Reverse version bump and skip case unsupported by old numpy

.travis.yml

@@ -30,7 +30,7 @@ addons:
    - dvipng
 
 install:
-  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv mkl python=2.6 numpy=1.9.1 scipy=0.14.0 nose=1.3.0 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv mkl python=2.6 numpy=1.7.1 scipy=0.11 nose=1.3.0 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
   - if [[ $TRAVIS_PYTHON_VERSION == '3.3' ]]; then conda create --yes -q -n pyenv mkl python=3.3 numpy=1.9.1 scipy=0.14.0 nose=1.3.4 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
   - source activate pyenv
 # pydot 1.2 broke support of python 2.6. They won't try to maintain it.

theano/tensor/tests/test_subtensor.py

@@ -342,7 +342,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
     def test_ellipsis(self):
         numpy_n = numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4))
         n = self.shared(numpy_n)
-        for length, op_type, slice_ in [
+        test_cases = [
             (0, self.sub, numpy.index_exp[...]),
             (1, self.sub, numpy.index_exp[..., 1]),
             (1, self.sub, numpy.index_exp[1, ...]),
@@ -351,9 +351,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
             (1, self.sub, numpy.index_exp[1, 2, 3, ...]),
             (3, DimShuffle, numpy.index_exp[..., [0, 2, 3]]),
             (1, DimShuffle,
-                 numpy.index_exp[numpy.newaxis, ...]),
+             numpy.index_exp[numpy.newaxis, ...])]
+        # The following test case is not supported by numpy before 1.9
+        numpy_version = [int(v) for v in numpy.version.version.split('.')[0:2]]
+        if numpy_version >= [1, 9]:
+            test_cases.append(
                 (1, AdvancedSubtensor,
-                 numpy.index_exp[..., numpy.newaxis, [1, 2]])]:
+                 numpy.index_exp[..., numpy.newaxis, [1, 2]]))
+
+        for length, op_type, slice_ in test_cases:
             numpy_tval = numpy_n[slice_]
             t = n[slice_]
             self.assertTrue(isinstance(t.owner.op, op_type))