lint, ci: pylint setup codecov

.github/workflows/ci-build.yml

@@ -5,14 +5,16 @@ on:
     branches: [ master ]
   pull_request:
     branches: [ master ]
+  schedule:
+    - cron: '0 0 * * 0'
 
 jobs:
   build:
 
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.11']
+        python-version: ["3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v4
@@ -25,13 +27,15 @@ jobs:
         python -m pip install --upgrade pip
         pip install -r requirements.txt
         pip install -r requirements-dev.txt
-        pip install pylint pytest-cov codecov
-    # - name: Linting with pylint
-    #   run: |
-    #     pylint tensornetwork
+    - name: Linting with pylint
+      run: |
+        pylint tensornetwork
     - name: Test with pytest
       run: |
-        pytest --cov=./
-    # - name: Uploading coverage report
-    #   run: |
-    #     codecov
+        pytest --cov=./ --cov-report xml
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v4
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: ./coverage.xml
+        fail_ci_if_error: true

.pylintrc

@@ -84,15 +84,23 @@ disable=
     wrong-import-order,
     wrong-import-position,
     consider-using-f-string,
+    consider-using-enumerate,
     too-many-branches,
+    unnecessary-dunder-call,
+    no-value-for-parameter,
+    implicit-str-concat,
+    redefined-builtin,
+    implicit-str-concat,
+    abstract-method,
+    arguments-differ,
+    possibly-used-before-assignment,
+    unnecessary-lambda-assignment,
+    unnecessary-list-index-lookup,
+    consider-using-generator,
 [REPORTS]
 # Set the output format. Available formats are text, parseable, colorized, msvs
 # (visual studio) and html
 output-format=text
-# Put messages in a separate file for each module / package specified on the
-# command line instead of printing them on stdout. Reports (if any) will be
-# written in a file name "pylint_global.[txt|html]".
-files-output=no
 # Tells whether to display a full report or only the messages
 # CHANGED:
 reports=no
@@ -142,7 +150,7 @@ max-module-lines=1000
 indent-string="    "
 [BASIC]
 # List of builtins function names that should not be used, separated by a comma
-bad-functions=map,filter,apply,input
+; bad-functions=map,filter,apply,input
 # Regular expression which should only match correct module names
 module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
 # Regular expression which should only match correct module level names
@@ -180,7 +188,7 @@ max-locals=15
 # Maximum number of return / yield for function / method body
 max-returns=6
 # Maximum number of branch for function / method body
-max-branchs=12
+max-branches=12
 # Maximum number of statements in function / method body
 max-statements=50
 # Maximum number of parents for a class (see R0901).
@@ -211,4 +219,4 @@ int-import-graph=
 [EXCEPTIONS]
 # Exceptions that will emit a warning when being caught. Defaults to
 # "Exception"
-overgeneral-exceptions=Exception
+overgeneral-exceptions=builtins.Exception

README.md

@@ -1,15 +1,14 @@
 <img src="https://user-images.githubusercontent.com/8702042/67589472-5a1d0e80-f70d-11e9-8812-64647814ae96.png" width="60%" height="60%">
 
-[![Build Status](https://travis-ci.org/google/TensorNetwork.svg?branch=master)](https://travis-ci.org/google/TensorNetwork)
-
+[![Continuous Integration](https://github.com/king-p3nguin/TensorNetwork2/actions/workflows/ci-build.yml/badge.svg?branch=master)](https://github.com/king-p3nguin/TensorNetwork2/actions/workflows/ci-build.yml)
+[![codecov](https://codecov.io/gh/king-p3nguin/TensorNetwork2/graph/badge.svg?token=gayPQVD9Y0)](https://codecov.io/gh/king-p3nguin/TensorNetwork2)
 
 A tensor network wrapper for TensorFlow, JAX, PyTorch, and Numpy.
 
-For an overview of tensor networks please see the following: 
+For an overview of tensor networks please see the following:
 
 - [Matrices as Tensor Network Diagrams](https://www.math3ma.com/blog/matrices-as-tensor-network-diagrams)
 
-
 - [Crash Course in Tensor Networks (video)](https://www.youtube.com/watch?v=YN2YBB0viKo)
 
 - [Hand-waving and interpretive dance: an introductory course on tensor networks](https://iopscience.iop.org/article/10.1088/1751-8121/aa6dc3)
@@ -28,8 +27,8 @@ More information can be found in our TensorNetwork papers:
 
 - [TensorNetwork for Machine Learning](https://arxiv.org/abs/1906.06329)
 
-
 ## Installation
+
 ```
 pip3 install tensornetwork
 ```
@@ -38,30 +37,32 @@ pip3 install tensornetwork
 
 For details about the TensorNetwork API, see the [reference documentation.](https://tensornetwork.readthedocs.io)
 
