Merge pull request #3030 from abergeron/flake8_temp

theano/compile/function_module.py

@@ -1417,8 +1417,7 @@ def _pickle_FunctionMaker(self):
         accept_inplace=self.accept_inplace,
         function_builder=self.function_builder,
         profile=self.profile,
-        on_unused_input=self.on_unused_input,
-        )
+        on_unused_input=self.on_unused_input)
     return (_constructor_FunctionMaker, (kwargs,))
 
 

theano/compile/mode.py

@@ -71,8 +71,7 @@ predefined_linkers = {
     'vm': gof.vm.VM_Linker(use_cloop=False),  # Use allow_gc Theano flag
     'cvm': gof.vm.VM_Linker(use_cloop=True),  # Use allow_gc Theano flag
     'vm_nogc': gof.vm.VM_Linker(allow_gc=False, use_cloop=False),
-    'cvm_nogc': gof.vm.VM_Linker(allow_gc=False, use_cloop=True),
-    }
+    'cvm_nogc': gof.vm.VM_Linker(allow_gc=False, use_cloop=True)}
 
 
 def register_linker(name, linker):
@@ -115,8 +114,7 @@ predefined_optimizers = {
     'fast_run': OPT_FAST_RUN,
     'fast_run_stable': OPT_FAST_RUN_STABLE,
     'fast_compile': OPT_FAST_COMPILE,
-    'stabilize': OPT_STABILIZE
-    }
+    'stabilize': OPT_STABILIZE}
 
 
 def register_optimizer(name, opt):

theano/compile/profilemode.py

@@ -378,22 +378,22 @@ class ProfileMode(Mode):
         print()
         print('Time since import %.3fs' % (total_time))
         print('Theano compile time: %.3fs (%.1f%% since import)' %
-              (compile_time, compile_time/total_time*100))
+              (compile_time, compile_time / total_time * 100))
         print('    Optimization time: %.3fs' % (other_time['optimizer_time']))
         print('    Linker time: %.3fs' % (other_time['linker_time']))
         print('Theano fct call %.3fs (%.1f%% since import)' %
-              (total_fct_time, total_fct_time/total_time*100))
+              (total_fct_time, total_fct_time / total_time * 100))
         print('   Theano Op time %.3fs %.1f%%(since import) %.1f%%'
-              '(of fct call)' % (local_time, local_time/total_time*100,
+              '(of fct call)' % (local_time, local_time / total_time * 100,
                                  time_pr_in_fct))
         print('   Theano function overhead in ProfileMode %.3fs %.1f%%'
               '(since import) %.1f%%(of fct call)' % (
-                  overhead_time, overhead_time/total_time*100,
+                  overhead_time, overhead_time / total_time * 100,
                   overhead_time_pourcent_fct_time))
         print('%i Theano fct call, %.3fs per call' %
               (total_fct_call, time_per_call))
         print('Rest of the time since import %.3fs %.1f%%' %
-              (unknown_time, unknown_time/total_time*100))
+              (unknown_time, unknown_time / total_time * 100))
 
         print()
         print('Theano fct summary:')
@@ -402,9 +402,9 @@ class ProfileMode(Mode):
         for key in fct_call:
             if fct_call[key] > 0:
                 print('   %4.1f%% %.3fs %.2es %d %s' %
-                      (fct_call_time[key]/total_fct_time*100,
+                      (fct_call_time[key] / total_fct_time * 100,
                        fct_call_time[key],
-                       fct_call_time[key]/fct_call[key],
+                       fct_call_time[key] / fct_call[key],
                        fct_call[key],
                        key.name))
             else:
@@ -448,7 +448,7 @@ class ProfileMode(Mode):
             sop_op[typ] += 1
             sop_cimpl.setdefault(typ, True)
             sop_cimpl[typ] = sop_cimpl[typ] and op_cimpl.get(a, False)
-            sop_call[typ] = sop_call.get(typ, 0)+op_call[a]
+            sop_call[typ] = sop_call.get(typ, 0) + op_call[a]
 
         # Print the summary per op class.
         print()
@@ -456,7 +456,7 @@ class ProfileMode(Mode):
         print('<% of local_time spent on this kind of Op> <cumulative %> '
               '<self seconds> <cumulative seconds> <time per call> [*] '
               '<nb_call> <nb_op> <nb_apply> <Op name>')
-        sotimes = [(t*100/local_time, t, a, sop_cimpl[a], sop_call[a],
+        sotimes = [(t * 100 / local_time, t, a, sop_cimpl[a], sop_call[a],
                     sop_op[a], sop_apply[a]) for a, t in iteritems(sop_time)]
         sotimes.sort()
         sotimes.reverse()
@@ -466,17 +466,17 @@ class ProfileMode(Mode):
                 assert t == 0
                 continue
             tot += t
-            ftot = tot*100/local_time
+            ftot = tot * 100 / local_time
             if ci:
                 msg = '*'
             else:
                 msg = ' '
             print('   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs  %.2es %s %5d %2d '
-                  '%2d %s' % (f, ftot, t, tot, t/nb_call, msg, nb_call,
+                  '%2d %s' % (f, ftot, t, tot, t / nb_call, msg, nb_call,
                               nb_op, nb_apply, a))
         print('   ... (remaining %i single Op account for %.2f%%(%.2fs) of '
               'the runtime)' %
-              (max(0, len(sotimes)-n_ops_to_print),
+              (max(0, len(sotimes) - n_ops_to_print),
                sum(soinfo[0] for soinfo in sotimes[n_ops_to_print:]),
                sum(soinfo[1] for soinfo in sotimes[n_ops_to_print:])))
 
@@ -486,7 +486,7 @@ class ProfileMode(Mode):
         op_flops = {}
         for a, t in iteritems(op_time):
             if hasattr(a, 'flops'):
-                op_flops[a] = a.flops*op_call[a]/t/1e6
+                op_flops[a] = a.flops * op_call[a] / t / 1e6
         flops_msg = ''
         if op_flops:
             flops_msg = ' <MFlops/s>'
@@ -500,7 +500,7 @@ class ProfileMode(Mode):
               '<self seconds> <cumulative seconds> <time per call> [*] %s '
               '<nb_call> <nb apply> <Op name>' % (flops_msg))
 
