except ValueError:
continue
#fig = plt.figure()
correlator.time_domain_signals = None
num_channels = 4
for channel in range(num_channels):
filename = "{c}.npy".format(c = channel)
with open("{d}/{t}/{f}".format(d = directory, t = timestamp, f = filename)) as f:
signal = np.load(f)
#subplot_sig = fig.add_subplot(4, 2, (2*channel) + 1)
#subplot_fft = fig.add_subplot(4, 2, (2*channel) + 2)
#subplot_sig.plot(signal)
#fft = np.abs(np.fft.rfft(signal))
#subplot_fft.plot(np.linspace(0, 400, len(fft)), fft)
signal = notch_filter(signal, fs, 0, args.f_start)
signal = notch_filter(signal, fs, args.f_stop, 400e6)
signal = time_domain_filter(signal, 10, 10)
if correlator.time_domain_signals == None:
correlator.time_domain_signals = np.ndarray((num_channels, len(signal)))
correlator.time_domain_signals[channel] = signal
#subplot_sig.plot(signal)
#fft = np.abs(np.fft.rfft(signal))
#subplot_fft.plot(np.linspace(0, 400, len(fft)), fft)
correlator.time_domain_axis = np.linspace(0,
len(correlator.time_domain_signals[0])/fs,
len(correlator.time_domain_signals[0]),
endpoint = False)
#plt.show()
df.df_impulse(args.d, t = timestamp)
exit()
num_channels = 4
for channel in range(num_channels):
filename = "{c}.npy".format(c=channel)
with open("{d}/{t}/{f}".format(d=directory,
t=timestamp,
f=filename)) as f:
signal = np.load(f)
#subplot_sig = fig.add_subplot(4, 2, (2*channel) + 1)
#subplot_fft = fig.add_subplot(4, 2, (2*channel) + 2)
#subplot_sig.plot(signal)
#fft = np.abs(np.fft.rfft(signal))
#subplot_fft.plot(np.linspace(0, 400, len(fft)), fft)
signal = notch_filter(signal, fs, 0, args.f_start)
signal = notch_filter(signal, fs, args.f_stop, 400e6)
signal = time_domain_filter(signal, 10, 10)
if correlator.time_domain_signals == None:
correlator.time_domain_signals = np.ndarray(
(num_channels, len(signal)))
correlator.time_domain_signals[channel] = signal
#subplot_sig.plot(signal)
#fft = np.abs(np.fft.rfft(signal))
#subplot_fft.plot(np.linspace(0, 400, len(fft)), fft)
correlator.time_domain_axis = np.linspace(
0,
len(correlator.time_domain_signals[0]) / fs,
len(correlator.time_domain_signals[0]),
endpoint=False)
#plt.show()
df.df_impulse(args.d, t=timestamp)
exit()
correlator.set_accumulation_len(args.acc_len)
correlator.add_cable_length_calibrations(
'/home/jgowans/workspace/directionFinder_backend/config/cable_length_calibration_actual_array.json'
)
correlator.add_frequency_bin_calibrations(
'/home/jgowans/workspace/directionFinder_backend/config/frequency_domain_calibration_through_chain.json'
)
df = DirectionFinder(correlator, array, args.f_start,
logger.getChild('df'))
if args.impulse == True:
df.set_time() # go into time mode
# 100 impulse filter len = 0.5 us
correlator.set_impulse_filter_len(100)
correlator.set_impulse_setpoint(args.impulse_setpoint)
correlator.re_sync()
time.sleep(0.1)
correlator.impulse_arm()
while True:
if args.impulse == True:
if df.fetch_impulse() == True:
correlator.save_time_domain_snapshots(df_raw_dir)
# not necessary to apply cal as it's done in the correlation routine
df.df_impulse(df_raw_dir)
else:
df.fetch_frequency_crosses()
correlator.save_frequency_correlations(df_raw_dir)
correlator.apply_frequency_domain_calibrations()
df.df_strongest_signal(args.f_start, args.f_stop, df_raw_dir)
os.mkdir(df_raw_dir)
array = AntennaArray.mk_from_config(args.array_geometry_file)
correlator = Correlator(logger = logger.getChild('correlator'))
correlator.set_accumulation_len(args.acc_len)
correlator.add_cable_length_calibrations('/home/jgowans/workspace/directionFinder_backend/config/cable_length_calibration_actual_array.json')
correlator.add_frequency_bin_calibrations('/home/jgowans/workspace/directionFinder_backend/config/frequency_domain_calibration_through_chain.json')
df = DirectionFinder(correlator, array, args.f_start, logger.getChild('df'))
if args.impulse == True:
df.set_time() # go into time mode
# 100 impulse filter len = 0.5 us
correlator.set_impulse_filter_len(100)
correlator.set_impulse_setpoint(args.impulse_setpoint)
correlator.re_sync()
time.sleep(0.1)
correlator.impulse_arm()
while True:
if args.impulse == True:
if df.fetch_impulse() == True:
correlator.save_time_domain_snapshots(df_raw_dir)
# not necessary to apply cal as it's done in the correlation routine
df.df_impulse(df_raw_dir)
else:
df.fetch_frequency_crosses()
correlator.save_frequency_correlations(df_raw_dir)
correlator.apply_frequency_domain_calibrations()
df.df_strongest_signal(args.f_start, args.f_stop, df_raw_dir)
