BUG: improve integer step selection in MaxNLocator

efiring · efiring · commit d12a00e2c581 · 2017-01-08T16:11:22.000-10:00
MaxNLocator with integer=True was unnecessarily restricting the
possible steps.  The restriction to integers is now done after
scaling, not before.
diff --git a/lib/matplotlib/tests/test_ticker.py b/lib/matplotlib/tests/test_ticker.py
@@ -32,9 +32,13 @@ def test_MaxNLocator_integer():
     test_value = np.array([-1, 0, 1, 2])
     assert_almost_equal(loc.tick_values(-0.1, 1.1), test_value)
 
-    test_value = np.array([-0.3, 0, 0.3, 0.6, 0.9, 1.2])
+    test_value = np.array([-0.25, 0, 0.25, 0.5, 0.75, 1.0])
     assert_almost_equal(loc.tick_values(-0.1, 0.95), test_value)
 
+    loc = mticker.MaxNLocator(nbins=5, integer=True, steps=[1, 1.5, 5, 6, 10])
+    test_value = np.array([0, 15, 30, 45, 60])
+    assert_almost_equal(loc.tick_values(1, 55), test_value)
+
 
 def test_LinearLocator():
     loc = mticker.LinearLocator(numticks=3)
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
@@ -1836,6 +1836,8 @@ def set_params(self, **kwargs):
                 raise ValueError(
                     "prune must be 'upper', 'lower', 'both', or None")
             self._prune = prune
+        if 'min_n_ticks' in kwargs:
+            self._min_n_ticks = max(1, kwargs['min_n_ticks'])
         if 'steps' in kwargs:
             steps = kwargs['steps']
             if steps is None:
@@ -1845,12 +1847,6 @@ def set_params(self, **kwargs):
             self._extended_steps = self._staircase(self._steps)
         if 'integer' in kwargs:
             self._integer = kwargs['integer']
-        if self._integer:
-            self._steps = np.array([n for n in self._steps
-                                    if _divmod(n, 1)[1] < 0.001])
-            self._extended_steps = self._staircase(self._steps)
-        if 'min_n_ticks' in kwargs:
-            self._min_n_ticks = max(1, kwargs['min_n_ticks'])
 
     def _raw_ticks(self, vmin, vmax):
         if self._nbins == 'auto':
@@ -1867,6 +1863,11 @@ def _raw_ticks(self, vmin, vmax):
         _vmax = vmax - offset
         raw_step = (vmax - vmin) / nbins
         steps = self._extended_steps * scale
+        if self._integer:
+            # For steps > 1, keep only integer values.
+            igood = (steps < 1) | (np.abs(steps - np.round(steps)) < 0.001)
+            steps = steps[igood]
+
         istep = np.nonzero(steps >= raw_step)[0][0]
 
         # Classic round_numbers mode may require a larger step.
