Yet more pep8 fixes.

theano/__init__.py

@@ -95,7 +95,6 @@ import scalar
 #import sparse
 import gradient
 from gradient import Rop, Lop, grad
-import gof
 
 if config.device.startswith('gpu') or config.init_gpu_device.startswith('gpu'):
     import theano.sandbox.cuda

theano/configdefaults.py

 import os
 import logging
 import subprocess
-import sys
 
 from theano.configparser import (
-        AddConfigVar, BoolParam, ConfigParam, EnumStr, IntParam, FloatParam,
-        StrParam, TheanoConfigParser)
+        AddConfigVar, BoolParam, ConfigParam, EnumStr, IntParam,
+        TheanoConfigParser)
 
 
 _logger = logging.getLogger('theano.configdefaults')
@@ -41,10 +40,13 @@ AddConfigVar('int_division',
         EnumStr('int', 'raise', 'floatX'),
         in_c_key=False)
 
-#gpu mean let the driver select the gpu. Needed in case of gpu in exclusive mode.
-#gpuX mean use the gpu number X.
+# gpu means let the driver select the gpu. Needed in case of gpu in
+# exclusive mode.
+# gpuX mean use the gpu number X.
 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.",
+        ("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."),
         EnumStr('cpu', 'gpu',
             'gpu0', 'gpu1', 'gpu2', 'gpu3',
             'gpu4', 'gpu5', 'gpu6', 'gpu7',
@@ -93,14 +95,16 @@ try:
                      stdin=dummy_stdin.fileno())
     # Keep the default linker the same as the one for the mode FAST_RUN
     AddConfigVar('linker',
-                 "Default linker used if the theano flags mode is Mode or ProfileMode",
+                 ("Default linker used if the theano flags mode is Mode "
+                  "or ProfileMode"),
                  EnumStr('c|py', 'py', 'c', 'c|py_nogc', 'c&py',
                      'vm', 'cvm', 'vm_nogc', 'cvm_nogc'),
                  in_c_key=False)
 except OSError:
     # g++ is not present, linker should default to python only
     AddConfigVar('linker',
-                 "Default linker used if the theano flags mode is Mode or ProfileMode",
+                 ("Default linker used if the theano flags mode is Mode "
+                  "or ProfileMode"),
                  EnumStr('py', 'c|py', 'c', 'c|py_nogc', 'c&py',
                      'vm', 'cvm', 'vm_nogc', 'cvm_nogc'),
                  in_c_key=False)
@@ -113,12 +117,14 @@ del dummy_stdin
 
 #Keep the default optimizer the same as the one for the mode FAST_RUN
 AddConfigVar('optimizer',
-        "Default optimizer. If not None, will use this linker with the Mode object(not ProfileMode or DebugMode)",
+        ("Default optimizer. If not None, will use this linker with the Mode "
+         "object (not ProfileMode or DebugMode)"),
         EnumStr('fast_run', 'merge', 'fast_compile', 'None'),
         in_c_key=False)
 
 AddConfigVar('on_opt_error',
-        "What to do when an optimization crashes: warn and skip it, or raise the exception",
+        ("What to do when an optimization crashes: warn and skip it, or raise "
+         "the exception"),
         EnumStr('warn', 'raise'),
         in_c_key=False)
 
@@ -160,16 +166,18 @@ AddConfigVar('nocleanup',
 # changed at runtime.
 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),
+        IntParam(0, lambda i: i in (0, 1, 2), allow_override=False),
         in_c_key=False)
 
 AddConfigVar('tensor.local_elemwise_fusion',
-        "Enable or not in fast_run mode(fast_run optimization) the elemwise fusion optimization",
+        ("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',
-        "Enable or not in fast_run mode(fast_run optimization) the gpu elemwise fusion optimization",
+        ("Enable or not in fast_run mode(fast_run optimization) the gpu "
+         "elemwise fusion optimization"),
         BoolParam(True),
         in_c_key=False)
 
@@ -179,7 +187,8 @@ AddConfigVar('lib.amdlibm',
         BoolParam(False))
 
 AddConfigVar('op.set_flops',
-        "currently used only in ConvOp. The profile mode will print the flops/s for the op.",
+        ("currently used only in ConvOp. The profile mode will print the "
+         "flops/s for the op."),
         BoolParam(False),
         in_c_key=False)
 
