def plot_thread():
while True:
plt.figure(0)
plt.clf()
idx, = np.where(fps.positions[0, :] > 0)
pos = fps.positions[:, idx]
plt.plot(pos[0, :], pos[1, :], label='Axis 1')
# plt.plot(pos[1, :], label='Axis 2')
# plt.plot(pos[2, :], label='Axis 3')
plt.xlim(pos[0, 0], pos[0, -1])
avg_x = running_mean(pos[1, :], 100)
plt.plot(pos[0, :len(avg_x)], avg_x, label='Axis 1 (100pts)')
peak_peak = 2. * (np.sum(np.abs(pos[1, :] - avg_x)) / len(avg_x))
ts = 1000.0 * np.average(np.diff(pos[0, :]))
plt.title('estimated sample time: {0:.2f} ms\n'
'peak-peak {1:.1f}nm'.format(ts, peak_peak * 1000.0)
)
np.save("interf_data.np", pos)
if 0:
plt.figure(1)
plt.clf()
fftplot(pos, [1], [2], column_names={1: 'x', 2: 'y'},
scale=1000.0,
)
plt.title('FFT [nm] vs [Hz]')
plt.pause(0.5)
def plot_thread():
while True:
plt.figure(0)
plt.clf()
idx, = np.where(fps.positions[0, :] > 0)
pos = fps.positions[:, idx]
plt.plot(pos[0, :], pos[1, :], label='Axis 1')
# plt.plot(pos[1, :], label='Axis 2')
# plt.plot(pos[2, :], label='Axis 3')
plt.xlim(pos[0, 0], pos[0, -1])
avg_x = running_mean(pos[1, :], 100)
plt.plot(pos[0, :len(avg_x)], avg_x, label='Axis 1 (100pts)')
peak_peak = 2. * (np.sum(np.abs(pos[1, :] - avg_x)) / len(avg_x))
ts = 1000.0 * np.average(np.diff(pos[0, :]))
plt.title('estimated sample time: {0:.2f} ms\n'
'peak-peak {1:.1f}nm'.format(ts, peak_peak * 1000.0))
np.save("interf_data.np", pos)
if 0:
plt.figure(1)
plt.clf()
fftplot(
pos,
[1],
[2],
column_names={
1: 'x',
2: 'y'
},
scale=1000.0,
)
plt.title('FFT [nm] vs [Hz]')
plt.pause(0.5)
def plot_loop(dev):
# t0 = time.time()
# dev.monitor(sample_rate=0.6554, wait_for=2048)
dev.monitor(sample_rate=2. * 0.3277, wait_timestamp=0.1)
# t1 = time.time()
dev.stop()
# print(pos)
# return
data = dev.position_data
# for i in range(num_pos):
# print('%-20s\t%f\t%f\t%f'
# '' % (dev._timestamps[i], dev._positions[0][i],
# dev._positions[1][i], dev._positions[2][i],)
# )
plt.figure(1)
plt.clf()
col_names = {1: 'x', 2: 'y', 3: 'z'}
colors = 'bgk'
ax1, ax2, freqs, spectra = fftplot(data, [1, 2, 3],
column_names=col_names,
left_label='Amplitude [nm]',
scale=1.0,
left_colors=colors)
plt.xlim(0, 400)
plt.ylim(0, 5.0)
plt.draw()
title = ['Sample rate: %f ms [data points=%d]'
'' % (dev.sample_rate, len(data[0, :]))
]
for i in range(1, 4):
max_i = np.argmax(spectra[:, i])
max_freq = freqs[max_i]
nm = spectra[max_i, i]
color = colors[i - 1]
# title.append('%s[%f]=%f nm' % (col_names[i], max_freq, nm))
# plt.axvline(max_freq)
plt.text(max_freq + 1, 0.9 * nm, '%.1f Hz' % max_freq, color=color)
# plt.text(max_freq + 1, 0.75 * nm, '%.2f nm' % nm, color=color)
# plt.text(max_freq, 0, '%.1f Hz' % max_freq, color=color)
plt.title('\n'.join(title))
plt.legend(loc='best')
plt.savefig('fft.png')
plt.figure(2)
plt.clf()
ts = data[0, :]
print('ts=%d filt=%d' % (len(ts), len(dev._filtered[0])))
plt.plot(ts, dev._filtered[0])
plt.plot(ts, data[1, :], alpha=0.1)
plt.pause(0.1)
plt.show()
np.save('test', data)
for i in range(3):
print('average position (ax=%d)' % i, np.average(dev._positions[i]))
# print('total positions in %g seconds: %d' % (t1 - t0, len(data[0, :])))
# print('sample rate %f (%d)' % (dev.sample_rate, dev._sample_rate))
dev._positions = [[] for i in range(3)]