1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
|
#!/usr/bin/env python
from __future__ import print_function, division
import yaml
import numpy as np
from scipy.stats import iqr
# on retina screens, the default plots are way too small
# by using Qt5 and setting QT_AUTO_SCREEN_SCALE_FACTOR=1
# Qt5 will scale everything using the dpi in ~/.Xresources
import matplotlib
matplotlib.use("Qt5Agg")
IDP_E_MINUS = 20
IDP_MU_MINUS = 22
SNOMAN_MASS = {
20: 0.511,
21: 0.511,
22: 105.658,
23: 105.658
}
def plot_hist(x, label=None):
# determine the bin width using the Freedman Diaconis rule
# see https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule
h = 2*iqr(x)/len(x)**(1/3)
n = max(int((np.max(x)-np.min(x))/h),10)
bins = np.linspace(np.min(x),np.max(x),n)
plt.hist(x, bins=bins, histtype='step', label=label)
def get_stats(x):
"""
Returns a tuple (mean, error mean, std, error std) for the values in x.
The formula for the standard error on the standard deviation comes from
https://stats.stackexchange.com/questions/156518.
"""
mean = np.mean(x)
std = np.std(x)
n = len(x)
u4 = np.mean((x-mean)**4)
error = np.sqrt((u4-(n-3)*std**4/(n-1))/n)/(2*std)
return mean, std/np.sqrt(n), std, error
if __name__ == '__main__':
import argparse
import matplotlib.pyplot as plt
import numpy as np
parser = argparse.ArgumentParser("plot fit results")
parser.add_argument("filenames", nargs='+', help="input files")
args = parser.parse_args()
for filename in args.filenames:
print(filename)
with open(filename) as f:
data = yaml.load(f.read())
dx = []
dy = []
dz = []
dT = []
thetas = []
likelihood_ratio = []
for event in data['data']:
# get the particle ID
id = event['mctk'][-1]['id']
mass = SNOMAN_MASS[id]
# for some reason it's the *second* track which seems to contain the
# initial energy
true_energy = event['mctk'][-1]['energy']
# The MCTK bank has the particle's total energy (except for neutrons)
# so we need to convert it into kinetic energy
ke = true_energy - mass
energy = event['ev'][0]['fit'][id]['energy']
dT.append(energy-ke)
true_posx = event['mcvx'][0]['posx']
posx = event['ev'][0]['fit'][id]['posx']
dx.append(posx-true_posx)
true_posy = event['mcvx'][0]['posy']
posy = event['ev'][0]['fit'][id]['posy']
dy.append(posy-true_posy)
true_posz = event['mcvx'][0]['posz']
posz = event['ev'][0]['fit'][id]['posz']
dz.append(posz-true_posz)
dirx = event['mctk'][-1]['dirx']
diry = event['mctk'][-1]['diry']
dirz = event['mctk'][-1]['dirz']
true_dir = [dirx,diry,dirz]
true_dir = np.array(true_dir)/np.linalg.norm(true_dir)
theta = event['ev'][0]['fit'][id]['theta']
phi = event['ev'][0]['fit'][id]['phi']
dir = [np.sin(theta)*np.cos(phi),np.sin(theta)*np.sin(phi),np.cos(theta)]
dir = np.array(dir)/np.linalg.norm(dir)
thetas.append(np.degrees(np.arccos(np.dot(true_dir,dir))))
fmin_electron = event['ev'][0]['fit'][IDP_E_MINUS]['fmin']
fmin_muon = event['ev'][0]['fit'][IDP_MU_MINUS]['fmin']
likelihood_ratio.append(fmin_muon-fmin_electron)
mean, mean_error, std, std_error = get_stats(dT)
print("dT = %.2g +/- %.2g" % (mean, mean_error))
print("std(dT) = %.2g +/- %.2g" % (std, std_error))
mean, mean_error, std, std_error = get_stats(dx)
print("dx = %4.2g +/- %.2g" % (mean, mean_error))
print("std(dx) = %4.2g +/- %.2g" % (std, std_error))
mean, mean_error, std, std_error = get_stats(dy)
print("dy = %4.2g +/- %.2g" % (mean, mean_error))
print("std(dy) = %4.2g +/- %.2g" % (std, std_error))
mean, mean_error, std, std_error = get_stats(dz)
print("dz = %4.2g +/- %.2g" % (mean, mean_error))
print("std(dz) = %4.2g +/- %.2g" % (std, std_error))
mean, mean_error, std, std_error = get_stats(thetas)
print("std(theta) = %4.2g +/- %.2g" % (std, std_error))
plt.figure(1)
plot_hist(dT, label=filename)
plt.xlabel("Kinetic Energy difference (MeV)")
plt.figure(2)
plot_hist(dx, label=filename)
plt.xlabel("X Position difference (cm)")
plt.figure(3)
plot_hist(dy, label=filename)
plt.xlabel("Y Position difference (cm)")
plt.figure(4)
plot_hist(dz, label=filename)
plt.xlabel("Z Position difference (cm)")
plt.figure(5)
plot_hist(thetas, label=filename)
plt.xlabel(r"$\theta$ (deg)")
plt.figure(6)
plot_hist(likelihood_ratio, label=filename)
plt.xlabel(r"Log Likelihood Ratio ($e/\mu$)")
plt.figure(1)
plt.legend()
plt.figure(2)
plt.legend()
plt.figure(3)
plt.legend()
plt.figure(4)
plt.legend()
plt.figure(5)
plt.legend()
plt.figure(6)
plt.legend()
plt.show()
|
