1.4 KiB
Executable File
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!!!