-        otimes = [(t*100/local_time, t, a, op_cimpl.get(a, 0),
+        otimes = [(t * 100 / local_time, t, a, op_cimpl.get(a, 0),
                    op_call.get(a, 0), op_apply.get(a, 0))
                   for a, t in iteritems(op_time)]
         otimes.sort()
@@ -511,23 +511,23 @@ class ProfileMode(Mode):
                 assert t == 0
                 continue
             tot += t
-            ftot = tot*100/local_time
+            ftot = tot * 100 / local_time
             if ci:
                 msg = '*'
             else:
                 msg = ' '
             if op_flops:
                 print('   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs  %.2es %s %7.1f '
-                      '%5d %2d %s' % (f, ftot, t, tot, t/nb_call, msg,
+                      '%5d %2d %s' % (f, ftot, t, tot, t / nb_call, msg,
                                       op_flops.get(a, -1), nb_call, nb_apply,
                                       a))
             else:
                 print('   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs  %.2es %s %5d %2d '
-                      '%s' % (f, ftot, t, tot, t/nb_call, msg, nb_call,
+                      '%s' % (f, ftot, t, tot, t / nb_call, msg, nb_call,
                               nb_apply, a))
         print('   ... (remaining %i Op account for %6.2f%%(%.2fs) of the '
               'runtime)' %
-              (max(0, len(otimes)-n_ops_to_print),
+              (max(0, len(otimes) - n_ops_to_print),
                sum(f for f, t, a, ci, nb_call, nb_op in
                    otimes[n_ops_to_print:]),
                sum(t for f, t, a, ci, nb_call, nb_op in
@@ -540,7 +540,7 @@ class ProfileMode(Mode):
             print('<% of local_time spent at this position> <cumulative %%> '
                   '<apply time> <cumulative seconds> <time per call> [*] '
                   '<nb_call> <Apply position> <Apply Op name>')
-            atimes = [(t*100/local_time, t, a,
+            atimes = [(t * 100 / local_time, t, a,
                        [v for k, v in iteritems(fct_call)
                         if k.maker.fgraph is a[1].fgraph][0])
                       for a, t in iteritems(apply_time)]
@@ -549,7 +549,7 @@ class ProfileMode(Mode):
             tot = 0
             for f, t, a, nb_call in atimes[:n_apply_to_print]:
                 tot += t
-                ftot = tot*100/local_time
+                ftot = tot * 100 / local_time
                 if nb_call == 0:
                     continue
                 if apply_cimpl.get(a[1], False):
@@ -558,11 +558,11 @@ class ProfileMode(Mode):
                     msg = ' '
                 print('   %4.1f%%  %5.1f%%  %5.3fs  %5.3fs %.2es  %s %i  '
                       '%2i %s' %
-                      (f, ftot, t, tot, t/nb_call, msg, nb_call, a[0],
+                      (f, ftot, t, tot, t / nb_call, msg, nb_call, a[0],
                        str(a[1])))
             print('   ... (remaining %i Apply instances account for '
                   '%.2f%%(%.2fs) of the runtime)' %
-                  (max(0, len(atimes)-n_apply_to_print),
+                  (max(0, len(atimes) - n_apply_to_print),
                    sum(f for f, t, a, nb_call in atimes[n_apply_to_print:]),
                    sum(t for f, t, a, nb_call in atimes[n_apply_to_print:])))
             print('(*) Op is running a c implementation')
@@ -603,7 +603,7 @@ Test them first, as they are not guaranteed to always provide a speedup.""")
                                       scal.RoundHalfAwayFromZero, scal.Log,
                                       scal.Log2, scal.Log10, scal.Log1p,
                                       scal.Exp, scal.Sqrt, scal.Abs, scal.Cos,
-                                      scal.Sin, scal.Tan,  scal.Tanh,
+                                      scal.Sin, scal.Tan, scal.Tanh,
                                       scal.Cosh, scal.Sinh,
                                       T.nnet.sigm.ScalarSigmoid,
                                       T.nnet.sigm.ScalarSoftplus]
@@ -661,9 +661,9 @@ Test them first, as they are not guaranteed to always provide a speedup.""")
         if not config.lib.amdlibm and any([exp_float32_op(a.op) and
                                            a.inputs[0].dtype == 'float32'
                                            for i, a in apply_time]):
-            print ("  - With the default gcc libm, exp in float32 is slower "
-                   "than in float64! Try Theano flag floatX=float64, or "
-                   "install amdlibm and set the theano flags lib.amdlibm=True")
+            print("  - With the default gcc libm, exp in float32 is slower "
+                  "than in float64! Try Theano flag floatX=float64, or "
+                  "install amdlibm and set the theano flags lib.amdlibm=True")
             printed_tip = True
 
         # tip 4
@@ -672,11 +672,11 @@ Test them first, as they are not guaranteed to always provide a speedup.""")
             if (isinstance(node.op, T.Dot) and
                     all([len(i.type.broadcastable) == 2
                          for i in node.inputs])):
-                print(("  - You have a dot operation that was not optimized to"
-                       " dot22 (which is faster). Make sure the inputs are "
-                       "float32 or float64, and are the same for both inputs. "
-                       "Currently they are: %s" %
-                       [i.type for i in node.inputs]))
+                print("  - You have a dot operation that was not optimized to"
+                      " dot22 (which is faster). Make sure the inputs are "
+                      "float32 or float64, and are the same for both inputs. "
+                      "Currently they are: %s" %
+                      [i.type for i in node.inputs])
                 printed_tip = True
 
