added whats new entry for CenteredNorm and expanded testing to setters

jpmattern · jpmattern · commit 6f10076a91eb · 2020-10-05T08:46:39.000-07:00
diff --git a/doc/users/next_whats_new/centerednorm.rst b/doc/users/next_whats_new/centerednorm.rst
@@ -0,0 +1,35 @@
+New CenteredNorm for symmetrical data around a center
+-----------------------------------------------------
+In cases where data is symmetrical around a center, for example, positive and
+negative anomalies around a center zero, `~.matplotlib.colors.CenteredNorm`
+is a new norm that automatically creates a symmetrical mapping around the
+center. This norm is well suited to be combined with a divergent colormap which
+uses an unsaturated color in its center.
+
+  .. plot::
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from matplotlib.colors import CenteredNorm
+
+    np.random.seed(20201004)
+    data = np.random.normal(size=(3, 4), loc=1)
+
+    fig, ax = plt.subplots()
+    pc = ax.pcolormesh(data, cmap=plt.get_cmap('RdGy'), norm=CenteredNorm())
+    fig.colorbar(pc)
+    ax.set_title('data centered around zero')
+
+    # add text annotation
+    for irow, data_row in enumerate(data):
+        for icol, val in enumerate(data_row):
+            ax.text(icol + 0.5, irow + 0.5, f'{val:.2f}', color='C0',
+                    size=16, va='center', ha='center')
+    plt.show()
+
+If the center of symmetry is different from 0, it can be set with the *vcenter*
+argument. To manually set the range of `~.matplotlib.colors.CenteredNorm`, use
+the *halfrange* argument.
+
+See :doc:`/tutorials/colors/colormapnorms` for an example and more details
+about data normalization.
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1342,7 +1342,7 @@ def halfrange(self, halfrange):
             self.vmax = None
         else:
             self._halfrange = abs(halfrange)
-            self._set_vmin_vmax()
+            #self._set_vmin_vmax()
 
     def __call__(self, value, clip=None):
         if self._halfrange is not None:
diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -360,25 +360,41 @@ def test_BoundaryNorm():
 
 
 def test_CenteredNorm():
+    np.random.seed(0)
+
     # Assert equivalence to symmetrical Normalize.
     x = np.random.normal(size=100)
     x_maxabs = np.max(np.abs(x))
     norm_ref = mcolors.Normalize(vmin=-x_maxabs, vmax=x_maxabs)
     norm = mcolors.CenteredNorm()
     assert_array_almost_equal(norm_ref(x), norm(x))
+
     # Check that vcenter is in the center of vmin and vmax
     # when vcenter is set.
     vcenter = int(np.random.normal(scale=50))
     norm = mcolors.CenteredNorm(vcenter=vcenter)
     norm.autoscale_None([1, 2])
     assert norm.vmax + norm.vmin == 2 * vcenter
+
     # Check that halfrange input works correctly.
     x = np.random.normal(size=10)
     norm = mcolors.CenteredNorm(vcenter=0.5, halfrange=0.5)
     assert_array_almost_equal(x, norm(x))
     norm = mcolors.CenteredNorm(vcenter=1, halfrange=1)
     assert_array_almost_equal(x, 2 * norm(x))
 
+    # Check that halfrange input works correctly and use setters.
+    norm = mcolors.CenteredNorm()
+    norm.vcenter = 2
+    norm.halfrange = 2
+    assert_array_almost_equal(x, 4 * norm(x))
+
+    # Check that prior to adding data, setting halfrange first has same effect.
+    norm = mcolors.CenteredNorm()
+    norm.halfrange = 2
+    norm.vcenter = 2
+    assert_array_almost_equal(x, 4 * norm(x))
+
 
 @pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]])
 def test_lognorm_invalid(vmin, vmax):
