black -> ruff

.pre-commit-config.yaml

@@ -1,22 +1,27 @@
 ci:
-    autoupdate_branch: 'devel'
-    autofix_prs: false
-    autoupdate_schedule: quarterly
+  autofix_prs: false
+  autoupdate_branch: devel
+  autoupdate_schedule: quarterly
 repos:
--   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v19.1.7
-    hooks:
-    -   id: clang-format
-        args: ['--style={BasedOnStyle: Mozilla, SortIncludes: false}']
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
-    hooks:
-    -   id: trailing-whitespace
--   repo: https://github.com/psf/black
-    rev: 24.10.0
-    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: v0.8.6
+  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/pre-commit/mirrors-clang-format
+  rev: v19.1.7
+  hooks:
+  - id: clang-format
+    args:
+    - '--style={BasedOnStyle: Mozilla, SortIncludes: false}'
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v5.0.0
+  hooks:
+  - id: trailing-whitespace

benchmark/timings-parallel.py

@@ -30,7 +30,7 @@ def generate_mixed_qp(n, n_eq, n_in, seed=1):
     q = np.random.randn(n)
     A = spa.random(m, n, density=0.15, data_rvs=np.random.randn, format="csc").toarray()
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)
 
@@ -49,7 +49,6 @@ def generate_mixed_qp(n, n_eq, n_in, seed=1):
 num_qps = 128
 
 for n, n_eq, n_in in problem_specs:
-
     print(f"\nProblem specs: {n=} {n_eq=} {n_in=}. Generating {num_qps} such problems.")
     problems = [generate_mixed_qp(n, n_eq, n_in, seed=j) for j in range(num_qps)]
     print(
@@ -86,7 +85,7 @@ def generate_mixed_qp(n, n_eq, n_in, seed=1):
     print("Solving batch of qps using solve_in_parallel with default thread config")
     tic = perf_counter_ns()
     proxsuite.proxqp.dense.solve_in_parallel(qps=qps_batch)
-    timings[f"solve_in_parallel_heuristics_threads"] = (perf_counter_ns() - tic) * 1e-6
+    timings["solve_in_parallel_heuristics_threads"] = (perf_counter_ns() - tic) * 1e-6
 
     print("Solving vector of qps serially")
     tic = perf_counter_ns()

bindings/python/proxsuite/__init__.py

@@ -1,5 +1,5 @@
 import platform
-import numpy  # for OpenMP proper linkage
+import numpy  # noqa F401 for OpenMP proper linkage
 
 machine = platform.machine()
 has_vectorization_instructions = not machine.startswith(

bindings/python/proxsuite/torch/__init__.py

@@ -1,5 +1,5 @@
 try:
-    import torch
+    import torch  # noqa F401
 except ImportError:
     import warnings
 

bindings/python/proxsuite/torch/qplayer.py

@@ -437,9 +437,9 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
                 if neq > 0:
                     kkt[:dim, dim : dim + n_eq] = A_i.transpose()
                     kkt[dim : dim + n_eq, :dim] = A_i
-                    kkt[dim + n_eq + n_in : dim + 2 * n_eq + n_in, dim : dim + n_eq] = (
-                        -np.eye(n_eq)
-                    )
+                    kkt[
+                        dim + n_eq + n_in : dim + 2 * n_eq + n_in, dim : dim + n_eq
+                    ] = -np.eye(n_eq)
                     kkt[
                         dim + n_eq + n_in : dim + 2 * n_eq + n_in,
                         dim + n_eq + 2 * n_in : 2 * dim + n_eq + 2 * n_in,
@@ -486,9 +486,9 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus, dl_ds_e, dl_ds_i):
                         rhs[dim + n_eq : dim + n_eq + n_in_sol][~active_set] = dl_dnus[
                             i
                         ][~active_set]
-                        rhs[dim + n_eq + n_in_sol : dim + n_eq + n_in][active_set] = (
-                            -dl_dnus[i][active_set]
-                        )
+                        rhs[dim + n_eq + n_in_sol : dim + n_eq + n_in][
+                            active_set
+                        ] = -dl_dnus[i][active_set]
                 if dl_ds_e != None:
                     if dl_ds_e.shape[0] != 0:
                         rhs[dim + n_eq + n_in : dim + 2 * n_eq + n_in] = -dl_ds_e[i]

examples/python/init_dense_qp.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/init_dense_qp_with_box.py

