Added ticker tests

pelson · pelson · commit bcdf06f516c5 · 2012-08-13T11:28:34.000+01:00
diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py
@@ -1003,26 +1003,28 @@ def tk_window_focus():
 
 default_test_modules = [
     'matplotlib.tests.test_agg',
+    'matplotlib.tests.test_axes',
     'matplotlib.tests.test_backend_svg',
     'matplotlib.tests.test_basic',
     'matplotlib.tests.test_cbook',
-    'matplotlib.tests.test_mlab',
-    'matplotlib.tests.test_transforms',
-    'matplotlib.tests.test_axes',
-    'matplotlib.tests.test_figure',
+    'matplotlib.tests.test_colorbar',
     'matplotlib.tests.test_dates',
-    'matplotlib.tests.test_spines',
+    'matplotlib.tests.test_delaunay',
+    'matplotlib.tests.test_figure',
     'matplotlib.tests.test_image',
-    'matplotlib.tests.test_simplification',
+    'matplotlib.tests.test_legend',
     'matplotlib.tests.test_mathtext',
+    'matplotlib.tests.test_mlab',
+    'matplotlib.tests.test_patches',
+    'matplotlib.tests.test_simplification',
+    'matplotlib.tests.test_spines',
     'matplotlib.tests.test_text',
+    'matplotlib.tests.test_ticker',
     'matplotlib.tests.test_tightlayout',
-    'matplotlib.tests.test_delaunay',
-    'matplotlib.tests.test_legend',
-    'matplotlib.tests.test_colorbar',
-    'matplotlib.tests.test_patches',
+    'matplotlib.tests.test_transforms',
     ]
 
+
 def test(verbosity=1):
     """run the matplotlib test suite"""
     old_backend = rcParams['backend']
diff --git a/lib/matplotlib/tests/test_ticker.py b/lib/matplotlib/tests/test_ticker.py
@@ -0,0 +1,51 @@
+from __future__ import print_function
+from nose.tools import assert_equal, assert_raises
+from numpy.testing import assert_almost_equal
+import numpy as np
+
+import matplotlib.ticker as mticker
+
+
+def test_MaxNLocator():
+    loc = mticker.MaxNLocator(nbins=5)
+    test_value = np.array([  20.,   40.,   60.,   80.,  100.])
+    assert_almost_equal(loc.tick_values(20, 100), test_value)
+
+    test_value = np.array([ 0., 0.0002, 0.0004, 0.0006, 0.0008, 0.001])
+    assert_almost_equal(loc.tick_values(0.001, 0.0001), test_value)
+
+    test_value = np.array([-1.0e+15, -5.0e+14, 0e+00, 5e+14, 1.0e+15])
+    assert_almost_equal(loc.tick_values(-1e15, 1e15), test_value)
+
+
+def test_LinearLocator():
+    loc = mticker.LinearLocator(numticks=3)
+    test_value = np.array([-0.8, -0.3,  0.2])
+    assert_almost_equal(loc.tick_values(-0.8, 0.2), test_value)
+
+
+def test_MultipleLocator():
+    loc = mticker.MultipleLocator(base=3.147)
+    test_value = np.array([-6.294, -3.147, 0., 3.147, 6.294, 9.441])
+    assert_almost_equal(loc.tick_values(-7, 10), test_value)
+
+
+def test_LogLocator():
+    loc = mticker.LogLocator(numticks=5)
+
+    # make sure the 0 case is covered with an exception
+    with assert_raises(ValueError):
+        loc.tick_values(0, 1000)
+
+    test_value = np.array([1.00000000e-03, 1.00000000e-01, 1.00000000e+01,
+                           1.00000000e+03, 1.00000000e+05, 1.00000000e+07])
+    assert_almost_equal(loc.tick_values(0.001, 1.1e5), test_value)
+
+    loc = mticker.LogLocator(base=2)
+    test_value = np.array([1., 2., 4., 8., 16., 32., 64., 128.])
+    assert_almost_equal(loc.tick_values(1, 100), test_value)
+
+
+if __name__=='__main__':
+    import nose
+    nose.runmodule(argv=['-s','--with-doctest'], exit=False)
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
@@ -1319,13 +1319,13 @@ class LogLocator(Locator):
     Determine the tick locations for log axes
     """
 
-    def __init__(self, base=10.0, subs=[1.0], numdecs=4):
+    def __init__(self, base=10.0, subs=[1.0], numdecs=4, numticks=15):
         """
         place ticks on the location= base**i*subs[j]
         """
         self.base(base)
         self.subs(subs)
-        self.numticks = 15
+        self.numticks = numticks
         self.numdecs = numdecs
 
     def base(self,base):
@@ -1359,7 +1359,9 @@ def tick_values(self, vmin, vmax):
             return ticklocs
 
         if vmin <= 0.0:
-            vmin = self.axis.get_minpos()
+            if self.axis is not None:
+                vmin = self.axis.get_minpos()
+
             if vmin <= 0.0 or not np.isfinite(vmin):
                 raise ValueError(
                     "Data has no positive values, and therefore can not be log-scaled.")
