[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

labs/approximation/approximation_plots.py

@@ -51,7 +51,7 @@ def plot_basis_functions(name="monomial"):
         if name == "chebychev":
             yvals = np.polynomial.Chebyshev.basis(i)(x)
         elif name == "monomial":
-            yvals = x ** i
+            yvals = x**i
 
         elif name == "linear":
             a, b = 0, 1

labs/integration/integration_algorithms.py

@@ -59,8 +59,8 @@ def quadrature_gauss_legendre_two(f, a=-1, b=1, n=10):
     xvals, weight_uni = np.polynomial.legendre.leggauss(n_dim)
     xvals_transformed = (b - a) * (xvals + 1.0) / 2.0 + a
 
-    weights = np.tile(np.nan, n_dim ** 2)
-    fvals = np.tile(np.nan, n_dim ** 2)
+    weights = np.tile(np.nan, n_dim**2)
+    fvals = np.tile(np.nan, n_dim**2)
 
     counter = 0
     for i, x in enumerate(xvals_transformed):

labs/linear_equations/linear_plots.py

@@ -15,7 +15,7 @@ def plot_operation_count():
     """Plot operation count."""
     dim = np.arange(10) + 1
     fig, ax = plt.subplots()
-    ax.plot(dim, dim / 3 + dim ** 2, label="LU")
-    ax.plot(dim, dim ** 3 + dim ** 2, label="Inverse")
+    ax.plot(dim, dim / 3 + dim**2, label="LU")
+    ax.plot(dim, dim**3 + dim**2, label="Inverse")
     ax.set_xlabel("Dimension")
     ax.legend()

labs/nonlinear_equations/nonlinear_algorithms.py

@@ -167,4 +167,4 @@ def fischer(u, v, sign):
     callable
 
     """
-    return u + v + sign * np.sqrt(u ** 2 + v ** 2)
+    return u + v + sign * np.sqrt(u**2 + v**2)

labs/nonlinear_equations/nonlinear_algorithms_tests.py

@@ -11,7 +11,7 @@ def test_1():
     """Bisection method is working."""
 
     def example(x):
-        return x ** 3 - 2
+        return x**3 - 2
 
     y = bisect(example, 1, 2)[0]
 
@@ -33,10 +33,10 @@ def test_3():
     """Newton method is working."""
 
     def _jacobian(x):
-        return 3 * x ** 2
+        return 3 * x**2
 
     def _value(x):
-        return x ** 3 - 2
+        return x**3 - 2
 
     def f(x):
         return _value(x), _jacobian(x)

labs/nonlinear_equations/nonlinear_plots.py

@@ -107,7 +107,7 @@ def plot_newtons_method():
     """
     # Define function for illustration.
     def f(x):
-        return x ** 5 - 3, 5 * x ** 4
+        return x**5 - 3, 5 * x**4
 
     # Set axis limits and get function values.
     xmin, xmax = 1.0, 2.55
@@ -170,7 +170,7 @@ def plot_secant_method():
     # Define function for illustration.
 
     def f(x):
-        return x ** 5 - 3
+        return x**5 - 3
 
     # Set axis limits and get function values.
     xmin, xmax = 1.0, 2.55

labs/nonlinear_equations/nonlinear_problems.py

@@ -10,7 +10,7 @@ def function_iteration_test_function(x):
 
 def bisection_test_function(x):
     """Get test function for bisection."""
-    return x ** 3 - 2
+    return x**3 - 2
 
 
 def newton_pathological_example_fjac(x, f):
@@ -20,7 +20,7 @@ def newton_pathological_example_fjac(x, f):
 
 def newton_pathological_example_fval(x):
     """Get Newton Pathological example function value."""
-    return np.cbrt(x) * np.exp(-(x ** 2))
+    return np.cbrt(x) * np.exp(-(x**2))
 
 
 def newton_pathological_example(x):

labs/nonlinear_equations/nonlinear_solutions_exercises.py

@@ -43,7 +43,7 @@ def test_exercise_3():
     """Test for exercise 3."""
 
     def func(x):
-        return x ** 4 - 2
+        return x**4 - 2
 
     fig, ax = plt.subplots()
     grid = np.linspace(1.1, 1.2)
@@ -53,7 +53,7 @@ def func(x):
     x = 2.3
     for it in range(50):
         print(it, x)
-        step = -(x ** 4 - 2) / (4 * x ** 3)
+        step = -(x**4 - 2) / (4 * x**3)
         x += step
         if abs(step) < 1e-10:
             break

labs/optimization/optimization_solutions_exercises.py

@@ -27,7 +27,7 @@ def test_exercise_2():
     df = pd.read_pickle(f"{dirname}/material/data-consumption-function.pkl")
 
     def construct_predicted_values(income, alpha, beta, gamma):
-        return alpha + beta * income ** gamma
+        return alpha + beta * income**gamma
 
     mock_rslt = [-91.1933, 0.5691, 1.0204]
     income = df["realgdp"].values