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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/>.
from __future__ import print_function, division
import yaml
try:
from yaml import CLoader as Loader
except ImportError:
from yaml.loader import SafeLoader as Loader
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")
SNOMAN_MASS = {
20: 0.511,
21: 0.511,
22: 105.658,
23: 105.658
}
AV_RADIUS = 600.0
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 chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
if __name__ == '__main__':
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
parser = argparse.ArgumentParser("plot fit results")
parser.add_argument("filenames", nargs='+', help="input files")
args = parser.parse_args()
fit_results = []
for filename in args.filenames:
print(filename)
with open(filename) as f:
data = yaml.load(f.read(),Loader=Loader)
for i, event in enumerate(data['data']):
for ev in event['ev']:
if 'fit' not in ev:
continue
for id, fit_result in [x for x in ev['fit'].iteritems() if isinstance(x[0],int)]:
# FIXME: Should I just store the particle ids in the YAML
# output as a list of particle ids instead of a single
# integer?
ids = map(int,chunks(str(id),2))
energy = 0.0
skip = False
for i, ke in zip(ids,np.atleast_1d(fit_result['energy'])):
energy += ke + SNOMAN_MASS[i]
# This is a bit of a hack. It appears that many times
# the fit will actually do much better by including a
# very low energy electron or muon. I believe the
# reason for this is that of course my likelihood
# function is not perfect (for example, I don't include
# the correct angular distribution for Rayleigh
# scattered light), and so the fitter often wants to
# add a very low energy electron or muon to fix things.
#
# Ideally I would fix the likelihood function, but for
# now we just discard any fit results which have a very
# low energy electron or muon.
if len(ids) > 1 and i == 20 and ke < 20.0:
skip = True
if len(ids) > 1 and i == 22 and ke < 200.0:
skip = True
if skip:
continue
# Calculate the approximate Ockham factor.
# See Chapter 20 in "Probability Theory: The Logic of Science" by Jaynes
#
# Note: This is a really approximate form by assuming that
# the shape of the likelihood space is equal to the average
# uncertainty in the different parameters.
w = len(ids)*np.log(0.1*0.001) + np.sum(np.log(fit_result['energy'])) + len(ids)*np.log(1e-4/(4*np.pi))
fit_results.append((
ev['run'],
ev['gtid'],
id,
fit_result['posx'],
fit_result['posy'],
fit_result['posz'],
fit_result['t0'],
energy,
fit_result['fmin'] - w,
fit_result['psi']/ev['nhit']))
# create a dataframe
# note: we have to first create a numpy structured array since there is no
# way to pass a list of data types to the DataFrame constructor. See
# https://github.com/pandas-dev/pandas/issues/4464
array = np.array(fit_results,
dtype=[('run',np.int), # run number
('gtid',np.int), # gtid
('id',np.int), # particle id
('x', np.double), # x
('y',np.double), # y
('z',np.double), # z
('t0',np.double), # t0
('ke',np.double), # kinetic energy
('fmin',np.double), # negative log likelihood
('psi',np.double)] # goodness of fit parameter
)
df = pd.DataFrame.from_records(array)
# get the best fit
df = df.sort_values('fmin').groupby(['run','gtid']).first()
# require r < 6 meters
df = df[np.sqrt(df.x.values**2 + df.y.values**2 + df.z.values**2) < AV_RADIUS]
# Note: Need to design and apply a psi based cut here, and apply the muon
# and neutron follower cuts.
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)
plt.hist(df_id.ke.values, bins=np.linspace(20,10e3,100), histtype='step')
plt.xlabel("Energy (MeV)")
plt.title(str(id))
plt.tight_layout()
plt.show()
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