def fft_analysis(groesse, hoehe, nummer):
#Daten mit Korrektur
dataname = groesse + '_' + hoehe + '_' + nummer
data = cassy.CassyDaten('daten/' + dataname + '.lab')
time = data.messung(1).datenreihe('t').werte
pres = data.messung(1).datenreihe('p_A1').werte
#Signale abschneiden
time, pres = analyse.untermenge_daten(time, pres, 0, tgrenz[dataname])
fourier = analyse.fourier_fft(time, pres)
freq = fourier[0]
amp = fourier[1]
peak = analyse.peakfinder_schwerpunkt(freq[3:100], amp[3:100])
delta = korrektur(time, pres)
return (2 * np.pi * peak, delta)
def test_fourier():
tmin = 100.
tmax = 500.
tstep = 0.2
freq = 0.123
t = np.arange(tmin, tmax, tstep)
y = np.cos(2. * np.pi * freq * t)
figure()
subplot(2, 1, 1)
title('Daten')
plot(t, y, '.')
xlabel('$t$ / s')
ylabel('amp')
grid()
subplot(2, 1, 2)
title('Fourierspektrum')
freq, amp = analyse.fourier(t, y)
freq_fft, amp_fft = analyse.fourier_fft(t, y)
plot(freq, amp, '.', color='blue', label="Fourier")
amp_fft = np.max(amp) / np.max(amp_fft) * amp_fft
plot(freq_fft, amp_fft, '.', color='red', label="FFT")
xlabel('$f$ / Hz')
ylabel('amp')
grid()
legend()
fpeak = analyse.peakfinder_schwerpunkt(freq, amp)
fpeak_fft = analyse.peakfinder_schwerpunkt(freq_fft, amp_fft)
axvline(fpeak, color='blue')
axvline(fpeak_fft, color='red')
xlim(0., np.max(freq))
ylim(0., max(np.max(amp), np.max(amp_fft)) * 1.1)
show()
grid()
subplot(2, 1, 2)
plot(t, U2)
ylabel('$U$ / V')
xlabel('t/s')
xlim(0, 0.04)
title('zweiter Schwingkreis')
grid()
subplots_adjust(hspace=0.5)
savefig('Images/Rohdaten_' + n[0] + 'sinnig.pdf')
plt.figure()
#FFT + Plot
w1_array, A1 = analyse.fourier_fft(t, U1)
w1 = analyse.peakfinder_schwerpunkt(w1_array, A1) #Hauptfrequenz1
print("Frequenz 1 aus fft: ", w1)
plt.plot(w1_array, A1 / max(A1))
plt.axvline(x=w1, color="darkred", linestyle="--")
plt.text(w1, 0.5, 'Max: {} Hz'.format(round(w1, 3)))
plt.title('FFT Schwingung 1')
plt.xlim(0, 4000)
plt.xlabel('Frequenz / Hz')
plt.ylabel('rel. Häufigkeiten')
plt.savefig(n[0] + 'sinnig_FFT1')
plt.figure()
w2_array, A2 = analyse.fourier_fft(t, U2)
w2 = analyse.peakfinder_schwerpunkt(w2_array, A2) #Hauptfrequenz2
plt.title("Schwebung bei $l_F$=52.7cm")
plt.ylabel("$U_1$ / V")
plt.xlabel("t / s")
plt.plot(t, U1)
scatter(peaks1[0], peaks1[1], color="r")
subplot(2, 1, 2)
plt.ylabel("$U_2$ / V")
plt.xlabel("t / s")
plt.plot(t, U2)
scatter(peaks2[0], peaks2[1], color="r")
plt.savefig("images/527_schwe.png")
plt.show()
plt.close()
omega_fft1, A1 = anal.fourier_fft(t, U1)
omega_fft2, A2 = anal.fourier_fft(t, U2)
plt.title("Fouriertransformierte der Schwebungen bei $l_F$=52.7cm")
plt.ylabel("Häufigkeit")
plt.xlabel("f / Hz")
plt.xlim(0.4, 0.8)