-
 ## Tutorials
 
 [Basic API tutorial](https://colab.research.google.com/drive/1Fp9DolkPT-P_Dkg_s9PLbTOKSq64EVSu)
 
 [Tensor Networks inside Neural Networks using Keras](https://colab.research.google.com/github/google/TensorNetwork/blob/master/colabs/Tensor_Networks_in_Neural_Networks.ipynb)
+
 ## Basic Example
 
 Here, we build a simple 2 node contraction.
+
 ```python
 import numpy as np
 import tensornetwork as tn
 
 # Create the nodes
-a = tn.Node(np.ones((10,))) 
+a = tn.Node(np.ones((10,)))
 b = tn.Node(np.ones((10,)))
 edge = a[0] ^ b[0] # Equal to tn.connect(a[0], b[0])
 final_node = tn.contract(edge)
 print(final_node.tensor) # Should print 10.0
 ```
 
-## Optimized Contractions.
+## Optimized Contractions
+
 Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges.
-We have `contract_between` and `contract_parallel` that do this automatically for your convenience. 
+We have `contract_between` and `contract_parallel` that do this automatically for your convenience.
 
 ```python
 # Contract all of the edges between a and b
@@ -70,13 +71,15 @@ c = tn.contract_between(a, b)
 # This is the same as above, but much shorter.
 c = a @ b
 
-# Contract all of edges that are parallel to edge 
+# Contract all of edges that are parallel to edge
 # (parallel means connected to the same nodes).
 c = tn.contract_parallel(edge)
 ```
 
 ## Split Node
-You can split a node by doing a singular value decomposition. 
+
+You can split a node by doing a singular value decomposition.
+
 ```python
 # This will return two nodes and a tensor of the truncation error.
 # The two nodes are the unitary matrices multiplied by the square root of the
@@ -88,9 +91,11 @@ u_s, vh_s, trun_error = tn.split_node(node, left_edges, right_edges)
 u, s, vh, trun_error = tn.split_node_full_svd(node, left_edges, right_edges)
 ```
 
-## Node and Edge names.
-You can optionally name your nodes/edges. This can be useful for debugging, 
+## Node and Edge names
+
+You can optionally name your nodes/edges. This can be useful for debugging,
 as all error messages will print the name of the broken edge/node.
+
 ```python
 node = tn.Node(np.eye(2), name="Identity Matrix")
 print("Name of node: {}".format(node.name))
@@ -101,15 +106,19 @@ final_result = tn.contract(edge, name="Trace Of Identity")
 print("Name of new node after contraction: {}".format(final_result.name))
 ```
 
-## Named axes.
+## Named axes
+
 To make remembering what an axis does easier, you can optionally name a node's axes.
+
 ```python
 a = tn.Node(np.zeros((2, 2)), axis_names=["alpha", "beta"])
 edge = a["beta"] ^ a["alpha"]
 ```
 
-## Edge reordering.
+## Edge reordering
+
 To assert that your result's axes are in the correct order, you can reorder a node at any time during computation.
+
 ```python
 a = tn.Node(np.zeros((1, 2, 3)))
 e1 = a[0]
@@ -121,8 +130,10 @@ a.reorder_edges([e3, e1, e2])
 print(a.tensor.shape) # Should print (3, 1, 2)
 ```
 
-## NCON interface.
+## NCON interface
+
 For a more compact specification of a tensor network and its contraction, there is `ncon()`. For example:
+
 ```python
 from tensornetwork import ncon
 a = np.ones((2, 2))
@@ -131,38 +142,43 @@ c = ncon([a, b], [(-1, 1), (1, -2)])
 print(c)
 ```
 
-## Different backend support.
+## Different backend support
+
 Currently, we support JAX, TensorFlow, PyTorch and NumPy as TensorNetwork backends.
 We also support tensors with Abelian symmetries via a `symmetric` backend, see the [reference
 documentation](https://tensornetwork.readthedocs.io/en/latest/block_sparse_tutorial.html) for more details.
 
 To change the default global backend, you can do:
+
 ```python
 tn.set_default_backend("jax") # tensorflow, pytorch, numpy, symmetric
 ```
+
 Or, if you only want to change the backend for a single `Node`, you can do:
+
 ```python
 tn.Node(tensor, backend="jax")
 ```
 
 If you want to run your contractions on a GPU, we highly recommend using JAX, as it has the closet API to NumPy.
 
 ## Disclaimer
+
 This library is in *alpha* and will be going through a lot of breaking changes. While releases will be stable enough for research, we do not recommend using this in any production environment yet.
 
 TensorNetwork is not an official Google product. Copyright 2019 The TensorNetwork Developers.
 
