import generador_placa_audio
import time
# %%
tiempo_medicion = 1
sample_rate = 10000
device = 'Dev7'
canales = ['ai1', 'ai6']
stream = generador_placa_audio.write(duracion=tiempo_medicion,
tipo='random',
amplitud=0.,
f_signal=10,
noise=False)
time.sleep(0.25)
time_vec, med = adquisicion_con_placa.medir(device, canales, sample_rate,
tiempo_medicion)
stream.stop_stream()
plt.plot(time_vec, med[0])
plt.plot(time_vec, med[1])
# %%
samples_per_channel = int(sample_rate * tiempo_medicion)
with nidaqmx.Task() as task:
ai_channel = task.ai_channels.add_ai_voltage_chan(
'{}/{}'.format(device, canales[0]), )
task.timing.cfg_samp_clk_timing(sample_rate,
samps_per_chan=samples_per_channel)
# ai_channel.ai_resolution = 2
print(ai_channel.ai_resolution)
# ai_channel.ai
canales = ['ai0']
fungen_perturb.write('output1 on')
fungen_control.write('output1 on')
fungen_control.write('VOLT % f' % 0.02)
fungen_control.write('FREQ % f' % 0.000001)
fungen_perturb.write('VOLT:OFFS % f' % 4)
fungen_perturb.write('FREQ % f' % 0.000001)
#fungen_perturb.write('VOLT % f' % 0.02)
#fungen_perturb.write('FREQ % f' % 0.000001)
tension_led = np.linspace(0, 8, num=100)
tension_fotodiodo = np.zeros_like(tension_led)
error_fotodiodo = np.zeros_like(tension_led)
for index, offset in enumerate(tension_led):
fungen_control.write('VOLT:OFFS % f' % offset)
time_vec, med = adquisicion_con_placa.medir(device,
canales,
sample_rate=1000,
duracion=1)
tension_fotodiodo[index] = np.mean(med)
error_fotodiodo[index] = np.std(med)
fungen_control.write('output1 off')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(tension_led, tension_fotodiodo)
ax.set_xlabel('Tension led perturbacion (V)')
ax.set_ylabel('Tension R fotodiodo (V)')
#fig.savefig('calibracion_led-fotodiodo_luzprendida_perturbprendido4V.png')
#np.savetxt('calibracion_ledperturbacion-fotodiodo_luzprendida_perturbprendido4V.dat', np.transpose([tension_led, tension_fotodiodo]))
# %%
#kps = [1, 5, 10, 20, 40, 70, 100]
kps = [25, 40]
#min_vals = [-5.0, -0.5, -5.0, -0.5, -5.0, -0.5, -5.0, -5.0]
#max_vals = list(-np.array(min_vals))
for canales_a_borrar in range(0,7):
canales = ['ai1','ai0','ai7','ai2','ai3','ai4','ai5','ai6']
min_vals = [-5.0, -0.5, -5.0, -0.5, -5.0, -0.5, -5.0, -5.0]
max_vals = list(-np.array(min_vals))
if canales_a_borrar < len(canales) - 1:
for i in range(canales_a_borrar):
canales.pop(1)
min_vals.pop(1)
max_vals.pop(1)
else:
raise ValueError('No borrar tantos canales')
sample_rate = int(250000/len(canales))
time_vec, med = adquisicion_con_placa.medir(device, canales, sample_rate,
tiempo_medicion)#, min_vals = min_vals, max_vals = max_vals)
corr = signal.correlate(med[0], med[-1], mode='full')
delay = (np.argmax(corr)/sample_rate)%max(time_vec)
fig, ax = plt.subplots(1, figsize=(6, 4))
ax.plot(time_vec, med[0], label = 'Canal 1')
ax.plot(time_vec, med[-1], label = 'Canal 6')
ax.legend()
ax.set_title('{} canales, Delay: {}s'.format(len(canales), delay))
fig.tight_layout()
#
# fname = '{}/freqgen{}Hz_srate{}_{}canales_consaltosdeganancia.dat'.format(folder, frecuencia, sample_rate, len(canales))
# coment = 'Medimos una señal de {}Hz en {} canales a fs {} con saltos de ganancia'.format(frecuencia, len(canales), sample_rate)
# if not os.path.isfile(fname):
# np.savetxt(fname, np.transpose([time_vec, med[0], med[-1]]), delimiter = ',', header = 'tiempo (s), tension (V)', footer=coment)
# fig.savefig('{}/{}Hz_{}canales.png'.format(folder, frecuencia, len(canales)))