Merge pull request #2744 from mohammadpz/pep8

theano/configdefaults.py

@@ -24,7 +24,7 @@ def floatX_convert(s):
 AddConfigVar('floatX',
              "Default floating-point precision for python casts",
              EnumStr('float64', 'float32', convert=floatX_convert,),
-)
+             )
 
 AddConfigVar('warn_float64',
              "Do an action when a tensor variable with float64 dtype is"
@@ -32,17 +32,17 @@ AddConfigVar('warn_float64',
              " gpu back-end and are slow with gamer GPUs.",
              EnumStr('ignore', 'warn', 'raise', 'pdb'),
              in_c_key=False,
-)
+             )
 
 AddConfigVar('cast_policy',
-        "Rules for implicit type casting",
+             'Rules for implicit type casting',
              EnumStr('custom', 'numpy+floatX',
-                # The 'numpy' policy was originally planned to provide a smooth
-                # transition from numpy. It was meant to behave the same as
-                # numpy+floatX, but keeping float64 when numpy would. However
-                # the current implementation of some cast mechanisms makes it
-                # a bit more complex to add than what was expected, so it is
-                # currently not available.
+                     # The 'numpy' policy was originally planned to provide a
+                     # smooth transition from numpy. It was meant to behave the
+                     # same asnumpy+floatX, but keeping float64 when numpy
+                     # would. However the current implementation of some cast
+                     # mechanisms makes it a bit more complex to add than what
+                     # was expected, so it is currently not available.
                      # numpy,
                      ),
              )
@@ -82,14 +82,14 @@ class DeviceParam(ConfigParam):
     def __str__(self):
         return '%s (cpu, gpu*, opencl*, cuda*) ' % (self.fullname,)
 
-AddConfigVar('device',
+AddConfigVar(
+    'device',
     ("Default device for computations. If gpu*, change the default to try "
      "to move computation to it and to put shared variable of float32 "
      "on it. Do not use upper case letters, only lower case even if "
      "NVIDIA use capital letters."),
     DeviceParam('cpu', allow_override=False),
-        in_c_key=False,
-        )
+    in_c_key=False,)
 
 AddConfigVar('gpuarray.init_device',
              """
@@ -99,7 +99,8 @@ AddConfigVar('gpuarray.init_device',
              StrParam(''),
              in_c_key=False)
 
-AddConfigVar('init_gpu_device',
+AddConfigVar(
+    'init_gpu_device',
     ("Initialize the gpu device to use, works only if device=cpu. "
      "Unlike 'device', setting this option will NOT move computations, "
      "nor shared variables, to the specified GPU. "
@@ -112,12 +113,14 @@ AddConfigVar('init_gpu_device',
             allow_override=False),
     in_c_key=False)
 
-AddConfigVar('force_device',
+AddConfigVar(
+    'force_device',
     "Raise an error if we can't use the specified device",
     BoolParam(False, allow_override=False),
     in_c_key=False)
 
-AddConfigVar('print_active_device',
+AddConfigVar(
+    'print_active_device',
     "Print active device at when the GPU device is initialized.",
     BoolParam(True, allow_override=False),
     in_c_key=False)
@@ -129,7 +132,8 @@ AddConfigVar('print_active_device',
 # scalable.
 # Also, please be careful not to modify the first item in the enum when adding
 # new modes, since it is the default mode.
-AddConfigVar('mode',
+AddConfigVar(
+    'mode',
     "Default compilation mode",
     EnumStr('Mode', 'ProfileMode', 'DebugMode', 'FAST_RUN',
             'FAST_COMPILE', 'PROFILE_MODE', 'DEBUG_MODE'),
@@ -209,7 +213,8 @@ AddConfigVar('allow_gc',
              in_c_key=False)
 
 # Keep the default optimizer the same as the one for the mode FAST_RUN
-AddConfigVar('optimizer',
+AddConfigVar(
+    'optimizer',
     ("Default optimizer. If not None, will use this linker with the Mode "
      "object (not ProfileMode(deprecated) or DebugMode)"),
     EnumStr('fast_run', 'merge', 'fast_compile', 'None'),
@@ -220,7 +225,8 @@ AddConfigVar('optimizer_verbose',
              BoolParam(False),
              in_c_key=False)
 
-AddConfigVar('on_opt_error',
+AddConfigVar(
+    'on_opt_error',
     ("What to do when an optimization crashes: warn and skip it, raise "
      "the exception, or fall into the pdb debugger."),
     EnumStr('warn', 'raise', 'pdb'),
@@ -246,13 +252,15 @@ def safe_no_home(home):
     return True
 
