Merge remote-tracking branch 'origin/main'

.idea/Masterprojekt-Campusnetz.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/.venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.14 (Masterprojekt)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.14" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.14" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.14 (Masterprojekt)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.14" project-jdk-type="Python SDK" />
 </project>

Netzqualität_Genauigkeit.py

@@ -0,0 +1,149 @@
+from dataclasses import dataclass
+from typing import Sequence, List, Dict
+import sympy as sp
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+@dataclass
+class Genauigkeitsmaße:
+
+
+    def helmertscher_punktfehler_3D(self, sigma_x: float, sigma_y: float, sigma_z: float) -> float:
+        sx = sp.sympify(sigma_x)
+        sy = sp.sympify(sigma_y)
+        sz = sp.sympify(sigma_z)
+
+        helmert_pf_3D = sp.sqrt(sx**2 + sy**2 + sz**2)
+        return float(helmert_pf_3D)
+
+
+    def helmertscher_punktfehler_3D_alle(
+        self,
+        sigma_x_list: Sequence[float],
+        sigma_y_list: Sequence[float],
+        sigma_z_list: Sequence[float],
+    ) -> List[float]:
+        if not (
+            len(sigma_x_list) == len(sigma_y_list) == len(sigma_z_list)
+        ):
+            raise ValueError("Listen sigma_x, sigma_y, sigma_z müssen gleich lang sein.")
+
+        return [
+            self.helmertscher_punktfehler_3D(sx, sy, sz)
+            for sx, sy, sz in zip(sigma_x_list, sigma_y_list, sigma_z_list)
+        ]
+
+
+    def fehlerellipse_standard_2D(
+        self,
+        Q_xx: float,
+        Q_yy: float,
+        Q_xy: float,
+        sigma_0: float,
+    ) -> Dict[str, float]:
+
+        Q_xx_s = sp.sympify(Q_xx)
+        Q_yy_s = sp.sympify(Q_yy)
+        Q_xy_s = sp.sympify(Q_xy)
+        sigma0_s = sp.sympify(sigma_0)
+
+        k = sp.sqrt((Q_xx_s - Q_yy_s)**2 + 4 * Q_xy_s**2)
+
+        Q_dmax = sp.Rational(1, 2) * (Q_xx_s + Q_yy_s + k)
+        Q_dmin = sp.Rational(1, 2) * (Q_xx_s + Q_yy_s - k)
+
+        s_max = sigma0_s * sp.sqrt(Q_dmax)
+        s_min = sigma0_s * sp.sqrt(Q_dmin)
+
+        theta_rad = sp.Rational(1, 2) * sp.atan2(2 * Q_xy_s, Q_xx_s - Q_yy_s)
+        theta_gon = theta_rad * 200 / sp.pi
+
+        return {
+            "Q_dmax": float(Q_dmax),
+            "Q_dmin": float(Q_dmin),
+            "s_max": float(s_max),
+            "s_min": float(s_min),
+            "theta_rad": float(theta_rad),
+            "theta_gon": float(theta_gon),
+        }
+
+
+
+    def fehlerellipse_konfidenz_2D(
+        self,
+        s_max: float,
+        s_min: float,
+        scale_value: float,
+    ) -> Dict[str, float]:
+
+        s_max_s = sp.sympify(s_max)
+        s_min_s = sp.sympify(s_min)
+        scale_s = sp.sympify(scale_value)
+
+        faktor = sp.sqrt(scale_s)
+
+        A_K = faktor * s_max_s
+        B_K = faktor * s_min_s
+
+        return {
+            "A_K": float(A_K),
+            "B_K": float(B_K),
+        }
+
+
+
+    def plot_ellipsen_alle(
+        self,
+        x_list: Sequence[float],
+        y_list: Sequence[float],
+        Q_xx_list: Sequence[float],
+        Q_yy_list: Sequence[float],
+        Q_xy_list: Sequence[float],
+        sigma_0: float,
+        scale_value: float | None = None,  # None = Standardellipse, sonst Konfidenzellipse
+    ) -> plt.Axes:
+
+        n = len(x_list)
+        if not (
+            n == len(y_list) == len(Q_xx_list) == len(Q_yy_list) == len(Q_xy_list)
+        ):
+            raise ValueError("Alle Listen müssen gleich lang sein (ein Eintrag pro Punkt).")
+
+        fig, ax = plt.subplots()
+
+        for i in range(n):
+            std = self.fehlerellipse_standard_2D(
+                Q_xx_list[i], Q_yy_list[i], Q_xy_list[i], sigma_0
+            )
+            s_max = std["s_max"]
+            s_min = std["s_min"]
+            theta = std["theta_rad"]
+
+            # ggf. Konfidenzellipse statt Standardellipse
+            if scale_value is not None:
+                konf = self.fehlerellipse_konfidenz_2D(s_max, s_min, scale_value)
+                A = konf["A_K"]
+                B = konf["B_K"]
+            else:
+                A = s_max
+                B = s_min
+
+            t = np.linspace(0, 2 * np.pi, 200)
+            ct = np.cos(theta)
+            st = np.sin(theta)
+
+            x_local = A * np.cos(t)
+            y_local = B * np.sin(t)
+
+            x_ell = x_list[i] + ct * x_local - st * y_local
+            y_ell = y_list[i] + st * x_local + ct * y_local
+
+            ax.plot(x_ell, y_ell, linewidth=0.8)
+            ax.plot(x_list[i], y_list[i], marker=".", color="k", markersize=3)
+
+        ax.set_aspect("equal", "box")
+        ax.grid(True)
+        ax.set_xlabel("x")
+        ax.set_ylabel("y")
+        return ax