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#!/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']
if id not in event['ev'][0]['fit']:
continue
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))))
if IDP_E_MINUS in event['ev'][0]['fit'] and IDP_MU_MINUS in event['ev'][0]['fit']:
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()
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