 
-AddConfigVar('home',
+AddConfigVar(
+    'home',
     "This config option was removed in 0.5: do not use it!",
     ConfigParam('', allow_override=False, filter=safe_no_home),
     in_c_key=False)
 
 
-AddConfigVar('nocleanup',
+AddConfigVar(
+    'nocleanup',
     "Suppress the deletion of code files that did not compile cleanly",
     BoolParam(False),
     in_c_key=False)
@@ -267,38 +275,44 @@ AddConfigVar('on_unused_input',
 # So changing it after import will not modify these global variables.
 # This could be done differently... but for now we simply prevent it from being
 # changed at runtime.
-AddConfigVar('tensor.cmp_sloppy',
+AddConfigVar(
+    'tensor.cmp_sloppy',
     "Relax tensor._allclose (0) not at all, (1) a bit, (2) more",
     IntParam(0, lambda i: i in (0, 1, 2), allow_override=False),
     in_c_key=False)
 
-AddConfigVar('tensor.local_elemwise_fusion',
+AddConfigVar(
+    'tensor.local_elemwise_fusion',
     ("Enable or not in fast_run mode(fast_run optimization) the elemwise "
      "fusion optimization"),
     BoolParam(True),
     in_c_key=False)
 
-AddConfigVar('gpu.local_elemwise_fusion',
+AddConfigVar(
+    'gpu.local_elemwise_fusion',
     ("Enable or not in fast_run mode(fast_run optimization) the gpu "
      "elemwise fusion optimization"),
     BoolParam(True),
     in_c_key=False)
 
 # http://developer.amd.com/CPU/LIBRARIES/LIBM/Pages/default.aspx
-AddConfigVar('lib.amdlibm',
+AddConfigVar(
+    'lib.amdlibm',
     "Use amd's amdlibm numerical library",
     BoolParam(False))
 
-AddConfigVar('gpuelemwise.sync',
+AddConfigVar(
+    'gpuelemwise.sync',
     "when true, wait that the gpu fct finished and check it error code.",
     BoolParam(True),
     in_c_key=False)
 
-AddConfigVar('traceback.limit',
+AddConfigVar(
+    'traceback.limit',
     "The number of stack to trace. -1 mean all.",
-# We default to 6 to be able to know where v1 + v2 is created in the
-# user script. The bigger this number is, the more run time it takes.
-# We need to default to 7 to support theano.tensor.tensor(...).
+    # We default to 6 to be able to know where v1 + v2 is created in the
+    # user script. The bigger this number is, the more run time it takes.
+    # We need to default to 7 to support theano.tensor.tensor(...).
     IntParam(7),
     in_c_key=False)
 
@@ -422,21 +436,24 @@ AddConfigVar('warn.sum_div_dimshuffle_bug',
              BoolParam(warn_default('0.3')),
              in_c_key=False)
 
-AddConfigVar('warn.subtensor_merge_bug',
+AddConfigVar(
+    'warn.subtensor_merge_bug',
     "Warn if previous versions of Theano (before 0.5rc2) could have given "
     "incorrect results when indexing into a subtensor with negative "
     "stride (for instance, for instance, x[a:b:-1][c]).",
     BoolParam(warn_default('0.5')),
     in_c_key=False)
 
-AddConfigVar('warn.gpu_set_subtensor1',
+AddConfigVar(
+    'warn.gpu_set_subtensor1',
     "Warn if previous versions of Theano (before 0.6) could have given "
     "incorrect results when moving to the gpu "
     "set_subtensor(x[int vector], new_value)",
     BoolParam(warn_default('0.6')),
     in_c_key=False)
 
-AddConfigVar('warn.vm_gc_bug',
+AddConfigVar(
+    'warn.vm_gc_bug',
     "There was a bug that existed in the default Theano configuration,"
     " only in the development version between July 5th 2012"
     " and July 30th 2012. This was not in a released version."
@@ -474,7 +491,8 @@ AddConfigVar('warn.inc_set_subtensor1',
              BoolParam(warn_default('0.7')),
              in_c_key=False)
 
-AddConfigVar('compute_test_value',
+AddConfigVar(
+    'compute_test_value',
     ("If 'True', Theano will run each op at graph build time, using "
      "Constants, SharedVariables and the tag 'test_value' as inputs "
      "to the function. This helps the user track down problems in the "
@@ -497,7 +515,8 @@ AddConfigVar('unpickle_function',
              BoolParam(True),
              in_c_key=False)
 
