +1351,26 @@ def test_minmax_dtypes(self):
             assert masked_array([-cmax, 0], mask=[0, 1]).max() == -cmax
             assert masked_array([cmax, 0], mask=[0, 1]).min() == cmax
 
+    @pytest.mark.parametrize("time_type", ["M8[s]", "m8[s]"])
+    def test_minmax_time_dtypes(self, time_type):
+        def minmax_with_mask(arr, mask):
+            masked_arr = masked_array(arr, mask=mask)
+            expected_min = arr[~np.array(mask, dtype=bool)].min()
+            expected_max = arr[~np.array(mask, dtype=bool)].max()
+
+            assert_array_equal(masked_arr.min(), expected_min)
+            assert_array_equal(masked_arr.max(), expected_max)
+
+        # Additional tests on max/min for time dtypes
+        x1 = np.array([1, 1, -2, 4, 5, -10, 10, 1, 2, -2**63+1], dtype=time_type)
+        x2 = np.array(['NaT', 1, -2, 4, 5, -10, 10, 1, 2, 3], dtype=time_type)
+        x3 = np.array(['NaT', 'NaT', -2, 4, 5, -10, 10, 1, 2, 3], dtype=time_type)
+        x_test = [x1, x2, x3]
+        m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0]
+
+        for x in x_test:
+            minmax_with_mask(x, m)
+
     def test_addsumprod(self):
         # Tests add, sum, product.
         (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d