         # tip 5
@@ -684,13 +684,13 @@ Test them first, as they are not guaranteed to always provide a speedup.""")
             node = a[1]
             if isinstance(node.op, RandomFunction):
                 printed_tip = True
-                print ("  - Replace the default random number generator by "
-                       "'from theano.sandbox.rng_mrg import MRG_RandomStreams "
-                       "as RandomStreams', as this is is faster. It is still "
-                       "experimental, but seems to work correctly.")
+                print("  - Replace the default random number generator by "
+                      "'from theano.sandbox.rng_mrg import MRG_RandomStreams "
+                      "as RandomStreams', as this is is faster. It is still "
+                      "experimental, but seems to work correctly.")
                 if config.device.startswith("gpu"):
-                    print ("     - MRG_RandomStreams is the only random number"
-                           " generator supported on the GPU.")
+                    print("     - MRG_RandomStreams is the only random number"
+                          " generator supported on the GPU.")
                 break
 
         if not printed_tip:

theano/gof/compiledir.py

@@ -63,8 +63,7 @@ compiledir_format_dict = {
     "theano_version": theano.__version__,
     "numpy_version": numpy.__version__,
     "gxx_version": gcc_version_str.replace(" ", "_"),
-    "hostname": socket.gethostname(),
-    }
+    "hostname": socket.gethostname()}
 
 
 def short_platform(r=None, p=None):

theano/gof/compilelock.py

@@ -108,7 +108,7 @@ def _get_lock(lock_dir=None, **kw):
                 raise Exception("For some unknow reason, the lock was already "
                                 "taken, but no start time was registered.")
             now = time.time()
-            if now - get_lock.start_time > config.compile.timeout/2:
+            if now - get_lock.start_time > config.compile.timeout / 2:
                 lockpath = os.path.join(get_lock.lock_dir, 'lock')
                 _logger.info('Refreshing lock %s', str(lockpath))
                 refresh_lock(lockpath)

theano/gof/optdb.py

@@ -312,7 +312,7 @@ class SequenceDB(DB):
         positions = list(self.__position__.items())
 
         def c(a, b):
-            return cmp(a[1], b[1])
+            return ((a[1] > b[1]) - (a[1] < b[1]))
         positions.sort(c)
 
         print("  position", positions, file=stream)

theano/gof/tests/test_vm.py

@@ -46,7 +46,7 @@ class TestCallbacks(unittest.TestCase):
 
     def test_callback_with_ifelse(self):
         a, b, c = tensor.scalars('abc')
-        f = function([a, b, c], ifelse(a, 2*b, 2*c),
+        f = function([a, b, c], ifelse(a, 2 * b, 2 * c),
                      mode=Mode(
                          optimizer=None,
                          linker=vm.VM_Linker(callback=self.callback)))
@@ -62,7 +62,7 @@ def test_c_thunks():
     if theano.config.cxx:
         cases.append(True)
     for c_thunks in cases:
-        f = function([a, b, c], ifelse(a, a*b, b*c),
+        f = function([a, b, c], ifelse(a, a * b, b * c),
                      mode=Mode(
                          optimizer=None,
                          linker=vm.VM_Linker(c_thunks=c_thunks,
@@ -86,7 +86,7 @@ def test_speed():
     def numpy_version(x, depth):
         z = x
         for d in xrange(depth):
-            z = (z+z)
+            z = (z + z)
         return z
 
     def time_numpy():
@@ -136,7 +136,7 @@ def test_speed():
 
         print("%s takes %f s/Kop" % (
             name,
-            (1000*(t_b-t_a) / (steps_b - steps_a))))
+            (1000 * (t_b - t_a) / (steps_b - steps_a))))
 
     time_linker('c|py', OpWiseCLinker)
     time_linker('vmLinker', vm.VM_Linker)
@@ -185,7 +185,7 @@ def test_speed_lazy():
 
         print("%s takes %f s/Kop" % (
             name,
-            (1000*(t_b-t_a) / (steps_b - steps_a))))
+            (1000 * (t_b - t_a) / (steps_b - steps_a))))
 
     time_linker('vmLinker', vm.VM_Linker)
     time_linker('vmLinker_nogc', lambda: vm.VM_Linker(allow_gc=False))
@@ -230,6 +230,8 @@ if run_memory_usage_tests:
             a = cuda.CudaNdarray(n)
             a.sum()
             assert c == sys.getrefcount(n)
+            # This is to confuse flake8
+            a = a
             del a
             if not i % 1000:
                 print('.', end=' ')

theano/gof/type.py

@@ -282,11 +282,7 @@ class PureType(object):
                 'Cannot convert Type %(othertype)s '
                 '(of Variable %(other)s) into Type %(self)s. '
                 'You can try to manually convert %(other)s into a %(self)s.'
-                % dict(
-                    othertype=other.type,
-                    other=other,
-                    self=self)
-                )
+                % dict(othertype=other.type, other=other, self=self))
         return other
 
     def is_valid_value(self, a):

theano/misc/elemwise_time_test.py

@@ -35,7 +35,7 @@ def ElemwiseOpTime(N, script=False, loops=1000):
     x = T.vector('x')
     np.random.seed(1235)
     v = np.random.random(N).astype(theano.config.floatX)
-    f = theano.function([x], 2*x + x*x)
+    f = theano.function([x], 2 * x + x * x)
     f1 = theano.function([x], T.tanh(x))
     if not script:
         if theano.config.openmp:

theano/sandbox/cuda/tests/test_fftconv.py

@@ -194,7 +194,7 @@ class TestConv3dFFT(unittest.TestCase):
 
         res_ref = f_ref()
         res_fft = f_fft()
-        utt.assert_allclose(res_ref, res_fft,  rtol=1e-05, atol=1e-05)
+        utt.assert_allclose(res_ref, res_fft, rtol=1e-05, atol=1e-05)
 
     def run_conv_full(self, inputs_shape, filters_shape, pad=False):
         inputs_val = numpy.random.random(inputs_shape).astype('float32')
@@ -220,7 +220,7 @@ class TestConv3dFFT(unittest.TestCase):
 
         res_ref = f_ref()
         res_fft = f_fft()
-        utt.assert_allclose(res_ref, res_fft,  rtol=1e-04, atol=1e-04)
+        utt.assert_allclose(res_ref, res_fft, rtol=1e-04, atol=1e-04)
 
     def test_valid(self):
         self.run_conv_valid(inputs_shape=(16, 20, 32, 16, 1),
@@ -299,7 +299,7 @@ class TestConv3dFFT(unittest.TestCase):
         res_ref = f_ref()
         res_fft = f_fft()
 
