Added tests for Asinh tick locations and improved handling of zero-straddling

rwpenney · rwpenney · commit 279c38b03881 · 2021-10-21T16:57:45.000+01:00
diff --git a/lib/matplotlib/tests/test_ticker.py b/lib/matplotlib/tests/test_ticker.py
@@ -457,6 +457,26 @@ def test_set_params(self):
         lctr.set_params(None)
         assert lctr.numticks == 23
 
+    def test_linear_values(self):
+        lctr = mticker.AsinhLocator(linear_width=100, numticks=11)
+
+        assert_almost_equal(lctr.tick_values(-1, 1),
+                            np.arange(-1, 1.01, 0.2))
+        assert_almost_equal(lctr.tick_values(-0.1, 0.1),
+                            np.arange(-0.1, 0.101, 0.02))
+        assert_almost_equal(lctr.tick_values(-0.01, 0.01),
+                            np.arange(-0.01, 0.0101, 0.002))
+
+    def test_wide_values(self):
+        lctr = mticker.AsinhLocator(linear_width=0.1, numticks=11)
+
+        assert_almost_equal(lctr.tick_values(-100, 100),
+                            [-100, -20, -5, -1, -0.2,
+                             0, 0.2, 1, 5, 20, 100])
+        assert_almost_equal(lctr.tick_values(-1000, 1000),
+                            [-1000, -100, -20, -3, -0.4,
+                             0, 0.4, 3, 20, 100, 1000])
+
 
 class TestScalarFormatter:
     offset_data = [
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
@@ -2591,7 +2591,7 @@ class AsinhLocator(Locator):
 
     This is very unlikely to have any use beyond the AsinhScale class.
     """
-    def __init__(self, linear_width, numticks=12):
+    def __init__(self, linear_width, numticks=11):
         """
         Parameters
         ----------
@@ -2618,12 +2618,14 @@ def __call__(self):
     def tick_values(self, vmin, vmax):
         # Construct a set of "on-screen" locations
         # that are uniformly spaced:
-        ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax]) / self.linear_width)
+        ymin, ymax = self.linear_width * np.arcsinh(np.array([vmin, vmax])
+                                                        / self.linear_width)
         ys = np.linspace(ymin, ymax, self.numticks)
-        if (ymin * ymax) < 0:
+        zero_dev = np.abs(ys / (ymax - ymin))
+        if (ymin * ymax) < 0 and min(zero_dev) > 1e-6:
             # Ensure that the zero tick-mark is included,
             # if the axis stradles zero
-            ys = np.hstack([ys, 0.0])
+            ys = np.hstack([ys[(zero_dev > 0.5 / self.numticks)], 0.0])
 
         # Transform the "on-screen" grid to the data space:
         xs = self.linear_width * np.sinh(ys / self.linear_width)
