Skip the tests that use scipy if scipy is missing

Some tests requires scipy. Scipy is an optional dependency, so it can be
missing leading to the tests to fail.

This commit decorate the tests that need scipy with the dec.skipif
decorator. This follows the guideline discussed in MDAnalysis#124.

testsuite/MDAnalysisTests/analysis/test_distances.py

@@ -16,14 +16,18 @@
 from __future__ import print_function
 
 import MDAnalysis
-import MDAnalysis.analysis.distances
+from MDAnalysisTests import module_not_found
 
-from numpy.testing import TestCase, assert_equal
+from numpy.testing import TestCase, assert_equal, dec
 import numpy as np
 
 
 class TestContactMatrix(TestCase):
+
+    @dec.skipif(module_not_found('scipy'),
+                "Test skipped because scipy is not available.")
     def setUp(self):
+        import MDAnalysis.analysis.distances
         self.coord = np.array([[1, 1, 1],
                                   [5, 5, 5],
                                   [1.1, 1.1, 1.1],

testsuite/MDAnalysisTests/analysis/test_leaflet.py

@@ -14,14 +14,16 @@
 # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
 #
 import MDAnalysis
-from MDAnalysis.analysis.leaflet import LeafletFinder
+from MDAnalysisTests import module_not_found
 
-from numpy.testing import TestCase, assert_equal
+from numpy.testing import TestCase, assert_equal, dec
 import numpy as np
 
 from MDAnalysisTests.datafiles import Martini_membrane_gro
 
 class TestLeafletFinder(TestCase):
+    @dec.skipif(module_not_found('scipy'),
+                "Test skipped because scipy is not available.")
     def setUp(self):
         self.universe = MDAnalysis.Universe(Martini_membrane_gro, Martini_membrane_gro)
         self.lipid_heads = self.universe.select_atoms("name PO4")
@@ -30,6 +32,7 @@ def tearDown(self):
         del self.universe
 
     def test_leaflet_finder(self):
+        from MDAnalysis.analysis.leaflet import LeafletFinder
         lfls = LeafletFinder(self.universe, self.lipid_heads, pbc=True)
         top_heads, bottom_heads = lfls.groups()
         # Make top be... on top.