diff options
Diffstat (limited to 'utils/plot')
| -rwxr-xr-x | utils/plot | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/utils/plot b/utils/plot new file mode 100755 index 0000000..c6d8b4f --- /dev/null +++ b/utils/plot @@ -0,0 +1,167 @@ +#!/usr/bin/env python +from __future__ import print_function, division +import yaml +import numpy as np +from scipy.stats import iqr +from matplotlib.lines import Line2D + +# 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 plot_legend(n): + plt.figure(n) + ax = plt.gca() + handles, labels = ax.get_legend_handles_labels() + new_handles = [Line2D([],[],c=h.get_edgecolor()) for h in handles] + plt.legend(handles=new_handles,labels=labels) + +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 = [] + t_electron = [] + t_muon = [] + 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) + if IDP_E_MINUS in event['ev'][0]['fit']: + t_electron.append(event['ev'][0]['fit'][IDP_E_MINUS]['time']) + if IDP_MU_MINUS in event['ev'][0]['fit']: + t_muon.append(event['ev'][0]['fit'][IDP_MU_MINUS]['time']) + + 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(7) + plot_hist(np.array(t_electron)/1e3/60.0, label=filename) + plt.xlabel(r"Electron Fit time (minutes)") + plt.figure(8) + plot_hist(np.array(t_muon)/1e3/60.0, label=filename) + plt.xlabel(r"Muon Fit time (minutes)") + + plot_legend(1) + plot_legend(2) + plot_legend(3) + plot_legend(4) + plot_legend(5) + plot_legend(6) + plot_legend(7) + plot_legend(8) + plt.show() |