-AddConfigVar('reoptimize_unpickled_function',
+AddConfigVar(
+    'reoptimize_unpickled_function',
     "Re-optimize the graph when a theano function is unpickled from the disk.",
     BoolParam(True, allow_override=True),
     in_c_key=False)
@@ -509,12 +528,13 @@ AddConfigVar('reoptimize_unpickled_function',
     == 'high', you should include a call to printing.min_informative_str
     on all important apply nodes.
 """
-AddConfigVar('exception_verbosity',
-        "If 'low', the text of exceptions will generally refer " \
-        + "to apply nodes with short names such as " \
-        + "Elemwise{add_no_inplace}. If 'high', some exceptions " \
-        + "will also refer to apply nodes with long descriptions " \
-        + """ like:
+AddConfigVar(
+    'exception_verbosity',
+    "If 'low', the text of exceptions will generally refer "
+    "to apply nodes with short names such as "
+    "Elemwise{add_no_inplace}. If 'high', some exceptions "
+    "will also refer to apply nodes with long descriptions "
+    """ like:
     A. Elemwise{add_no_inplace}
             B. log_likelihood_v_given_h
             C. log_likelihood_h""",
@@ -570,14 +590,16 @@ AddConfigVar('openmp_elemwise_minsize',
              in_c_key=False,
              )
 
-AddConfigVar('check_input',
+AddConfigVar(
+    'check_input',
     "Specify if types should check their input in their C code. "
     "It can be used to speed up compilation, reduce overhead "
     "(particularly for scalars) and reduce the number of generated C "
     "files.",
     BoolParam(True))
 
-AddConfigVar('cache_optimizations',
+AddConfigVar(
+    'cache_optimizations',
     "WARNING: work in progress, does not work yet. "
     "Specify if the optimization cache should be used. This cache will "
     "any optimized graph and its optimization. Actually slow downs a lot "

theano/configparser.py

@@ -217,8 +217,8 @@ def AddConfigVar(name, doc, configparam, root=config, in_c_key=True):
                 _i_am_a_config_class = True
             setattr(root.__class__, sections[0], SubObj())
         newroot = getattr(root, sections[0])
-        if (not getattr(newroot, '_i_am_a_config_class', False)
-                or isinstance(newroot, type)):
+        if (not getattr(newroot, '_i_am_a_config_class', False) or
+                isinstance(newroot, type)):
             raise TypeError(
                 'Internal config nodes must be config class instances',
                 newroot)
@@ -235,7 +235,8 @@ def AddConfigVar(name, doc, configparam, root=config, in_c_key=True):
         if not callable(configparam.default):
             configparam.__get__()
         else:
-            # We do not want to evaluate now the default value when it is a callable.
+            # We do not want to evaluate now the default value
+            # when it is a callable.
             try:
                 fetch_val_for_key(configparam.fullname)
                 # The user provided a value, filter it now.

theano/gradient.py

 """Driver for gradient calculations."""
-
-__authors__ = "James Bergstra, Razvan Pascanu, Arnaud Bergeron, Ian Goodfellow"
-__copyright__ = "(c) 2011, Universite de Montreal"
-__license__ = "3-clause BSD License"
-__contact__ = "theano-dev <theano-dev@googlegroups.com>"
-
-__docformat__ = "restructuredtext en"
-
 import __builtin__
 from itertools import izip
 import logging
 import time
 import warnings
-_logger = logging.getLogger('theano.gradient')
 
 import numpy  # for numeric_grad
-np = numpy
 
 import theano
 
@@ -26,6 +16,15 @@ from theano.gof.null_type import NullType, null_type
 from theano.gof.op import get_debug_values
 from theano.compile import ViewOp
 
