PEP8: curand_rng.py

theano/sandbox/cuda/rng_curand.py

@@ -2,10 +2,10 @@
 Define CURAND_RandomStreams - backed by CURAND
 """
 
-__authors__   = "James Bergstra"
+__authors__ = "James Bergstra"
 __copyright__ = "(c) 2011, University of Montreal"
-__license__   = "3-clause BSD License"
-__contact__   = "theano-dev@googlegroups.com"
+__license__ = "3-clause BSD License"
+__contact__ = "theano-dev@googlegroups.com"
 
 import sys
 import numpy
@@ -15,14 +15,16 @@ from theano.tensor import (get_vector_length, cast, opt)
 from theano.compile import optdb
 from theano.gof import local_optimizer, Variable
 
+
 config = theano.config
 
+
 class CURAND_Base(theano.gof.Op):
     """ Base class for a random number generator implemented in CURAND.
 
-    The random number generator itself is an opaque reference managed by CURAND.
-    This Op uses a generic-typed shared variable to point to a CObject that
-    encapsulates this opaque reference.
+    The random number generator itself is an opaque reference managed by
+    CURAND.  This Op uses a generic-typed shared variable to point to a CObject
+    that encapsulates this opaque reference.
 
     Each random variable is created with a generator of False.
     The actual random number generator is allocated from the seed, on the first
@@ -30,8 +32,8 @@ class CURAND_Base(theano.gof.Op):
 
     :note:
         One caveat is that the random number state is simply not serializable.
-        Consequently, attempts to serialize functions compiled with these random
-        numbers will fail.
+        Consequently, attempts to serialize functions compiled with these
+        random numbers will fail.
 
     """
     def __init__(self, output_type, seed, destructive):
@@ -60,13 +62,14 @@ class CURAND_Base(theano.gof.Op):
                 )
 
     def __eq__(self, other):
-        return type(self)==type(other) and self._config()==other._config()
+        return type(self) == type(other) and self._config() == other._config()
 
     def __hash__(self):
         return hash((type(self), self._config()))
+
     def __str__(self):
-        return self.__class__.__name__+"{inplace=%s, out_dtype=%s}"%(
-            self.destructive, self.output_type)
+        return (self.__class__.__name__ + "{inplace=%s, out_dtype=%s}" %
+                (self.destructive, self.output_type))
 
     def make_node(self, generator, size):
         return theano.gof.Apply(self, [generator, size],
@@ -90,9 +93,9 @@ class CURAND_Base(theano.gof.Op):
 
         o_gen, sample = self(generator, cast(v_size, 'int32'))
 
-        sample.generator = generator       # for user
-        sample.update = (generator, o_gen) # for CURAND_RandomStreams
-        generator.default_update = o_gen   # for pfunc uses this attribute
+        sample.generator = generator        # for user
+        sample.update = (generator, o_gen)  # for CURAND_RandomStreams
+        generator.default_update = o_gen    # for pfunc uses this attribute
         return sample
 
     def c_headers(self):
@@ -216,7 +219,7 @@ class CURAND_Base(theano.gof.Op):
             cudaThreadSynchronize();
         }
         //////// </ code generated by CURAND_Base>
-        """ %locals()
+        """ % locals()
 
     def c_code_cache_version(self):
         return (2,)
@@ -230,7 +233,7 @@ class CURAND_Normal(CURAND_Base):
                 CudaNdarray_DEV_DATA(%(o_sample)s),
                 n_elements,
                 0.0, 1.0);
-        """%kwargs
+        """ % kwargs
 
 
 class CURAND_Uniform(CURAND_Base):
@@ -240,7 +243,7 @@ class CURAND_Uniform(CURAND_Base):
         return """ curandGenerateUniform(*gen,
                 CudaNdarray_DEV_DATA(%(o_sample)s),
                 n_elements);
-        """%kwargs
+               """ % kwargs
 
 
 class CURAND_RandomStreams(object):
@@ -254,7 +257,7 @@ class CURAND_RandomStreams(object):
         """
         self._start_seed = seed
         self._cur_seed = seed
-        self._has_lost_states = False #True if self.state_updates is incomplete
+        self._has_lost_states = False  # True if self.state_updates incomplete
         self.state_updates = []
 
     def updates(self):
@@ -267,7 +270,7 @@ class CURAND_RandomStreams(object):
         """Return a unique seed for initializing a random variable.
         """
         self._cur_seed += 1
-        return self._cur_seed -1
+        return self._cur_seed - 1
 
     def __getstate__(self):
         rval = dict(self.__dict__)
@@ -283,19 +286,22 @@ 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)
+            assert all([isinstance(i, int) or isinstance(i, Variable)
                 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
+            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.state_updates.append(u.update)
-        rval = u * (high-low) + low
+        rval = u * (high - low) + low
         if u.type.broadcastable != rval.type.broadcastable:
-            raise NotImplementedError( 'Increase the size to match the broadcasting pattern of `low` and `high` arguments')
+            raise NotImplementedError(
+                'Increase the size to match the broadcasting pattern of '
+                'low and `high` arguments'
+            )
         return  rval
 
     def normal(self, size=None, avg=0.0, std=1.0, ndim=None,
@@ -303,23 +309,27 @@ class CURAND_RandomStreams(object):
         """
         Return symbolic tensor of normally-distributed numbers.
 
-        :param: size: Can be a list of integer or Theano variable(ex: the shape of other Theano Variable)
+        :param: size: Can be a list of integer or Theano variable(ex: the shape
+        of other Theano Variable)
         """
         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)
+            assert all([isinstance(i, int) or isinstance(i, Variable)
                 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
+            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.state_updates.append(u.update)
         rval = u * std + avg
         if u.type.broadcastable != rval.type.broadcastable:
-            raise NotImplementedError( 'Increase the size to match the broadcasting pattern of `low` and `high` arguments')
+            raise NotImplementedError(
+                'Increase the size to match the broadcasting pattern of `low`'
+                'and `high` arguments'
+            )
         return  rval
 
 
@@ -332,4 +342,5 @@ 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')