path: root/utils/plot-energy

#!/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

# Data cleaning bitmasks.
DC_MUON           = 0x1
DC_JUNK           = 0x2
DC_CRATE_ISOTROPY = 0x4
DC_QVNHIT         = 0x8
DC_NECK           = 0x10
DC_FLASHER        = 0x20
DC_ESUM           = 0x40
DC_OWL            = 0x80
DC_OWL_TRIGGER    = 0x100
DC_FTS            = 0x200

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
    import sys

    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:
                    fit_results.append((
                        ev['run'],
                        ev['gtr'],
                        ev['nhit'],
                        ev['gtid'],
                        ev['dc'],
                        0,
                        np.nan,
                        np.nan,
                        np.nan,
                        np.nan,
                        np.nan,
                        np.nan,
                        np.nan))
                    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['gtr'],
                        ev['nhit'],
                        ev['gtid'],
                        ev['dc'],
                        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
                            ('gtr',np.double),   # 50 MHz clock in ns
                            ('nhit',np.int),     # number of PMTs hit
                            ('gtid',np.int),     # gtid
                            ('dc',np.int),       # data cleaning word
                            ('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)

    # remove events 200 microseconds after a muon
    muons = df[(df.dc & DC_MUON) != 0]

    print(len(df))
    print("nmuons = %i" % len(muons))

    df = df[(df.dc & DC_MUON) == 0]

    if muons.size:
        # FIXME: need to deal with 50 MHz clock rollover
        df = df[~np.any((df.gtr.values > muons.gtr.values[:,np.newaxis]) & (df.gtr.values <= (muons.gtr.values[:,np.newaxis] + 200e3)),axis=0)]

    print(len(df))

    # perform prompt event data cleaning
    df = df[df.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK) == 0]

    print(len(df))

    # apply prompt event selection
    df = df[df.nhit >= 100]

    print(len(df))

    # get rid of events which don't have a fit
    nan = np.isnan(df.fmin.values)
    df = df[~nan]

    if np.count_nonzero(nan):
        print("skipping %i events because they are missing fit information!" % np.count_nonzero(nan),file=sys.stderr)

    # 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()