def Ti_histogram(T13, T23, bins=None, range_T13=None, range_T23=None, norm = None, legend = '', title = '', figlabel = '', save_fig = False):
plot_functions.histogram(T23, "$T_{23} [ns]$", "entries/bin", bins=bins, range=range_T23, f = False, title = title , legend = legend)
if save_fig == True:
plt.savefig('plot/T23%s.pdf' % figlabel, format = 'pdf')
plot_functions.histogram(T13, "$T_{13} [ns]$", "entries/bin", bins = bins, range=range_T23, f = False, title = title, legend = legend)
if save_fig == True:
plt.savefig('plot/T13%s.pdf' % figlabel, format = 'pdf')
plot_functions.hist2d(T23, T13, "$T_{23} [ns]$", "$T_{13} [ns]$", bins=bins, range_x = range_T23, range_y = range_T13, norm = norm, title = title , legend = legend )
if save_fig == True:
plt.savefig('plot/T13_T23%s.pdf' % figlabel, format = 'pdf')
r, p = pearsonr(T23, T13)
print("r, p T23 and T13:", r, p)
return
def tof_beta_histogram(TOF, T12, x, l, beta, bins=None, range_TOF=(-5, 20.), range_T12=None, range_x = (-20., 300.), range_beta = (0., 3.), legend = '', title = '', figlabel = '', save_fig = False):
plot_functions.histogram(TOF, "TOF[ns]", "entries/bin", bins = bins, range = range_TOF, f = False, title = title, legend = legend)
if save_fig is True:
plt.savefig('plot/TOF%s.pdf' % figlabel, format = 'pdf')
plot_functions.histogram(T12, "T12[ns]", "entries/bin", bins = bins, range = range_T12, f = False, title = title, legend = legend)
if save_fig is True:
plt.savefig('plot/T12%s.pdf' % figlabel, format = 'pdf')
plot_functions.histogram(x, "x [cm]", "entries/bin", bins=bins , range = range_x, f = False, title = title, legend = legend)
if save_fig is True:
plt.savefig('plot/x%s.pdf' % figlabel, format = 'pdf')
plot_functions.histogram(beta, "$beta [cm/ns]$", "entries/bin", bins= bins , range = range_beta, f = False, title = title, legend = legend)
if save_fig is True:
plt.savefig('plot/beta%s.pdf' % figlabel, format = 'pdf')
plot_functions.hist2d(TOF, l, "TOF [ns]", "l[cm]", bins=bins, range_x = range_TOF, range_y = (110., 280.), norm = LogNorm())
if save_fig is True:
plt.savefig('plot/tof_l_2dhist%s.pdf' % figlabel, format = 'pdf')
return
plot_functions.multiple_histogram(theta[mask],
phi[mask],
"theta[mask]",
"phi[mask]",
bins=45,
range_var1=(-numpy.pi, numpy.pi),
range_var2=(0., numpy.pi * 2))
plot_functions.multiple_histogram(x3, y3, "x3", "y3", bins=45)
plot_functions.multiple_histogram(x3[mask],
y3[mask],
"x3[mask]",
"y3[mask]",
bins=45)
plot_functions.multiple_histogram(x1, y1, "x1", "y1", bins=45)
plot_functions.multiple_histogram(x1[mask],
y1[mask],
"x1[mask]",
"y1[mask]",
bins=45)
plot_functions.histogram(E[mask],
"E[mask]",
"",
bins=None,
range=(0., 1000),
f=False,
density=False,
title='',
legend='')
plt.ion()
plt.show()
options = vars(options_parser.parse_args())
input_file_sim = options['input_file_simulation']
save_fig = options['save_fig']
position = options['s3_position']
E, P, beta, x1, y1, theta, phi, x3, y3, f = numpy.loadtxt(input_file_sim,
unpack=True)
mask = (f == 1)
title = 'Simulazione Monte Carlo per x = %d cm ' % position
range = (position - 20., position + 20.)
plot_functions.histogram(x1[mask] * 100,
"$x_{t}$[cm]",
"entries/bin",
bins=150,
range=range,
f=False,
density=False,
title=title,
legend='')
delay_T13 = 26.1
delay_T23 = 26.2
res = 0.
TOF_sim = signal_propagation_functions.Time_Of_Flight(
x1[mask], x3[mask], y1[mask], y3[mask], 0., beta[mask])
T13_sim, _ = signal_propagation_functions.DT_13(x1[mask],
x3[mask],
y3[mask],
delay_T13,
TOF_sim,
#E[MeV], P [MeV], beta, x1[m], y1[cm], theta, phi, x3[m], y3[cm], flag
E, P, beta, x1, y1, theta, phi, x3, y3, f = numpy.loadtxt(input_file,
unpack=True)
mask = f > 0.5
print("efficienza/tot:", numpy.sum(f), len(f))
delay_T13 = numpy.ones(int(numpy.sum(f))) * 25.0 #ns
delay_T23 = numpy.ones(int(numpy.sum(f))) * 25.1 #ns
delay_T12 = delay_T13
z_13 = 1.22
if (plot_flag == True):
plot_functions.histogram(E,
'E [MeV]',
"entries/bin",
bins=70,
range=(150., 3000),
f=False,
title='Spettro degli eventi generati',
density=False)
plot_functions.multiple_histogram(theta,
phi,
'$\Theta[rad]$',
"$\Phi[rad]$",
bins=70,
range_var1=(-numpy.pi, numpy.pi),
range_var2=(0., numpy.pi * 2),
title='')
plot_functions.multiple_histogram(theta[mask],
phi[mask],
"$\Theta_{S3}[rad]$",