requirements.txt

@@ -1,4 +1,2 @@
-numpy==2.4.3
-matplotlib==3.10.8
-ipykernel==7.2.0
-scipy==1.17.1
+numpy
+matplotlib

src/gauss/gauss_7_3.py

@@ -1,12 +0,0 @@
-# %%
-import numpy as np
-
-R = 1.0 * np.array(
-    [
-        [2, 3, 1],
-        [1, -2, -1],
-        [4, 1, -3],
-    ]
-)
-
-L = 1.0 * np.array([8, 3, 6])

src/gauss/toleranz.py

@@ -1,21 +0,0 @@
-#%%
-# Python initialisieren
-import numpy as np
-
-# Parameter
-G=1.*np.array([
-    [2,3,1,8],
-    [1,-2,-1,-3],
-    [4,1,-3,6]])
-
-# Berechnung
-m = np.max(G.shape)
-n = np.linalg.norm(G)
-e = np.finfo(np.float64).eps
-tol = m * n * e
-
-print(m)
-print(n)
-print(e)
-print(tol)
-# %%

src/scipy_integrate.py

@@ -1,41 +0,0 @@
-# %%
-# Python initialisieren
-import matplotlib.pyplot as plt
-import numpy as np
-import scipy.integrate as ig
-
-# Parameter
-x_0 = 0.0
-x_E = np.pi
-N = 11
-n = 5
-pr = 6
-lw = 3
-fig = 1
-
-# Funktion
-f = lambda x: np.sin(x)
-
-# Berechnungen
-for k in range(0, n):
-    x_data = np.linspace(x_0, x_E, N)
-    y_data = f(x_data)
-    I = ig.trapezoid(y=y_data, x=x_data)
-    J = ig.simpson(y=y_data, x=x_data)
-    print(f"I = {I:#.16g}")
-    print(f"J = {J:#.16g}")
-    N *= 2
-
-# Ausgabe
-print(f"I = {I:#.{pr}g}")
-print(f"J = {J:#.{pr}g}")
-
-# Plot
-fh = plt.figure(fig)
-plt.plot(x_data, y_data, linewidth=lw)
-plt.xlabel("x")
-plt.ylabel("y")
-plt.grid(visible=True)
-plt.axis("image")
-plt.show()
-# %%

src/serie_3.py

@@ -1,61 +0,0 @@
-# %%
-import numpy as np
-import matplotlib.pyplot as plt
-
-# %%
-print(60 * "-")
-print(__file__)
-print("Aufgabe 2. Interpolationspolynome gemäss NEWTON-Schema berechnen")
-# punkte [[x0, x1, x2, xn], [y0, y1, y2, yn]]
-punkte = [
-    [np.array([4, 8]), np.array([1, -1])],
-    [np.array([-1, 1, 2]), np.array([15, 5, 9])],
-    [np.array([-1, 0, 1, 2]), np.array([-5, -1, -1, 1])],
-]
-
-for punkt in punkte:
-
-    # Parameter
-    x_data = punkt[0]
-    y_data = punkt[1]
-    x_0 = x_data[0]
-    x_E = x_data[-1]
-    N = 201
-    lw = 3
-    fig = 1
-
-    # Berechnung
-    n = np.size(y_data)
-    TAB = np.block([[y_data], [np.zeros((n - 1, n))]])
-    c_data = np.zeros(n)
-    c_data[0] = y_data[0]
-
-    for i in range(1, n):
-        for j in range(1, n):
-            TAB[i][j] = (TAB[i - 1][j] - TAB[i - 1][j - 1]) / (
-                x_data[j] - x_data[j - i]
-            )
-        c_data[i] = TAB[i][i]
-
-    # Funktionen:
-    def p(x):
-        d = 1
-        y = c_data[0]
-        for k in range(1, n):
-            d = d * (x - x_data[k - 1])
-            y = y + c_data[k] * d
-        return y
-
-    # Daten
-    u_data = np.linspace(x_0, x_E, N)
-    v_data = p(u_data)
-
-    fh = plt.figure(fig)
-    plt.plot(u_data, v_data, linewidth=lw)
-    plt.plot(x_data, y_data, "o", linewidth=lw)
-    plt.xlabel(r"$x$")
-    plt.ylabel(r"$y$")
-    plt.grid(visible=True)
-    plt.axis("image")
-
-print(60 * "-")