Use better contour defaults with logarithmic norms (#10565)

* Remove default re-definition

* colors arg should take priority over default cmaps

* Update test_plot.py

* Update test_plot.py

* add log norm test

* Add tests

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update test_plot.py

* Update test_plot.py

* Update test_plot.py

* Update test_plot.py

* Update whats-new.rst

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Illviljan

doc/whats-new.rst

@@ -43,6 +43,8 @@ Deprecations
 
 Bug fixes
 ~~~~~~~~~
+- Fix contour plots not normalizing the colors correctly when using for example logarithmic norms. (:issue:`10551`, :pull:`10565`)
+  By `Jimmy Westling <https://github.com/illviljan>`_.
 - Fix distribution of ``auto_complex`` keyword argument for open_datatree (:issue:`10631`, :pull:`10632`).
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
 - Warn instead of raise in case of misconfiguration of ``unlimited_dims`` originating from dataset.encoding, to prevent breaking users workflows (:issue:`10647`, :pull:`10648`).

xarray/plot/dataarray_plot.py

@@ -1594,7 +1594,7 @@ def newplotfunc(
             kwargs["levels"] = cmap_params["levels"]
             # if colors == a single color, matplotlib draws dashed negative
             # contours. we lose this feature if we pass cmap and not colors
-            if isinstance(colors, str):
+            if colors is not None:
                 cmap_params["cmap"] = None
                 kwargs["colors"] = colors
 

xarray/plot/utils.py

@@ -931,9 +931,6 @@ def _process_cmap_cbar_kwargs(
 
     cbar_kwargs = {} if cbar_kwargs is None else dict(cbar_kwargs)
 
-    if "contour" in func.__name__ and levels is None:
-        levels = 7  # this is the matplotlib default
-
     # colors is mutually exclusive with cmap
     if cmap and colors:
         raise ValueError("Can't specify both cmap and colors.")

xarray/tests/test_plot.py

@@ -1776,6 +1776,18 @@ def test_levels(self) -> None:
         artist = self.plotmethod(levels=3)
         assert artist.extend == "neither"
 
+    def test_colormap_norm(self) -> None:
+        # Using a norm should plot a nice colorbar and look consistent with pcolormesh.
+        norm = mpl.colors.LogNorm(0.1, 1e1)
+
+        with pytest.warns(UserWarning):
+            artist = self.plotmethod(norm=norm, add_colorbar=True)
+
+        actual = artist.colorbar.locator()
+        expected = np.array([0.01, 0.1, 1.0, 10.0])
+
+        np.testing.assert_allclose(actual, expected)
+
 
 @pytest.mark.slow
 class TestContour(Common2dMixin, PlotTestCase):
@@ -1792,32 +1804,30 @@ def test_colors(self) -> None:
         artist = self.plotmethod(colors="k")
         assert artist.cmap.colors[0] == "k"
 
+        # 2 colors, will repeat every other tick:
         artist = self.plotmethod(colors=["k", "b"])
-        assert self._color_as_tuple(artist.cmap.colors[1]) == (0.0, 0.0, 1.0)
+        assert artist.cmap.colors[:2] == ["k", "b"]
 
+        # 4 colors, will repeat every 4th tick:
         artist = self.darray.plot.contour(
             levels=[-0.5, 0.0, 0.5, 1.0], colors=["k", "r", "w", "b"]
         )
-        assert self._color_as_tuple(artist.cmap.colors[1]) == (1.0, 0.0, 0.0)
-        assert self._color_as_tuple(artist.cmap.colors[2]) == (1.0, 1.0, 1.0)
+        assert artist.cmap.colors[:5] == ["k", "r", "w", "b"]
+
         # the last color is now under "over"
-        assert self._color_as_tuple(artist.cmap._rgba_over) == (0.0, 0.0, 1.0)
+        assert self._color_as_tuple(artist.cmap.get_over()) == (0.0, 0.0, 1.0)
 
     def test_colors_np_levels(self) -> None:
         # https://github.com/pydata/xarray/issues/3284
         levels = np.array([-0.5, 0.0, 0.5, 1.0])
         artist = self.darray.plot.contour(levels=levels, colors=["k", "r", "w", "b"])
         cmap = artist.cmap
         assert isinstance(cmap, mpl.colors.ListedColormap)
-        # non-optimal typing in matplotlib (ArrayLike)
-        # https://github.com/matplotlib/matplotlib/blob/84464dd085210fb57cc2419f0d4c0235391d97e6/lib/matplotlib/colors.pyi#L133
-        colors = cast(np.ndarray, cmap.colors)
 
-        assert self._color_as_tuple(colors[1]) == (1.0, 0.0, 0.0)
-        assert self._color_as_tuple(colors[2]) == (1.0, 1.0, 1.0)
+        assert artist.cmap.colors[:5] == ["k", "r", "w", "b"]  # type: ignore[attr-defined]
+
         # the last color is now under "over"
-        assert hasattr(cmap, "_rgba_over")
-        assert self._color_as_tuple(cmap._rgba_over) == (0.0, 0.0, 1.0)
+        assert self._color_as_tuple(cmap.get_over()) == (0.0, 0.0, 1.0)
 
     def test_cmap_and_color_both(self) -> None:
         with pytest.raises(ValueError):
@@ -1841,6 +1851,18 @@ def test_single_level(self) -> None:
         self.plotmethod(levels=[0.1])
         self.plotmethod(levels=1)
 
+    def test_colormap_norm(self) -> None:
+        # Using a norm should plot a nice colorbar and look consistent with pcolormesh.
+        norm = mpl.colors.LogNorm(0.1, 1e1)
+
+        with pytest.warns(UserWarning):
+            artist = self.plotmethod(norm=norm, add_colorbar=True)
+
+        actual = artist.colorbar.locator()
+        expected = np.array([0.01, 0.1, 1.0, 10.0])
+
+        np.testing.assert_allclose(actual, expected)
+
 
 class TestPcolormesh(Common2dMixin, PlotTestCase):
     plotfunc = staticmethod(xplt.pcolormesh)