 ## Citation
+
 If you are using TensorNetwork for your research please cite this work using the following bibtex entry:
 
 ```
 @misc{roberts2019tensornetwork,
-      title={TensorNetwork: A Library for Physics and Machine Learning}, 
+      title={TensorNetwork: A Library for Physics and Machine Learning},
       author={Chase Roberts and Ashley Milsted and Martin Ganahl and Adam Zalcman and Bruce Fontaine and Yijian Zou and Jack Hidary and Guifre Vidal and Stefan Leichenauer},
       year={2019},
       eprint={1905.01330},
       archivePrefix={arXiv},
       primaryClass={physics.comp-ph}
 }
 ```
-

requirements-dev.txt

@@ -1,7 +1,8 @@
 tensorflow
 pytest
-pytest-xdist
+pytest-cov
 torch
 jax
 jaxlib
 pylint
+black

tensornetwork/backends/abstract_backend.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Callable, Optional, Sequence, Tuple, Type, Union
 
 import numpy as np
 
@@ -363,7 +363,7 @@ def eigh(self, matrix: Tensor):
     def eigs(
         self,
         A: Callable,
-        args: Optional[List[Tensor]] = None,
+        args: Optional[list[Tensor]] = None,
         initial_state: Optional[Tensor] = None,
         shape: Optional[Tuple[int, ...]] = None,
         dtype: Optional[Type[np.number]] = None,  # pylint: disable=no-member
@@ -372,7 +372,7 @@ def eigs(
         tol: float = 1e-8,
         which: str = "LR",
         maxiter: Optional[int] = None,
-    ) -> Tuple[Tensor, List]:
+    ) -> Tuple[Tensor, list]:
         """Arnoldi method for finding the lowest eigenvector-eigenvalue pairs
         of a linear operator `A`. `A` is a callable implementing the
         matrix-vector product. If no `initial_state` is provided then
@@ -413,7 +413,7 @@ def eigs(
     def eigsh(
         self,
         A: Callable,
-        args: Optional[List[Tensor]] = None,
+        args: Optional[list[Tensor]] = None,
         initial_state: Optional[Tensor] = None,
         shape: Optional[Tuple[int, ...]] = None,
         dtype: Optional[Type[np.number]] = None,  # pylint: disable=no-member
@@ -422,7 +422,7 @@ def eigsh(
         tol: float = 1e-8,
         which: str = "LR",
         maxiter: Optional[int] = None,
-    ) -> Tuple[Tensor, List]:
+    ) -> Tuple[Tensor, list]:
         """Lanczos method for finding the lowest eigenvector-eigenvalue pairs
         of a symmetric (hermitian) linear operator `A`. `A` is a callable
         implementing the matrix-vector product. If no `initial_state` is provided
@@ -463,7 +463,7 @@ def eigsh(
     def eigsh_lanczos(
         self,
         A: Callable,
-        args: Optional[List[Tensor]] = None,
+        args: Optional[list[Tensor]] = None,
         initial_state: Optional[Tensor] = None,
         shape: Optional[Tuple[int, ...]] = None,
         dtype: Optional[Type[np.number]] = None,  # pylint: disable=no-member
@@ -473,7 +473,7 @@ def eigsh_lanczos(
         delta: float = 1e-8,
         ndiag: int = 20,
         reorthogonalize: bool = False,
-    ) -> Tuple[Tensor, List]:
+    ) -> Tuple[Tensor, list]:
         """
         Lanczos method for finding the lowest eigenvector-eigenvalue pairs
         of `A`.
@@ -517,7 +517,7 @@ def gmres(
         self,
         A_mv: Callable,
         b: Tensor,
-        A_args: Optional[List] = None,
+        A_args: Optional[list] = None,
         A_kwargs: Optional[dict] = None,
         x0: Optional[Tensor] = None,
         tol: float = 1e-05,
@@ -639,8 +639,7 @@ def gmres(
 
             x0 = self.reshape(x0, (N,))
 
-        if num_krylov_vectors > N:
-            num_krylov_vectors = N
+        num_krylov_vectors = min(num_krylov_vectors, N)
 
         if tol < 0:
             raise ValueError(f"tol = {tol} must be positive.")
@@ -668,7 +667,7 @@ def _gmres(
         self,
         A_mv: Callable,
         b: Tensor,
-        A_args: List,
+        A_args: list,
         A_kwargs: dict,
         x0: Tensor,
         tol: float,

tensornetwork/backends/backend_factory.py

@@ -30,7 +30,7 @@
 }
 
 # we instantiate each backend only once and store it here
-_INSTANTIATED_BACKENDS = dict()
+_INSTANTIATED_BACKENDS = {}
 
 
 def get_backend(
@@ -39,7 +39,7 @@ def get_backend(
     if isinstance(backend, abstract_backend.AbstractBackend):
         return backend
     if backend not in _BACKENDS:
-        raise ValueError("Backend '{}' does not exist".format(backend))
+        raise ValueError(f"Backend '{backend}' does not exist")
 
     if backend in _INSTANTIATED_BACKENDS:
         return _INSTANTIATED_BACKENDS[backend]