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')
plt.show()
#Autocorrelate smooth amplitude
ac = PhyPraKit.autocorrelate(smoothamplitude)
#Plot Maximumarray
plt.plot(ac)
plt.title("Autocorrelate")
plt.show()
#Find minimums and maximums of autocorrelate and take differences
maxac = maximumfinder(ac)
minac = minimumfinder(ac)
print(maxac)
print(minac)
plt.plot(ac)
plt.title("Autocorrelate")
' processing file ' + fname)
# read data from PicoScope
units, data = ppk.readPicoScope(fname, prlevel=2)
t = data[0]
a = data[1]
print("** Fourier Spectrum")
freq, amp = ppk.FourierSpectrum(t, a, fmax=20)
# freq, amp = ppk.Fourier_fft(t, a) # use fast algorithm
frequency = freq[np.where(amp == max(amp))]
print(" --> Frequenz mit max. Amplitude: ", frequency)
# calculate autocorrelation function
print("** Autocorrelation Function")
ac_a = ppk.autocorrelate(a)
ac_t = t - t[0]
# run peak finder
width = 80
# use convoluted template filter
pidx = ppk.convolutionPeakfinder(ac_a, width, th=0.4)
if len(pidx) > 3:
print(" --> %i auto-correlation peaks found" % (len(pidx)))
pidx[0] = 0 # first peak is at 0 by construction
tp, ap = np.array(ac_t[pidx]), np.array(ac_a[pidx])
else:
print("*!!* not enough peaks found - tune peakfinder parameters!")
sys.exit(1)
# Filter peaks and dips: keep only largest ones
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:
print("*!!* not enough correlation peaks found")
#Plot erstellen