for i in range(numRuns):
# take data
dataA = getData(ps, nSamples, channel='A')
dataB = getData(ps, nSamples, channel='B')
mean_A = np.mean(dataA)
mean_B = np.mean(dataB)
# save waveforms
wvout.write('A\n' + ', '.join(map(str, dataA)) + '\n\n')
wvout.write('B\n' + ', '.join(map(str, dataB)) + '\n\n')
# do the FT
spectrumA = np.fft.fft(np.array(dataA), nSamples)
spectrumB = np.fft.fft(np.array(dataB), nSamples)
freqs = np.fft.fftfreq(nSamples, sampling_interval)
f_B = calc_frequency(freqs, spectrumB)
f_A = calc_frequency(freqs, spectrumA)
print("FrequencyA = %f Hz" % f_A)
print("FrequencyB = %f Hz" % f_B)
# save the datapoint
fout.write(
'{f_in:.4f}\t{mean_A:.5f}\t{f_A:.4f}\t{mean_B:.5f}\t{f_B:.4f}\n'.
format(f_in=freq, mean_A=mean_A, f_A=f_A, mean_B=mean_B, f_B=f_B))
# plot the real signal
while ax.lines:
ax.lines.pop()
ax.plot(t, dataA, label='A', color='C0')
ax.plot(t, dataB, label='B', color='C1')
ps.setSimpleTrigger('A', 1.0, 'Falling', timeout_ms=100, enabled=True)
(sampling_interval, nSamples,
maxSamples) = configure_sampling(ps, 2 * half_p_us / 1e6)
# initialise the x axes
t = np.linspace(0, nSamples * sampling_interval, num=nSamples)
freqs = np.fft.fftfreq(nSamples, sampling_interval)
# set up plot -- does not work atm
fig, [ax1, ax2] = plt.subplots(ncols=1, nrows=2)
va_line, = ax1.plot(t, np.zeros(nSamples))
vb_line, = ax1.plot(t, np.zeros(nSamples))
f_line, = ax2.plot(freqs, np.zeros(len(freqs)))
plt.draw()
for i in range(nRuns):
dataA = getData(ps, nSamples, channel='A')
# do the FT
spectrum = np.fft.fft(np.array(dataA), nSamples)
fhz = calc_frequency(freqs, spectrum)
print('Frequency: %f Hz' % fhz)
va_line.set_ydata(dataA)
f_line.set_ydata(abs(spectrum))
plt.draw()
ps.stop()
ps.close()
plt.show()
ps.setSimpleTrigger('A', 1.0, 'Falling', timeout_ms=100, enabled=True)
(sampling_interval, nSamples,
maxSamples) = configure_sampling(ps, 2 * half_p_us / 1e6, multiplicity=mult)
dataA = getData(ps, nSamples, channel='A')
dataB = getData(ps, nSamples, channel='B')
ps.stop()
ps.close()
# do the FT
spectrum = np.fft.fft(np.array(dataA), nSamples)
spectrumB = np.fft.fft(np.array(dataB), nSamples)
freqs = np.fft.fftfreq(nSamples, sampling_interval)
print("FrequencyA = %f Hz" % calc_frequency(freqs, spectrum))
print("FrequencyB = %f Hz" % calc_frequency(freqs, spectrumB))
# plot the real signal
t = np.linspace(0, sampling_interval * nSamples, num=nSamples)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 8))
set_as_time(ax, fontsize=fontsize)
ax.plot(t, dataA, label='A')
ax.plot(t, dataB, label='B')
ax.legend(fontsize=fontsize)
# plot the FT'ed signal
# set_as_freq(ax1)
# ax1.plot(freqs, abs(spectrum), label='A')
# ax1.plot(freqs, abs(spectrumB), label='B')
from utils.analysis_utils import calc_frequency
import matplotlib.pyplot as plt
import numpy as np
with open('data/fourier.csv') as fin:
lines = fin.readlines()
lines = [[float(x) for x in line.split(',')] for line in lines]
data = zip(*lines)
freqs = list(data[0])
spectrum = list(data[1])
print(freqs[0:6])
print(spectrum[0:6])
print('Frequency = %f Hz' % calc_frequency(freqs, spectrum))
# frequency(freqs, spectrum)
# read signal
dataA = getData(ps, nSamples, channel='A')
dataB = getData(ps, nSamples, channel='B')
# replot
lineA.set_ydata(dataA)
lineB.set_ydata(dataB)
plt.pause(0.01)
# save the output waveforms
wvout.write(', '.join(map(str, dataB)) + '\n')
# measure the frequency of A
spectrum = np.fft.fft(np.array(dataA), nSamples)
freqs = np.fft.fftfreq(nSamples, sampling_interval)
f_measured = calc_frequency(freqs, spectrum)
# measure the means
meanA = np.mean(dataA)
meanB = np.mean(dataB)
at_this_phase.append(meanB)
# save the processed data
print('FrequencyA = %f Hz;\tmeanA = %f V;\tmeanB = %f V' %
(f_measured, meanA, meanB))
fout.write('%d\t%.5f\t%d\t%.5f\t%.5f\t%.5f\n' %
((fid * len(phase_shifts) + phid) * numRuns + run, f,
phase, f_measured, meanA, meanB))
# output results at this phase
mean_at_this_phase = np.mean(at_this_phase)
# do the runs
for run in range(nRuns):
# measure
dataA = getData(ps, nSamples, channel='A')
dataB = getData(ps, nSamples, channel='B')
meanA = np.mean(dataA)
meanB = np.mean(dataB)
sigA = np.std(dataA)
sigB = np.std(dataB)
# do the FT
spectrumA = np.fft.fft(np.array(dataA), nSamples)
spectrumB = np.fft.fft(np.array(dataB), nSamples)
freqA = calc_frequency(freqs, spectrumA)
freqB = calc_frequency(freqs, spectrumB)
print("FrequencyA = %.4e Hz;\tv_A = %f +/- %f V" % (freqA, meanA, sigA))
print("FrequencyB = %.4e Hz;\tv_B = %f +/- %f V" % (freqB, meanB, sigB))
fout.write('%.3f\t%.4f\t%.5f\t%.5f\t%.4f\t%.5f\t%.5f\n' % (v, freqA, meanA, sigA, freqB, meanB, sigB))
wvout.write('expected voltage = %.3f V\n' % v)
wvout.write('A\n' + ', '.join(map(str, dataA)) + '\n\n')
wvout.write('B\n' + ', '.join(map(str, dataB)) + '\n\n')
# replot realtime
lineA.set_ydata(dataA)
lineB.set_ydata(dataB)
specA.set_ydata(spectrumA)
specB.set_ydata(spectrumB)
plt.pause(0.01)