matplotlib
diff --git a/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+9-5Lines changed: 9 additions & 5 deletions b/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+9-5Lines changed: 9 additions & 5 deletions
diff --git a/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+10Lines changed: 10 additions & 0 deletions b/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+10Lines changed: 10 additions & 0 deletions
@@ -1038,6 +1038,12 @@ def __call__(self, value, clip=None):
 class LogNorm(Normalize):
     """Normalize a given value to the 0-1 range on a log scale."""
 
+    def _check_vmin_vmax(self):
+        if self.vmin > self.vmax:
+            raise ValueError("minvalue must be less than or equal to maxvalue")
+        elif self.vmin <= 0:
+            raise ValueError("minvalue must be positive")
+
     def __call__(self, value, clip=None):
         if clip is None:
             clip = self.clip
@@ -1047,12 +1053,9 @@ def __call__(self, value, clip=None):
         result = np.ma.masked_less_equal(result, 0, copy=False)
 
         self.autoscale_None(result)
+        self._check_vmin_vmax()
         vmin, vmax = self.vmin, self.vmax
-        if vmin > vmax:
-            raise ValueError("minvalue must be less than or equal to maxvalue")
-        elif vmin <= 0:
-            raise ValueError("values must all be positive")
-        elif vmin == vmax:
+        if vmin == vmax:
             result.fill(0)
         else:
             if clip:
@@ -1078,6 +1081,7 @@ def __call__(self, value, clip=None):
     def inverse(self, value):
         if not self.scaled():
             raise ValueError("Not invertible until scaled")
+        self._check_vmin_vmax()
         vmin, vmax = self.vmin, self.vmax
 
         if np.iterable(value):
 
@@ -145,6 +145,16 @@ def test_BoundaryNorm():
     assert np.all(bn(vals).mask)
 
 
+@pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]])
+def test_lognorm_invalid(vmin, vmax):
+    # Check that invalid limits in LogNorm error
+    norm = mcolors.LogNorm(vmin=vmin, vmax=vmax)
+    with pytest.raises(ValueError):
+        norm(1)
+    with pytest.raises(ValueError):
+        norm.inverse(1)
+
+
 def test_LogNorm():
     """
     LogNorm ignored clip, now it has the same