Add missing black settings

pyproject.toml

@@ -0,0 +1,3 @@
+[tool.black]
+line-length = 100
+target-version = ['py36', 'py37', 'py38', 'py39']

qiskit_finance/applications/optimization/portfolio_diversification.py

@@ -28,9 +28,7 @@ class PortfolioDiversification(OptimizationApplication):
         [1]: GG. Cornuejols and R. Tutuncu, Optimization methods in finance, 2006
     """
 
-    def __init__(
-        self, similarity_matrix: np.ndarray, num_assets: int, num_clusters: int
-    ) -> None:
+    def __init__(self, similarity_matrix: np.ndarray, num_assets: int, num_clusters: int) -> None:
         """
         Args:
             similarity_matrix: An asset-to-asset similarity matrix, such as the covariance matrix.
@@ -63,9 +61,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
                 for j in range(self._num_assets)
             )
         )
-        mdl.add_constraint(
-            mdl.sum(y[j] for j in range(self._num_assets)) == self._num_clusters
-        )
+        mdl.add_constraint(mdl.sum(y[j] for j in range(self._num_assets)) == self._num_clusters)
         for i in range(self._num_assets):
             mdl.add_constraint(mdl.sum(x[(i, j)] for j in range(self._num_assets)) == 1)
         for j in range(self._num_assets):

qiskit_finance/applications/optimization/portfolio_optimization.py

@@ -67,9 +67,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         op.from_docplex(mdl)
         return op
 
-    def portfolio_expected_value(
-        self, result: Union[OptimizationResult, np.ndarray]
-    ) -> float:
+    def portfolio_expected_value(self, result: Union[OptimizationResult, np.ndarray]) -> float:
         """Returns the portfolio expected value based on the result.
 
         Args:
@@ -81,9 +79,7 @@ def portfolio_expected_value(
         x = self._result_to_x(result)
         return np.dot(self._expected_returns, x)
 
-    def portfolio_variance(
-        self, result: Union[OptimizationResult, np.ndarray]
-    ) -> float:
+    def portfolio_variance(self, result: Union[OptimizationResult, np.ndarray]) -> float:
         """Returns the portfolio variance based on the result
 
         Args:

qiskit_finance/circuit/library/payoff_functions/european_call_delta_objective.py

@@ -35,18 +35,14 @@ def __init__(
         """
         # map strike price to {0, ..., 2^n-1}
         num_values = 2 ** num_state_qubits
-        strike_price = (
-            (strike_price - bounds[0]) / (bounds[1] - bounds[0]) * (num_values - 1)
-        )
+        strike_price = (strike_price - bounds[0]) / (bounds[1] - bounds[0]) * (num_values - 1)
         strike_price = int(np.ceil(strike_price))
 
         # create comparator
         comparator = IntegerComparator(num_state_qubits, strike_price)
 
         # initialize circuit
-        qr_state = QuantumRegister(
-            comparator.num_qubits - comparator.num_ancillas, "state"
-        )
+        qr_state = QuantumRegister(comparator.num_qubits - comparator.num_ancillas, "state")
         qr_work = QuantumRegister(comparator.num_ancillas, "work")
         super().__init__(qr_state, qr_work, name="ECD")
 

qiskit_finance/circuit/library/payoff_functions/fixed_income_pricing_objective.py

@@ -79,12 +79,7 @@ def __init__(
         g = np.zeros(dimensions)
         for t in range(time_steps):
             h += cash_flow[t] / (1 + initial_interests[t]) ** (t + 1)
-            g += (
-                -(t + 1)
-                * cash_flow[t]
-                * pca_matrix[t, :]
-                / (1 + initial_interests[t]) ** (t + 2)
-            )
+            g += -(t + 1) * cash_flow[t] * pca_matrix[t, :] / (1 + initial_interests[t]) ** (t + 2)
 
