content: import numpy as np
  import matplotlib.pyplot as plt
  from scipy.stats import linregress
  from scipy.optimize import curve_fit
  
  oberwelle3, amplitude3 = np.genfromtxt('Rohdaten/dreieckspannung.txt',unpack=True)
  plt.plot(oberwelle3, amplitude3,'k.',label="Messdaten")
  #plt.legend(loc='best')
  plt.grid()
  #plt.xlim(0,1.5)
  plt.xscale('log')
  plt.yscale('log')
  plt.xlabel(r'Oberwellen')
  plt.ylabel(r'$\mathrm{U}/V$')
  plt.tight_layout()
  #plt.show()
  #plt.savefig('build/50ohm.pdf')
  
  ####################
  newX = np.logspace(-4, 1, base=10)  # Makes a nice domain for the fitted curves.
  
                                     # This avoids the sorting and the swarm of lines.
  
  # Let's fit an exponential function.
  # This looks like a line on a lof-log plot.
  def myExpFunc(x, a, b):
      return a * np.power(x, b)
  popt, pcov = curve_fit(myExpFunc, oberwelle3, amplitude3)
  plt.plot(newX, myExpFunc(newX, *popt), 'r-',
           label="Fit".format(*popt))
  plt.xlim(10**-2, 10**1)
  plt.ylim(10**-0.5, 10**1)
  print('Exponential Fit: y = (a*(x**b))')
  print('\ta = popt[0] = {0}\n\tb = popt[1] = {1}'.format(*popt))
  ####################
  
  
  plt.legend(loc='best')
  #plt.show()
  plt.savefig('build/dreieckspannung.pdf')
  