matplotlib
diff --git a/‎lib/matplotlib/image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/image.py
+3-3Lines changed: 3 additions & 3 deletions b/‎lib/matplotlib/image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/image.py
+3-3Lines changed: 3 additions & 3 deletions
diff --git a/‎lib/matplotlib/tests/test_image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_image.py
+10-9Lines changed: 10 additions & 9 deletions b/‎lib/matplotlib/tests/test_image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_image.py
+10-9Lines changed: 10 additions & 9 deletions
@@ -532,9 +532,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                 # we have re-set the vmin/vmax to account for small errors
                 # that may have moved input values in/out of range
                 s_vmin, s_vmax = vrange
-                if isinstance(self.norm, mcolors.LogNorm):
-                    if s_vmin < 0:
-                        s_vmin = max(s_vmin, np.finfo(scaled_dtype).eps)
+                if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0:
+                    # Don't give 0 or negative values to LogNorm
+                    s_vmin = np.finfo(scaled_dtype).eps
                 with cbook._setattr_cm(self.norm,
                                        vmin=s_vmin,
                                        vmax=s_vmax,
 
@@ -1233,23 +1233,24 @@ def test_imshow_quantitynd():
     fig.canvas.draw()
 
 
+@pytest.mark.parametrize('x', [-1, 1])
 @check_figures_equal(extensions=['png'])
-def test_huge_range_log(fig_test, fig_ref):
-    data = np.full((5, 5), -1, dtype=np.float64)
+def test_huge_range_log(fig_test, fig_ref, x):
+    # parametrize over bad lognorm -1 values and large range 1 -> 1e20
+    data = np.full((5, 5), x, dtype=np.float64)
     data[0:2, :] = 1E20
 
     ax = fig_test.subplots()
-    im = ax.imshow(data, norm=colors.LogNorm(vmin=100, vmax=data.max()),
-                   interpolation='nearest', cmap='viridis')
+    ax.imshow(data, norm=colors.LogNorm(vmin=1, vmax=data.max()),
+              interpolation='nearest', cmap='viridis')
 
-    data = np.full((5, 5), -1, dtype=np.float64)
+    data = np.full((5, 5), x, dtype=np.float64)
     data[0:2, :] = 1000
 
-    cmap = copy(plt.get_cmap('viridis'))
-    cmap.set_under('w')
     ax = fig_ref.subplots()
-    im = ax.imshow(data, norm=colors.Normalize(vmin=100, vmax=data.max()),
-                   interpolation='nearest', cmap=cmap)
+    cmap = plt.get_cmap('viridis').with_extremes(under='w')
+    ax.imshow(data, norm=colors.Normalize(vmin=1, vmax=data.max()),
+              interpolation='nearest', cmap=cmap)
 
 
 @check_figures_equal()