@@ -1,6 +1,5 @@
 import proxsuite
 import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/init_dense_qp_with_other_options.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/init_dense_qp_with_timings.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/init_with_default_options.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/loading_sparse_qp.py

@@ -1,14 +1,12 @@
 import proxsuite
+from util import generate_mixed_qp
 
 # load a qp object using qp problem dimensions
 n = 10
 n_eq = 2
 n_in = 2
 qp = proxsuite.proxqp.sparse.QP(n, n_eq, n_in)
 
-import numpy as np
-import scipy.sparse as spa
-from util import generate_mixed_qp
 
 
 # load a qp2 object using matrix masks

examples/python/overview-simple.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/qplayer_sudoku.py

@@ -3,13 +3,11 @@
 import time
 import argparse
 import numpy as np
-import scipy.sparse as spa
 
 try:
     import torch
     import torch.nn as nn
     import torch.optim as optim
-    from torch.autograd import Variable
     from torch.nn.parameter import Parameter
 
     import cvxpy as cp

examples/python/solve_dense_qp.py

@@ -1,6 +1,5 @@
 import proxsuite
 import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/solve_dense_qp_with_setting.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/solve_without_api.py

@@ -1,6 +1,5 @@
 import proxsuite
 import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/solve_without_api_and_option.py

@@ -1,6 +1,4 @@
 import proxsuite
-import numpy as np
-import scipy.sparse as spa
 from util import generate_mixed_qp
 
 

examples/python/update_dense_qp_ws_previous_result.py

@@ -1,5 +1,4 @@
 import proxsuite
-import numpy as np
 from util import generate_mixed_qp
 
 

examples/python/update_sparse_qp.py

@@ -1,5 +1,4 @@
 import proxsuite
-import numpy as np
 from util import generate_mixed_qp
 
 

examples/python/util.py

@@ -24,7 +24,7 @@ def generate_mixed_qp(n, sparse=False, seed=1, reg=1e-2, dens1=0.075):
         A = A.toarray()
         P = P.toarray()
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)
 

pyproject.toml

@@ -20,3 +20,7 @@ requires = [
     ]
 build-backend = "cmeel.build"
 configure-args = ["-DBUILD_TESTING:BOOL=OFF","-DBUILD_PYTHON_INTERFACE:BOOL=ON","-DBUILD_WITH_VECTORIZATION_SUPPORT:BOOL=ON","-DINSTALL_DOCUMENTATION:BOOL=OFF","-DBUILD_WITH_OPENMP_SUPPORT=OFF"]
+
+[tool.ruff.lint]
+ignore = [ "E741" ]
+exclude = [ "cmake-module/*", "bindings/python/external/nanobind/*" ]

test/src/dense_qp_solve.py

@@ -348,8 +348,8 @@ def test_initializing_with_None(self):
         A = None
         b = None
         C = None
-        u = None
-        l = None
+        _u = None
+        _l = None
 
         results = proxsuite.proxqp.dense.solve(
             H,

test/src/dense_qp_wrapper.py

@@ -41,7 +41,7 @@ def generate_mixed_qp(n, seed=1, reg=0.01):
         order="C"
     )
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)
 

test/src/parallel_qp_solve.py

@@ -37,7 +37,7 @@ def generate_mixed_qp(n, seed=1):
         order="C"
     )
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)
 
@@ -162,8 +162,8 @@ def test_sparse_parallel_custom_BatchQP(self):
 
         for i in range(batch_size):
             H, g, A, b, C, u, l = generate_mixed_qp(n, seed=i)
-            n_eq = A.shape[0]
-            n_in = C.shape[0]
+            _n_eq = A.shape[0]
+            _n_in = C.shape[0]
 
             H_ = H != 0.0
             A_ = A != 0.0

test/src/sparse_qp_solve.py

@@ -37,7 +37,7 @@ def generate_mixed_qp(n, seed=1):
     q = np.random.randn(n)
     A = spa.random(m, n, density=0.15, data_rvs=np.random.randn, format="csc")
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)
 

test/src/sparse_qp_wrapper.py

@@ -37,7 +37,7 @@ def generate_mixed_qp(n, seed=1, reg=0.01):
     q = np.random.randn(n)
     A = spa.random(m, n, density=0.15, data_rvs=np.random.randn, format="csc")
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
+    _delta = np.random.rand(m)  # To get inequality
     u = A @ v
     l = -1.0e20 * np.ones(m)