plt.plot(omega_fft1, A1)
plt.plot(omega_fft2, A2)
plt.show()
#---------------------------------------------------
#Schwebung bei lF=0.781 m
#---------------------------------------------------
t, U1, U2 = c_open("781cm/Schwebung.lab")
U1 = np.array(U1) - np.average(U1)
voltage = voltage - offset
figure(1)
title('Pendel')
subplot(2, 1, 1)
plot(timeValues, voltage)
grid()
xlabel('Zeit / s')
ylabel('Spannung / V')
einhuellende = analyse.exp_einhuellende(timeValues, voltage, voltageError)
plot(timeValues, +einhuellende[0] * exp(-einhuellende[2] * timeValues))
plot(timeValues, -einhuellende[0] * exp(-einhuellende[2] * timeValues))
subplot(2, 1, 2)
fourier = analyse.fourier_fft(timeValues, voltage)
frequency = fourier[0]
amplitude = fourier[1]
plot(frequency, amplitude)
grid()
xlabel('Frequenz / Hz')
ylabel('Amplitude')
maximumIndex = amplitude.argmax()
xlim(frequency[max(0, maximumIndex - 10)],
frequency[min(maximumIndex + 10, len(frequency))])
peak = analyse.peakfinder_schwerpunkt(frequency, amplitude)
axvline(peak)
L = 0.667
g = ((2 * np.pi * peak)**2) * L
dis = 0
i = 0
while i < len(ind) - 1:
dis = dis + v[ind[i + 1]] - v[ind[i]]
i = i + 1
dis = dis / (len(ind) - 1)
return dis
data = cassy.CassyDaten('Gleichsinnig/gleich.koppl.eisen.lab')
U1 = data.messung(1).datenreihe('U_B1').werte
U2 = data.messung(1).datenreihe('U_B2').werte
t = data.messung(1).datenreihe('t').werte
w1, A1 = analyse.fourier_fft(t, U1)
w1 = analyse.peakfinder_schwerpunkt(w1, A1)
print("Frequenz 1 aus fft: ", w1)
w2, A2 = analyse.fourier_fft(t, U2)
w2 = analyse.peakfinder_schwerpunkt(w2, A2)
print("Frequenz 2 aus fft: ", w2)
w_mean = (w1 + w2) / 2
w_std = np.sqrt((w1 - w_mean)**2 + (w2 - w_mean)**2)
print("mittlere Frequenz aus fft", w_mean, "+-", w_std)
fig = figure()
subplot(2, 1, 1)
plot(t, U1)
# Daten laden
data1 = cassy.CassyDaten('daten/spektrum_1.2.lab')
time1 = data1.messung(1).datenreihe('t').werte
volt1 = data1.messung(1).datenreihe('U_A1').werte
data2 = cassy.CassyDaten('daten/spektrum_2.lab')
time2 = data2.messung(1).datenreihe('t').werte
volt2 = data2.messung(1).datenreihe('U_A1').werte
data3 = cassy.CassyDaten('daten/spektrum_3.1.lab')
time3 = data3.messung(1).datenreihe('t').werte
volt3 = data3.messung(1).datenreihe('U_A1').werte
# Fourier
fourier1 = analyse.fourier_fft(time1, volt1)
fourier2 = analyse.fourier_fft(time2, volt2)
fourier3 = analyse.fourier_fft(time3, volt3)
fre1 = fourier1[0]
amp1 = fourier1[1]
fre2 = fourier2[0]
amp2 = fourier2[1]
fre3 = fourier3[0]
amp3 = fourier3[1]
# Neagtive Frequenzen wegschneiden
fre1, amp1 = analyse.untermenge_daten(fre1, amp1, 0, 5000)
fre2, amp2 = analyse.untermenge_daten(fre2, amp2, 0, 5000)
