rng_curand.py has been modified in order to respect the flake8 style.

theano/sandbox/cuda/rng_curand.py

-"""
-Define CURAND_RandomStreams - backed by CURAND.
-
-"""
 from __future__ import absolute_import, print_function, division
-
-__authors__ = "James Bergstra"
-__copyright__ = "(c) 2011, University of Montreal"
-__license__ = "3-clause BSD License"
-__contact__ = "theano-dev@googlegroups.com"
-
 import numpy
 import theano.gof
 from theano.compat import PY3
@@ -17,12 +7,21 @@ from theano.tensor import (get_vector_length, cast, opt)
 from theano.compile import optdb
 from theano.gof import local_optimizer, Variable
 
+__authors__ = "James Bergstra"
+__copyright__ = "(c) 2011, University of Montreal"
+__license__ = "3-clause BSD License"
+__contact__ = "theano-dev@googlegroups.com"
+
+"""
+Define CURAND_RandomStreams - backed by CURAND.
+
+"""
 
 config = theano.config
 
 
 class CURAND_Base(GpuOp):
-    """ 
+    """
     Base class for a random number generator implemented in CURAND.
 
     The random number generator itself is an opaque reference managed by
@@ -70,8 +69,7 @@ class CURAND_Base(GpuOp):
         Return a tuple of attributes that define the Op.
 
         """
-        return (
-                self.destructive,
+        return (self.destructive,
                 self.output_type,
                 self.seed,
                 )
@@ -88,7 +86,7 @@ class CURAND_Base(GpuOp):
 
     def make_node(self, generator, size):
         return theano.gof.Apply(self, [generator, size],
-                [generator.type(), self.output_type()])
+                                [generator.type(), self.output_type()])
 
     @classmethod
     def new_auto_update(cls, generator, ndim, dtype, size, seed):
@@ -101,10 +99,9 @@ class CURAND_Base(GpuOp):
         v_size = theano.tensor.as_tensor_variable(size)
         if ndim is None:
             ndim = get_vector_length(v_size)
-        self = cls(
-                output_type=CudaNdarrayType((False,) * ndim),
-                seed=seed,
-                destructive=False)
+        self = cls(output_type=CudaNdarrayType((False,) * ndim),
+                   seed=seed,
+                   destructive=False)
 
         o_gen, sample = self(generator, cast(v_size, 'int32'))
 
@@ -282,7 +279,7 @@ class CURAND_RandomStreams(object):
     RandomStreams instance that creates CURAND-based random variables.
 
     One caveat is that generators are not serializable.
-    
+
     Parameters
     ----------
     seed : int
@@ -319,7 +316,7 @@ class CURAND_RandomStreams(object):
         return rval
 
     def uniform(self, size, low=0.0, high=1.0, ndim=None,
-            dtype=config.floatX):
+                dtype=config.floatX):
         """
         Return symbolic tensor of uniform numbers.
 
@@ -327,14 +324,14 @@ class CURAND_RandomStreams(object):
         if isinstance(size, tuple):
             msg = "size must be a tuple of int or a Theano variable"
             assert all([isinstance(i, int) or isinstance(i, Variable)
-                for i in size]), msg
+                        for i in size]), msg
         else:
             msg = "size must be a tuple of int or a Theano variable"
             assert isinstance(size, Variable) and size.ndim == 1, msg
         generator = theano.shared(False)  # makes a generic
         s_size = theano.tensor.as_tensor_variable(size)
         u = CURAND_Uniform.new_auto_update(generator, ndim, dtype, s_size,
-                self.next_seed())
+                                           self.next_seed())
         self.state_updates.append(u.update)
         rval = u * (high - low) + low
         if u.type.broadcastable != rval.type.broadcastable:
@@ -342,10 +339,10 @@ class CURAND_RandomStreams(object):
                 'Increase the size to match the broadcasting pattern of '
                 'low and `high` arguments'
             )
-        return  rval
+        return rval
 
     def normal(self, size=None, avg=0.0, std=1.0, ndim=None,
-            dtype=config.floatX):
+               dtype=config.floatX):
         """
         Return symbolic tensor of normally-distributed numbers.
 
@@ -359,14 +356,14 @@ class CURAND_RandomStreams(object):
         if isinstance(size, tuple):
             msg = "size must be a tuple of int or a Theano variable"
             assert all([isinstance(i, int) or isinstance(i, Variable)
-                for i in size]), msg
+                        for i in size]), msg
         else:
             msg = "size must be a tuple of int or a Theano variable"
             assert isinstance(size, Variable) and size.ndim == 1, msg
         generator = theano.shared(False)  # makes a generic
         s_size = theano.tensor.as_tensor_variable(size)
         u = CURAND_Normal.new_auto_update(generator, ndim, dtype, s_size,
-                self.next_seed())
+                                          self.next_seed())
         self.state_updates.append(u.update)
         rval = u * std + avg
         if u.type.broadcastable != rval.type.broadcastable:
@@ -374,7 +371,7 @@ class CURAND_RandomStreams(object):
                 'Increase the size to match the broadcasting pattern of `low`'
                 'and `high` arguments'
             )
-        return  rval
+        return rval
 
 
 @local_optimizer([CURAND_Base])
@@ -386,5 +383,5 @@ def local_destructive(node):
         return new_op.make_node(*node.inputs).outputs
     return False
 optdb.register('CURAND_destructive',
-        opt.in2out(local_destructive, ignore_newtrees=True), 99, 'fast_run',
-                   'inplace')
+               opt.in2out(local_destructive, ignore_newtrees=True),
+               99, 'fast_run', 'inplace')