bump black version (#617) (#619)

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com> (cherry picked from commit 5d20d2e) Co-authored-by: Takashi Imamichi <31178928+t-imamichi@users.noreply.github.com>
qiskit-community · May 8, 2024 · 23b565f · 23b565f
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diff --git a/docs/tutorials/07_examples_vehicle_routing.ipynb b/docs/tutorials/07_examples_vehicle_routing.ipynb
@@ -297,9 +297,7 @@
     "        my_obj = list(instance.reshape(1, n**2)[0]) + [0.0 for x in range(0, n - 1)]\n",
     "        my_ub = [1 for x in range(0, n**2 + n - 1)]\n",
     "        my_lb = [0 for x in range(0, n**2)] + [0.1 for x in range(0, n - 1)]\n",
-    "        my_ctype = \"\".join([\"I\" for x in range(0, n**2)]) + \"\".join(\n",
-    "            [\"C\" for x in range(0, n - 1)]\n",
-    "        )\n",
+    "        my_ctype = \"\".join([\"I\" for x in range(0, n**2)]) + \"\".join([\"C\" for x in range(0, n - 1)])\n",
     "\n",
     "        my_rhs = (\n",
     "            2 * ([K] + [1 for x in range(0, n - 1)])\n",

diff --git a/docs/tutorials/08_cvar_optimization.ipynb b/docs/tutorials/08_cvar_optimization.ipynb
@@ -213,6 +213,7 @@
     "objectives = {alpha: [] for alpha in alphas}  # set of tested objective functions w.r.t. alpha\n",
     "results = {}  # results of minimum eigensolver w.r.t alpha\n",
     "\n",
+    "\n",
     "# callback to store intermediate results\n",
     "def callback(i, params, obj, stddev, alpha):\n",
     "    # we translate the objective from the internal Ising representation\n",

diff --git a/qiskit_optimization/algorithms/qrao/magic_rounding.py b/qiskit_optimization/algorithms/qrao/magic_rounding.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -446,9 +446,11 @@ def round(self, rounding_context: RoundingContext) -> RoundingResult:
                     [int(bit) for bit in soln]
                 ),
                 probability=count / self._shots,
-                status=OptimizationResultStatus.SUCCESS
-                if rounding_context.encoding.problem.is_feasible([int(bit) for bit in soln])
-                else OptimizationResultStatus.INFEASIBLE,
+                status=(
+                    OptimizationResultStatus.SUCCESS
+                    if rounding_context.encoding.problem.is_feasible([int(bit) for bit in soln])
+                    else OptimizationResultStatus.INFEASIBLE
+                ),
             )
             for soln, count in soln_counts.items()
         ]

diff --git a/qiskit_optimization/algorithms/qrao/semideterministic_rounding.py b/qiskit_optimization/algorithms/qrao/semideterministic_rounding.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -68,9 +68,11 @@ def round(self, rounding_context: RoundingContext) -> RoundingResult:
                 x=np.asarray(rounded_vars),
                 fval=rounding_context.encoding.problem.objective.evaluate(rounded_vars),
                 probability=1.0,
-                status=OptimizationResultStatus.SUCCESS
-                if rounding_context.encoding.problem.is_feasible(rounded_vars)
-                else OptimizationResultStatus.INFEASIBLE,
+                status=(
+                    OptimizationResultStatus.SUCCESS
+                    if rounding_context.encoding.problem.is_feasible(rounded_vars)
+                    else OptimizationResultStatus.INFEASIBLE
+                ),
             )
         ]
 

diff --git a/qiskit_optimization/converters/integer_to_binary.py b/qiskit_optimization/converters/integer_to_binary.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -77,7 +77,7 @@ def convert(self, problem: QuadraticProgram) -> QuadraticProgram:
                 if x.vartype == Variable.Type.INTEGER:
                     new_vars = self._convert_var(x.name, x.lowerbound, x.upperbound)
                     self._conv[x] = new_vars
-                    for (var_name, _) in new_vars:
+                    for var_name, _ in new_vars:
                         self._dst.binary_var(var_name)
                 else:
                     if x.vartype == Variable.Type.CONTINUOUS:
@@ -164,7 +164,11 @@ def _substitute_int_var(self):
         linear, linear_constant = self._convert_linear_coefficients_dict(
             self._src.objective.linear.to_dict(use_name=True)
         )
-        quadratic, q_linear, q_constant, = self._convert_quadratic_coefficients_dict(
+        (
+            quadratic,
+            q_linear,
+            q_constant,
+        ) = self._convert_quadratic_coefficients_dict(
             self._src.objective.quadratic.to_dict(use_name=True)
         )
 

diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -1,6 +1,6 @@
 coverage>=4.4.0
 matplotlib>=2.1
-black[jupyter]~=22.0
+black[jupyter]~=24.1
 pylint>=2.16.2
 pylatexenc>=1.4
 stestr>=4.1.0

diff --git a/test/algorithms/qrao/test_magic_rounding.py b/test/algorithms/qrao/test_magic_rounding.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -328,9 +328,11 @@ def make_solution_sample(
         x=x,
         fval=problem.objective.evaluate(x),
         probability=probability,
-        status=OptimizationResultStatus.SUCCESS
-        if problem.is_feasible(x)
-        else OptimizationResultStatus.INFEASIBLE,
+        status=(
+            OptimizationResultStatus.SUCCESS
+            if problem.is_feasible(x)
+            else OptimizationResultStatus.INFEASIBLE
+        ),
     )