-        utt.assert_allclose(res_ref, res_fft,  rtol=1e-04, atol=1e-04)
+        utt.assert_allclose(res_ref, res_fft, rtol=1e-04, atol=1e-04)
 
     def test_opt_convtransp3d_fft(self):
         inputs_shape = (2, 9, 16, 12, 10)

theano/sandbox/cuda/tests/test_type.py

-import six.moves.cPickle as pickle
 import os.path
-import sys
 
 from nose.tools import assert_raises
 import numpy

theano/sandbox/gpuarray/opt.py

@@ -255,7 +255,7 @@ def local_gpu_elemwise(node):
     scal_op = op.scalar_op
     name = op.name
     if name:
-        name = 'Gpu'+name
+        name = 'Gpu' + name
     res = GpuElemwise(scal_op, name=name,
                       inplace_pattern=copy.copy(op.inplace_pattern),
                       nfunc_spec=op.nfunc_spec)
@@ -349,7 +349,7 @@ def local_gpu_pdbbreakpoint_op(node):
         nb_monitored_vars = len(node.outputs)
         for i in range(nb_monitored_vars):
 
-            inp = old_inputs[i+1]
+            inp = old_inputs[i + 1]
             out = old_outputs[i]
 
             input_is_from_gpu = (inp.owner and

theano/scalar/tests/test_basic_sympy.py

@@ -25,12 +25,12 @@ def test_grad():
     op = SymPyCCode([xs], xs**2)
     zt = op(xt)
     ztprime = theano.grad(zt, xt)
-    assert ztprime.owner.op.expr == 2*xs
+    assert ztprime.owner.op.expr == 2 * xs
 
 
 def test_multivar_grad():
-    op = SymPyCCode([xs, ys], xs**2 + ys**3)
+    op = SymPyCCode([xs, ys], xs ** 2 + ys ** 3)
     zt = op(xt, yt)
     dzdx, dzdy = theano.grad(zt, [xt, yt])
-    assert dzdx.owner.op.expr == 2*xs
-    assert dzdy.owner.op.expr == 3*ys**2
+    assert dzdx.owner.op.expr == 2 * xs
+    assert dzdy.owner.op.expr == 3 * ys ** 2

theano/tensor/elemwise.py

@@ -136,16 +136,15 @@ class DimShuffle(Op):
                 # returning False for numpy integers.
                 # See <http://projects.scipy.org/numpy/ticket/2235>.
                 if not isinstance(j, (int, numpy.integer)):
-                    raise TypeError(
-                            "DimShuffle indices must be python ints.")
+                    raise TypeError("DimShuffle indices must be python ints.")
                 if j >= len(input_broadcastable):
                     raise ValueError(("new_order[%d] is %d, but the input "
                         "only has %d axes.") %
                         (i, j, len(input_broadcastable)))
                 if j in new_order[(i + 1):]:
-                    raise ValueError((
-                    "The same input dimension may not appear twice in the "
-                    "list of output dimensions", (new_order)))
+                    raise ValueError("The same input dimension may not appear "
+                                     "twice in the list of output dimensions",
+                                     new_order)
 
         # list of dimensions of the input to drop
         self.drop = []
@@ -158,8 +157,8 @@ class DimShuffle(Op):
                 else:
                     # we cannot drop non-broadcastable dimensions
                     raise ValueError(
-                            "You cannot drop a non-broadcastable dimension.",
-                            (input_broadcastable, new_order))
+                        "You cannot drop a non-broadcastable dimension.",
+                        (input_broadcastable, new_order))
 
         # this is the list of the original dimensions that we keep
         self.shuffle = [x for x in new_order if x != 'x']
@@ -246,7 +245,7 @@ class DimShuffle(Op):
             raise TypeError(res)
 
         # transpose
-        res = res.transpose(self.shuffle+self.drop)
+        res = res.transpose(self.shuffle + self.drop)
 
         # augment
         shape = list(res.shape[:len(self.shuffle)])
@@ -339,26 +338,27 @@ class DimShuffle(Op):
         #                       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, '
-                            'PyArray_DATA(%(basename)s), PyArray_ITEMSIZE(%(basename)s), '
-                            # borrow only the writable flag from the base
-                            # the NPY_OWNDATA flag will default to 0.
-                            '(NPY_ARRAY_WRITEABLE*PyArray_ISWRITEABLE(%(basename)s)), NULL)'),
-                'if (%(res)s == NULL) %(fail)s;',
-                # recalculate flags: CONTIGUOUS, FORTRAN, ALIGNED
-                'PyArray_UpdateFlags(%(res)s, NPY_ARRAY_UPDATE_ALL)',
-                # we are making a view in both inplace and non-inplace cases
-"""
+            # create a new array,
+            ('%(res)s = (PyArrayObject*)PyArray_New(&PyArray_Type, '
+             '' + str(nd_out) + ', dimensions, '
+             'PyArray_TYPE(%(basename)s), strides, '
+             'PyArray_DATA(%(basename)s), PyArray_ITEMSIZE(%(basename)s), '
+             # borrow only the writable flag from the base
+             # the NPY_OWNDATA flag will default to 0.
+             '(NPY_ARRAY_WRITEABLE*PyArray_ISWRITEABLE(%(basename)s)), '
+             'NULL)'),
+            'if (%(res)s == NULL) %(fail)s;',
+            # recalculate flags: CONTIGUOUS, FORTRAN, ALIGNED
+            'PyArray_UpdateFlags(%(res)s, NPY_ARRAY_UPDATE_ALL)',
+            # we are making a view in both inplace and non-inplace cases
+            """
 #if NPY_API_VERSION < 0x00000007
 PyArray_BASE(%(res)s) = (PyObject*)%(basename)s;
 #else
 PyArray_SetBaseObject(%(res)s, (PyObject*)%(basename)s);
 #endif
 """
-                '}']
+            '}']
 
         full_code = statements(check_input_nd
                 + clear_output
@@ -529,8 +529,8 @@ class Elemwise(OpenMPOp):
         """
         inputs = list(map(as_tensor_variable, inputs))
         shadow = self.scalar_op.make_node(
-                *[get_scalar_type(dtype=i.type.dtype).make_variable()
-                  for i in inputs])
+            *[get_scalar_type(dtype=i.type.dtype).make_variable()
+              for i in inputs])
 
         target_length = max([input.type.ndim for input in inputs])
 
