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#!/usr/bin/env python
# Copyright (c) 2019, Anthony Latorre <tlatorre at uchicago>
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <https://www.gnu.org/licenses/>.
"""
Script to plot reconstructed quantities for instrumentals. To run it just run:
$ ./plot-dc [list of fit results]
This will produce corner plots showing the distribution of the high level
variables used in the contamination analysis for all the different instrumental
backgrounds and external muons.
"""
from __future__ import print_function, division
import numpy as np
from scipy.stats import iqr, poisson
from matplotlib.lines import Line2D
from scipy.stats import iqr, norm, beta
from scipy.special import spence
from itertools import chain
particle_id = {20: 'e', 22: r'\mu'}
def plot_hist2(df, muons=False):
for id, df_id in sorted(df.groupby('id')):
if id == 20:
plt.subplot(2,3,1)
elif id == 22:
plt.subplot(2,3,2)
elif id == 2020:
plt.subplot(2,3,4)
elif id == 2022:
plt.subplot(2,3,5)
elif id == 2222:
plt.subplot(2,3,6)
if muons:
plt.hist(np.log10(df_id.ke.values/1000), bins=np.linspace(0,4.5,100), histtype='step')
plt.xlabel("log10(Energy (GeV))")
else:
bins = np.logspace(np.log10(20),np.log10(10e3),21)
plt.hist(df_id.ke.values, bins=bins, histtype='step')
plt.gca().set_xscale("log")
major = np.array([10,100,1000,10000])
minor = np.unique(list(chain(*list(range(i,i*10,i) for i in major[:-1]))))
minor = np.setdiff1d(minor,major)
plt.gca().set_xticks(major)
plt.gca().set_xticks(minor,minor=True)
plt.xlabel("Energy (MeV)")
plt.title('$' + ''.join([particle_id[int(''.join(x))] for x in grouper(str(id),2)]) + '$')
if len(df):
plt.tight_layout()
def plot_hist(df, muons=False):
for id, df_id in sorted(df.groupby('id')):
if id == 20:
plt.subplot(3,4,1)
elif id == 22:
plt.subplot(3,4,2)
elif id == 2020:
plt.subplot(3,4,5)
elif id == 2022:
plt.subplot(3,4,6)
elif id == 2222:
plt.subplot(3,4,7)
elif id == 202020:
plt.subplot(3,4,9)
elif id == 202022:
plt.subplot(3,4,10)
elif id == 202222:
plt.subplot(3,4,11)
elif id == 222222:
plt.subplot(3,4,12)
if muons:
plt.hist(np.log10(df_id.ke.values/1000), bins=np.linspace(0,4.5,100), histtype='step')
plt.xlabel("log10(Energy (GeV))")
else:
plt.hist(df_id.ke.values, bins=np.linspace(20,10e3,100), histtype='step')
plt.xlabel("Energy (MeV)")
plt.title(str(id))
if len(df):
plt.tight_layout()
if __name__ == '__main__':
import argparse
import numpy as np
import pandas as pd
import sys
import h5py
from sddm.plot_energy import *
from sddm.plot import *
from sddm import setup_matplotlib
parser = argparse.ArgumentParser("plot fit results")
parser.add_argument("filenames", nargs='+', help="input files")
parser.add_argument("--save", action='store_true', default=False, help="save corner plots for backgrounds")
parser.add_argument("--nhit-thresh", type=int, default=None, help="nhit threshold to apply to events before processing (should only be used for testing to speed things up)")
args = parser.parse_args()
setup_matplotlib(args.save)
import matplotlib.pyplot as plt
# Loop over runs to prevent using too much memory
evs = []
data_filenames = [filename for filename in args.filenames if 'SNO' in filename]
mc_filenames = [filename for filename in args.filenames if 'SNO' not in filename]
if len(data_filenames):
rhdr = pd.concat([read_hdf(filename, "rhdr").assign(filename=filename) for filename in data_filenames],ignore_index=True)
for run, df in rhdr.groupby('run'):
evs.append(get_events(df.filename.values, merge_fits=True, nhit_thresh=args.nhit_thresh))
if len(mc_filenames):
evs.append(get_events(mc_filenames, merge_fits=True, nhit_thresh=args.nhit_thresh))
ev = pd.concat(evs)
ev = ev[ev.prompt & ~np.isnan(ev.fmin)]
ev = ev[ev.ke > 20]
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print("Noise events")
print(ev[ev.noise][['psi','x','y','z','id1','id2']])
print("Muons")
print(ev[ev.muon][['psi','r','id1','id2','id3','energy1','energy2','energy3']])
print("Neck")
print(ev[ev.neck & ev.psi < 6][['psi','r','id1','cos_theta']])
print("Flashers")
print(ev[ev.flasher & ev.udotr > 0])
print("Signal")
print(ev[ev.signal])
# save as PDF b/c EPS doesn't support alpha values
if args.save:
plot_corner_plot(ev[ev.breakdown],"Breakdowns",save="breakdown_corner_plot")
plot_corner_plot(ev[ev.muon],"Muons",save="muon_corner_plot")
plot_corner_plot(ev[ev.flasher],"Flashers",save="flashers_corner_plot")
plot_corner_plot(ev[ev.neck],"Neck",save="neck_corner_plot")
plot_corner_plot(ev[ev.noise],"Noise",save="noise_corner_plot")
plot_corner_plot(ev[ev.signal],"Signal",save="signal_corner_plot")
else:
plot_corner_plot(ev[ev.breakdown],"Breakdowns")
plot_corner_plot(ev[ev.muon],"Muons")
plot_corner_plot(ev[ev.flasher],"Flashers")
plot_corner_plot(ev[ev.neck],"Neck")
plot_corner_plot(ev[ev.noise],"Noise")
plot_corner_plot(ev[ev.signal],"Signal")
fig = plt.figure()
plot_hist2(ev[ev.flasher])
despine(fig,trim=True)
plt.suptitle("Flashers")
fig = plt.figure()
plot_hist2(ev[ev.muon],muons=True)
despine(fig,trim=True)
plt.suptitle("Muons")
plt.show()
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