Ruff fixes + version bump

dsp/cone_transforms.py

@@ -44,7 +44,7 @@ def sum_of_K_reprs(cls, reprs: list[KRepresentation]) -> KRepresentation:
         offset = np.sum([K.offset for K in reprs])
 
         def f_concave(x: np.ndarray) -> cp.Expression:
-            nones = any([K.concave_expr(x) is None for K in reprs])
+            nones = any(K.concave_expr(x) is None for K in reprs)
             return cp.sum([K.concave_expr(x) for K in reprs]) if not nones else None
 
         concave_expr = f_concave
@@ -214,7 +214,6 @@ def K_repr_ax(a: cp.Expression) -> KRepresentation:
 
 class LocalToGlob:
     def __init__(self, x_variables: list[cp.Variable], y_variables: list[cp.Variable]) -> None:
-
         self.outer_x_vars = x_variables
         self.var_to_glob: dict[int, tuple[int, int]] = {}
 
@@ -296,7 +295,6 @@ def K_repr_FxGy(
     local_to_glob: LocalToGlob,
     switched: bool = False,
 ) -> KRepresentation:
-
     z = cp.Variable(Fx.shape)
     constraints = [z >= Fx]
 
@@ -335,7 +333,6 @@ def K_repr_bilin(
 def get_cone_repr(
     const: list[Constraint], variables: list[cp.Variable]
 ) -> tuple[dict[int, np.ndarray], np.ndarray, ConeDims]:
-
     assert all(isinstance(v, cp.Variable) for v in variables)
 
     # TODO: CVXPY does not have a stable API for getting the cone representation that is
@@ -504,7 +501,6 @@ def create_sparse_matrix_from_columns(
     local_to_glob: LocalToGlob,
     var_to_mat_mapping: dict[int, sp.csc_matrix],
 ) -> sp.csc_matrix:
-
     cols = []
     for v in variables:
         start, end = local_to_glob.var_to_glob[v.id]

dsp/parser.py

@@ -40,7 +40,6 @@ def affine_error_message(affine_vars: list[cp.Variable]) -> str:
 
 class Parser:
     def __init__(self, convex_vars: set[cp.Variable], concave_vars: set[cp.Variable]) -> None:
-
         self.convex_vars: set[cp.Variable] = convex_vars
         self.concave_vars: set[cp.Variable] = concave_vars
         if self.convex_vars & self.concave_vars:
@@ -170,7 +169,6 @@ def parse_expr_variables(self, expr: cp.Expression, switched: bool, **kwargs: di
     def parse_expr_repr(
         self, expr: cp.Expression, switched: bool, local_to_glob: LocalToGlob
     ) -> KRepresentation:
-
         K_repr = self._parse_expr(expr, switched, repr_parse=True, local_to_glob=local_to_glob)
         assert isinstance(K_repr, KRepresentation)
         return K_repr
@@ -192,7 +190,6 @@ def contains_curvature_lumping(expr: cp.Expression) -> bool:
     def _parse_expr(
         self, expr: cp.Expression, switched: bool, repr_parse: bool, **kwargs: dict
     ) -> KRepresentation | None:
-
         # constant
         if not expr.variables():
             assert expr.size == 1

dsp/problem.py

@@ -88,7 +88,6 @@ def dualized_problem(
         minimization_vars: Iterable[cp.Variable],
         maximization_vars: Iterable[cp.Variable],
     ) -> tuple[list[Constraint], Objective]:
-
         parser = initialize_parser(obj_expr, minimization_vars, maximization_vars, constraints)
 
         local_to_glob_y = LocalToGlob(parser.convex_vars, parser.concave_vars)
@@ -147,7 +146,7 @@ def value(self) -> float | None:
     def is_dsp(self) -> bool:
         # try to form x_prob and catch the exception
         try:
-            self.x_prob
+            self.x_prob  # noqa
             return True
         except DSPError:
             return False
@@ -269,7 +268,7 @@ def is_dsp(obj: cp.Problem | SaddlePointProblem | cp.Expression) -> bool:
         return obj.is_dsp()
     elif isinstance(obj, cp.Problem):
         all_SE_atoms = get_problem_SE_atoms(obj)
-        return obj.is_dcp() and all([atom.is_dsp() for atom in all_SE_atoms])
+        return obj.is_dcp() and all(atom.is_dsp() for atom in all_SE_atoms)
     elif isinstance(obj, cp.Expression):
         return is_dsp_expr(obj)
     else:

dsp/saddle_atoms.py

@@ -59,7 +59,7 @@ def is_dsp(self) -> bool:
         raise NotImplementedError
 
     def numeric(self, values: list[np.ndarray | None]) -> np.ndarray | None:
-        if any([v is None for v in values]):
+        if any(v is None for v in values):
             return None
         return self._numeric(values)
 