@@ -567,9 +567,9 @@ class Elemwise(OpenMPOp):
                 for ob, ib in izip(out_broadcastables[overwriter],
                                   inputs[overwritten].type.broadcastable):
                     if ib and not ob:
-                        raise ValueError((
+                        raise ValueError(
                             "Operation cannot be done inplace on an input "
-                            "with broadcasted dimensions."))
+                            "with broadcasted dimensions.")
 
         out_dtypes = [o.type.dtype for o in shadow.outputs]
         if any(inputs[i].type.dtype != out_dtypes[o]
@@ -767,7 +767,7 @@ class Elemwise(OpenMPOp):
                 res = theano.tensor.constant(numpy.asarray(r.data), dtype=r.type.dtype)
                 return DimShuffle((), ['x'] * nd, inplace=False)(res)
             new_r = Elemwise(node.op, {})(
-                    *[transform(ipt) for ipt in node.inputs])
+                *[transform(ipt) for ipt in node.inputs])
             return new_r
         ret = []
         for scalar_igrad, ipt in izip(scalar_igrads, inputs):
@@ -1041,15 +1041,15 @@ class Elemwise(OpenMPOp):
 
         # We generate the C code of the inner loop using the scalar op
         task_code = self.scalar_op.c_code(
-                Apply(self.scalar_op,
-                      [get_scalar_type(dtype=input.type.dtype).make_variable()
-                          for input in node.inputs],
-                      [get_scalar_type(dtype=output.type.dtype).make_variable()
-                          for output in node.outputs]),
-                nodename + '_scalar_',
-                ["%s_i" % s for s in _inames],
-                ["%s_i" % s for s in onames],
-                sub)
+            Apply(self.scalar_op,
+                  [get_scalar_type(dtype=input.type.dtype).make_variable()
+                   for input in node.inputs],
+                  [get_scalar_type(dtype=output.type.dtype).make_variable()
+                   for output in node.outputs]),
+            nodename + '_scalar_',
+            ["%s_i" % s for s in _inames],
+            ["%s_i" % s for s in onames],
+            sub)
         code = """
         {
             %(defines)s
@@ -1379,7 +1379,7 @@ class CAReduce(Op):
     def __str__(self):
         if self.axis is not None:
             return "Reduce{%s}{%s}" % (
-                    self.scalar_op, ", ".join(str(x) for x in self.axis))
+                self.scalar_op, ", ".join(str(x) for x in self.axis))
         else:
             return "Reduce{%s}" % self.scalar_op
 
@@ -1462,8 +1462,8 @@ class CAReduce(Op):
 
         if hasattr(self, 'acc_dtype') and self.acc_dtype is not None:
             acc_type = TensorType(
-                    broadcastable=node.outputs[0].broadcastable,
-                    dtype=self.acc_dtype)
+                broadcastable=node.outputs[0].broadcastable,
+                dtype=self.acc_dtype)
             adtype = acc_type.dtype_specs()[1]
         else:
             adtype = odtype
@@ -1510,12 +1510,12 @@ class CAReduce(Op):
 
         # Allocate output buffer
         alloc += cgen.make_declare(
-                [list(range(nnested)) + ['x'] * len(axis)],
-                [odtype], dict(sub, lv0=oname))
+            [list(range(nnested)) + ['x'] * len(axis)],
+            [odtype], dict(sub, lv0=oname))
         alloc += cgen.make_alloc([order1], odtype, sub)
         alloc += cgen.make_checks(
-                [list(range(nnested)) + ['x'] * len(axis)],
-                [odtype], dict(sub, lv0=oname))
+            [list(range(nnested)) + ['x'] * len(axis)],
+            [odtype], dict(sub, lv0=oname))
 
         if adtype != odtype:
             # Allocate accumulation buffer
@@ -1523,12 +1523,12 @@ class CAReduce(Op):
             sub['olv'] = aname
 
             alloc += cgen.make_declare(
-                    [list(range(nnested)) + ['x'] * len(axis)],
-                    [adtype], dict(sub, lv0=aname))
+                [list(range(nnested)) + ['x'] * len(axis)],
+                [adtype], dict(sub, lv0=aname))
             alloc += cgen.make_alloc([order1], adtype, sub)
             alloc += cgen.make_checks(
-                    [list(range(nnested)) + ['x'] * len(axis)],
-                    [adtype], dict(sub, lv0=aname))
+                [list(range(nnested)) + ['x'] * len(axis)],
+                [adtype], dict(sub, lv0=aname))
 
         if hasattr(self.scalar_op, 'identity'):
             identity = self.scalar_op.identity
@@ -1608,8 +1608,8 @@ for(int i=0;i<PyArray_NDIM(%(iname)s);i++){
         else:
             all_code = [task0_decl + code1]
         loop = cgen.make_loop_careduce(
-                [order, list(range(nnested)) + ['x'] * len(axis)],
-                [idtype, adtype], all_code, sub)
+            [order, list(range(nnested)) + ['x'] * len(axis)],
+            [idtype, adtype], all_code, sub)
 
