update to local git repo

Semester 3/ELMESS/Labor_04.md

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+5.3 Zeitkonstanten, Least Squares Parameteridentifikation
+´´´matlab
+scss
+% Import necessary libraries
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy.optimize import curve_fit
+
+% Load data from file
+data = np.loadtxt('data.csv', delimiter=',')
+
+% Define functions for plotting
+def plot_time_series(x, y):
+fig, ax = plt.subplots()
+  ax.plot(x, y)
+  ax.set_xlabel('Time (s)')
+  ax.set_ylabel('Temperature (°C)')
+  ax.grid(True)
+  return fig, ax
+
+% Define functions for fitting the curves
+def fit_curve(x, y, t0, T0, Tb):
+  A = np.exp(-Tb * x / T0)
+  B = np.log(1 + (A / (1 - A)))
+  return A * np.exp(B * x / T0)
+
+def fit_saturation(x, y, t0, T0, Tb):
+  def saturation(x, t0, T0, Tb):
+    return T0 + (Tb - T0) * np.exp(-x / t0)
+  return curve_fit(saturation, x, y, p0=[t0, T0, Tb])[0]
+
+% Fit curves to data
+fig1, ax1 = plot_time_series(data[:, 0], data[:, 1])
+ax1.plot(fit_curve(data[:, 0], data[:, 1], t0=30, T0=25, Tb=10))
+fig2, ax2 = plot_time_series(data[:, 0], data[:, 2])
+ax2.plot(fit_saturation(data[:, 0], data[:, 2], t0=30, T0=25, Tb=10))
+
+% Calculate difference between time constants
+T_diff = np.abs(fit_curve(data[:, 0], data[:, 2], t0=30, T0=25, Tb=10)[0] - fit_saturation(data[:, 0], data[:, 2], t0=30, T0=25, Tb=10)[0])
+
+% Print result
+print('The difference between the time constants of the two sensors is:', T_diff)
+´´´
+5.4 Parameter B des NTC
+EASY STUFF!!!