         # compute overall offset using lower bound for x (corresponding to x = min)
         low = [bound[0] for bound in bounds]

qiskit_finance/circuit/library/probability_distributions/lognormal.py

@@ -129,12 +129,10 @@ def __init__(
             # indexing 'ij' yields the "column-based" indexing
             meshgrid = np.meshgrid(
                 *[
-                    np.linspace(
-                        bound[0], bound[1], num=2 ** num_qubits[i]  # type: ignore
-                    )
+                    np.linspace(bound[0], bound[1], num=2 ** num_qubits[i])  # type: ignore
                     for i, bound in enumerate(bounds)
                 ],
-                indexing="ij"
+                indexing="ij",
             )
             # flatten into a list of points
             x = list(zip(*[grid.flatten() for grid in meshgrid]))  # type: ignore

qiskit_finance/circuit/library/probability_distributions/normal.py

@@ -176,12 +176,10 @@ def __init__(
             # indexing 'ij' yields the "column-based" indexing
             meshgrid = np.meshgrid(
                 *[
-                    np.linspace(
-                        bound[0], bound[1], num=2 ** num_qubits[i]  # type: ignore
-                    )
+                    np.linspace(bound[0], bound[1], num=2 ** num_qubits[i])  # type: ignore
                     for i, bound in enumerate(bounds)
                 ],
-                indexing="ij"
+                indexing="ij",
             )
             # flatten into a list of points
             x = list(zip(*[grid.flatten() for grid in meshgrid]))
@@ -225,9 +223,7 @@ def bounds(self) -> Union[Tuple[float, float], List[Tuple[float, float]]]:
 
 
 def _check_dimensions_match(num_qubits, mu, sigma, bounds):
-    num_qubits = (
-        [num_qubits] if not isinstance(num_qubits, (list, np.ndarray)) else num_qubits
-    )
+    num_qubits = [num_qubits] if not isinstance(num_qubits, (list, np.ndarray)) else num_qubits
     dim = len(num_qubits)
 
     if mu is not None:

qiskit_finance/data_providers/_base_data_provider.py

@@ -110,9 +110,7 @@ def get_period_return_mean_vector(self) -> np.ndarray:
                 "No data loaded, yet. Please run the method run() first to load the data."
             ) from ex
         _div_func = np.vectorize(BaseDataProvider._divide)
-        period_returns = (
-            _div_func(np.array(self._data)[:, 1:], np.array(self._data)[:, :-1]) - 1
-        )
+        period_returns = _div_func(np.array(self._data)[:, 1:], np.array(self._data)[:, :-1]) - 1
         self.period_return_mean = cast(np.ndarray, np.mean(period_returns, axis=1))
         return self.period_return_mean
 
@@ -158,9 +156,7 @@ def get_period_return_covariance_matrix(self) -> np.ndarray:
                 "No data loaded, yet. Please run the method run() first to load the data."
             ) from ex
         _div_func = np.vectorize(BaseDataProvider._divide)
-        period_returns = (
-            _div_func(np.array(self._data)[:, 1:], np.array(self._data)[:, :-1]) - 1
-        )
+        period_returns = _div_func(np.array(self._data)[:, 1:], np.array(self._data)[:, :-1]) - 1
         self.period_return_cov = np.cov(period_returns)
         return self.period_return_cov
 

qiskit_finance/data_providers/data_on_demand_provider.py

@@ -109,7 +109,5 @@ def run(self) -> None:
                     price_evolution.append(q["ask_price"])
                 self._data.append(price_evolution)
             except Exception as ex:  # pylint: disable=broad-except
-                raise QiskitFinanceError(
-                    "Accessing NASDAQ Data on Demand failed."
-                ) from ex
+                raise QiskitFinanceError("Accessing NASDAQ Data on Demand failed.") from ex
             http.clear()