fre3, amp3 = analyse.untermenge_daten(fre3, amp3, 0, 5000)
while i < len(ind)-1:
dis = dis + v[ind[i+1]]-v[ind[i]]
i = i+1
dis = dis/(len(ind)-1)
return dis
data = cassy.CassyDaten('Schwebung_1_250n.lab')
U1 = data.messung(1).datenreihe('U_B1').werte
t = data.messung(1).datenreihe('t').werte
ind_max = find_peaks_cwt(U1,np.arange(1,20))
ind_max = ind_max[:20]
ind_max = ind_max[3:]
w_array,A = analyse.fourier_fft(t,U1)
ind_w1=analyse.peak(w_array,A,np.argmax(w_array>950),np.argmax(w_array>1020))
ind_w2=analyse.peak(w_array,A,np.argmax(w_array>1100),np.argmax(w_array>1160))
plot(w_array,A/max(A))
plt.axvline(x=w_array[int(ind_w1)], color="darkred", linestyle = "--")
plt.text(w_array[int(ind_w1)],0.7,'max_1:{} Hz'.format(np.round(w_array[int(ind_w1)],4)))
plt.axvline(x=w_array[int(ind_w2)], color="darkred", linestyle = "--")
plt.text(w_array[int(ind_w2)],0.5,'max_2:{} Hz'.format(np.round(w_array[int(ind_w2)],4)))
xlim(0,4000)
plt.savefig('Images/Schwebung_FFT')
err1=w_array[int(np.ceil(ind_w1))]-w_array[int(np.floor(ind_w1))]
err2=w_array[int(np.ceil(ind_w2))]-w_array[int(np.floor(ind_w2))]
#Ausgabe der Werte
print('f+={} +- {}'.format(w_array[int(ind_w1)],err1))
print('f-={} +- {}'.format(w_array[int(ind_w2)],err2))
ax2.set_ylabel('$U_2$ / V')
ax2.set_xlabel('$t$ / ms')
ax2.set_xlim(0,10)
ax2.grid()
plt.subplots_adjust(hspace=0)
plt.savefig('plots/fundamental_roh.pdf', format='pdf', dpi=1200)
plt.show()
'''
# Frequenzspektrum
data1 = cassy.CassyDaten('daten/fundamental_3.lab')
time1 = data1.messung(1).datenreihe('t').werte
vol1_1 = data1.messung(1).datenreihe('U_A1').werte
vol1_2 = data1.messung(1).datenreihe('U_A2').werte
fre1_1, amp1_1 = analyse.fourier_fft(time1, vol1_1)
fre1_2, amp1_2 = analyse.fourier_fft(time1, vol1_2)
data2 = cassy.CassyDaten('daten/fundamental_2.lab')
time2 = data2.messung(1).datenreihe('t').werte
vol2_1 = data2.messung(1).datenreihe('U_A1').werte
vol2_2 = data2.messung(1).datenreihe('U_A2').werte
fre2_1, amp2_1 = analyse.fourier_fft(time2, vol2_1)
fre2_2, amp2_2 = analyse.fourier_fft(time2, vol2_2)
peak1_1 = analyse.peakfinder_schwerpunkt(fre1_1[5:600], amp1_1[5:600])
peak1_2 = analyse.peakfinder_schwerpunkt(fre1_2[5:600], amp1_2[5:600])
peak2_1 = analyse.peakfinder_schwerpunkt(fre2_1[5:600], amp2_1[5:600])
peak2_2 = analyse.peakfinder_schwerpunkt(fre2_2[5:600], amp2_2[5:600])
plt.ylabel(u"U1/V")
plt.setp(ax1.get_xticklabels(), visible=False)
plt.grid()
ax2 = plt.subplot(gs[1, 0], sharex=ax1)
plt.plot(t[j], U2[j], color='red')
plt.ylabel(u"U2/V")
plt.xlabel(u"t/s")
ax2.yaxis.tick_right()
plt.grid()
plt.subplots_adjust(hspace=.0)
plt.savefig("Images/Messung " + str(i) + "_" + name[j] + ".jpg")
#Fourieranalyse
