Added AutoLocator for minor ticks and further tweaks of zero-crossing logic

Author: rwpenney

examples/scales/asinh_demo.py

@@ -41,7 +41,7 @@
 import matplotlib.pyplot as plt
 
 # Prepare sample values for variations on y=x graph:
-x = np.linspace(-3, 6, 100)
+x = np.linspace(-3, 6, 500)
 
 ########################################
 # Compare "symlog" and "asinh" behaviour on sample y=x graph,

lib/matplotlib/scale.py

@@ -529,7 +529,9 @@ def get_transform(self):
 
     def set_default_locators_and_formatters(self, axis):
         axis.set(major_locator=AsinhLocator(self.linear_width),
-                 major_formatter='{x:.3g}')
+                 minor_locator=AutoLocator(),
+                 major_formatter='{x:.3g}',
+                 minor_formatter=NullFormatter())
 
 class LogitTransform(Transform):
     input_dims = output_dims = 1

lib/matplotlib/ticker.py

@@ -2635,7 +2635,7 @@ def tick_values(self, vmin, vmax):
                                                         / self.linear_width)
         ys = np.linspace(ymin, ymax, self.numticks)
         zero_dev = np.abs(ys / (ymax - ymin))
-        if (ymin * ymax) < 0 and min(zero_dev) > 0:
+        if (ymin * ymax) < 0:
             # Ensure that the zero tick-mark is included,
             # if the axis straddles zero
             ys = np.hstack([ys[(zero_dev > 0.5 / self.numticks)], 0.0])
@@ -2647,7 +2647,7 @@ def tick_values(self, vmin, vmax):
         # Round the data-space values to be intuitive decimal numbers:
         decades = (
             np.where(xs >= 0, 1, -1) *
-            np.power(10, np.where(zero_xs, 1.0,
+            np.power(10, np.where(zero_xs, 0.0,
                                   np.floor(np.log10(np.abs(xs)
                                                     + zero_xs*1e-6))))
         )