         end = ""
         if adtype != odtype:
@@ -1789,19 +1789,16 @@ class CAReduceDtype(CAReduce):
                 int32='int64',
                 uint8='uint32',
                 uint16='uint32',
-                uint32='uint64',
-                ).get(idtype, idtype)
+                uint32='uint64').get(idtype, idtype)
         if dtype is None:
             # If input has a discrete dtype, upcast it to 64
             return dict(
-                    int8='int64',
-                    int16='int64',
-                    int32='int64',
-                    uint8='uint64',
-                    uint16='uint64',
-                    uint32='uint64',
-                    ).get(idtype, idtype)
-
+                int8='int64',
+                int16='int64',
+                int32='int64',
+                uint8='uint64',
+                uint16='uint64',
+                uint32='uint64').get(idtype, idtype)
         else:
             # The important is that the accumulator dtype does not
             # lose precision. Then, the result can be downcasted.
@@ -1811,16 +1808,15 @@ class CAReduceDtype(CAReduce):
         acc_dtype = self.acc_dtype
         if acc_dtype is None:
             return dict(
-                    int8='int64',
-                    int16='int64',
-                    int32='int64',
-                    uint8='uint64',
-                    uint16='uint64',
-                    uint32='uint64',
-                    float16='float32',
-                    float32='float64',
-                    complex64='complex128',
-                    ).get(idtype, idtype)
+                int8='int64',
+                int16='int64',
+                int32='int64',
+                uint8='uint64',
+                uint16='uint64',
+                uint32='uint64',
+                float16='float32',
+                float32='float64',
+                complex64='complex128').get(idtype, idtype)
         elif (acc_dtype in theano.tensor.continuous_dtypes and
               idtype in theano.tensor.discrete_dtypes):
             # Specifying a continuous accumulator for discrete input is OK
@@ -1830,17 +1826,17 @@ class CAReduceDtype(CAReduce):
             upcasted_dtype = scalar.upcast(idtype, acc_dtype)
             if acc_dtype != upcasted_dtype:
                 raise TypeError(
-                        'Cannot build %s node with input dtype %s '
-                        'and acc_dtype %s, as precision would be lost. '
-                        'To correct this error, you can:\n'
-                        '  - not specify acc_dtype, or\n'
-                        '  - use an acc_dtype at least as precise as %s.\n'
-                        '  - specify "dtype" instead of "acc_dtype", so '
-                        'the reduction will be precise, but the result will '
-                        'be casted into "dtype" at the end.\n'
-                        'If you are expecting the precision loss, you can '
-                        'use tensor.cast(..., dtype="%s"), on your input.'
-                        % (self, idtype, acc_dtype, upcasted_dtype, acc_dtype))
+                    'Cannot build %s node with input dtype %s '
+                    'and acc_dtype %s, as precision would be lost. '
+                    'To correct this error, you can:\n'
+                    '  - not specify acc_dtype, or\n'
+                    '  - use an acc_dtype at least as precise as %s.\n'
+                    '  - specify "dtype" instead of "acc_dtype", so '
+                    'the reduction will be precise, but the result will '
+                    'be casted into "dtype" at the end.\n'
+                    'If you are expecting the precision loss, you can '
+                    'use tensor.cast(..., dtype="%s"), on your input.'
+                    % (self, idtype, acc_dtype, upcasted_dtype, acc_dtype))
             return acc_dtype
 
     def make_node(self, input):
@@ -2071,7 +2067,7 @@ class Prod(CAReduceDtype):
             where_zeros = T.eq(prod_in, 0.0)
             sum_where_zeros = T.sum(where_zeros, axis=self.axis)
             groups_with_single_zero = T.eq(sum_where_zeros, 1).dimshuffle(
-                    new_dims)
+                new_dims)
             # tensor with 0 everywhere except for those places where
             # a 0 part of a group with a single zero was to be found
             where_single_zero = groups_with_single_zero * where_zeros
@@ -2081,7 +2077,7 @@ class Prod(CAReduceDtype):
             # only take ProdWithoutZeros for the groups with single zeros
             # with non-null incoming gradient
             where_to_take_prod_without_zeros = (
-                        groups_with_single_zero * where_gz_not_zero)
+                groups_with_single_zero * where_gz_not_zero)
             # preprocess the original input so that we set 0 everywhere
             # except for groups that contain a single zero, to avoid computing
             # multiplications on other groups
@@ -2090,16 +2086,16 @@ class Prod(CAReduceDtype):
             # this is pretty efficient already (no multiplication if 0), but
             # it'd be even better if we had a lazy if per element
             prod_without_zeros = ProdWithoutZeros(axis=self.axis)(
-                    prod_without_zeros_in)
+                prod_without_zeros_in)
             prod_without_zeros = prod_without_zeros.dimshuffle(new_dims)
 
             groups_without_zeros = T.eq(sum_where_zeros, 0).dimshuffle(
-                    new_dims)
+                new_dims)
 
             final_grad = T.switch(
-                    groups_without_zeros,
-                    grad_case_without_zeros,
-                    T.switch(where_single_zero, prod_without_zeros, 0.0) * gz)
+                groups_without_zeros,
+                grad_case_without_zeros,
+                T.switch(where_single_zero, prod_without_zeros, 0.0) * gz)
 
             return [final_grad]
 

theano/tensor/inplace.py

@@ -29,9 +29,6 @@ def _scal_inplace(symbol):
         if not r.owner:
             return False
         op = r.owner.op
-#         print op, rval, r.owner and op == rval
-#         print op.inplace_pattern, rval.inplace_pattern, op.inplace_pattern == rval.inplace_pattern
-#         print op.scalar_op, rval.scalar_op, op.scalar_op == rval.scalar_op
         return r.owner.op == rval
 
     pprint.assign(chk, printing.FunctionPrinter(symbolname.replace('_inplace', '=')))
@@ -348,17 +345,13 @@ def conj_inplace(a):
 pprint.assign(add_inplace, printing.OperatorPrinter('+=', -2, 'either'))
 pprint.assign(mul_inplace, printing.OperatorPrinter('*=', -1, 'either'))
 pprint.assign(sub_inplace, printing.OperatorPrinter('-=', -2, 'left'))
-pprint.assign(neg_inplace, printing.OperatorPrinter('-=',  0, 'either'))
+pprint.assign(neg_inplace, printing.OperatorPrinter('-=', 0, 'either'))
 pprint.assign(true_div_inplace, printing.OperatorPrinter('/=', -1, 'left'))
 pprint.assign(int_div_inplace, printing.OperatorPrinter('//=', -1, 'left'))
 pprint.assign(pow_inplace, printing.OperatorPrinter('**=', 1, 'right'))
 
 
 def transpose_inplace(x, **kwargs):
-    """Perform a transpose on a tensor without copying the underlying storage"""
+    "Perform a transpose on a tensor without copying the underlying storage"
     dims = list(range(x.ndim - 1, -1, -1))
     return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)
-
-#pprint.assign(transpose_inplace, printing.MemberPrinter('T'))
-
-

theano/tensor/shared_randomstreams.py

@@ -24,11 +24,11 @@ def randomstate_constructor(value, name=None, strict=False,
     if not borrow:
         value = copy.deepcopy(value)
     return RandomStateSharedVariable(
-            type=raw_random.random_state_type,
-            value=value,
-            name=name,
-            strict=strict,
-            allow_downcast=allow_downcast)
+        type=raw_random.random_state_type,
+        value=value,
+        name=name,
+        strict=strict,
+        allow_downcast=allow_downcast)
 
