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)