Further refinements to documentation and test coverage

Author: rwpenney

doc/api/colors_api.rst

@@ -21,6 +21,7 @@ Classes
    :toctree: _as_gen/
    :template: autosummary.rst
 
+   AsinhNorm
    BoundaryNorm
    Colormap
    CenteredNorm

lib/matplotlib/colors.py

@@ -1662,6 +1662,12 @@ class AsinhNorm(Normalize):
     or negative values. Unlike the `SymLogNorm`, the transition between
     these linear and logarithmic regions is smooth, which may reduce
     the risk of visual artifacts.
+
+    Parameters
+    ----------
+    linear_width : float, default: 1
+        The effective width of the linear region, beyond which
+        the transformation becomes asymptotically logarithmic
     """
 
     @property

lib/matplotlib/tests/test_scale.py

@@ -6,6 +6,7 @@
     LogTransform, InvertedLogTransform,
     SymmetricalLogTransform)
 import matplotlib.scale as mscale
+from matplotlib.ticker import AsinhLocator, LogFormatterSciNotation
 from matplotlib.testing.decorators import check_figures_equal, image_comparison
 
 import numpy as np
@@ -261,6 +262,28 @@ def test_base_init(self):
         assert s7._base == 7
         assert s7._subs == (2, 4)
 
+    def test_fmtloc(self):
+        class DummyAxis:
+            def __init__(self):
+                self.fields = {}
+            def set(self, **kwargs):
+                self.fields.update(**kwargs)
+            def set_major_formatter(self, f):
+                self.fields['major_formatter'] = f
+
+        ax0 = DummyAxis()
+        s0 = AsinhScale(axis=ax0, base=0)
+        s0.set_default_locators_and_formatters(ax0)
+        assert isinstance(ax0.fields['major_locator'], AsinhLocator)
+        assert isinstance(ax0.fields['major_formatter'], str)
+
+        ax5 = DummyAxis()
+        s7 = AsinhScale(axis=ax5, base=5)
+        s7.set_default_locators_and_formatters(ax5)
+        assert isinstance(ax5.fields['major_locator'], AsinhLocator)
+        assert isinstance(ax5.fields['major_formatter'],
+                          LogFormatterSciNotation)
+
     def test_bad_scale(self):
         fig, ax = plt.subplots()