+np = numpy
+__authors__ = "James Bergstra, Razvan Pascanu, Arnaud Bergeron, Ian Goodfellow"
+__copyright__ = "(c) 2011, Universite de Montreal"
+__license__ = "3-clause BSD License"
+__contact__ = "theano-dev <theano-dev@googlegroups.com>"
+
+__docformat__ = "restructuredtext en"
+_logger = logging.getLogger('theano.gradient')
+
 # we can't do "import theano.tensor"
 # tensor depends on theano.compile
 # theano.compile depends on theano.gradient (this file)
@@ -467,16 +466,17 @@ def grad(cost, wrt, consider_constant=None,
             g_cost = known_grads[cost]
         else:
             g_cost = _float_ones_like(cost)
-        # g_cost may be Disconnected or NullType. A creative use of the function,
-        # sure, but nonetheless one we can and should support. So before we try
-        # to cast it make sure it even has a dtype
+        # g_cost may be Disconnected or NullType. A creative use of the
+        # function, sure, but nonetheless one we can and should support.
+        # So before we try to cast it make sure it even has a dtype
         if (hasattr(g_cost.type, 'dtype') and
                 cost.type.dtype not in tensor.discrete_dtypes):
-            # Here we enforce the constraint that floating point variables have
-            # the same dtype as their gradient.
+                # Here we enforce the constraint that floating point variables
+                # have the same dtype as their gradient.
                 g_cost = g_cost.astype(cost.type.dtype)
         # DO NOT enforce g_cost to be 0 if cost is an integer.
-        # This is to be enforced by the Op.grad method for the Op that outputs cost.
+        # This is to be enforced by the Op.grad method for the
+        # Op that outputs cost.
         if hasattr(g_cost.type, 'dtype'):
             assert g_cost.type.dtype not in tensor.discrete_dtypes
 
@@ -494,7 +494,7 @@ def grad(cost, wrt, consider_constant=None,
                 'float' not in str(g_var.type.dtype)):
             raise TypeError("Gradients must always be NullType, "
                             "DisconnectedType, or continuous, but grad was "
-                            "given a known_grad of type "+str(g_var.type))
+                            "given a known_grad of type " + str(g_var.type))
 
         # DO NOT check that these gradients are equal to 0 if var is int
         # The gradient is allowed to be non-zero on var in that case
@@ -734,8 +734,8 @@ def _node_to_pattern(node):
             if not isinstance(output_pattern, list):
                 raise TypeError(
                     '%s.connection_pattern should return' %
-                    node.op + ' a list of lists, but element %d' % ii
-                    + 'is %s of type %s.' % (output_pattern,
+                    node.op + ' a list of lists, but element %d' % ii +
+                    'is %s of type %s.' % (output_pattern,
                                            type(output_pattern)))
     else:
         connection_pattern = [[True for output in node.outputs]
@@ -846,10 +846,10 @@ def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):
 
                 if ipt not in var_to_app_to_idx:
                     # This object here *must* be an OrderedDict, because
-                    # we iterate over its keys when adding up the terms of
-                    # the gradient on ipt. If it is a regular dict, the grad
-                    # method will return something that is analytically correct,
-                    # but whose order of doing additions depends on the memory
+                    # we iterate over its keys when adding up the terms of the
+                    # gradient on ipt. If it is a regular dict, the grad method
+                    # will return something that is analytically correct, but
+                    # whose order of doing additions depends on the memory
                     # location of the apply nodes.
                     var_to_app_to_idx[ipt] = OrderedDict()
                 app_to_idx = var_to_app_to_idx[ipt]
@@ -923,8 +923,8 @@ def _populate_grad_dict(var_to_app_to_idx,
         grad_dict: A dictionary mapping variables to their gradients.
                    Should be populated by grad function, which should:
                        -Set the gradient with respect to the cost to 1
-                       -Load all gradients from known_grads, possibly overriding
-                        the cost
+                       -Load all gradients from known_grads, possibly
+                        overriding the cost
                        -Set the gradient for disconnected
                         inputs to a variable with type DisconnectedType()
 
@@ -1004,10 +1004,10 @@ def _populate_grad_dict(var_to_app_to_idx,
                 # call the op's grad method
 
                 # Each Op's grad function requires inputs and output_grads
-                # If the Op destroys any input, but the grad expression uses it,
-                # then chances are the resulting graph will have a dependency
-                # cycle. We avoid this cycle by passing (symbolic) copies of
-                # each destroyed input.
+                # If the Op destroys any input, but the grad expression uses
+                # it, then chances are the resulting graph will have a
+                # dependency cycle. We avoid this cycle by passing (symbolic)
+                # copies of each destroyed input.
                 try:
                     dinputs = [node.inputs[x[0]] for x in
                                node.op.destroy_map.values()]
@@ -1030,9 +1030,10 @@ def _populate_grad_dict(var_to_app_to_idx,
                 # If an output is of an integer dtype, then we just leave it
                 # alone.
                 # DO NOT force integer variables to have zero grad. This causes
-                # bugs where we fail to detect disconnected or undefined gradients.
-                # DO NOT force integer variables to have integer dtype. This is
-                # a violation of the op contract.
+                # bugs where we fail to detect disconnected or undefined
+                # gradients.
+                # DO NOT force integer variables to have integer dtype.
+                # This is a violation of the op contract.
                 new_output_grads = []
                 for o, og in zip(node.outputs, output_grads):
                     o_dt = getattr(o.type, 'dtype', None)
@@ -1063,12 +1064,13 @@ def _populate_grad_dict(var_to_app_to_idx,
                     assert (getattr(ng.type, 'dtype', None)
                             not in theano.tensor.discrete_dtypes)
 
