fixed a bug in the setting of output array flags in DimShuffle.c_code

theano/tensor/elemwise.py

@@ -224,15 +224,19 @@ class DimShuffle(Op):
         for i in xrange(nd_out-2,-1, -1):
             strides_statements.append("if (strides[%(i)s] == 0) strides[%(i)s] = strides[%(i)s+1] * dimensions[%(i)s+1]"%dict(i=str(i)))
 
+        #
+        # PyObject* PyArray_New(PyTypeObject* subtype, int nd, npy_intp* dims, int type_num,
+        #                       npy_intp* strides, void* data, int itemsize, int flags, PyObject* obj)
+        #
         close_bracket = [
                 #create a new array, 
                 ('%(res)s = (PyArrayObject*)PyArray_New(&PyArray_Type, '
                             '' + str(nd_out) + ', dimensions, '
                             'PyArray_TYPE(%(basename)s), strides, '
-                            '%(basename)s->data, %(basename)s->descr->elsize, '
+                            '%(basename)s->data, PyArray_ITEMSIZE(%(basename)s), '
                             #borrow only the writable flag from the base
                             # the NPY_OWNDATA flag will default to 0.
-                            'PyArray_ISWRITEABLE(%(basename)s), NULL)'),
+                            '(NPY_WRITEABLE*PyArray_ISWRITEABLE(%(basename)s)), NULL)'),
                 #recalculate flags: CONTIGUOUS, FORTRAN, ALIGNED
                 'PyArray_UpdateFlags(%(res)s, NPY_UPDATE_ALL)',
                 #we are making a view in both inplace and non-inplace cases

theano/tensor/tests/test_misc.py

+import sys
 import numpy, theano
 from theano import tensor
 
 def test_bug_2009_06_02():
 
-    y = tensor.lvector()
-    f = theano.function([y], tensor.stack(y[0] / 2))
+    y = tensor.lvector('y')
+    #f = theano.function([y], tensor.stack(y[0] / 2))
+    #f = theano.function([y], tensor.join(0,tensor.shape_padleft(y[0] / 2,1)))
+    f = theano.function([y], tensor.int_div(tensor.DimShuffle(y[0].broadcastable, ['x'])(y[0]), 2))
+    sys.stdout.flush()
+    print f(numpy.ones(1) * 3)
+    #z = tensor.lscalar('z')
+    #f = theano.function([z], tensor.DimShuffle([], ['x'])(z) / 2)
 
-    print f(numpy.ones(1) * 2)