errors = numpy.sqrt(n)
bin_centers = 0.5 * (bins[1:] + bins[:-1])
mask = (n > 0.)
x = bin_centers[mask]
y = n[mask]
dy = errors[mask]
plt.figure("Time distribution")
plt.errorbar(x, y, yerr=dy, fmt='o')
p0 = (10., 0.1)
opt, pcov = curve_fit(fit_functions.exponential, x, y, sigma = dy, p0=p0)
param_errors = numpy.sqrt(pcov.diagonal())
residuals = y - fit_functions.exponential(x, *opt)
chi2 = numpy.sum((residuals/dy)**2)
ndof = len(y)-len(opt)
param_names = ['amplitude', 'rate']
param_units = ['', 'Hz']
legend = plot_functions.fit_legend(opt, param_errors, param_names, param_units, chi2, ndof)
title = '%d eventi %d secondi, 24/03/21' % (len(t), t_run)
bin_grid = numpy.linspace(*range, 1000)
plt.plot(bin_grid, fit_functions.exponential(bin_grid, *opt), '-r', label = legend)
plot_functions.set_plot("$\Delta t [s]$", "entries/bin", title = title)
plt.ion()
plt.show()
def convolution_and_fit(T_sim, T_measured, xlabel, ylabel, data_bins, data_range, sim_bins, sim_range, title = None, figlabel='', save_fig = False):
#Calcola la spline
plt.figure()
n, bins, patches = plt.hist(T_sim, bins = sim_bins, range = sim_range)
bin_centers = 0.5 * (bins[1:] + bins[:-1])
x = bin_centers
y = n
polynomial_f = interp1d(x, y, kind='linear')
plt.plot(x, polynomial_f(x), '-', label = 'Spline geometria')
plot_functions.set_plot(xlabel, ylabel, title='Simulazione Monte Carlo')
if save_fig ==True:
plt.savefig('risoluzione/plot/spline%s.pdf' % figlabel, format = 'pdf')
data_mean = T_measured.mean()
p0 = [0.15, len(T_measured)/data_bins, data_mean - 1., 4., data_mean, 0.9]
bounds = (0.1, 0., data_mean*0.7, 1., data_mean*0.7 , 0.3 ), (0.4, numpy.inf, data_mean*1.3, 6., data_mean*1.3, 1.)
#calcola i parametri della doppia gaussiana e calcola la convoluzione
opt_true, pcov_true = plot_functions.fit2gauss(T_measured, xlabel, ylabel, bins = data_bins, range=data_range, f = True, p0=p0,
bounds = bounds, title = title)
if save_fig ==True:
plt.savefig('risoluzione/plot/2gauss_fit%s.pdf' % figlabel, format = 'pdf')
convolved_fit_function = fit_functions.create_convolution(polynomial_f, fit_functions.two_gauss)
#Fa il fit
plt.figure()
ni, bins, pat = plt.hist(T_measured, bins = data_bins, range=data_range)
x = 0.5 * (bins[1:] + bins[:-1])
nj = ni
mask_fit = nj>0
middle_point = (min(x[mask_fit]) + max(x[mask_fit])) * 0.5
p0 = numpy.copy(opt_true)
p0[2] = middle_point - (opt_true[4] - opt_true[2])
p0[4] = middle_point
bounds = (0.1, 0., p0[2]*0.9, 1., p0[4]*0.9 , 0.4 ), (0.4, numpy.inf, p0[2]*1.1, 6., p0[4]*1.1, 1.)
opt, pcov = curve_fit(convolved_fit_function, x[mask_fit], nj[mask_fit], p0 = p0, bounds = bounds)
chi2 = (nj[mask_fit] - convolved_fit_function(x[mask_fit], *opt))**2 / nj[mask_fit]
chi2 = chi2.sum()
ndof = len(nj[mask_fit])- len(opt)
param_errors = numpy.sqrt(pcov.diagonal())
param_values = numpy.copy(opt)
param_values[2] = param_values[2] - middle_point
param_values[4] = param_values[4] - middle_point
param_names = ['fraction', 'norm', '$\mu_{1}$', '$\sigma_1$', '$\mu_{2}$', '$\sigma_2$']
param_units = ['', 'ns$^{-1}$', 'ns', 'ns', 'ns', 'ns']
legend = plot_functions.fit_legend(param_values, param_errors, param_names, param_units, chi2, ndof)
x_grid = x[mask_fit]
y = convolved_fit_function(x_grid, *opt)
plt.plot(x_grid, y, '-', label = legend)
plot_functions.set_plot(xlabel, ylabel, title = title)
if save_fig ==True:
plt.savefig('risoluzione/plot/conv_fit%s.pdf' % figlabel, format = 'pdf')
plt.figure()
middle_point = (bins[-1] + bins[0]) * 0.5
integral = numpy.sum(fit_functions.two_gauss(x_grid, *opt))
plt.plot(x_grid - middle_point, fit_functions.two_gauss(x_grid, *opt)/integral, 'r-', label='Response function')
plot_functions.set_plot('Time [s]', 'pdf', title = 'Risoluzione ')
if save_fig ==True:
plt.savefig('risoluzione/plot/risoluzione%s.pdf' % figlabel, format = 'pdf')
return opt_true, pcov_true, param_values, pcov
electron_down.append(-25.)
