fpath = "C:\\Users\\LiquidSpot\\PycharmProjects\\CGDA\Blatt 5\\signal.csv" t = PhyPraKit.readCSV(fpath) #print(t) #Datenformat: Comma seperated values(CSV) #Datenformat t = Liste von listen #Numpy list for time and amplitude time = np.zeros(10000); amplitude = np.zeros(10000); time = t[1][0] amplitude = t[1][1] #Smooth amplitude by mean, deltaA as difference between original and smooth Amplitude smoothamplitude = PhyPraKit.meanFilter(amplitude,5) deltaA = amplitude - smoothamplitude #Plot amplitude over time plt.plot(time,amplitude) plt.title('amplitude over time') plt.show() #Plot smooth amplitude over time plt.plot(time,smoothamplitude) plt.title('Smooth amplitude over time') plt.show() #Plot deltaAmplitude over time plt.plot(time,deltaA) plt.title('delta Amplitude over time')
import kafe #Bestimmung m_e ___________________________________________________________________________ M = 0.14174 #Angaben in kg M_F = 0.0154 o_me = 0.1 * 10**-3 #Fehler von der Wage me, o_me = (M + 1. / 3. * M_F), o_me + 1 / 3 * o_me #Bestimmung von T ___________________________________________________________________________ hlines, data = ppk.readCSV('HandyPendel.csv') t = data[0] a = data[2] #Glätten der Funktion a_smooth = ppk.meanFilter(a, 7) # calculate autocorrelation function ac_a = ppk.autocorrelate(a_smooth) ac_t = t # find maxima and minima using convolution peak finder width = 3 pidxac = ppk.convolutionPeakfinder(ac_a, width, th=0.66) didxac = ppk.convolutionPeakfinder(-ac_a, width, th=0.66) if len(pidxac) > 1: print(" --> %i auto-correlation peaks found" % (len(pidxac))) pidxac[0] = 0 # first peak is at 0 by construction ac_tp, ac_ap = np.array(ac_t[pidxac]), np.array(ac_a[pidxac]) ac_td, ac_ad = np.array(ac_t[didxac]), np.array(ac_a[didxac]) else: