matplotlib
diff --git a/‎lib/matplotlib/__init__.py
Lines changed: 1 addition & 0 deletions b/‎lib/matplotlib/__init__.py
Lines changed: 1 addition & 0 deletions
diff --git a/‎lib/matplotlib/mlab.py
Lines changed: 10 additions & 13 deletions b/‎lib/matplotlib/mlab.py
Lines changed: 10 additions & 13 deletions
diff --git a/‎lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf
5.76 KB b/‎lib/matplotlib/tests/baseline_images/vp_coverage/blank.pdf
5.76 KB
diff --git a/‎lib/matplotlib/tests/baseline_images/vp_coverage/blank.png
10.2 KB b/‎lib/matplotlib/tests/baseline_images/vp_coverage/blank.png
10.2 KB
@@ -1360,6 +1360,7 @@ def tk_window_focus():
     'matplotlib.tests.test_tightlayout',
     'matplotlib.tests.test_transforms',
     'matplotlib.tests.test_triangulation',
+    'matplotlib.tests.test_vpcoverage',
     ]
 
 
 
@@ -3656,12 +3656,13 @@ def stineman_interp(xi,x,y,yp=None):
                                   1/(dy1+dy2),))
     return yi
 
+
 class GaussianKDE(object):
     """
     Representation of a kernel-density estimate using Gaussian kernels.
 
     Call signature::
-    kde = gaussian_kde(dataset, 'silverman')
+    kde = GaussianKDE(dataset, bw_method='silverman')
 
     Parameters
     ----------
@@ -3715,8 +3716,8 @@ def __init__(self, dataset, bw_method=None):
             self.covariance_factor = self.scotts_factor
         elif bw_method == 'silverman':
             self.covariance_factor = self.silverman_factor
-        elif np.isscalar(bw_method):
-            if not isinstance(bw_method, six.string_types):
+        elif (np.isscalar(bw_method) and not
+              isinstance(bw_method, six.string_types)):
                 self._bw_method = 'use constant'
                 self.covariance_factor = lambda: bw_method
         elif callable(bw_method):
@@ -3726,10 +3727,10 @@ def __init__(self, dataset, bw_method=None):
             msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
                   "or a callable."
             raise ValueError(msg)
-        
+
         # Computes the covariance matrix for each Gaussian kernel using
         # covariance_factor().
-        
+
         self.factor = self.covariance_factor()
         # Cache covariance and inverse covariance of the data
         if not hasattr(self, '_data_inv_cov'):
@@ -3780,14 +3781,9 @@ def evaluate(self, points):
 
         dim, num_m = np.array(points).shape
         if dim != self.dim:
-            if dim == 1 and num_m == self.dim:
-                # points was passed in as a row vector
-                points = np.reshape(points, (self.dim, 1))
-                num_m = 1
-            else:
-                msg = "points have dimension %s, dataset has dimension %s" % (
-                    dim, self.dim)
-                raise ValueError(msg)
+            msg = "points have dimension %s, dataset has dimension %s" % (
+                dim, self.dim)
+            raise ValueError(msg)
 
         result = np.zeros((num_m,), dtype=np.float)
 
@@ -3812,6 +3808,7 @@ def evaluate(self, points):
 
     __call__ = evaluate
 
+
 ##################################################
 # Code related to things in and around polygons
 ##################################################
Original file line number	Diff line number	Diff line change
`@@ -1360,6 +1360,7 @@ def tk_window_focus():`
`1360`	`1360`	`'matplotlib.tests.test_tightlayout',`
`1361`	`1361`	`'matplotlib.tests.test_transforms',`
`1362`	`1362`	`'matplotlib.tests.test_triangulation',`
	`1363`	`+ 'matplotlib.tests.test_vpcoverage',`
`1363`	`1364`	`]`
`1364`	`1365`
`1365`	`1366`