Improved testing of locator edge-cases, and patched constrained_layout misstep

Author: rwpenney

examples/scales/asinh_demo.py

@@ -59,8 +59,8 @@
 
 ########################################
 # Compare "asinh" graphs with different scale parameter "linear_width":
-fig2 = plt.figure()
-axs = fig2.subplots(1, 3, sharex=True, constrained_layout=True)
+fig2 = plt.figure(constrained_layout=True)
+axs = fig2.subplots(1, 3, sharex=True)
 for ax, a0 in zip(axs, (0.2, 1.0, 5.0)):
     ax.set_title('linear_width={:.3g}'.format(a0))
     ax.plot(x, x, label='y=x')

lib/matplotlib/scale.py

@@ -512,7 +512,8 @@ def __init__(self, axis, *, linear_width=1.0, **kwargs):
         Parameters
         ----------
         linear_width : float, default: 1
-            The scale parameter defining the extent of the quasi-linear region,
+            The scale parameter (elsewhere referred to as :math:`a_0`)
+            defining the extent of the quasi-linear region,
             and the coordinate values beyond which the transformation
             becomes asympoticially logarithmic.
         """

lib/matplotlib/tests/test_ticker.py

@@ -477,6 +477,18 @@ def test_wide_values(self):
                             [-1000, -100, -20, -3, -0.4,
                              0, 0.4, 3, 20, 100, 1000])
 
+    def test_near_zero(self):
+        """Check that manually injected zero will supersede nearby tick"""
+        lctr = mticker.AsinhLocator(linear_width=100, numticks=3)
+
+        assert_almost_equal(lctr.tick_values(-1.1, 0.9), [ -1.0, 0.0, 0.9])
+
+    def test_fallback(self):
+        lctr = mticker.AsinhLocator(1.0, numticks=11)
+
+        assert_almost_equal(lctr.tick_values(100, 101),
+                            np.arange(100, 101.01, 0.1))
+
 
 class TestScalarFormatter:
     offset_data = [

lib/matplotlib/ticker.py

@@ -2639,9 +2639,10 @@ def tick_values(self, vmin, vmax):
                                                     + zero_xs*1e-6))))
         )
         qs = decades * np.round(xs / decades)
+        ticks = np.array(sorted(set(qs)))
 
-        if len(qs) > self.numticks // 2:
-            return np.array(sorted(set(qs)))
+        if len(ticks) > self.numticks // 2:
+            return ticks
         else:
             return np.linspace(vmin, vmax, self.numticks)