Add flake8 and spell check, whitelist dictionary, docstrings

.github/CONTRIBUTING.md

@@ -1,7 +1,7 @@
 # How to contribute
 
 We welcome contributions from external contributors, and this document
-describes how to merge code changes into this `opt_einsum`. 
+describes how to merge code changes into this `opt_einsum`.
 
 ## Getting Started
 
@@ -34,7 +34,7 @@ describes how to merge code changes into this `opt_einsum`.
   integration returns checkmarks,
   and multiple core developers give "Approved" reviews.
 
-# Additional Resources
+## Additional Resources
 
 * [General GitHub documentation](https://help.github.com/)
 * [PR best practices](http://codeinthehole.com/writing/pull-requests-and-other-good-practices-for-teams-using-github/)
@@ -115,4 +115,3 @@ available at [http://contributor-covenant.org/version/1/4][version]
 
 [homepage]: http://contributor-covenant.org
 [version]: http://contributor-covenant.org/version/1/4/
-

README.md

@@ -1,14 +1,15 @@
+
+# Optimized Einsum
+
 [![Build Status](https://travis-ci.org/dgasmith/opt_einsum.svg?branch=master)](https://travis-ci.org/dgasmith/opt_einsum)
 [![codecov](https://codecov.io/gh/dgasmith/opt_einsum/branch/master/graph/badge.svg)](https://codecov.io/gh/dgasmith/opt_einsum)
 [![Anaconda-Server Badge](https://anaconda.org/conda-forge/opt_einsum/badges/version.svg)](https://anaconda.org/conda-forge/opt_einsum)
 [![PyPI](https://img.shields.io/pypi/v/opt_einsum.svg)](https://pypi.org/project/opt-einsum/#description)
 [![PyPIStats](https://img.shields.io/pypi/dm/opt_einsum)](https://pypistats.org/packages/opt-einsum)
 [![Documentation Status](https://readthedocs.org/projects/optimized-einsum/badge/?version=latest)](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest)
-[![DOI](http://joss.theoj.org/papers/10.21105/joss.00753/status.svg)](https://doi.org/10.21105/joss.00753)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.00753/status.svg)](https://doi.org/10.21105/joss.00753)
 
-
-Optimized Einsum: A tensor contraction order optimizer
-======================================================
+## Optimized Einsum: A tensor contraction order optimizer
 
 Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g.,
 [`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html),
@@ -17,7 +18,9 @@ Optimized einsum can significantly reduce the overall execution time of einsum-l
 [`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum),
 )
 by optimizing the expression's contraction order and dispatching many
-operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized
+operations to canonical BLAS, cuBLAS, or other specialized routines.
+
+Optimized
 einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch,
 Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially
 any library which conforms to a standard API. See the
@@ -70,23 +73,20 @@ The following capabilities are enabled by `opt_einsum`:
 
 Please see the [documentation](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest) for more features!
 
-
 ## Installation
 
-`opt_einsum` can either be installed via `pip install opt_einsum` or from conda `conda install opt_einsum -c conda-forge`. See the installation [documenation](http://optimized-einsum.readthedocs.io/en/latest/install.html) for further methods.
+`opt_einsum` can either be installed via `pip install opt_einsum` or from conda `conda install opt_einsum -c conda-forge`. See the installation [documentation](http://optimized-einsum.readthedocs.io/en/latest/install.html) for further methods.
 
 ## Citation
 
 If this code has benefited your research, please support us by citing:
 
 Daniel G. A. Smith and Johnnie Gray, opt_einsum - A Python package for optimizing contraction order for einsum-like expressions. *Journal of Open Source Software*, **2018**, 3(26), 753
 
-DOI: https://doi.org/10.21105/joss.00753
+DOI: <https://doi.org/10.21105/joss.00753>
 
 ## Contributing
 
 All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
 
 A detailed overview on how to contribute can be found in the [contributing guide](https://github.com/dgasmith/opt_einsum/blob/master/.github/CONTRIBUTING.md).
-
-

opt_einsum/backends/dispatch.py

@@ -69,7 +69,7 @@ def get_func(func, backend='numpy', default=None):
         return fn
 
 
-# mark libs with einsum, else try to use tensordot/tranpose as much as possible
+# mark libs with einsum, else try to use tensordot/transpose as much as possible
 _has_einsum = {}
 
 

opt_einsum/blas.py

@@ -91,7 +91,7 @@ def can_blas(inputs, result, idx_removed, shapes=None):
     if inputs[0] == inputs[1]:
         return 'DOT'
 
-    # DDOT doesnt make sense if you have to tranpose - prefer einsum
+    # DDOT does not make sense if you have to transpose - prefer einsum
     elif sets[0] == sets[1]:
         return 'DOT/EINSUM'
 
@@ -107,7 +107,7 @@ def can_blas(inputs, result, idx_removed, shapes=None):
     elif input_left[-rs:] == input_right[-rs:]:
         return 'GEMM'
 
-    # GEMM tranpose left
+    # GEMM transpose left
     elif input_left[:rs] == input_right[:rs]:
         return 'GEMM'
 
@@ -216,7 +216,7 @@ def tensor_blas(view_left, input_left, view_right, input_right, index_result, id
     elif input_left[-rs:] == input_right[-rs:]:
         new_view = np.dot(view_left.reshape(dim_left, dim_removed), view_right.reshape(dim_right, dim_removed).T)
 
