matplotlib
diff --git a/‎doc/users/dflt_style_changes.rst
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diff --git a/‎lib/matplotlib/tests/test_ticker.py
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diff --git a/‎lib/matplotlib/ticker.py
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+33-13Lines changed: 33 additions & 13 deletions b/‎lib/matplotlib/ticker.py
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+33-13Lines changed: 33 additions & 13 deletions
@@ -969,6 +969,11 @@ or create a new `~matplotlib.ticker.MaxNLocator`::
   import matplotlib.ticker as mticker
   ax.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10])
 
+The algorithm used by `~matplotlib.ticker.MaxNLocator` has been
+improved, and this may change the choice of tick locations in some
+cases.  This also affects `~matplotlib.ticker.AutoLocator`, which
+uses ``MaxNLocator`` internally.
+
 For a log-scaled axis the default locator is the
 `~matplotlib.ticker.LogLocator`.  Previously the maximum number
 of ticks was set to 15, and could not be changed. Now there is a
 
@@ -32,7 +32,7 @@ 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([-0.25, 0, 0.25, 0.5, 0.75, 1])
+    test_value = np.array([-0.3, 0, 0.3, 0.6, 0.9, 1.2])
     assert_almost_equal(loc.tick_values(-0.1, 0.95), test_value)
 
 
 
@@ -1698,6 +1698,33 @@ def __init__(self, *args, **kwargs):
         self.set_params(**self.default_params)
         self.set_params(**kwargs)
 
+    @staticmethod
+    def _validate_steps(steps):
+        if not np.iterable(steps):
+            raise ValueError('steps argument must be a sequence of numbers '
+                             'from 1 to 10')
+        steps = np.asarray(steps)
+        if np.any(np.diff(steps) <= 0):
+            raise ValueError('steps argument must be uniformly increasing')
+        if steps[-1] > 10 or steps[0] < 1:
+            warnings.warn('Steps argument should be a sequence of numbers\n'
+                          'increasing from 1 to 10, inclusive. Behavior with\n'
+                          'values outside this range is undefined, and will\n'
+                          'raise a ValueError in future versions of mpl.')
+        if steps[0] != 1:
+            steps = np.hstack((1, steps))
+        if steps[-1] != 10:
+            steps = np.hstack((steps, 10))
+        return steps
+
+    @staticmethod
+    def _staircase(steps):
+        # Make an extended staircase within which the needed
+        # step will be found.  This is probably much larger
+        # than necessary.
+        flights = (0.1 * steps[:-1], steps, 10 * steps[1])
+        return np.hstack(flights)
+
     def set_params(self, **kwargs):
         """Set parameters within this locator."""
         if 'nbins' in kwargs:
@@ -1719,23 +1746,16 @@ def set_params(self, **kwargs):
         if 'steps' in kwargs:
             steps = kwargs['steps']
             if steps is None:
-                self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10]
+                self._steps = np.array([1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10])
             else:
-                if int(steps[-1]) != 10:
-                    steps = list(steps)
-                    steps.append(10)
-                self._steps = steps
-            # Make an extended staircase within which the needed
-            # step will be found.  This is probably much larger
-            # than necessary.
-            flights = (0.1 * np.array(self._steps[:-1]),
-                       self._steps,
-                       [10 * self._steps[1]])
-            self._extended_steps = np.hstack(flights)
+                self._steps = self._validate_steps(steps)
+            self._extended_steps = self._staircase(self._steps)
         if 'integer' in kwargs:
             self._integer = kwargs['integer']
         if self._integer:
-            self._steps = [n for n in self._steps if _divmod(n, 1)[1] < 0.001]
+            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'])