TST/MNT fix errors for overlapping vmin, vcenter, vmax

jklymak · jklymak · commit 0aa344b6ad2f · 2018-10-07T08:08:10.000-07:00
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1040,7 +1040,7 @@ def __call__(self, value, clip=None):
         # or vmax.  We could error out if some stop points are
         # overrun, but this lets things keep going...
 
-        if not np.all(np.diff(self._data_points) >= 0):
+        if not np.all(np.diff(self._data_points) > 0):
              raise ValueError("data points must increase monotonically")
         ind = np.where((self._data_points >= vmin) &
                        (self._data_points <= vmax))[0]
@@ -1154,8 +1154,8 @@ def __call__(value, clip=None):
                 super().__call(value, clip=clip)
             except ValueError:
                 # improve the more general error message.
-                raise ValueError('vmin, vcenter, and vmax must '
-                                 'increment monotonically for DivergingNorm '
+                raise ValueError('vmin, vcenter, and vmax must increment '
+                                 'monotonically for DivergingNorm '
                                  'to work.')
 
 
diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -270,26 +270,17 @@ def test_DivergingNorm_Odd():
 
 
 def test_DivergingNorm_VminEqualsVcenter():
-    norm = mcolors.DivergingNorm(vmin=-2, vcenter=-2, vmax=2)
-    vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
-    expected = np.array([0.5, 0.625, 0.75, 0.875, 1.0])
-    assert_array_equal(norm(vals), expected)
+    vals = np.arange(50)
+    with pytest.raises(ValueError):
+        norm = mcolors.DivergingNorm(vmin=10, vcenter=10, vmax=20)
+        norm(vals)
 
 
 def test_DivergingNorm_VmaxEqualsVcenter():
-    norm = mcolors.DivergingNorm(vmin=-2, vcenter=2, vmax=2)
-    vals = np.array([-2.0, -1.0, 0.0, 1.0, 1.999, 2.0])
-    expected = np.array([0.0, 0.125, 0.25, 0.375, 0.499875, 1.0])
-    assert_array_equal(norm(vals), expected)
-
-
-def test_DivergingNorm_VsAllEqual():
-    v = 10
-    mcolors.DivergingNorm = mcolors.DivergingNorm
-    norm = mcolors.DivergingNorm(vmin=v, vcenter=v, vmax=v)
-    vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
-    expected = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
-    assert_array_equal(norm(vals), expected)
+    vals = np.arange(50)
+    with pytest.raises(ValueError):
+        norm = mcolors.DivergingNorm(vmin=0, vcenter=10, vmax=10)
+        norm(vals)
 
 
 def test_DivergingNorm_VminGTVcenter():
