def plot_channel_histogram(time_diff,
channel_start,
channel_stop,
n_bins,
fit_function=None,
param_names=None,
param_units=None,
p0=None,
bounds=(-numpy.inf, numpy.inf),
save_fig=False,
range_hist=None,
x_min=0.,
label='',
ex_int=(numpy.inf, -numpy.inf),
title=''):
print('\n')
legend = '%s%d eventi' % (label, len(time_diff))
bin_width = int(1000 * (range_hist[1] - range_hist[0]) / n_bins)
ylabel = 'ev./%d ns ' % (bin_width)
if fit_function is not None:
plt.subplot(2, 1, 1)
bins, n, dn = plot_functions.plot_histogram(time_diff,
"$\Delta t$ [$\mu$s]",
ylabel,
n_bins=n_bins,
range=range_hist,
title=title,
legend=legend,
fmt='.b',
as_scatter=True)
opt, pcov = plot_functions.do_fit(bins,
n,
dn,
param_names,
param_units,
fit_function=fit_function,
p0=p0,
bounds=bounds,
x_min=x_min,
ex_int=ex_int)
l_likelihood = functions.gauss_log_likelihood(bins, n, dn,
fit_function, *opt)
plt.subplot(2, 1, 2)
residuals = n - fit_function(bins, *opt)
plot_functions.scatter_plot(bins,
residuals,
"$\Delta t$ [$\mu$s]",
'Res.',
dx=None,
dy=dn,
title='')
#if save_fig == True:
# figlabel = 'dt_%d_%d_%s.pdf' % (channel_start, channel_stop, label)
# plt.savefig('%s' % figlabel , format = 'pdf')
return l_likelihood
else:
bins, n, dn = plot_functions.plot_histogram(time_diff,
"$\Delta t$ [$\mu$s]",
ylabel,
n_bins=n_bins,
range=range_hist,
title=title,
legend=legend,
fmt='.b',
as_scatter=True)
return bins, n, dn
legend = '%d' % len(ch_stop)
bins, n, dn = plot_functions.plot_histogram(time_stop,
"time [$\mu$s]",
"",
n_bins=n_bins,
range=range_hist,
title=title,
legend=legend,
fmt='.b',
as_scatter=True)
opt_two_expo, pcov_two_expo = plot_functions.do_fit(
bins,
n,
dn,
param_names_2exp,
param_units_2exp,
fit_function=functions.two_expo,
p0=p0,
bounds=bounds,
x_min=fit_min,
x_max=x_max,
ex_int=ex_int)
#opt_expo, pcov_expo = plot_functions.do_fit(bins, n, dn, param_names = param_names, param_units = param_units, fit_function = functions.exponential, p0 = None, bounds = (-numpy.inf, numpy.inf), x_min = x_min, x_max = x_max)
plt.subplot(2, 1, 2)
residuals_two_expo = n - functions.two_expo(bins, *opt_two_expo)
plot_functions.scatter_plot(bins,
residuals_two_expo,
'time [$\mu$s]',
'residuals',
dx=None,
dy=dn,
title='')
#FIT DEI DATI CON SOPRA VERSO L'ALTO
index, channel_diff_up, time_diff_up = utilities.mask_array(ch, time, ch_start, ch_stop_up)
range_hist = (time_diff_up[time_diff_up > 0.].min(), x_max)
plt.figure()
plt.subplot(2, 1, 1)
l_likelihood = two_expo_fit.plot_channel_histogram(time_diff_up, ch_start, ch_stop_up, n_bins = n_bins_up, fit_function = functions.two_expo_integral, param_names = param_names_2exp, param_units = param_units_2exp, p0 = p0 , bounds = bounds, x_min = x_min, range_hist = range_hist, save_fig=save_fig)
#FIT DEI DATI CON SOPRA VERSO IL BASSO
index, channel_diff_down, time_diff_down = utilities.mask_array(ch, time, ch_start, ch_stop_down)
range_hist = (time_diff_down[time_diff_down > 0.].min(), x_max)
plt.figure()
plt.subplot(2, 1, 1)