@@ -101,7 +101,8 @@ def __init__(self, Fx: cp.Expression, Gy: cp.Expression) -> None:
         if (not self.bilinear) and (not Gy.is_nonneg()):
             warnings.warn(
                 "Gy is non-positive. The y domain of saddle_inner is Gy >="
-                " 0. The implicit constraint Gy >= 0 will be added to the problem."
+                " 0. The implicit constraint Gy >= 0 will be added to the problem.",
+                stacklevel=1,
             )
 
         self.Fx = Fx
@@ -226,7 +227,8 @@ def __init__(self, exponents: cp.Expression, weights: cp.Expression) -> None:
             warnings.warn(
                 "Weights are non-positive. The domain of weighted log-sum-exp is y >="
                 " 0. The implicit constraint y >= 0 will be added to"
-                " the problem."
+                " the problem.",
+                stacklevel=1,
             )
 
         self.concave_composition = not weights.is_affine()
@@ -560,7 +562,8 @@ def __init__(self, x: cp.Expression, y: cp.Expression) -> None:
             warnings.warn(
                 "Weights are non-positive. The domain of weighted_norm2 is y >="
                 " 0. The implicit constraint y >= 0 will be added to"
-                " the problem."
+                " the problem.",
+                stacklevel=1,
             )
 
         self.concave_composition = not y.is_affine()

dsp/saddle_extremum.py

@@ -20,7 +20,6 @@ def __init__(
         f: cp.Expression,
         constraints: Iterable[cp.Constraint],
     ) -> None:
-
         self.f = f
         self._parser = None
 
@@ -44,7 +43,7 @@ def _validate_arguments(self, constraints: list[cp.Constraint]) -> None:
     def is_dsp(self) -> bool:
         try:
             self.parser  # noqa
-            return all([c.is_dcp() for c in self.constraints])
+            return all(c.is_dcp() for c in self.constraints)
         except DSPError:
             return False
 
@@ -81,7 +80,6 @@ def __init__(
         f: cp.Expression,
         constraints: Iterable[cp.Constraint],
     ) -> None:
-
         super().__init__(f, constraints)
 
         self._concave_vars = set(filter(lambda v: isinstance(v, dsp.LocalVariable), f.variables()))
@@ -99,7 +97,6 @@ def concave_variables(self) -> list[cp.Variable]:
     @property
     def parser(self) -> Parser:
         if self._parser is None:
-
             parser = initialize_parser(
                 self.f,
                 minimization_vars=self.other_variables,
@@ -155,7 +152,6 @@ def __init__(
         f: cp.Expression,
         constraints: Iterable[cp.Constraint],
     ) -> None:
-
         super().__init__(f, constraints)
 
         self._convex_vars = set(filter(lambda v: isinstance(v, dsp.LocalVariable), f.variables()))

pyproject.toml

@@ -3,8 +3,8 @@ requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
 
 [tool.ruff]
-select = ["E", "F", "I", "UP", "B", "ANN", "N", "C4"]
+lint.select = ["E", "F", "I", "UP", "B", "ANN", "N", "C4"]
 line-length = 100
-target-version = "py37"
-ignore = ["N806", "ANN101", "ANN102", "N801", "N802", "N803", "ANN401"]
+target-version = "py38"
+lint.ignore = ["N806", "ANN101", "ANN102", "N801", "N802", "N803", "ANN401"]
 exclude = ["tests"]

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = dsp-cvxpy
-version = 0.1.0
+version = 0.1.1
 author = Philipp Schiele, Eric Luxenberg, Stephen Boyd
 author_email = [email protected], [email protected], [email protected]
 url = https://github.com/cvxgrp/dsp
@@ -23,6 +23,3 @@ dev = tox
 
 [options.package_data]
 * = README.md
-
-[flake8]
-max-line-length = 100