-                # If config.compute_test_value is turned on, check that the gradients
-                # on the outputs of this node have the right shape.
-                # We also check the gradient on the inputs later--both checks are needed,
-                # because some gradients are only ever specified by the user, not computed
-                # by Op.grad, and some gradients are only computed and returned, but never
-                # passed as another node's output grads.
+                # If config.compute_test_value is turned on, check that the
+                # gradients on the outputs of this node have the right shape.
+                # We also check the gradient on the inputs later--both checks
+                # are needed, because some gradients are only ever specified
+                # by the user, not computed by Op.grad, and some gradients are
+                # only computed and returned, but never passed as another
+                # node's output grads.
                 for idx, packed in enumerate(izip(node.outputs,
                                              new_output_grads)):
                     orig_output, new_output_grad = packed
@@ -1104,7 +1106,7 @@ def _populate_grad_dict(var_to_app_to_idx,
 #                    raise ValueError(
 #                        "%s returned the wrong type for gradient terms."
 #                        " Sparse inputs must have sparse grads and dense"
-#                            " inputs must have dense grad. Got %s, expected %s" % (
+#                        " inputs must have dense grad. Got %s, expected %s" %(
 #                            str(node.op), ig.type, i.type))
 
             # must convert to list in case the op returns a tuple
@@ -1138,7 +1140,8 @@ def _populate_grad_dict(var_to_app_to_idx,
                          'the grad_undefined or grad_unimplemented helper '
                          'functions.') % node.op)
 
-                # Check that the gradient term for this input has the right shape
+                # Check that the gradient term for this input
+                # has the right shape
                 if hasattr(term, 'shape'):
                     orig_ipt = inputs[i]
                     for orig_ipt_v, term_v in get_debug_values(orig_ipt, term):
@@ -1384,12 +1387,13 @@ class numeric_grad(object):
 
         total_size = __builtin__.sum(prod(sh) for sh in shapes)
 
-        working_dtype = __builtin__.min((self.type_eps[dt], dt)
-                                        for dt in dtypes)[1]
+        working_dtype = __builtin__.min(
+            (self.type_eps[dt], dt) for dt in dtypes)[1]
 
         # create un-initialized memory
         x = numpy.ndarray((total_size,), dtype=working_dtype)
-        if (not out_type is None) and (out_type.startswith('complex')):
+        # (not out_type is None) --> (out_type is not None) ???
+        if (out_type is not None) and (out_type.startswith('complex')):
             gx = numpy.ndarray((total_size,), dtype=out_type)
         else:
             gx = numpy.ndarray((total_size,), dtype=working_dtype)
@@ -1974,6 +1978,7 @@ def disconnected_grad(x):
 class GradClip(ViewOp):
     # See doc in user fct grad_clip
     __props__ = ()
+
     def __init__(self, clip_lower_bound, clip_upper_bound):
         # We do not put those member in __eq__ or __hash__
         # as they do not influence the perform of this op.

theano/ifelse.py

@@ -10,15 +10,6 @@ which value to report. Note also that `switch` is an elemwise operation (so
 it picks each entry of a matrix according to the condition) while `ifelse`
 is a global operation with a scalar condition.
 """
-
-__docformat__ = 'restructedtext en'
-__authors__ = ("Razvan Pascanu "
-               "James Bergstra "
-               "Dumitru Erhan "
-               "David Warde-Farley")
-__copyright__ = "(c) 2010, Universite de Montreal"
-__contact__ = "Razvan Pascanu <r.pascanu@gmail>"
-
 from copy import deepcopy
 from itertools import izip
 import logging
@@ -34,6 +25,15 @@ from theano.tensor import opt
 from theano.scan_module.scan_utils import find_up
 from theano.scan_module.scan_utils import clone
 
