Fix W291: trailing whitespace

theano/compile/__init__.py

@@ -2,7 +2,7 @@ from theano.compile.ops import (
         DeepCopyOp, deep_copy_op, register_deep_copy_op_c_code,
         Shape, shape, register_shape_c_code,
         Shape_i, register_shape_i_c_code,
-        ViewOp, view_op, register_view_op_c_code, FromFunctionOp, 
+        ViewOp, view_op, register_view_op_c_code, FromFunctionOp,
         as_op, Rebroadcast, register_rebroadcast_c_code,
         SpecifyShape, specify_shape, register_specify_shape_c_code)
 

theano/compile/function_module.py

@@ -933,7 +933,7 @@ class FunctionMaker(object):
             found_graph_in_db = None
             # The sole purpose of this loop is to set 'need_optimize' by
             # going through graph_db, looking for graph that has the same
-            # computation performed. 
+            # computation performed.
             for graph_old, graph_optimized in graph_db.iteritems():
                 inputs_old = graph_old.inputs
                 outputs_old = graph_old.outputs
@@ -1031,7 +1031,7 @@ class FunctionMaker(object):
             print 'graph not found in graph_db, optimizing the graph'
             self.fgraph.variables = set(gof.graph.variables(
                 self.fgraph.inputs, self.fgraph.outputs))
-            # check_integrity parameters was added to ignore 
+            # check_integrity parameters was added to ignore
             #"excess cached variables" errors. Works that way
             # but once again the error couldbe worth
             # investigating.
@@ -1123,7 +1123,7 @@ class FunctionMaker(object):
         # Check if some input variables are unused
         self._check_unused_inputs(inputs, outputs, on_unused_input)
 
-        # Make a list of (SymbolicInput|SymblicInputKits, indices, [SymbolicInput,...]), one 
+        # Make a list of (SymbolicInput|SymblicInputKits, indices, [SymbolicInput,...]), one
         # tuple for each input. (See Function.indices for more details)
         indices = [[input] + self.expand_in(input, _inputs) for input in inputs]
 
@@ -1156,7 +1156,7 @@ class FunctionMaker(object):
                 if theano.config.cache_optimizations:
                     optimizer_profile = self.optimize_graph_with_cache(
                         optimizer, inputs, outputs)
-                else:    
+                else:
                     optimizer_profile = optimizer(fgraph)
                     
                 end_optimizer = time.time()

theano/gof/cutils.py

@@ -5,7 +5,7 @@ import sys
 from theano.compat import PY3
 from theano.gof.compilelock import get_lock, release_lock
 from theano import config
-import cmodule 
+import cmodule
 
 # TODO These two lines may be removed in the future, when we are 100% sure
 # noone has an old cutils_ext.so lying around anymore.

theano/gof/sched.py

@@ -11,10 +11,10 @@ from theano.compat import cmp
 # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
 # of the Software, and to permit persons to whom the Software is furnished to do
 # so, subject to the following conditions:
-# 
+#
 # The above copyright notice and this permission notice shall be included in all
 # copies or substantial portions of the Software.
-# 
+#
 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

theano/misc/cpucount.py

 # Copyright (c) 2006-2008, R Oudkerk
 # All rights reserved.
-# 
+#
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
 # are met:
-# 
+#
 # 1. Redistributions of source code must retain the above copyright
 #    notice, this list of conditions and the following disclaimer.
 # 2. Redistributions in binary form must reproduce the above copyright
@@ -13,7 +13,7 @@
 # 3. Neither the name of author nor the names of any contributors may be
 #    used to endorse or promote products derived from this software
 #    without specific prior written permission.
-# 
+#
 # THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
 # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE

theano/sandbox/gpuarray/nnet.py

@@ -490,7 +490,7 @@ class GpuSoftmax (Op):
         if config.gpuarray.sync:
             cnda_thread_sync = "GpuArray_sync(&%(zz)s->ga);" % dict(zz=zz)
         else:
-            cnda_thread_sync = ""  
+            cnda_thread_sync = ""
         return """
         if (PyGpuArray_NDIM(%(x)s) != 2)
         {
@@ -686,7 +686,7 @@ class GpuSoftmaxWithBias (Op):
         if config.gpuarray.sync:
             cnda_thread_sync = "GpuArray_sync(&%(zz)s->ga);" % dict(zz=zz)
         else:
-            cnda_thread_sync = "" 
+            cnda_thread_sync = ""
         return """
         if (PyGpuArray_NDIM(%(x)s) != 2)
         {

theano/sparse/basic.py

@@ -1222,7 +1222,7 @@ class GetItem2d(gof.op.Op):
                 start = ind.start
                 stop = ind.stop
                 step = ind.step
-                # If start or stop or step are None, make them a Generic 
+                # If start or stop or step are None, make them a Generic
                 # constant. Else, they should be converted to Tensor Variables
                 # of dimension 1 and int/uint dtype.
                 if scipy_ver < [0, 14] and ind.step != None:
@@ -1239,7 +1239,7 @@ class GetItem2d(gof.op.Op):
                         raise ValueError((
                             "Impossible to index into a sparse matrix with "
                             "slice where step=%s" % step),
-                            step.ndim, step.dtype)                    
+                            step.ndim, step.dtype)
 
                 if start is None:
                     start = generic_None

theano/tensor/nnet/tests/speed_test_conv.py

@@ -42,9 +42,9 @@ global_rng = N.random.RandomState(3423489)
 
 dmatrix4 = T.TensorType('float64', (False, False, False, False))
 
-def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns, 
-        unroll_batch=0, unroll_kern=0, img=T.dmatrix(), validate=True, 
-        conv_op_py=False, do_print=True, repeat=1, 
+def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
+        unroll_batch=0, unroll_kern=0, img=T.dmatrix(), validate=True,
+        conv_op_py=False, do_print=True, repeat=1,
         unroll_patch=False, unroll_patch_size=False, verbose=0):
 
         # build actual input images
@@ -56,7 +56,7 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
         kerns4 = dmatrix4()
 
         # for each layer
-        ntot = 0 
+        ntot = 0
         tctot = 0
         tpytot = 0
 
@@ -76,7 +76,7 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
             if conv_mode == 'full':
                 padimg_shp = N.array(imshp[1:]) + 2*(N.array(kshp) - N.array([1, 1]))
                 padimg = N.zeros(N.r_[bsize, imshp[0], padimg_shp])
-                padimg[:, :, kshp[0]-1:-kshp[0]+1, 
+                padimg[:, :, kshp[0]-1:-kshp[0]+1,
                              kshp[1]-1:-kshp[1]+1] = imgval
 
             outshp = N.hstack((nkern, ConvOp.getOutputShape(imshp[1:], kshp, ss, conv_mode)))
@@ -136,9 +136,9 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
 
         return tctot, tpytot, ntot
 
-def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, 
+def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
         unroll_batch=0, unroll_kern=0, img=T.dmatrix(),
-        do_print=True, repeat=1, 
+        do_print=True, repeat=1,
         unroll_patch=False, unroll_patch_size=False, verbose=0):
 
         # build actual input images
@@ -150,7 +150,7 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
         kerns4 = dmatrix4()
 
         # for each layer
-        ntot = 0 
+        ntot = 0
         tctot = 0
         tpytot = 0
 
@@ -195,7 +195,7 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
 
 def speed_multilayer_conv():
         # calculate the speed up of different combination of unroll
-        # put the paramter to the same you will try. 
+        # put the paramter to the same you will try.
         
