linters: add ruff & isort (#441)

* sort pre-commit file

* pre-commit: add ruff

* pre-commit run -a

* manual ruff fixes

* README: add ruff badge

* pre-commit: add isort

* pre-commit run -a

* ignore previous commit in blames

* add changelog entry

* replace black & isort by ruff

* ruff: more linters

* clean config files

* pre-commit run -a

* ignore previous commit in blames

* fix NPY002

ref. https://numpy.org/doc/stable/reference/random/legacy.html

* remove useless code

* fix dtype conversion

Author: nim65s

.git-blame-ignore-revs

@@ -1,2 +1,8 @@
 # pre-commit run -a (Guilhem Saurel, 2022-07-27)
 4af05ec6781f9da65b81af8e3af8d69213f99e85
+
+# pre-commit run -a (Guilhem Saurel, 2024-02-17)
+48fb48c83f0456de2fb612ef55df8ad789824d87
+
+# pre-commit run -a (Guilhem Saurel, 2024-02-19)
+0bae435330ee475f8dbb11bf5e672284d294d9b3

.pre-commit-config.yaml

@@ -1,20 +1,29 @@
 ci:
-    autoupdate_branch: 'devel'
+  autoupdate_branch: devel
 repos:
--   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v17.0.6
-    hooks:
-    -   id: clang-format
-        args: ['--style={BasedOnStyle: Google, SortIncludes: false, Standard: Cpp03}']
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
-    hooks:
-    -   id: trailing-whitespace
--   repo: https://github.com/psf/black
-    rev: 24.2.0
-    hooks:
-    -   id: black
--   repo: https://github.com/cheshirekow/cmake-format-precommit
-    rev: v0.6.13
-    hooks:
-    - id: cmake-format
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.2.2
+  hooks:
+  - id: ruff
+    args:
+    - --fix
+    - --exit-non-zero-on-fix
+  - id: ruff-format
+- repo: https://github.com/cheshirekow/cmake-format-precommit
+  rev: v0.6.13
+  hooks:
+  - id: cmake-format
+- repo: https://github.com/pappasam/toml-sort
+  rev: v0.23.1
+  hooks:
+  - id: toml-sort-fix
+- repo: https://github.com/pre-commit/mirrors-clang-format
+  rev: v17.0.6
+  hooks:
+  - id: clang-format
+    args:
+    - '--style={BasedOnStyle: Google, SortIncludes: false, Standard: Cpp03}'
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.5.0
+  hooks:
+  - id: trailing-whitespace

CHANGELOG.md

@@ -13,6 +13,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - Support for `std::unique_ptr` as a return types with `StdUniquePtrCallPolicies` and `boost::python::default_call_policies` ([#433](https://github.com/stack-of-tasks/eigenpy/pull/433))
 - Support for `std::unique_ptr` as an internal reference with `ReturnInternalStdUniquePtr` ([#433](https://github.com/stack-of-tasks/eigenpy/pull/433))
 - Support for `Eigen::Simplicial{LLT,LDLT}` and `Eigen::Cholmod{Simplicial,Supernodal}{LLT,LDLT}` Cholesky de compositions ([#438](https://github.com/stack-of-tasks/eigenpy/pull/438))
+- Switch to ruff for lints, format, and import sort ([#441](https://github.com/stack-of-tasks/eigenpy/pull/441))
 
 ### Fixed
 - Fix the issue of missing exposition of Eigen types with __int64 scalar type ([#426](https://github.com/stack-of-tasks/eigenpy/pull/426))

README.md

@@ -8,6 +8,7 @@ EigenPy — Versatile and efficient Python bindings between Numpy and Eigen
   <a href="https://anaconda.org/conda-forge/eigenpy"><img src="https://img.shields.io/conda/vn/conda-forge/eigenpy.svg" alt="Conda Version"/></a>
   <a href="https://badge.fury.io/py/eigenpy"><img src="https://badge.fury.io/py/eigenpy.svg" alt="PyPI version"></a>
   <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black"></a>
+  <a href="https://github.com/astral-sh/ruff"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" alt="Linter: ruff"></a>
 </p>
 
 **EigenPy** is an open-source framework that allows the binding of the famous [Eigen](http://eigen.tuxfamily.org) C++ library in Python via Boost.Python.

benchmarks/bench-switch.py

@@ -1,13 +1,11 @@
-from __future__ import print_function
-
-import eigenpy
-import numpy as np
-
 import time  # noqa
 import timeit  # noqa
 