qiskit_finance/data_providers/exchange_data_provider.py

@@ -104,9 +104,7 @@ def run(self) -> None:
             stock_data = None
             name = self._stockmarket + "/" + ticker_name
             try:
-                stock_data = quandl.get(
-                    name, start_date=self._start, end_date=self._end
-                )
+                stock_data = quandl.get(name, start_date=self._start, end_date=self._end)
             except quandl.AuthenticationError as ex:
                 raise QiskitFinanceError("Quandl invalid token.") from ex
             except quandl.NotFoundError as ex:
@@ -120,16 +118,10 @@ def run(self) -> None:
             try:
                 self._data.append(stock_data["Close"])
             except KeyError as ex:
-                raise QiskitFinanceError(
-                    "Cannot parse Quandl '{}'output.".format(name)
-                ) from ex
+                raise QiskitFinanceError("Cannot parse Quandl '{}'output.".format(name)) from ex
 
         if stocks_notfound or stocks_forbidden:
-            err_msg = (
-                "Stocks not found: {}. ".format(stocks_notfound)
-                if stocks_notfound
-                else ""
-            )
+            err_msg = "Stocks not found: {}. ".format(stocks_notfound) if stocks_notfound else ""
             if stocks_forbidden:
                 err_msg += (
                     "You do not have permission to view this data. "

qiskit_finance/data_providers/random_data_provider.py

@@ -79,15 +79,13 @@ def run(self) -> None:
         generator = np.random.default_rng(self._seed)
         self._data = []
         for _ in self._tickers:
-            d_f = pd.DataFrame(
-                generator.standard_normal(length)
-            ).cumsum() + generator.integers(1, 101)
+            d_f = pd.DataFrame(generator.standard_normal(length)).cumsum() + generator.integers(
+                1, 101
+            )
             trimmed = np.maximum(d_f[0].values, np.zeros(len(d_f[0].values)))
             trimmed_list = trimmed.tolist()
             # find index of first 0 element
-            zero_idx = next(
-                (idx for idx, val in enumerate(trimmed_list) if val == 0), -1
-            )
+            zero_idx = next((idx for idx, val in enumerate(trimmed_list) if val == 0), -1)
             if zero_idx >= 0:
                 # set to 0 all values after first 0
                 trimmed_list = [

qiskit_finance/data_providers/wikipedia_data_provider.py

@@ -89,9 +89,7 @@ def run(self) -> None:
             stock_data = None
             name = "WIKI" + "/" + ticker_name
             try:
-                stock_data = quandl.get(
-                    name, start_date=self._start, end_date=self._end
-                )
+                stock_data = quandl.get(name, start_date=self._start, end_date=self._end)
             except quandl.AuthenticationError as ex:
                 raise QiskitFinanceError("Quandl invalid token.") from ex
             except quandl.NotFoundError as ex:

test/applications/test_european_call_pricing.py

@@ -61,9 +61,7 @@ def test_to_estimation_problem(self):
         self.assertEqual(est_problem.objective_qubits, [num_qubits])
         self.assertTrue(Operator(est_problem.state_preparation).equiv(expected_circ))
         # pylint: disable=not-callable
-        self.assertEqual(
-            linear_function.post_processing(0.5), est_problem.post_processing(0.5)
-        )
+        self.assertEqual(linear_function.post_processing(0.5), est_problem.post_processing(0.5))
 
 
 if __name__ == "__main__":