l_likelihood = two_expo_fit.plot_channel_histogram(time_diff_down, ch_start, ch_stop_down, n_bins = n_bins_up, fit_function = functions.two_expo_integral, param_names = param_names_2exp, param_units = param_units_2exp, p0 = p0 , bounds = bounds, x_min = x_min, range_hist = range_hist, save_fig=save_fig)
#AGGREGANDO I DATI: SOPRA E SOTTO
ch_stop = numpy.concatenate((channel_diff_up, channel_diff_down))
time_stop = numpy.concatenate((time_diff_up, time_diff_down))
plt.figure()
title = ''
legend = '%d' % len(time_stop)
bins, n, dn = plot_functions.plot_histogram(time_stop, "time [$\mu$s]", "", n_bins = n_bins, range = range_hist, title = title, legend = legend, fmt = '.b', as_scatter = True)
opt, pcov = plot_functions.do_fit(bins, n, dn, param_names_2exp, param_units_2exp, fit_function = functions.two_expo_integral, p0 = p0, bounds = bounds, x_min = x_min, x_max = x_max)
bins_grid = numpy.linspace(0., 20., 1000)
plt.plot(bins_grid, functions.two_expo(bins_grid, *opt))
plt.ion()
plt.show()
plot_functions.scatter_plot(bins_center,
asimmetry,
'dt [$\mu$s]',
'Asimmetry ',
dy=asimmetry_err,
title='')
p0 = [0.1, 3., 0.0, 0.1]
bounds = (0., 0., -numpy.inf, -numpy.inf), (0.3, numpy.inf, 2 * numpy.pi,
+numpy.inf)
param_names = ['Amplitude', '$\omega$', '$\phi$', 'costant']
param_units = ['', 'MHz', 'rad', '']
opt_wave, pcov_wave = plot_functions.do_fit(bins_center,
asimmetry,
asimmetry_err,
param_names,
param_units,
functions.wave,
p0=p0,
bounds=bounds,
x_min=x_min,
x_max=x_max)
plt.subplot(3, 1, 2)
plot_functions.scatter_plot(bins_center,
asimmetry,
'dt [$\mu$s]',
'Asimmetry ',
dy=asimmetry_err,
title='')
p0 = [0.1, 3., 0.0, 0.1, 0.1]
bounds = (0., 0., -numpy.inf, -numpy.inf,
-numpy.inf), (0.3, numpy.inf, 2 * numpy.pi, +numpy.inf,
numpy.inf)
n_bins=n_bins,
x0=p0,
bounds=None,
fit_min=fit_min,
range_hist=(0., 15.),
title=title,
output_file=output_file)
plt.subplot(3, 1, (1, 2))
bounds = (0.0, 0.01, 0.02, 1.5, 0.), (numpy.inf, 1., 1.2, 5., 1000)
opt, pcov = plot_functions.do_fit(bin_center,
n,
dn,
param_names=param_names_2exp,
param_units=param_units_2exp,
fit_function=functions.two_expo_integral,
p0=p0,
show=True,
bounds=bounds,
draw_on_points=True,
output_file=output_file)
#MONTE CARLO SOTTRAENDO NOISE
plt.figure(figsize=[6.4, 7.])
n = n - functions.exponential(bin_center, norm_noise, tau_noise,
cost_noise)
mask = (n > 0.)
bin_center = bin_center[mask]
n = n[mask]
dn = numpy.sqrt(n)
minus_two_ll_two_expo, bin_center, n, dn = likelihood_fit(
index, channel_diff_up, time_diff_up = utilities.mask_array(
ch_up, time_up, 1, 4)
mask = time_diff_up > 0.
time_diff_up = time_diff_up[mask]
print(min(time_diff_up))
bins, n, dn = two_expo_fit.plot_channel_histogram(time_diff_up,
1,
4,
n_bins=n_bins,
range_hist=range_hist,
title=title,
label='stop up, ')
plot_functions.do_fit(bins,
n,
dn,
param_names,
param_units,
fit_function=functions.exponential,
p0=None)
plt.xlim(-1., 15)
plt.ylim(-1., 25)
index, channel_diff_up, time_diff_up = utilities.mask_array(
ch_up, time_up, 1, 3)
plt.subplot(2, 1, 2)
mask = time_diff_up > 0.
time_diff_up = time_diff_up[mask]
print(min(time_diff_up))
bins, n, dn = two_expo_fit.plot_channel_histogram(time_diff_up,
1,
3,
n_bins=n_bins,