         validate = False  # we don't validate the result to have it much faster!
         repeat = 3

theano/tensor/opt.py

@@ -1929,16 +1929,16 @@ def local_useless_slice(node):
     if isinstance(node.op, Subtensor):
         slices = get_idx_list(node.inputs, node.op.idx_list)
         last_slice = len(slices)
-        for s in slices[::-1]: 
+        for s in slices[::-1]:
             # check if slice and then check slice indices
             if (isinstance(s, slice) and s.start is None and s.stop is None
-                and (s.step is None or T.extract_constant(s.step) == 1)): 
+                and (s.step is None or T.extract_constant(s.step) == 1)):
                     last_slice -= 1
             else:
                 break
         # check if we removed something
         if last_slice < len(slices):
-            subtens = Subtensor(slices[:last_slice]) 
+            subtens = Subtensor(slices[:last_slice])
             sl_ins = Subtensor.collapse(slices[:last_slice],
                                         lambda x: isinstance(x, T.Variable))
             out = subtens(node.inputs[0], *sl_ins)
@@ -5619,8 +5619,8 @@ else:
 # # Remove consider_constant #
 # ############################
 
-# Although the ops ConsiderConstant, ZeroGrad and DisconnectedGrad 
-# just returns the input, it should be removed from the graph to 
+# Although the ops ConsiderConstant, ZeroGrad and DisconnectedGrad
+# just returns the input, it should be removed from the graph to
 # make sure all possible optimizations can be applied.
 register_canonicalize(gof.OpRemove(theano.gradient.consider_constant_),
     'fast_compile', 'fast_run', name='remove_consider_constant')

theano/tensor/signal/tests/test_downsample.py

@@ -465,7 +465,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
     def test_max_pool_2d_2D_same_size(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
         test_input_array = numpy.array([[[
-            [1., 2., 3., 4.], 
+            [1., 2., 3., 4.],
             [5., 6., 7., 8.]
         ]]])
         test_answer_array = numpy.array([[[

theano/tensor/tests/test_opt.py

@@ -1586,7 +1586,7 @@ def test_local_useless_slice():
     f_opt = theano.function([x], o, mode=mode_opt)
     test_inp = numpy.random.randint(-10, 10, (4, 4)).astype('float32')
     assert all(f_opt(test_inp)  == f_unopt(test_inp)),\
-           "The optimization caused a mismatch in the result" 
+           "The optimization caused a mismatch in the result"
     # test to see if the slice is truely gone
     apply_node = f_opt.maker.fgraph.toposort()[0]
     subtens = apply_node.op
@@ -1599,7 +1599,7 @@ def test_local_useless_slice():
     f_opt_check = theano.function([z], o2, mode=mode_opt)
     f_opt_check_apply = theano.function([z], o3, mode=mode_opt)
 
-    # The optimization shouldn't apply here 
+    # The optimization shouldn't apply here
     apply_node = f_opt_check.maker.fgraph.toposort()[0]
     subtens = apply_node.op
     assert [isinstance(idx, slice) for idx in subtens.idx_list].count(True) == 2

theano/tests/run_tests_in_batch.py

@@ -153,7 +153,7 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
     assert rval == 0
     noseids_file = '.noseids'
 
-    with open(noseids_file, 'rb') as f: 
+    with open(noseids_file, 'rb') as f:
         data = cPickle.load(f)
 
     ids = data['ids']

theano/tests/test_gradient.py

@@ -568,7 +568,7 @@ def test_subgraph_grad():
     w2 = theano.shared(np.random.randn(4, 2))
     a1 = theano.tensor.tanh(theano.tensor.dot(x, w1))
     a2 = theano.tensor.tanh(theano.tensor.dot(a1, w2))
-    cost2 = theano.tensor.sqr(a2 - t).sum() 
+    cost2 = theano.tensor.sqr(a2 - t).sum()
     cost2 += theano.tensor.sqr(w2.sum())
     cost1 = theano.tensor.sqr(w1.sum())
     
@@ -591,7 +591,7 @@ def test_subgraph_grad():
     param_grads = []
     for i in xrange(2):
         param_grad, next_grad = theano.subgraph_grad(
-            wrt=params[i], end=grad_ends[i], 
+            wrt=params[i], end=grad_ends[i],
             start=next_grad, cost=costs[i]
         )
         next_grad = OrderedDict(zip(grad_ends[i], next_grad))