w, A = analyse.fourier_fft(t[j], U1[j])
#w,A = analyse.fourier_fft(t[j],U2[j])
range1 = [(0.6, 0.63), (0.6, 0.63), (0.6, 0.63),
(0.5, 0.64)] #in diesen beiden Bereichen sollen der erste
range2 = [(0.6, 0.63), (0.63, 0.64), (0.64, 0.7),
(0.69, 0.8)] #bzw. der zweite Peak "eingefangen" werden
ind_w1 = list(A).index(
max(A[np.argmax(w > range1[i - 1][0]):np.argmax(
w > range1[i - 1][1])]), np.argmax(w > range1[i - 1][0]))
ind_w2 = list(A).index(
max(A[np.argmax(w > range2[i - 1][0]):np.argmax(
w > range2[i - 1][1])]), np.argmax(w > range2[i - 1][0]))
#Fourierplot
plt.figure()
"""
Created on Mon Sep 16 21:19:03 2019
@author: simon
"""
from praktikum import analyse
from praktikum import cassy
import numpy as np
import matplotlib.pyplot as plt
data = cassy.CassyDaten('daten/mittel_20_3.lab')
timeVal = data.messung(1).datenreihe('t').werte
pres = data.messung(1).datenreihe('p_A1').werte
f, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(timeVal, pres, color='red')
freq, amp = analyse.fourier_fft(timeVal, pres)
ax2.plot(freq[0:8000], amp[0:8000], color='red')
ax1.set_ylabel("Druckdifferenz / hPa")
ax2.set_ylabel("Amplitude")
ax2.set_xlabel("$f$ / Hz")
ax1.set_xlabel("$t$ / $s$")
ax1.set_xlim(0, 8)
ax2.set_xlim(0, 5)
plt.rcParams["figure.figsize"] = (12, 6)
plt.rcParams['axes.titlesize'] = 'large'
plt.rcParams['axes.labelsize'] = 'large'
f.tight_layout()
plt.savefig('plots/rohdaten.pdf')
plt.show()
plt.close()
fplus = np.array([])
fminus = np.array([])
dic_file = {1:'ohne2', 2:'ohne3', 3:'eisen2'}
dic_fre = {1: np.array([57,67,70,78]), 2:np.array([64,68,68,72]), 3:np.array([20,29,40,49])}
freqs = np.array([])
amps = np.array([])
for i in range(1,4,1):
data = cassy.CassyDaten('daten/schwebung_' + dic_file[i] + '_1.lab')
time = data.messung(1).datenreihe('t').werte
vol = data.messung(1).datenreihe('U_A1').werte
vol2 = data.messung(1).datenreihe('U_A2').werte
freq, amp = analyse.fourier_fft(time, vol)
freq2, amp2 = analyse.fourier_fft(time, vol2)
(freq, amp) = analyse.untermenge_daten(freq, amp, 0, 50000)
(freq2, amp2) = analyse.untermenge_daten(freq2, amp2, 0, 50000)
peak1 = analyse.peakfinder_schwerpunkt(freq[dic_fre[i][0]:dic_fre[i][1]], amp[dic_fre[i][0]:dic_fre[i][1]])
peak2 = analyse.peakfinder_schwerpunkt(freq[dic_fre[i][2]:dic_fre[i][3]], amp[dic_fre[i][2]:dic_fre[i][3]])
ymax1 = amp[dic_fre[i][0]:dic_fre[i][1]].argmax()
ymax2 = amp[dic_fre[i][2]:dic_fre[i][3]].argmax()
fplus = np.concatenate((fplus, np.array([peak1, np.abs(peak1-freq[dic_fre[i][0]+ymax1])])))
fminus = np.concatenate((fminus, np.array([peak2, np.abs(peak2-freq[dic_fre[i][2]+ymax2])])))
amps = np.concatenate((amps, amp))
freqs = np.concatenate((freqs, freq))
'''
