plot.py

import matplotlib.pyplot as plt
import numpy as np
import csv
from scipy.optimize import curve_fit

x=np.genfromtxt("RCKreis.csv", delimiter=",",unpack=True,usecols=0)
y=np.genfromtxt("RCKreis.csv", delimiter=",",unpack=True,usecols=1)

params, covariance_matrix = np.polyfit(x, np.log(y), deg=1, cov=True)
x_plot = np.linspace(0,3,100)
uncertainties = np.sqrt(np.diag(covariance_matrix))
for name, value, uncertainty in zip('ab', params, uncertainties): 
    print(f'{name} = {value:8.3f} ± {uncertainty:.5f}')


plt.plot(
   x_plot,
    params[0] * x_plot + params[1],
    label='Lineare Regression',
    linewidth=3,
)
plt.plot(x,np.log(y),'r.', label = 'Messwerte')
plt.xlabel(r'$t \,/\, \mathrm{ms}$')
plt.ylabel(r'$ln\left(\frac{U_C}{U_0}\right)$')
plt.legend()



plt.tight_layout()
plt.savefig('plot.pdf')