Don't modify arrays when masking values for log.

QuLogic · QuLogic · commit 48eef460e8c8 · 2021-06-29T05:00:47.000-04:00
NumPy 1.21.0 fixed a bug with `np.ma.masked_where` where `copy=False`
now modifies the input if it is masked, which we do not want to do.
`np.ma.array` defaults to not copying the data, but copies the mask
(adding the new mask), which is what we wanted with `copy=False`.
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1545,11 +1545,11 @@ class LogNorm(Normalize):
 
     def autoscale(self, A):
         # docstring inherited.
-        super().autoscale(np.ma.masked_less_equal(A, 0, copy=False))
+        super().autoscale(np.ma.array(A, mask=(A <= 0)))
 
     def autoscale_None(self, A):
         # docstring inherited.
-        super().autoscale_None(np.ma.masked_less_equal(A, 0, copy=False))
+        super().autoscale_None(np.ma.array(A, mask=(A <= 0)))
 
 
 @_make_norm_from_scale(
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -1233,6 +1233,34 @@ def test_imshow_quantitynd():
     fig.canvas.draw()
 
 
+@check_figures_equal(extensions=['png'])
+def test_norm_change(fig_test, fig_ref):
+    # LogNorm should not mask anything invalid permanently.
+    data = np.full((5, 5), 1, dtype=np.float64)
+    data[0:2, :] = -1
+
+    masked_data = np.ma.array(data, mask=False)
+    masked_data.mask[0:2, 0:2] = True
+
+    cmap = plt.get_cmap('viridis').with_extremes(under='w')
+
+    ax = fig_test.subplots()
+    im = ax.imshow(data, norm=colors.LogNorm(vmin=0.5, vmax=1),
+                   extent=(0, 5, 0, 5), interpolation='nearest', cmap=cmap)
+    im.set_norm(colors.Normalize(vmin=-2, vmax=2))
+    im = ax.imshow(masked_data, norm=colors.LogNorm(vmin=0.5, vmax=1),
+                   extent=(5, 10, 5, 10), interpolation='nearest', cmap=cmap)
+    im.set_norm(colors.Normalize(vmin=-2, vmax=2))
+    ax.set(xlim=(0, 10), ylim=(0, 10))
+
+    ax = fig_ref.subplots()
+    ax.imshow(data, norm=colors.Normalize(vmin=-2, vmax=2),
+              extent=(0, 5, 0, 5), interpolation='nearest', cmap=cmap)
+    ax.imshow(masked_data, norm=colors.Normalize(vmin=-2, vmax=2),
+              extent=(5, 10, 5, 10), interpolation='nearest', cmap=cmap)
+    ax.set(xlim=(0, 10), ylim=(0, 10))
+
+
 @pytest.mark.parametrize('x', [-1, 1])
 @check_figures_equal(extensions=['png'])
 def test_huge_range_log(fig_test, fig_ref, x):