-    # Tranpose left
+    # Transpose left
     elif input_left[:rs] == input_right[:rs]:
         new_view = np.dot(view_left.reshape(dim_removed, dim_left).T, view_right.reshape(dim_removed, dim_right))
 

opt_einsum/contract.py

@@ -245,7 +245,7 @@ def contract_path(*operands, **kwargs):
     num_ops = len(input_list)
 
     # Compute naive cost
-    # This isnt quite right, need to look into exactly how einsum does this
+    # This is not quite right, need to look into exactly how einsum does this
     # indices_in_input = input_subscripts.replace(',', '')
 
     inner_product = (sum(len(x) for x in input_sets) - len(indices)) > 0
@@ -375,7 +375,7 @@ def _tensordot(x, y, axes, backend='numpy'):
 # Rewrite einsum to handle different cases
 def contract(*operands, **kwargs):
     """
-    contract(subscripts, *operands, out=None, dtype=None, order='K', casting='safe', use_blas=True, optimize=True, memory_limit=None, backend='numpy')
+    contract(subscripts, *operands, out=None, dtype=None, order='K', casting='safe', use_blas=True, optimize=True, memory_limit=None, backend='numpy') 
 
     Evaluates the Einstein summation convention on the operands. A drop in
     replacement for NumPy's einsum function that optimizes the order of contraction

opt_einsum/parser.py

@@ -121,7 +121,8 @@ def alpha_canonicalize(equation):
 
 def find_output_str(subscripts):
     """
-    Find the output string for the inputs ``subscripts`` under canonical einstein summation rules. That is, repeated indices are summed over by default.
+    Find the output string for the inputs ``subscripts`` under canonical einstein summation rules.
+    That is, repeated indices are summed over by default.
 
     Examples
     --------

opt_einsum/path_random.py

@@ -276,7 +276,7 @@ def thermal_chooser(queue, remaining, nbranch=8, temperature=1, rel_temperature=
     chosen, = random_choices(range(n), weights=energies)
     cost, k1, k2, k12 = choices.pop(chosen)
 
-    # put the other choise back in the heap
+    # put the other choice back in the heap
     for other in choices:
         heapq.heappush(queue, other)
 

opt_einsum/paths.py

@@ -702,7 +702,7 @@ def _tree_to_sequence(c):
         return []
 
     c = [c]  # list of remaining contractions (lower part of columns shown above)
-    t = []  # list of elementary tensors (upper part of colums)
+    t = []  # list of elementary tensors (upper part of columns)
     s = []  # resulting contraction sequence
 
     while len(c) > 0:
@@ -970,7 +970,7 @@ def __call__(self, inputs, output, size_dict, memory_limit=None):
             # nothing left to do after single axis reductions!
             return _tree_to_sequence(simple_tree_tuple(inputs_done))
 
-        # a list of all neccessary contraction expressions for each of the
+        # a list of all necessary contraction expressions for each of the
         # disconnected subgraphs and their size
         subgraph_contractions = inputs_done
         subgraph_contractions_size = [1] * len(inputs_done)

whitelist.txt

@@ -0,0 +1,162 @@
+0rc1
+10pt
+11pt
+12pt
+16x16
+32x32
+a4paper
+abap
+autodoc
+autogenerated
+autograd
+Backends
+backendseq
+bbfreeze
+bdist
+blas
+borland
+bw
+C0301
+caes
+cba
+cfg
+cmd
+cmdclass
+cmin
+conda
+consts
+crossref
+cupy
+cx
+datestamp
+DDOT
+Deduplicate
+dep
+dereference
+detailmenu
+dirname
+documentclass
+dtype
+einsum
+execfile
+favicon
+fi
+FILEVERSION
+fmt
+fn
+func
+GEMM
+GEMV
+gh
+gitattributes
+githubs
+hadamard
+Hadamard
+hardlink
+hashable
+hashtable
+howto
+htaccess
+htbp
+https
+ico
+idx
+iij
+ij
+ik
+inds
+ja
+ja
+jax
+jieba
+jk
+jkk
+js
+letterpaper
+lgtm
+lhs
+libs
+lru
+manni
+mem
+method1
+method2
+modindex
+moduleauthor
+monokai
+nczeczulin
+ndim
+nl
+no
+NUM
+numpy
+numpytensordot
+opensearch
+outputless
+pagerefs
+papersize
+paraiso
+parentdir
+pep440
+perldoc
+pointsize
+prepended
+prodcuts
+PRODUCTVERSION
+py2exe
+Pygments
+pylint
+quickstart
+recurse
+refnames
+rhs
+ro
+rrt
+rst
+runtime
+s1
+s2
+sdist
+sectionauthor
+Ses
+sig
+sourcedist
+sourcelink
+sparsify
+sphinxstrong
+sphinxtitleref
+subdependencies
+subgraph
+subgraphs
+subst
+sv
+TDOT
+tempdir
+tensordot
+Tensordot
+test5
+texinfo
+Texinfo
+theano
+titleref
+tmp
+toctree
+toplevel
+trac
+transpose
+uncomparable
+undoc
+unparseable
+v0
+VCS
+Vectordot
+versioneer
+versionfile
+x3
+xcode
+xhtml
+zh
+zh
+zipball
+eq
+0b100101