# check for / read command line arguments
if len(sys.argv) == 2:
fname = sys.argv[1]
else:
fname = "Wellenform.csv"
print('\n*==* script ' + sys.argv[0]+ ' executing \n',\
' 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)))
#phi=ppk.meanFilter(phi_c, width=10) #
# * use, if sampling rate is too high
if len(phi) > 2000:
print(" resampling")
phi, t = ppk.resample(phi, t, n=int(len(phi) /
1000)) # average n samples into 1
# numerical differentiation with numpy
print("** numerical derivative")
dt = t[1] - t[0]
omega = np.gradient(phi, dt)
# calculate fourier spectrum
print("** Fourier Spectrum")
freq, amp = ppk.FourierSpectrum(t, phi, fmax=1.)
# freq, amp = ppk.Fourier_fft(t, phi) # fast algorithm
frequency = freq[np.where(amp == max(amp))]
print(" --> Frequenz: ", frequency)
# run a peak finder
# first, determine width of typical peaks and dips
width = 0.5 * len(t) / (t[-1] - t[0]) / frequency
# use convoluted template filter
peakind = ppk.convolutionPeakfinder(phi, width, th=0.53)
dipind = ppk.convolutionPeakfinder(-phi, width, th=0.53)
if len(peakind) > 5:
print(" --> %i peaks and %i dips found" % (len(peakind), len(dipind)))
tp, phip = np.array(t[peakind]), np.array(phi[peakind])
td, phid = np.array(t[dipind]), np.array(phi[dipind])
else:
#Create Array of differences of Maximums
deltaMax = deltaArray(maxac)
deltaMin = deltaArray(minac)
print(deltaMax)
print(deltaMin)
deltaMin = (-1)*deltaMin
deltaNew = deltaMin + deltaMax
# plot data as histogram
nbin = 10
binc, bine, patches = plt.hist(deltaNew, nbin,facecolor='g', log=False, alpha=1)
plt.title("Histogramm of MaxDifferences")
plt.show()
#Calculate Period and Uncertainity from Histogram data
Perioddata = PhyPraKit.histstat(binc, bine, patches)
print("Period by amplitude alone = " + str(Perioddata[0]))
print("?? = " + str(Perioddata[1]))
print("Uncertainity = " + str(Perioddata[2]))
print("True Period in s = " + str(Perioddata[0]/0.05555555555))
print("True Uncertantiy = " + str(Perioddata[2]/0.05555555555))
#Stelle Frequenz Spektrum dar
fourierspec = PhyPraKit.FourierSpectrum(time,smoothamplitude)
plt.plot(fourierspec[0],fourierspec[1])
plt.title("Frequenz Spektrum des Signals")
print('Frequency by Fourier Analysis = ' +str(findMaximumxPos(*fourierspec)))
plt.show()