test/applications/test_portfolio_diversification.py

@@ -51,19 +51,13 @@ def assertEqualQuadraticProgram(self, actual, expected):
         # Test objective
         self.assertEqual(actual.objective.sense, expected.objective.sense)
         self.assertEqual(actual.objective.constant, expected.objective.constant)
-        self.assertDictEqual(
-            actual.objective.linear.to_dict(), expected.objective.linear.to_dict()
-        )
+        self.assertDictEqual(actual.objective.linear.to_dict(), expected.objective.linear.to_dict())
         self.assertDictEqual(
             actual.objective.quadratic.to_dict(), expected.objective.quadratic.to_dict()
         )
         # Test constraint
-        self.assertEqual(
-            len(actual.linear_constraints), len(expected.linear_constraints)
-        )
-        for act_lin, exp_lin in zip(
-            actual.linear_constraints, expected.linear_constraints
-        ):
+        self.assertEqual(len(actual.linear_constraints), len(expected.linear_constraints))
+        for act_lin, exp_lin in zip(actual.linear_constraints, expected.linear_constraints):
             self.assertEqual(act_lin.sense, exp_lin.sense)
             self.assertEqual(act_lin.rhs, exp_lin.rhs)
             self.assertEqual(act_lin.linear.to_dict(), exp_lin.linear.to_dict())
@@ -96,12 +90,8 @@ def test_to_quadratic_program(self):
         linear = {"x_0_0": 1, "x_0_1": 0.8, "x_1_0": 0.8, "x_1_1": 1}
         expected_op.maximize(linear=linear)
         expected_op.linear_constraint(linear={"y_0": 1, "y_1": 1}, sense="==", rhs=1)
-        expected_op.linear_constraint(
-            linear={"x_0_0": 1, "x_0_1": 1}, sense="==", rhs=1
-        )
-        expected_op.linear_constraint(
-            linear={"x_1_0": 1, "x_1_1": 1}, sense="==", rhs=1
-        )
+        expected_op.linear_constraint(linear={"x_0_0": 1, "x_0_1": 1}, sense="==", rhs=1)
+        expected_op.linear_constraint(linear={"x_1_0": 1, "x_1_1": 1}, sense="==", rhs=1)
         expected_op.linear_constraint(linear={"x_0_0": 1, "y_0": -1}, sense="==", rhs=0)
         expected_op.linear_constraint(linear={"x_1_1": 1, "y_1": -1}, sense="==", rhs=0)
         expected_op.linear_constraint(linear={"x_0_0": 1, "y_0": -1}, sense="<=", rhs=0)
@@ -128,9 +118,7 @@ def test_smilarity_matrix(self):
             num_clusters=self.q,
         )
         portfolio_diversification.similarity_matrix = np.array([[0, 1], [1, 0]])
-        self.assertEqual(
-            portfolio_diversification.similarity_matrix.tolist(), [[0, 1], [1, 0]]
-        )
+        self.assertEqual(portfolio_diversification.similarity_matrix.tolist(), [[0, 1], [1, 0]])
 
     def test_num_assets(self):
         """test num_assets"""

test/applications/test_portfolio_optimization.py

@@ -53,19 +53,13 @@ def assertEqualQuadraticProgram(self, actual, expected):
         # Test objective
         self.assertEqual(actual.objective.sense, expected.objective.sense)
         self.assertEqual(actual.objective.constant, expected.objective.constant)
-        self.assertDictEqual(
-            actual.objective.linear.to_dict(), expected.objective.linear.to_dict()
-        )
+        self.assertDictEqual(actual.objective.linear.to_dict(), expected.objective.linear.to_dict())
         self.assertDictEqual(
             actual.objective.quadratic.to_dict(), expected.objective.quadratic.to_dict()
         )
         # Test constraint
-        self.assertEqual(
-            len(actual.linear_constraints), len(expected.linear_constraints)
-        )
-        for act_lin, exp_lin in zip(
-            actual.linear_constraints, expected.linear_constraints
-        ):
+        self.assertEqual(len(actual.linear_constraints), len(expected.linear_constraints))
+        for act_lin, exp_lin in zip(actual.linear_constraints, expected.linear_constraints):
             self.assertEqual(act_lin.sense, exp_lin.sense)
             self.assertEqual(act_lin.rhs, exp_lin.rhs)
             self.assertEqual(act_lin.linear.to_dict(), exp_lin.linear.to_dict())
@@ -115,9 +109,7 @@ def test_portfolio_expected_value(self):
         )
         result_x = np.array([0, 1, 0, 1])
         expected_value = np.dot(self.expected_returns, result_x)
-        self.assertEqual(
-            portfolio_optimization.portfolio_expected_value(result_x), expected_value
-        )
+        self.assertEqual(portfolio_optimization.portfolio_expected_value(result_x), expected_value)
 