+
+__docformat__ = 'restructedtext en'
+__authors__ = ("Razvan Pascanu "
+               "James Bergstra "
+               "Dumitru Erhan "
+               "David Warde-Farley")
+__copyright__ = "(c) 2010, Universite de Montreal"
+__contact__ = "Razvan Pascanu <r.pascanu@gmail>"
+
 _logger = logging.getLogger('theano.ifelse')
 
 
@@ -338,13 +338,13 @@ def ifelse(condition, then_branch, else_branch, name=None):
             # converted into one, then we try to do that.
             # This case happens when one of the elements has a GPU type,
             # for instance a shared variable that was silently moved to GPU.
-            if (isinstance(then_branch_elem.type, TensorType)
-                    and not isinstance(else_branch_elem.type, TensorType)):
+            if (isinstance(then_branch_elem.type, TensorType) and not
+                    isinstance(else_branch_elem.type, TensorType)):
                 else_branch_elem = then_branch_elem.type.filter_variable(
                     else_branch_elem)
 
-            elif (isinstance(else_branch_elem.type, TensorType)
-                    and not isinstance(then_branch_elem.type, TensorType)):
+            elif (isinstance(else_branch_elem.type, TensorType) and not
+                    isinstance(then_branch_elem.type, TensorType)):
                 then_branch_elem = else_branch_elem.type.filter_variable(
                     then_branch_elem)
 
@@ -471,7 +471,7 @@ def ifelse_lift_single_if_through_acceptable_ops(main_node):
     ts = node.inputs[1:][:op.n_outs]
     fs = node.inputs[1:][op.n_outs:]
 
-    outs = main_node.outputs
+    # outs = main_node.outputs
     mop = main_node.op
     true_ins = []
     false_ins = []
@@ -486,8 +486,8 @@ def ifelse_lift_single_if_through_acceptable_ops(main_node):
             false_ins.append(x)
     true_eval = mop(*true_ins, **dict(return_list=True))
     false_eval = mop(*false_ins, **dict(return_list=True))
-    #true_eval  = clone(outs, replace = dict(zip(node.outputs, ts)))
-    #false_eval = clone(outs, replace = dict(zip(node.outputs, fs)))
+    # true_eval  = clone(outs, replace = dict(zip(node.outputs, ts)))
+    # false_eval = clone(outs, replace = dict(zip(node.outputs, fs)))
 
     nw_outs = ifelse(node.inputs[0], true_eval, false_eval, return_list=True)
     return nw_outs
@@ -567,8 +567,8 @@ class CondMerge(gof.Optimizer):
                 if merging_node.op.name:
                     mn_name = merging_node.op.name
                 pl_name = '?'
-                mn_n_ts = len(mn_ts)
-                mn_n_fs = len(mn_fs)
+                # mn_n_ts = len(mn_ts)
+                # mn_n_fs = len(mn_fs)
                 if proposal.op.name:
                     pl_name = proposal.op.name
                 new_ifelse = IfElse(
@@ -673,8 +673,8 @@ def cond_merge_random_op(main_node):
             if merging_node.op.name:
                 mn_name = merging_node.op.name
             pl_name = '?'
-            mn_n_ts = len(mn_ts)
-            mn_n_fs = len(mn_fs)
+            # mn_n_ts = len(mn_ts)
+            # mn_n_fs = len(mn_fs)
             if proposal.op.name:
                 pl_name = proposal.op.name
             new_ifelse = IfElse(

theano/tests/test_flake8.py

@@ -17,13 +17,8 @@ except ImportError:
     flake8_available = False
 
 whitelist_flake8 = [
-    "updates.py",
     "__init__.py",
-    "configparser.py",
-    "ifelse.py",
     "version.py",
-    "configdefaults.py",
-    "gradient.py",
     "compat/python2x.py",
     "compat/six.py",
     "compat/__init__.py",

theano/updates.py

 """Defines Updates object for storing a (SharedVariable, new_value) mapping.
 
 """
+from theano.compat.python2x import OrderedDict
+
+from theano.compile.sharedvalue import SharedVariable
+import logging
+import warnings
+
+
 __authors__ = "theano-dev"
 __copyright__ = "(c) 2010, Universite de Montreal"
 __license__ = "3-clause BSD License"
@@ -8,12 +15,7 @@ __contact__ = "theano-dev <theano-dev@googlegroups.com>"
 
 __docformat__ = "restructuredtext en"
 
-from theano.compat.python2x import OrderedDict
-
-from theano.compile.sharedvalue import SharedVariable
-import logging
 logger = logging.getLogger('theano.updates')
-import warnings
 
 
 # Must be an OrderedDict or updates will be applied in a non-deterministic