@@ -244,8 +253,14 @@ AddConfigVar('numpy.seterr_invalid',
 ### To disable some warning about old bug that are fixed now.
 ###
 AddConfigVar('warn.ignore_bug_before',
-             "If 'None', we warn about all Theano bugs found by default. If 'all', we don't warn about Theano bugs found by default. If a version, we print only the warnings relative to Theano bugs found after that version. Warning for specific bugs can be configured with specific [warn] flags.",
-             EnumStr('None', 'all', '0.3','0.4', '0.4.1', '0.5', allow_override=False),
+             ("If 'None', we warn about all Theano bugs found by default. "
+              "If 'all', we don't warn about Theano bugs found by default. "
+              "If a version, we print only the warnings relative to Theano "
+              "bugs found after that version. "
+              "Warning for specific bugs can be configured with specific "
+              "[warn] flags."),
+             EnumStr('None', 'all', '0.3', '0.4', '0.4.1', '0.5',
+                     allow_override=False),
              in_c_key=False)
 
 
@@ -263,34 +278,48 @@ def warn_default(version):
 
 
 AddConfigVar('warn.argmax_pushdown_bug',
-             "Warn if in past version of Theano we generated a bug with the theano.tensor.nnet.nnet.local_argmax_pushdown optimization. Was fixed 27 may 2010",
+             ("Warn if in past version of Theano we generated a bug with the "
+              "theano.tensor.nnet.nnet.local_argmax_pushdown optimization. "
+              "Was fixed 27 may 2010"),
              BoolParam(warn_default('0.3')),
              in_c_key=False)
 
 AddConfigVar('warn.gpusum_01_011_0111_bug',
-             "Warn if we are in a case where old version of Theano had a silent bug with GpuSum pattern 01,011 and 0111 when the first dimensions was bigger then 4096. Was fixed 31 may 2010",
+             ("Warn if we are in a case where old version of Theano had a "
+              "silent bug with GpuSum pattern 01,011 and 0111 when the first "
+              "dimensions was bigger then 4096. Was fixed 31 may 2010"),
              BoolParam(warn_default('0.3')),
              in_c_key=False)
 
 AddConfigVar('warn.sum_sum_bug',
-             "Warn if we are in a case where Theano version between version 9923a40c7b7a and the 2 august 2010(fixed date), generated an error in that case. This happen when their is 2 consecutive sum in the graph, bad code was generated. Was fixed 2 August 2010",
+             ("Warn if we are in a case where Theano version between version "
+              "9923a40c7b7a and the 2 august 2010 (fixed date), generated an "
+              "error in that case. This happens when there are 2 consecutive "
+              "sums in the graph, bad code was generated. "
+              "Was fixed 2 August 2010"),
              BoolParam(warn_default('0.3')),
              in_c_key=False)
 
 AddConfigVar('warn.sum_div_dimshuffle_bug',
-             "Warn if previous versions of Theano (between rev. 3bd9b789f5e8, 2010-06-16, and cfc6322e5ad4, 2010-08-03) would have given incorrect result. This bug was triggered by sum of division of dimshuffled tensors.",
+             ("Warn if previous versions of Theano (between rev. "
+              "3bd9b789f5e8, 2010-06-16, and cfc6322e5ad4, 2010-08-03) "
+              "would have given incorrect result. This bug was triggered by "
+              "sum of division of dimshuffled tensors."),
              BoolParam(warn_default('0.3')),
              in_c_key=False)
 
 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]).",
+        "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('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 graph before it gets optimized.",
+        ("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 "
+         "graph before it gets optimized."),
         EnumStr('off', 'ignore', 'warn', 'raise'),
         in_c_key=False)
 
@@ -310,5 +339,5 @@ AddConfigVar('exception_verbosity',
         A. Elemwise{add_no_inplace}
                 B. log_likelihood_v_given_h
                 C. log_likelihood_h""",
-        EnumStr('low','high'),
+        EnumStr('low', 'high'),
         in_c_key=False)

theano/updates.py

@@ -12,6 +12,7 @@ from theano.compile.sharedvalue  import SharedVariable
 import logging
 logger = logging.getLogger('theano.updates')
 
+
 class Updates(dict):
     """
     Dict-like mapping from SharedVariable keys to their new values.
@@ -30,7 +31,9 @@ class Updates(dict):
 
             return super(Updates, self).__setitem__(key, value)
         else:
-            raise TypeError('Updates keys must inherit from SharedVariable', key)
+            raise TypeError('Updates keys must inherit from SharedVariable',
+                    key)
+
     def update(self, other):
         for key, val in dict(other).iteritems():
             if key in self:
@@ -50,4 +53,3 @@ class Updates(dict):
         rval.update(other)
         rval.update(self)
         return rval
-