 
 class RandomStreams(raw_random.RandomStreamsBase):

theano/tensor/slinalg.py

@@ -25,15 +25,14 @@ except ImportError:
     imported_scipy = False
 
 MATRIX_STRUCTURES = (
-        'general',
-        'symmetric',
-        'lower_triangular',
-        'upper_triangular',
-        'hermitian',
-        'banded',
-        'diagonal',
-        'toeplitz',
-        )
+    'general',
+    'symmetric',
+    'lower_triangular',
+    'upper_triangular',
+    'hermitian',
+    'banded',
+    'diagonal',
+    'toeplitz')
 
 
 class Cholesky(Op):
@@ -168,8 +167,8 @@ class Solve(Op):
         assert A.ndim == 2
         assert b.ndim in [1, 2]
         otype = tensor.tensor(
-                broadcastable=b.broadcastable,
-                dtype=(A * b).dtype)
+            broadcastable=b.broadcastable,
+            dtype=(A * b).dtype)
         return Apply(self, [A, b], [otype])
 
     def perform(self, node, inputs, output_storage):
@@ -294,7 +293,7 @@ class EigvalshGrad(Op):
         (a, b, gw) = inputs
         w, v = scipy.linalg.eigh(a, b, lower=self.lower)
         gA = v.dot(numpy.diag(gw).dot(v.T))
-        gB = - v.dot(numpy.diag(gw*w).dot(v.T))
+        gB = - v.dot(numpy.diag(gw * w).dot(v.T))
 
         # See EighGrad comments for an explanation of these lines
         out1 = self.tri0(gA) + self.tri1(gA).T

theano/tensor/subtensor.py

@@ -155,16 +155,16 @@ def get_canonical_form_slice(theslice, length):
                                   1), 1)
                 stop_plus_len = stop + length
                 stop = switch(
-                        lt(stop, 0),
-                        # stop < 0
-                        switch(
-                            lt(stop_plus_len, 0),
-                            # stop + len < 0
-                            0,
-                            # stop + len >= 0
-                            stop_plus_len),
-                        # stop >= 0: use min(stop, length)
-                        switch(lt(stop, length), stop, length))
+                    lt(stop, 0),
+                    # stop < 0
+                    switch(
+                        lt(stop_plus_len, 0),
+                        # stop + len < 0
+                        0,
+                        # stop + len >= 0
+                        stop_plus_len),
+                    # stop >= 0: use min(stop, length)
+                    switch(lt(stop, length), stop, length))
                 return slice(0, stop, 1), 1
             elif is_stop_length:
                 # start:length:1
@@ -173,16 +173,16 @@ def get_canonical_form_slice(theslice, length):
                                  length, 1), 1
                 start_plus_len = start + length
                 start = switch(
-                        lt(start, 0),
-                        # start < 0
-                        switch(
-                            lt(start_plus_len, 0),
-                            # start + len < 0
-                            0,
-                            # start + len >= 0
-                            start_plus_len),
-                        # start >= 0: use min(start, length)
-                        switch(lt(start, length), start, length))
+                    lt(start, 0),
+                    # start < 0
+                    switch(
+                        lt(start_plus_len, 0),
+                        # start + len < 0
+                        0,
+                        # start + len >= 0
+                        start_plus_len),
+                    # start >= 0: use min(start, length)
+                    switch(lt(start, length), start, length))
                 return slice(start, length, 1), 1
 
         # This is the generic case.
@@ -614,10 +614,8 @@ class Subtensor(Op):
         helper_c_code
         """
 
-        return {
-            "c_prefix": "PyArray",
-            "strides_mul": 1,
-            }
+        return {"c_prefix": "PyArray",
+                "strides_mul": 1}
 
     @staticmethod
     def helper_c_code(node, name, inputs, outputs, sub, idx_list, view_ndim,

theano/tensor/var.py

@@ -389,17 +389,15 @@ class _tensor_py_operators:
                     axis = i
 
         if advanced:
-            if (axis is not None
-                and all(isinstance(a, slice) and
-                        equal_slices(a, slice(None)) for a in args[:axis])
-                and all(isinstance(a, slice) and
-                        equal_slices(a, slice(None)) for a in args[axis + 1:])
-                and isinstance(args[axis], (
-                    numpy.ndarray,
-                    list,
-                    TensorVariable,
-                    TensorConstant,
-                    theano.tensor.sharedvar.TensorSharedVariable))):
+            if (axis is not None and
+                all(isinstance(a, slice) and
+                    equal_slices(a, slice(None)) for a in args[:axis]) and
+                all(isinstance(a, slice) and
+                    equal_slices(a, slice(None)) for a in args[axis + 1:]) and
+                isinstance(args[axis],
+                           (numpy.ndarray, list,
+                            TensorVariable, TensorConstant,
+                            theano.tensor.sharedvar.TensorSharedVariable))):
                 return self.take(args[axis], axis)
             else:
                 return theano.tensor.subtensor.advanced_subtensor(self, *args)

theano/tests/breakpoint.py

@@ -88,7 +88,7 @@ class PdbBreakpoint(Op):
         for i in range(len(monitored_vars)):
             # Every output i is a view of the input i+1 because of the input
             # condition.
-            new_op.view_map[i] = [i+1]
+            new_op.view_map[i] = [i + 1]
             new_op.inp_types.append(monitored_vars[i].type)
 
