pep8 elemwise

theano/tensor/elemwise.py

@@ -278,7 +278,8 @@ class DimShuffle(Op):
         #get the copy / view of the input depending on whether we're doingi
         # things inplace or not.
         if self.inplace:
-            get_base = ['{ PyArrayObject * %(basename)s = %(input)s', 'Py_INCREF((PyObject*)%(basename)s)']
+            get_base = [
+                '{ PyArrayObject * %(basename)s = %(input)s', 'Py_INCREF((PyObject*)%(basename)s)']
         else:
             get_base = [('{ PyArrayObject * %(basename)s = (PyArrayObject*)PyArray_FromAny((PyObject*)%(input)s, NULL,'
                     '0, 0, NPY_ALIGNED|NPY_ENSURECOPY, NULL)')]
@@ -286,7 +287,8 @@ class DimShuffle(Op):
         shape_statements = ['npy_intp dimensions[%i]' % nd_out]
         for i, o in enumerate(self.new_order):
             if o != 'x':
-                shape_statements += [('dimensions[' + str(i) + '] = %(basename)s->dimensions[' + str(o) + ']')]
+                shape_statements += [('dimensions[' + str(
+                    i) + '] = %(basename)s->dimensions[' + str(o) + ']')]
             else:
                 shape_statements += [('dimensions[' + str(i) + '] = 1')]
 
@@ -295,7 +297,8 @@ class DimShuffle(Op):
         #set the strides of the non-broadcasted dimensions
         for i, o in enumerate(self.new_order):
             if o != 'x':
-                strides_statements += [('strides[' + str(i) + '] = %(basename)s->strides[' + str(o) + ']')]
+                strides_statements += [('strides[' + str(i)
+                     + '] = %(basename)s->strides[' + str(o) + ']')]
             else:
                 strides_statements += [('strides[' + str(i) + '] = 0')]
 
@@ -311,7 +314,8 @@ class DimShuffle(Op):
                 '-1] = %(basename)s->descr->elsize'
             )
         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)))
+            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,
@@ -619,7 +623,6 @@ class Elemwise(Op):
 
         return rval
 
-
     def connection_pattern(self, node):
 
         if hasattr(self.scalar_op, 'connection_pattern'):
@@ -686,7 +689,7 @@ class Elemwise(Op):
 
             theano.config.compute_test_value = prev_setting
 
-        if not isinstance(scalar_igrads,(list,tuple)):
+        if not isinstance(scalar_igrads, (list, tuple)):
             raise TypeError('%s.grad returned %s instead of list or tuple' %
                     (str(self.scalar_op), str(type(scalar_igrads))))
 
@@ -1340,7 +1343,8 @@ class CAReduce(Op):
             alloc += """
 for(int i=0;i<%(iname)s->nd;i++){
   if(PyArray_DIMS(%(iname)s)[i]==0 && tosum[i]){
-    PyErr_Format(PyExc_ValueError, "Input of CAReduce{%(scal_name)s} has zero-size on axis %%d",i);
+    PyErr_Format(PyExc_ValueError,
+         "Input of CAReduce{%(scal_name)s} has zero-size on axis %%d",i);
     %(fail)s;
   }
 }
@@ -1718,7 +1722,7 @@ class Prod(CAReduceDtype):
         out = self(*inp)
 
         if out.dtype[0:3] in ('int', 'uin'):
-            return [ prod_in.zeros_like().astype(theano.config.floatX) ]
+            return [prod_in.zeros_like().astype(theano.config.floatX)]
 
         # Prepare the broadcasting that is used everywhere to broadcast
         # over the original groups (ie. broadcast over the elements of a given