+import numpy as np
 from IPython import get_ipython
 
+import eigenpy
+
 ipython = get_ipython()
 
 quat = eigenpy.Quaternion()

pyproject.toml

@@ -0,0 +1,11 @@
+[tool.ruff]
+extend-exclude = ["cmake"]
+
+[tool.ruff.lint]
+extend-select = ["I", "NPY", "RUF", "UP", "W"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["eigenpy"]
+
+[tool.tomlsort]
+all = true

python/eigenpy/__init__.py

@@ -3,4 +3,4 @@
 #
 
 from .eigenpy_pywrap import *  # noqa
-from .eigenpy_pywrap import __version__, __raw_version__  # noqa
+from .eigenpy_pywrap import __raw_version__, __version__  # noqa

unittest/python/decompositions/sparse/cholmod/test_CholmodSimplicialLDLT.py

@@ -1,19 +1,20 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+rng = np.random.default_rng()
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 A = csc_matrix(A)
 
 llt = eigenpy.CholmodSimplicialLDLT(A)
 
 assert llt.info() == eigenpy.ComputationInfo.Success
 
-X = np.random.rand(dim, 20)
+X = rng.rand((dim, 20))
 B = A.dot(X)
 X_est = llt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/decompositions/sparse/cholmod/test_CholmodSimplicialLLT.py

@@ -1,19 +1,21 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+rng = np.random.default_rng()
+
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 A = csc_matrix(A)
 
 llt = eigenpy.CholmodSimplicialLLT(A)
 
 assert llt.info() == eigenpy.ComputationInfo.Success
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = llt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/decompositions/sparse/cholmod/test_CholmodSupernodalLLT.py

@@ -1,19 +1,21 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+rng = np.random.default_rng()
+
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 A = csc_matrix(A)
 
 llt = eigenpy.CholmodSupernodalLLT(A)
 
 assert llt.info() == eigenpy.ComputationInfo.Success
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = llt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/decompositions/sparse/test_Accelerate.py

@@ -1,21 +1,23 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
+rng = np.random.default_rng()
+
 
 def test(SolverType: type):
     dim = 100
-    A = np.random.rand(dim, dim)
-    A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
     A = csc_matrix(A)
 
     llt = SolverType(A)
 
     assert llt.info() == eigenpy.ComputationInfo.Success
 
-    X = np.random.rand(dim, 20)
+    X = rng.random((dim, 20))
     B = A.dot(X)
     X_est = llt.solve(B)
     #    import pdb; pdb.set_trace()

unittest/python/decompositions/sparse/test_SimplicialLDLT.py

@@ -1,11 +1,13 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+rng = np.random.default_rng()
+
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 A = csc_matrix(A)
 
@@ -20,7 +22,7 @@
 LDU = L @ D @ U
 assert eigenpy.is_approx(LDU.toarray(), A.toarray())
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = ldlt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/decompositions/sparse/test_SimplicialLLT.py

@@ -1,11 +1,13 @@
-import eigenpy
-
 import numpy as np
 from scipy.sparse import csc_matrix
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+rng = np.random.default_rng()
+
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 A = csc_matrix(A)
 
@@ -19,7 +21,7 @@
 LU = L @ U
 assert eigenpy.is_approx(LU.toarray(), A.toarray())
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = llt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/test_LDLT.py

@@ -1,10 +1,12 @@
-import eigenpy
 import numpy as np
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
+rng = np.random.default_rng()
 
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 ldlt = eigenpy.LDLT(A)
 
@@ -16,7 +18,7 @@
     np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))), A
 )
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = ldlt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/test_LLT.py

@@ -1,18 +1,19 @@
-import eigenpy
-
 import numpy as np
 
+import eigenpy
+
 dim = 100
-A = np.random.rand(dim, dim)
+rng = np.random.default_rng()
 
-A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
+A = rng.random((dim, dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
 llt = eigenpy.LLT(A)
 
 L = llt.matrixL()
 assert eigenpy.is_approx(L.dot(np.transpose(L)), A)
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = llt.solve(B)
 assert eigenpy.is_approx(X, X_est)

unittest/python/test_MINRES.py

@@ -1,12 +1,14 @@
-import eigenpy
 import numpy as np
 
+import eigenpy
+
 dim = 100
+rng = np.random.default_rng()
 A = np.eye(dim)
 
 minres = eigenpy.MINRES(A)
 
-X = np.random.rand(dim, 20)
+X = rng.random((dim, 20))
 B = A.dot(X)
 X_est = minres.solve(B)
 print("A.dot(X_est):", A.dot(X_est))

unittest/python/test_complex.py

@@ -1,16 +1,15 @@
-from __future__ import print_function
-
 import numpy as np
-from complex import real, imag, ascomplex
+from complex import ascomplex, imag, real
 
 rows = 10
 cols = 20
+rng = np.random.default_rng()
 
 
 def test(dtype):
     Z = np.zeros((rows, cols), dtype=dtype)
-    Z.real = np.random.rand(rows, cols)
-    Z.imag = np.random.rand(rows, cols)
+    Z.real = rng.random((rows, cols))
+    Z.imag = rng.random((rows, cols))
 
     Z_real = real(Z)
     assert (Z_real == Z.real).all()

unittest/python/test_dimensions.py

@@ -1,5 +1,3 @@
-from __future__ import print_function
-
 import eigenpy
 
 quat = eigenpy.Quaternion()