         # Build the Apply node
@@ -125,7 +125,7 @@ class PdbBreakpoint(Op):
         else:
             # Simply return views on the monitored variables
             for i in range(len(output_storage)):
-                output_storage[i][0] = inputs[i+1]
+                output_storage[i][0] = inputs[i + 1]
 
     def grad(self, inputs, output_gradients):
         return ([DisconnectedType()()] + output_gradients)

theano/tests/disturb_mem.py

@@ -20,4 +20,4 @@ def disturb_mem():
     ms = int(ms)
     n = ms % 1000
     m = ms // 1000
-    l = [[0]*m for i in xrange(n)]
+    l = [[0] * m for i in xrange(n)]

theano/tests/test_dictionary_output.py

@@ -12,8 +12,8 @@ class dictionary_output_checker(unittest.TestCase):
         '''
 
         x = T.scalar()
-        f = theano.function([x], outputs={'a': x, 'c': x*2,
-                                          'b': x*3, '1': x*4})
+        f = theano.function([x], outputs={'a': x, 'c': x * 2,
+                                          'b': x * 3, '1': x * 4})
 
         outputs = f(10.0)
 

theano/tests/test_flake8.py

@@ -6,7 +6,6 @@ from nose.plugins.skip import SkipTest
 import os
 from fnmatch import fnmatch
 import theano
-from theano.compat import PY3
 try:
     import flake8.engine
     import flake8.main
@@ -18,6 +17,10 @@ __authors__ = ("Saizheng Zhang")
 __copyright__ = "(c) 2015, Universite de Montreal"
 __contact__ = "Saizheng Zhang <saizhenglisa..at..gmail.com>"
 
+# This corresponds to "line too long" and it's too much of a pain with
+# all of the C code
+ignore = ('E501',)
+
 whitelist_flake8 = [
     "compat/six.py",  # This is bundled code that will be deleted, don't fix it
     "__init__.py",
@@ -74,7 +77,6 @@ whitelist_flake8 = [
     "tensor/extra_ops.py",
     "tensor/nlinalg.py",
     "tensor/blas_c.py",
-    "tensor/io.py",
     "tensor/elemwise_cgen.py",
     "tensor/raw_random.py",
     "tensor/blas_scipy.py",
@@ -93,7 +95,6 @@ whitelist_flake8 = [
     "tensor/tests/test_merge.py",
     "tensor/tests/test_gc.py",
     "tensor/tests/test_complex.py",
-    "tensor/tests/test_type_other.py",
     "tensor/tests/test_io.py",
     "tensor/tests/test_sharedvar.py",
     "tensor/tests/test_fourier.py",
@@ -160,8 +161,6 @@ whitelist_flake8 = [
     "sandbox/cuda/opt.py",
     "sandbox/cuda/blas.py",
     "sandbox/cuda/blocksparse.py",
-    "sandbox/cuda/GpuConv3D.py",
-    "sandbox/cuda/extra_ops.py",
     "sandbox/cuda/rng_curand.py",
     "sandbox/cuda/fftconv.py",
     "sandbox/cuda/kernel_codegen.py",
@@ -172,7 +171,6 @@ whitelist_flake8 = [
     "sandbox/cuda/tests/test_gradient.py",
     "sandbox/cuda/tests/test_neighbours.py",
     "sandbox/cuda/tests/test_conv_cuda_ndarray.py",
-    "sandbox/cuda/tests/test_type.py",
     "sandbox/cuda/tests/test_var.py",
     "sandbox/cuda/tests/test_opt.py",
     "sandbox/cuda/tests/test_blas.py",
@@ -235,7 +233,6 @@ whitelist_flake8 = [
     "misc/pycuda_utils.py",
     "misc/pycuda_example.py",
     "misc/ordered_set.py",
-    "misc/windows.py",
     "misc/strutil.py",
     "misc/gnumpy_utils.py",
     "misc/may_share_memory.py",
@@ -250,7 +247,6 @@ whitelist_flake8 = [
     "misc/tests/test_pycuda_example.py",
     "misc/hooks/reindent.py",
     "misc/hooks/check_whitespace.py",
-    "sparse/sharedvar.py",
     "sparse/type.py",
     "sparse/__init__.py",
     "sparse/opt.py",
@@ -308,15 +304,13 @@ def test_format_flake8():
     """
     if not flake8_available:
         raise SkipTest("flake8 is not installed")
-    if PY3:
-        raise SkipTest("not testing in python3 since 2to3 ran")
     total_errors = 0
     for path in list_files():
         rel_path = os.path.relpath(path, theano.__path__[0])
         if rel_path in whitelist_flake8:
             continue
         else:
-            error_num = flake8.main.check_file(path)
+            error_num = flake8.main.check_file(path, ignore=ignore)
             total_errors += error_num
     if total_errors > 0:
         raise AssertionError("FLAKE8 Format not respected")
@@ -332,7 +326,8 @@ def print_files_information_flake8():
     non_infracting_files = []
     for path in list_files():
         rel_path = os.path.relpath(path, theano.__path__[0])
-        number_of_infractions = flake8.main.check_file(path)
+        number_of_infractions = flake8.main.check_file(path,
+                                                       ignore=ignore)
         if number_of_infractions > 0:
             if rel_path not in whitelist_flake8:
                 infracting_files.append(rel_path)
@@ -359,7 +354,8 @@ def check_all_files(dir_path=theano.__path__[0], pattern='*.py'):
     for (dir, _, files) in os.walk(dir_path):
         for f in files:
             if fnmatch(f, pattern):
-                error_num = flake8.main.check_file(os.path.join(dir, f))
+                error_num = flake8.main.check_file(os.path.join(dir, f),
+                                                   ignore=ignore)
                 if error_num > 0:
                     path = os.path.relpath(os.path.join(dir, f),
                                            theano.__path__[0])

theano/tests/test_printing.py

@@ -495,17 +495,17 @@ def test_scan_debugprint3():
 def test_scan_debugprint4():
 
     def fn(a_m2, a_m1, b_m2, b_m1):
-        return a_m1+a_m2, b_m1+b_m2
+        return a_m1 + a_m2, b_m1 + b_m2
 
     a0 = theano.shared(numpy.arange(2))
     b0 = theano.shared(numpy.arange(2))
 
-    (a, b), _ = theano.scan(fn, outputs_info=[
-        {'initial': a0, 'taps': [-2, -1]},
-        {'initial': b0, 'taps': [-2, -1]}],
-                            n_steps=5)
+    (a, b), _ = theano.scan(
+        fn, outputs_info=[{'initial': a0, 'taps': [-2, -1]},
+                          {'initial': b0, 'taps': [-2, -1]}],
+        n_steps=5)
 
-    final_result = a+b
+    final_result = a + b
     output_str = theano.printing.debugprint(final_result, file='str')
     lines = []
     for line in output_str.split('\n'):