     def test_portfolio_variance(self):
         """test portfolio_variance"""

test/circuit/test_european_call_delta_objective.py

@@ -53,9 +53,7 @@ def test_application(self):
                 Aer,
             )  # pylint: disable=unused-import,import-outside-toplevel
         except ImportError as ex:  # pylint: disable=broad-except
-            self.skipTest(
-                "Aer doesn't appear to be installed. Error: '{}'".format(str(ex))
-            )
+            self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
             return
 
         num_qubits = 3
@@ -105,9 +103,7 @@ def test_application(self):
         q_i = QuantumInstance(
             Aer.get_backend("qasm_simulator"), seed_simulator=125, seed_transpiler=80
         )
-        iae = IterativeAmplitudeEstimation(
-            epsilon_target=0.01, alpha=0.05, quantum_instance=q_i
-        )
+        iae = IterativeAmplitudeEstimation(epsilon_target=0.01, alpha=0.05, quantum_instance=q_i)
         result = iae.estimate(problem)
         self.assertAlmostEqual(result.estimation_processed, 0.8079816552117238)
 

test/circuit/test_european_call_pricing_objective.py

@@ -42,9 +42,7 @@ def test_ecev_circuit(self):
         rescaling_factor = 0.1
         strike_price = 0.5
         bounds = (0, 2)
-        ecev = EuropeanCallPricingObjective(
-            num_qubits, strike_price, rescaling_factor, bounds
-        )
+        ecev = EuropeanCallPricingObjective(num_qubits, strike_price, rescaling_factor, bounds)
 
         breakpoints = [0, strike_price]
         slopes = [0, 1]
@@ -70,9 +68,7 @@ def test_application(self):
                 Aer,
             )  # pylint: disable=unused-import,import-outside-toplevel
         except ImportError as ex:  # pylint: disable=broad-except
-            self.skipTest(
-                "Aer doesn't appear to be installed. Error: '{}'".format(str(ex))
-            )
+            self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
             return
 
         bounds = np.array([0.0, 7.0])
@@ -116,9 +112,7 @@ def test_application(self):
         q_i = QuantumInstance(
             Aer.get_backend("qasm_simulator"), seed_simulator=125, seed_transpiler=80
         )
-        iae = IterativeAmplitudeEstimation(
-            epsilon_target=0.01, alpha=0.05, quantum_instance=q_i
-        )
+        iae = IterativeAmplitudeEstimation(epsilon_target=0.01, alpha=0.05, quantum_instance=q_i)
         result = iae.estimate(problem)
         self.assertAlmostEqual(result.estimation_processed, 1.0127253837345427)
 

test/circuit/test_fixed_income_pricing_objective.py

@@ -57,9 +57,7 @@ def test_application(self):
                 Aer,
             )  # pylint: disable=unused-import,import-outside-toplevel
         except ImportError as ex:  # pylint: disable=broad-except
-            self.skipTest(
-                "Aer doesn't appear to be installed. Error: '{}'".format(str(ex))
-            )
+            self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
             return
 
         a_n = np.eye(2)
@@ -99,9 +97,7 @@ def test_application(self):
         q_i = QuantumInstance(
             Aer.get_backend("qasm_simulator"), seed_simulator=2, seed_transpiler=2
         )
-        iae = IterativeAmplitudeEstimation(
-            epsilon_target=0.01, alpha=0.05, quantum_instance=q_i
-        )
+        iae = IterativeAmplitudeEstimation(epsilon_target=0.01, alpha=0.05, quantum_instance=q_i)
         result = iae.estimate(problem)
 
         # compare to precomputed solution