re worked the tests in test_mlab to work with the modified GaussianKDE

Author: grdlok

lib/matplotlib/tests/test_mlab.py

@@ -2757,110 +2757,59 @@ def get_z(x, y):
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
 
-#-----------------------------------------------------------------
-#-----------------------------------------------------------------
+#*****************************************************************
+#*****************************************************************
+
 
 class ksdensity_test():
+    
+
     def test_kde_integer_input(self):
         """Regression test for #1181."""
         x1 = np.arange(5)
-        kde = mlab.ksdensity(x1 + x1)
+        kde = mlab.GaussianKDE(x1)
         y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869, 0.13480721]
-        assert_array_almost_equal(kde["result"], y_expected, decimal=6)
+        assert_array_almost_equal(kde(x1), y_expected, decimal=6)
 
-    @knownfailureif(True)
     def test_gaussian_kde_covariance_caching(self):
         x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
         xs = np.linspace(-10, 10, num=5)
         # These expected values are from scipy 0.10, before some changes to
         # gaussian_kde. They were not compared with any external reference.
         y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754, 0.01664475]
 
-        # Set the bandwidth, then reset it to the default.
-        kde = mlab.ksdensity(x1, 0.5)
-        y2 = kde['result']
-
-        assert_array_almost_equal(y_expected, y2, decimal=7)
-
-        kde = mlab.ksdensity(xs, 'scott')
-        y2 = kde['result']
+        # set it to the default bandwidth.
+        kde2 = mlab.GaussianKDE(x1, 'scott')
+        y2 = kde2(xs)
 
         assert_array_almost_equal(y_expected, y2, decimal=7)
 
-    @knownfailureif(True)
-    def test_gaussian_kde_subclassing(self):
-        x1 = np.array([-7, -5, 1, 4, 5], dtype=np.float)
-        xs = np.linspace(-10, 10, num=50)
-
-        # gaussian_kde itself
-        kde = mlab.ksdensity(xs)
-        ys = kde['result']
-
-        y_expected = [0.06292987, 0.06346938, 0.05860291, 0.08657652, 0.07904017]
-
-        assert_array_almost_equal(y_expected, ys, decimal=6)
-
-
-    @knownfailureif(True)
-    def test_kde_1d(self):
-        #some basic tests comparing to normal distribution
-        np.random.seed(8765678)
-        n_basesample = 500
-        xn = np.random.randn(n_basesample)
-        xnmean = xn.mean()
-        xnstd = xn.std(ddof=1)
-
-        xs = np.linspace(-7, 7, 501)
-
-        # get kde for original sample
-        gkde = mlab.ksdensity(xn)
-
-        # evaluate the density function for the kde for some points
-        kdepdf = gkde['result']
-        intervall = xs[1] - xs[0]
-
-        normpdf = np.random.normal(loc=xnmean, scale=xnstd)
-
-        assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
-        prob1 = gkde.integrate_box_1d(xnmean, np.inf)
-        prob2 = gkde.integrate_box_1d(-np.inf, xnmean)
-        assert_almost_equal(prob1, 0.5, decimal=1)
-        assert_almost_equal(prob2, 0.5, decimal=1)
-        assert_almost_equal(gkde.integrate_box(xnmean, np.inf), prob1, decimal=13)
-        assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), prob2, decimal=13)
-
-        assert_almost_equal(gkde.integrate_kde(gkde),
-                        (kdepdf**2).sum()*intervall, decimal=2)
-        assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
-                        (kdepdf*normpdf).sum()*intervall, decimal=2)
-
     def test_kde_bandwidth_method(self):
 
         np.random.seed(8765678)
         n_basesample = 50
         xn = np.random.randn(n_basesample)
 
         # Default
-        gkde = mlab.ksdensity(xn)
+        gkde = mlab.GaussianKDE(xn)
         # Supply a callable
-        gkde2 =mlab.ksdensity(xn, 'scott')
-        assert_almost_equal(gkde['result'].all(), gkde2['result'].all())
+        gkde2 = mlab.GaussianKDE(xn, 'scott')
         # Supply a scalar
-        #gkde3 = mlab.ksdensity(xn, bw_method=gkde.factor)
+        gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
 
         xs = np.linspace(-7,7,51)
-        kdepdf = mlab.ksdensity(xs)
-        kdepdf2 = mlab.ksdensity(xs, 'scott')
-        #kdepdf3 = gkde3.evaluate(xs)
-        #assert_almost_equal(kdepdf, kdepdf3)
+        kdepdf = gkde.evaluate(xs)
+        kdepdf2 = gkde2.evaluate(xs)
+        assert_almost_equal(kdepdf.all(), kdepdf2.all())
+        kdepdf3 = gkde3.evaluate(xs)
+        assert_almost_equal(kdepdf.all(), kdepdf3.all())
 
-        assert_raises(ValueError, mlab.ksdensity, xn, bw_method='wrongstring')
 
 
+#*****************************************************************
+#*****************************************************************
 
 
-#-----------------------------------------------------------------
-#-----------------------------------------------------------------
 if __name__ == '__main__':
     import nose
     import sys