Fix #639: unskip and harden the vcenter colormap test

ultraplot/axes/plot.py

@@ -4168,6 +4168,7 @@ def _parse_cmap(
         extend=None,
         vmin=None,
         vmax=None,
+        vcenter=None,
         discrete=None,
         default_cmap=None,
         default_discrete=True,
@@ -4193,6 +4194,8 @@ def _parse_cmap(
             The colormap extend setting.
         vmin, vmax : float, optional
             The normalization range.
+        vcenter : float, optional
+            The center value for diverging normalizers.
         sequential, diverging, cyclic, qualitative : bool, optional
             Toggle various colormap types.
         discrete : bool, optional
@@ -4227,19 +4230,26 @@ def _parse_cmap(
         # with explicit vmin/vmax is ambiguous. String / single-element list or
         # tuple specs are just names for ``constructor.Norm`` and accept
         # vmin/vmax as kwargs.
-        if (vmin is not None or vmax is not None) and isinstance(
+        if (vmin is not None or vmax is not None or vcenter is not None) and isinstance(
             norm, mcolors.Normalize
         ):
             raise ValueError(
-                "If 'norm' is a Normalize instance, 'vmin' and 'vmax' must not be "
+                "If 'norm' is a Normalize instance, 'vmin', 'vmax', and 'vcenter' must not be "
                 "set. Pass them through the Normalize constructor, or specify "
-                "'norm' as a string / list / tuple to let vmin and vmax apply."
+                "'norm' as a string / list / tuple to let vmin, vmax, and vcenter apply."
             )
         if isinstance(norm, mcolors.Normalize):
             vmin = norm.vmin
             vmax = norm.vmax
         vmin = _not_none(vmin=vmin, norm_kw_vmin=norm_kw.pop("vmin", None))
         vmax = _not_none(vmax=vmax, norm_kw_vmax=norm_kw.pop("vmax", None))
+        vcenter = _not_none(
+            vcenter=vcenter, norm_kw_vcenter=norm_kw.pop("vcenter", None)
+        )
+        if vcenter is not None:
+            norm_kw["vcenter"] = vcenter
+            if norm is None:
+                norm = "diverging"
         extend = _not_none(extend, "neither")
         modes = {
             key: kwargs.pop(key, None)

ultraplot/tests/test_2dplots.py

@@ -11,18 +11,39 @@
 import ultraplot as uplt, warnings
 
 
-@pytest.mark.skip("not sure what this does")
-@pytest.mark.mpl_image_compare
 def test_colormap_vcenter(rng):
     """
-    Test colormap vcenter.
+    Test that explicit `vcenter` configures a diverging normalizer centered at `vcenter`.
     """
     fig, axs = uplt.subplots(ncols=3)
     data = 10 * rng.random((10, 10)) - 3
-    axs[0].pcolor(data, vcenter=0)
-    axs[1].pcolor(data, vcenter=1)
-    axs[2].pcolor(data, vcenter=2)
-    return fig
+    m0 = axs[0].pcolor(data, vcenter=0)
+    m1 = axs[1].pcolor(data, vcenter=1)
+    m2 = axs[2].pcolor(data, vcenter=2)
+
+    # In discrete mode (default), m.norm is DiscreteNorm wrapping DivergingNorm (_norm)
+    assert m0.norm._norm.vcenter == pytest.approx(0)
+    assert m1.norm._norm.vcenter == pytest.approx(1)
+    assert m2.norm._norm.vcenter == pytest.approx(2)
+
+    # The underlying diverging norm maps vcenter to 0.5 (center of colormap)
+    assert m0.norm._norm(0) == pytest.approx(0.5)
+    assert m1.norm._norm(1) == pytest.approx(0.5)
+    assert m2.norm._norm(2) == pytest.approx(0.5)
+
+    # Verify continuous mode (discrete=False) where DivergingNorm is directly used
+    _, ax = uplt.subplots()
+    m_cont = ax.pcolor(data, vcenter=1.5, discrete=False)
+    assert isinstance(m_cont.norm, uplt.DivergingNorm)
+    assert m_cont.norm.vcenter == pytest.approx(1.5)
+    assert m_cont.norm(1.5) == pytest.approx(0.5)
+
+    # Verify passing vcenter via norm_kw works identically
+    _, ax_kw = uplt.subplots()
+    m_kw = ax_kw.pcolor(data, norm_kw={"vcenter": 1.5}, discrete=False)
+    assert isinstance(m_kw.norm, uplt.DivergingNorm)
+    assert m_kw.norm.vcenter == pytest.approx(1.5)
+    assert m_kw.norm(1.5) == pytest.approx(0.5)
 
 
 @pytest.mark.mpl_image_compare