def update_plot_signal_noise(self, temp_norm_hist, decay_params, decay_cov,
current_time):
decay_error = np.sqrt(np.diag(decay_cov))
signal_amp = decay_params[0]
signal_std = decay_error[0]
self.signal_std_arr = np.append(self.signal_std_arr, signal_std)
# time
time_arr = np.append(self.plot_signal_amp.getData()[0], current_time)
# signal amplitude
self.plot_signal_amp.setData(
time_arr, np.append(self.plot_signal_amp.getData()[1], signal_amp))
# confidence bands
upper = pg.PlotDataItem(
self.plot_signal_amp.getData()[0],
self.plot_signal_amp.getData()[1] + 2 * self.signal_std_arr)
lower = pg.PlotDataItem(
self.plot_signal_amp.getData()[0],
self.plot_signal_amp.getData()[1] - 2 * self.signal_std_arr)
self.plot_signal_err.setCurves(upper, lower)
# last signal amplitude
self.plot_signal_line.setValue(signal_amp)
# noise amplitude
self.plot_noise_amp.setData(
time_arr,
np.append(self.plot_noise_amp.getData()[1], self.noise_std))
# restrict y range to the maximum amplitude
self.ax_signal_noise.setLimits(
yMax=np.max(self.plot_signal_amp.getData()[1]) * 1.1)
# noise fit to t^(-1/2)
if len(time_arr) > 3:
noise_data = self.plot_noise_amp.getData()[1]
noise_A, noise_A_err = get_noise_fit(noise_data, time_arr)
# extrapolate to the time at which the noise amplitude is equal to the max signal (t_1=(A/max_signal)**2)
# if there are no data points before
init_time = min((noise_A / signal_amp)**2, time_arr[0])
interpolated_meas_time = np.linspace(init_time, time_arr[-1],
len(time_arr) * 100)
fit_noise_amplitude = noise_amp_fun(interpolated_meas_time,
noise_A)
# update noise fit
self.plot_noise_fit.setData(interpolated_meas_time,
fit_noise_amplitude)
if self.noise_std < signal_amp:
self.ax_signal_noise.setTitle(
'Correlation signal and noise (T={:.0f} s)'.format(
(noise_A / signal_amp)**2))
else:
self.ax_signal_noise.setTitle('Correlation signal and noise')
def update_plot_signal_noise(self, temp_norm_hist, decay_params, decay_cov, current_time):
decay_error = np.sqrt(np.diag(decay_cov))
signal_amp = decay_params[0]
signal_std = decay_error[0]
self.signal_std_arr = np.append(self.signal_std_arr, signal_std)
# time
time_arr = np.append(self.plot_signal_amp.get_xdata(), current_time)
# signal amplitude
self.plot_signal_amp.set_ydata(np.append(self.plot_signal_amp.get_ydata(), signal_amp))
self.plot_signal_amp.set_xdata(time_arr)
# confidence bands
self.plot_signal_err.remove()
self.plot_signal_err = self.ax_signal_noise.fill_between(self.plot_signal_amp.get_xdata(),
y1=self.plot_signal_amp.get_ydata()+2*self.signal_std_arr,
y2=(self.plot_signal_amp.get_ydata()-2*self.signal_std_arr).clip(min=0),
color=FIT_ERROR_COLOR)
# last signal amplitude
self.plot_signal_line.set_ydata(signal_amp)
# noise amplitude
self.plot_noise_amp.set_ydata(np.append(self.plot_noise_amp.get_ydata(), self.noise_std))
self.plot_noise_amp.set_xdata(time_arr)
# noise fit to t^(-1/2)
if len(time_arr) > 3:
noise_data = self.plot_noise_amp.get_ydata()
noise_A, noise_A_err = get_noise_fit(noise_data, time_arr)
# extrapolate to the time at which the noise amplitude is equal to the max signal (t_1=(A/max_signal)**2)
# if there are no data points before
# max_signal = np.max(noise_data)
# init_time = min((noise_A/max_signal)**2, time_arr[0])
interpolated_meas_time = np.linspace(time_arr[0], time_arr[-1], len(time_arr)*100)
fit_noise_amplitude = np.interp(interpolated_meas_time, time_arr, noise_amp_fun(time_arr, noise_A))
# update noise fit
# self.plot_noise_fit.set_ydata(noise_amp_fun(time_arr, noise_A))
self.plot_noise_fit.set_ydata(fit_noise_amplitude)
self.plot_noise_fit.set_xdata(interpolated_meas_time)
if self.noise_std < signal_amp:
self.ax_signal_noise.set_title('Correlation signal and noise (T={:.0f} s)'.format((noise_A/signal_amp)**2))
else:
self.ax_signal_noise.set_title('Correlation signal and noise')
self.ax_signal_noise.relim()
self.ax_signal_noise.autoscale()
fit_function_name=fit_function_name,
expected_tau=expected_tau)
decay_error = np.sqrt(np.diag(decay_cov))
signal_amplitude = decay_params[0]
signal_amplitude_std = decay_error[0]
signal_decay_tau = decay_params[1]
signal_decay_std = decay_error[1]
if len(decay_params) > 2:
signal_time_offset = decay_params[2]
signal_time_offset_std = decay_error[2]
signal_noise_amplitudes = np.array(amplitudes)
experimental_time = signal_noise_amplitudes[:, 0]
noise_amplitudes = signal_noise_amplitudes[:, 4]
noise_A, noise_A_err = get_noise_fit(noise_amplitudes, experimental_time)
if noise_amplitudes[-1] < signal_amplitude:
crossing_time = (noise_A / signal_amplitude)**2
crossing_time_std = crossing_time * np.sqrt(
(signal_amplitude_std / signal_amplitude)**2 +
(noise_A_err / noise_A)**2)
else:
crossing_time = crossing_time_std = 0.0
# experimental time in the header, it should be the same as total_time
exp_time = header['TTResult_StopAfter'] * 1e-3 # in s
# log some info to file
logger.info("Counts on SYNC: {}.".format(synccnt))
logger.info("Counts on INPUT: {}.".format(inputcnt))
logger.info("Average counts on SYNC: {:.0f} cps.".format(synccnt /