Apply isort to theano.sparse sub-package modules

theano/sparse/__init__.py

 import sys
 
+
 try:
     import scipy
 
@@ -19,8 +20,8 @@ except ImportError:
 
 from theano.sparse.type import *
 
+
 if enable_sparse:
+    from theano.sparse import opt, sharedvar
     from theano.sparse.basic import *
-    from theano.sparse import opt
-    from theano.sparse import sharedvar
     from theano.sparse.sharedvar import sparse_constructor as shared

theano/sparse/basic.py

@@ -13,17 +13,16 @@ http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps
 import sys
 
 import numpy as np
+import scipy.sparse
 from numpy.lib.stride_tricks import as_strided
 from six import integer_types
 
-import scipy.sparse
-
 import theano
-from theano import gof, tensor, scalar, config
-from theano.gradient import DisconnectedType
-from theano.sparse.utils import hash_from_sparse
-from theano.gradient import grad_not_implemented, grad_undefined
+from theano import config, gof, scalar, tensor
+from theano.gradient import DisconnectedType, grad_not_implemented, grad_undefined
 from theano.sparse.type import SparseType, _is_sparse
+from theano.sparse.utils import hash_from_sparse
+
 
 sparse_formats = ["csc", "csr"]
 

theano/sparse/opt.py

@@ -3,20 +3,20 @@ import scipy
 
 import theano
 from theano import gof, scalar, tensor
-
-from theano.tensor import blas
-from theano.tensor.opt import register_specialize, register_canonicalize
-from theano.sparse import (
+from theano.sparse import basic as sparse
+from theano.sparse.basic import (
     CSC,
     CSR,
-    csm_properties,
+    csm_data,
     csm_grad,
-    usmm,
     csm_indices,
     csm_indptr,
-    csm_data,
+    csm_properties,
+    usmm,
 )
-from theano.sparse import basic as sparse
+from theano.tensor import blas
+from theano.tensor.opt import register_canonicalize, register_specialize
+
 
 _is_sparse_variable = sparse._is_sparse_variable
 _is_dense = sparse._is_dense

theano/sparse/sandbox/sp.py

@@ -11,10 +11,9 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
 import numpy as np
 from scipy import sparse as scipy_sparse
 
-
 import theano
 import theano.sparse
-from theano import sparse, Op, tensor
+from theano import Op, sparse, tensor
 
 
 def register_specialize(lopt, *tags, **kwargs):

theano/sparse/sandbox/sp2.py

 import numpy as np
-
-import theano
 import scipy.sparse
 
+import theano
 from theano import gof, tensor
-from theano.tensor import discrete_dtypes, float_dtypes
-from theano.tensor.opt import register_specialize
-from theano.sparse.basic import (
-    as_sparse_variable,
-    SparseType,
-    add_s_s,
-    neg,
-    mul_s_s,
-    mul_s_d,
-    dot,
-    CSMProperties,
-    CSM,
-    _is_sparse_variable,
-    _is_dense_variable,
+from theano.sparse.basic import (  # To maintain compatibility
     CSC,
+    CSM,
     CSR,
-    csm_properties,
+    AddSSData,
+    Cast,
+    CSMProperties,
+    HStack,
+    MulSV,
+    Remove0,
+    SamplingDot,
+    SparseType,
+    StructuredAddSV,
+    VStack,
+    _is_dense_variable,
+    _is_sparse,
+    _is_sparse_variable,
+    add_s_s,
+    add_s_s_data,
+    as_sparse_variable,
+    bcast,
+    ccast,
     csm_data,
     csm_indices,
     csm_indptr,
+    csm_properties,
     csm_shape,
-    _is_sparse,
-    # To maintain compatibility
-    Remove0,
-    remove0,
-    Cast,
-    bcast,
-    wcast,
-    icast,
-    lcast,
-    fcast,
     dcast,
-    ccast,
-    zcast,
-    HStack,
+    dot,
+    fcast,
     hstack,
-    VStack,
-    vstack,
-    AddSSData,
-    add_s_s_data,
-    MulSV,
+    icast,
+    lcast,
+    mul_s_d,
+    mul_s_s,
     mul_s_v,
-    structured_monoid,
-    structured_sigmoid,
+    neg,
+    remove0,
+    sampling_dot,
+    structured_add,
+    structured_add_s_v,
     structured_exp,
     structured_log,
-    structured_pow,
-    structured_minimum,
     structured_maximum,
-    structured_add,
-    StructuredAddSV,
-    structured_add_s_v,
-    SamplingDot,
-    sampling_dot,
+    structured_minimum,
+    structured_monoid,
+    structured_pow,
+    structured_sigmoid,
+    vstack,
+    wcast,
+    zcast,
 )
 
-# Probability Ops are currently back in sandbox, because they do not respect
-# Theano's Op contract, as their behaviour is not reproducible: calling
-# the perform() method twice with the same argument will yield different
-# results.
-# from theano.sparse.basic import (
-#    Multinomial, multinomial, Poisson, poisson,
-#    Binomial, csr_fbinomial, csc_fbinomial, csr_dbinomial, csc_dbinomial)
-
 # Also for compatibility
 from theano.sparse.opt import (
     MulSDCSC,
-    mul_s_d_csc,
     MulSDCSR,
-    mul_s_d_csr,
     MulSVCSR,
-    mul_s_v_csr,
-    StructuredAddSVCSR,
-    structured_add_s_v_csr,
     SamplingDotCSR,
-    sampling_dot_csr,
+    StructuredAddSVCSR,
     local_mul_s_d,
     local_mul_s_v,
-    local_structured_add_s_v,
     local_sampling_dot_csr,
+    local_structured_add_s_v,
+    mul_s_d_csc,
+    mul_s_d_csr,
+    mul_s_v_csr,
+    sampling_dot_csr,
+    structured_add_s_v_csr,
 )
+from theano.tensor import discrete_dtypes, float_dtypes
+from theano.tensor.opt import register_specialize
+
+
+# Probability Ops are currently back in sandbox, because they do not respect
+# Theano's Op contract, as their behaviour is not reproducible: calling
+# the perform() method twice with the same argument will yield different
+# results.
+# from theano.sparse.basic import (
+#    Multinomial, multinomial, Poisson, poisson,
+#    Binomial, csr_fbinomial, csc_fbinomial, csr_dbinomial, csc_dbinomial)
 
 
 # Alias to maintain compatibility

theano/sparse/sharedvar.py

 import copy
+
 import scipy.sparse
-from theano.compile import shared_constructor, SharedVariable
+
+from theano.compile import SharedVariable, shared_constructor
 from theano.sparse.basic import SparseType, _sparse_py_operators
 
 

theano/sparse/type.py

 import numpy as np
 
+
 try:
     import scipy.sparse
 
@@ -7,9 +8,10 @@ try:
 except ImportError:
     imported_scipy = False
 
+from six import string_types
+
 import theano
 from theano import gof
-from six import string_types
 
 
 def _is_sparse(x):