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Diffstat (limited to 'utils/plot.py')
| -rwxr-xr-x | utils/plot.py | 167 |
1 files changed, 0 insertions, 167 deletions
diff --git a/utils/plot.py b/utils/plot.py deleted file mode 100755 index c6d8b4f..0000000 --- a/utils/plot.py +++ /dev/null @@ -1,167 +0,0 @@ -#!/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() |