material_depth = numpy.array(material_depth)
electron_up = numpy.array(electron_up)
electron_down = numpy.array(electron_down)
epsilon_electron = numpy.array(epsilon_electron)
epsilon_muon = numpy.array(epsilon_muon)
epsilon = epsilon_electron * epsilon_muon
electron_e_mean_range = spline_electron(37.25)
title = '%s' % (element)
plt.figure()
plt.plot(material_depth, epsilon_electron, '-r', label='Elettroni')
plt.plot(material_depth, epsilon_muon, '-b', label='Muoni')
plot_functions.set_plot("$\Delta x$ [cm]", "$N_i$/$N_{tot}$", title=title)
if save_fig == True:
plt.savefig('mu_ele_epsilon_%s.pdf' % element, format='pdf')
plt.figure()
title = '%s' % (element)
plt.plot(material_depth, epsilon, '-')
plot_functions.set_plot("$\Delta x$ [cm]", "$\epsilon$", title=title)
plt.axvline(electron_e_mean_range, color='g')
if save_fig == True:
plt.savefig('epsilon_%s.pdf' % element, format='pdf')
plt.figure()
plt.plot(material_depth, electron_up, '-', label='up')
plt.plot(material_depth, electron_down, '-', label='down')
plt.ylim(-0.1, 1.1)
'Scintillator6': ('dati_15_04/Vss_40mv_6.txt', 'dati_15_04/Vss_70mv_6.txt',
'dati_20_04/s6.txt', 'dati_20_04/s6.txt')
}
plt.figure()
date = '15/04/21'
for scintillator in data_dict:
data_file = data_dict[scintillator][0]
Vss, counts, time = numpy.loadtxt(data_file, unpack=True)
#Vth = 40. #mV
title = '%s' % (date)
rate = counts / time
dcounts = numpy.sqrt(counts)
drate = dcounts / time
plt.errorbar(Vss, rate, yerr=drate, label=scintillator)
plot_functions.set_plot('$V_a$ [V]', 'Rate [Hz]', title=title)
plt.figure()
for scintillator in data_dict:
data_file = data_dict[scintillator][1]
Vss, counts, time = numpy.loadtxt(data_file, unpack=True)
#Vth = 70. #mV
title = '%s' % (date)
rate = counts / time
dcounts = numpy.sqrt(counts)
drate = dcounts / time
plt.errorbar(Vss, rate, yerr=drate, label=scintillator)
plot_functions.set_plot('$V_a$ [V]', 'Rate [Hz]', title=title)
date = '20/04/21'
plt.figure()
utilities.rate_and_saturation(t, ch0, ch1)
t_run = utilities.acquisition_duration(t)
T12, tau = utilities.TAC_scale(ch0, ch1)
mask_0 = T12 > 0.0
T12 = T12[mask_0]
T12 = numpy.concatenate((t12, T12))
mean = numpy.mean(T12)
print("mean", mean)
bins = 101
range = (0., 70.)
title = '%d eventi %d secondi, %s' % (len(T12), t_run, '31/03/21')
plt.figure()
n, bins = numpy.histogram(T12, bins = bins, range = range, density = None)
n = n/n.max()
n, bins, patches = plt.hist(bins[1:], weights=n, bins = bins, label = '', alpha = 0.4)
plot_functions.set_plot('$T_{12}$ [ns]', 'a.u.', title = title)
#T12 = T12 - mean
#x = (geometry.X1*100 - T12/numpy.abs(m))*0.5
#plot_functions.histogram(x, "x [cm]", "", f = False, bins = bins)
plt.ion()
plt.show()
input_file, unpack=True)
plt.figure()
plt.subplot(2, 1, 1)
plt.errorbar(x,
mean1_13,
yerr=dmean1_13,
fmt='.r',
label='$\mu_{1}$ $T_{13}$')
plt.errorbar(x,
mean2_13,
yerr=dmean2_13,
fmt='.b',
label='$\mu_{2}$ $T_{13}$')
plot_functions.set_plot('x [cm]', '$\mu(T_{13})$', title='')
plt.subplot(2, 1, 2)
plt.errorbar(x,
mean1_23,
yerr=dmean1_23,
fmt='.r',
label='$\mu_{1}$ $T_{23}$')
plt.errorbar(x,
mean2_23,
yerr=dmean2_23,
fmt='.b',
label='$\mu_{2}$ $T_{23}$')
plot_functions.set_plot('x [cm]', '$\mu(T_{23})$', title='')
if save_fig == True: