Avoid in-place multiplication of a large value to an array with small…

… integer dtype (#8867)

* Avoid inplace multiplication

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* Update test_plot.py

* Update test_plot.py

* Update dataarray_plot.py

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xarray/plot/dataarray_plot.py

@@ -1848,9 +1848,10 @@ def _center_pixels(x):
         # missing data transparent.  We therefore add an alpha channel if
         # there isn't one, and set it to transparent where data is masked.
         if z.shape[-1] == 3:
-            alpha = np.ma.ones(z.shape[:2] + (1,), dtype=z.dtype)
+            safe_dtype = np.promote_types(z.dtype, np.uint8)
+            alpha = np.ma.ones(z.shape[:2] + (1,), dtype=safe_dtype)
             if np.issubdtype(z.dtype, np.integer):
-                alpha *= 255
+                alpha[:] = 255
             z = np.ma.concatenate((z, alpha), axis=2)
         else:
             z = z.copy()

xarray/tests/test_plot.py

@@ -2040,15 +2040,17 @@ def test_normalize_rgb_one_arg_error(self) -> None:
         for vmin2, vmax2 in ((-1.2, -1), (2, 2.1)):
             da.plot.imshow(vmin=vmin2, vmax=vmax2)
 
-    def test_imshow_rgb_values_in_valid_range(self) -> None:
-        da = DataArray(np.arange(75, dtype="uint8").reshape((5, 5, 3)))
+    @pytest.mark.parametrize("dtype", [np.uint8, np.int8, np.int16])
+    def test_imshow_rgb_values_in_valid_range(self, dtype) -> None:
+        da = DataArray(np.arange(75, dtype=dtype).reshape((5, 5, 3)))
         _, ax = plt.subplots()
         out = da.plot.imshow(ax=ax).get_array()
         assert out is not None
-        dtype = out.dtype
-        assert dtype is not None
-        assert dtype == np.uint8
+        actual_dtype = out.dtype
+        assert actual_dtype is not None
+        assert actual_dtype == np.uint8
         assert (out[..., :3] == da.values).all()  # Compare without added alpha
+        assert (out[..., -1] == 255).all()  # Compare alpha
 
     @pytest.mark.filterwarnings("ignore:Several dimensions of this array")
     def test_regression_rgb_imshow_dim_size_one(self) -> None: