models

#!/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 final fit results along with sidebands for the dark matter
analysis. To run it just run:

    $ ./plot-energy [list of fit results]

Currently it will plot energy distributions for external muons, michel
electrons, atmospheric events with neutron followers, and prompt signal like
events. Each of these plots will have a different subplot for the particle ID
of the best fit, i.e. single electron, single muon, double electron, electron +
muon, or double muon.
"""
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")
            plt.xlabel("Energy (MeV)")
            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.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")
    args = parser.parse_args()

    setup_matplotlib(args.save)

    import matplotlib.pyplot as plt

    ev, fits = get_events(args.filenames)

    # First, do basic data cleaning which is done for all events.
    ev = ev[ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ITC | DC_BREAKDOWN) == 0]

    # 00-orphan cut
    ev = ev[(ev.gtid & 0xff) != 0]

    print("number of events after data cleaning = %i" % np.count_nonzero(ev.prompt))

    # Now, we select events tagged by the muon tag which should tag only
    # external muons. We keep the sample of muons since it's needed later to
    # identify Michel electrons and to apply the muon follower cut
    muons = ev[(ev.dc & DC_MUON) != 0]

    print("number of muons = %i" % len(muons))

    # Try to identify Michel electrons. Currently, the event selection is based
    # on Richie's thesis. Here, we do the following:
    #
    # 1. Apply more data cleaning cuts to potential Michel electrons
    # 2. Nhit >= 100
    # 3. It must be > 800 ns and less than 20 microseconds from a prompt event
    #    or a muon
    michel = ev[ev.michel]

    print("number of michel events = %i" % len(michel))

    print("number of events after neutron follower cut = %i" % np.count_nonzero(ev.prompt & (~ev.atm)))

    # remove events 200 microseconds after a muon
    ev = ev.groupby('run',group_keys=False).apply(muon_follower_cut)

    # Get rid of muon events in our main event sample
    ev = ev[(ev.dc & DC_MUON) == 0]

    prompt = ev[ev.prompt & ~ev.atm]

    atm = ev[ev.atm]

    print("number of events after muon cut = %i" % len(prompt))

    # Check to see if there are any events with missing fit information
    atm_ra = atm[['run','gtid']].to_records(index=False)
    muons_ra = muons[['run','gtid']].to_records(index=False)
    prompt_ra = prompt[['run','gtid']].to_records(index=False)
    michel_ra = michel[['run','gtid']].to_records(index=False)
    fits_ra = fits[['run','gtid']].to_records(index=False)

    if len(atm_ra) and np.count_nonzero(~np.isin(atm_ra,fits_ra)):
        print_warning("skipping %i atmospheric events because they are missing fit information!" % np.count_nonzero(~np.isin(atm_ra,fits_ra)))

    if len(muons_ra) and np.count_nonzero(~np.isin(muons_ra,fits_ra)):
        print_warning("skipping %i muon events because they are missing fit information!" % np.count_nonzero(~np.isin(muons_ra,fits_ra)))

    if len(prompt_ra) and np.count_nonzero(~np.isin(prompt_ra,fits_ra)):
        print_warning("skipping %i signal events because they are missing fit information!" % np.count_nonzero(~np.isin(prompt_ra,fits_ra)))

    if len(michel_ra) and np.count_nonzero(~np.isin(michel_ra,fits_ra)):
        print_warning("skipping %i Michel events because they are missing fit information!" % np.count_nonzero(~np.isin(michel_ra,fits_ra)))

    # Now, we merge the event info with the fitter info.
    #
    # Note: This means that the dataframe now contains multiple rows for each
    # event, one for each fit hypothesis.
    atm = pd.merge(fits,atm,how='inner',on=['run','gtid'])
    muons = pd.merge(fits,muons,how='inner',on=['run','gtid'])
    michel = pd.merge(fits,michel,how='inner',on=['run','gtid'])
    prompt = pd.merge(fits,prompt,how='inner',on=['run','gtid'])

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

    if np.count_nonzero(nan):
        print_warning("skipping %i signal events because the negative log likelihood is nan!" % len(prompt[nan].groupby(['run','gtid'])))

    prompt = prompt[~nan]

    nan_atm = np.isnan(atm.fmin.values)

    if np.count_nonzero(nan_atm):
        print_warning("skipping %i atmospheric events because the negative log likelihood is nan!" % len(atm[nan_atm].groupby(['run','gtid'])))

    atm = atm[~nan_atm]

    nan_muon = np.isnan(muons.fmin.values)

    if np.count_nonzero(nan_muon):
        print_warning("skipping %i muons because the negative log likelihood is nan!" % len(muons[nan_muon].groupby(['run','gtid'])))

    muons = muons[~nan_muon]

    nan_michel = np.isnan(michel.fmin.values)

    if np.count_nonzero(nan_michel):
        print_warning("skipping %i michel electron events because the negative log likelihood is nan!" % len(michel[nan_michel].groupby(['run','gtid'])))

    michel = michel[~nan_michel]

    # get the best fit
    prompt = prompt.sort_values('fmin').groupby(['run','gtid']).nth(0)
    atm = atm.sort_values('fmin').groupby(['run','gtid']).nth(0)
    michel_best_fit = michel.sort_values('fmin').groupby(['run','gtid']).nth(0)
    muon_best_fit = muons.sort_values('fmin').groupby(['run','gtid']).nth(0)
    muons = muons[muons.id == 22].sort_values('fmin').groupby(['run','gtid'],as_index=False).nth(0).reset_index(level=0,drop=True)

    # require (r/r_psup)^3 < 0.9
    prompt = prompt[prompt.r_psup < 0.9]
    atm = atm[atm.r_psup < 0.9]

    print("number of events after radius cut = %i" % len(prompt))

    # require psi < 6
    prompt = prompt[prompt.psi < 6]
    atm = atm[atm.psi < 6]

    print("number of events after psi cut = %i" % len(prompt))

    fig = plt.figure()
    plot_hist2(prompt)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("prompt.pdf")
        plt.savefig("prompt.eps")
    else:
        plt.suptitle("Without Neutron Follower")
    fig = plt.figure()
    plot_hist2(atm)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("atm.pdf")
        plt.savefig("atm.eps")
    else:
        plt.suptitle("With Neutron Follower")
    fig = plt.figure()
    plot_hist2(michel_best_fit)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("michel_electrons.pdf")
        plt.savefig("michel_electrons.eps")
    else:
        plt.suptitle("Michel Electrons")
    fig = plt.figure()
    plot_hist2(muon_best_fit,muons=True)
    despine(fig,trim=True)
    if len(muon_best_fit):
        plt.tight_layout()
    if args.save:
        plt.savefig("external_muons.pdf")
        plt.savefig("external_muons.eps")
    else:
        plt.suptitle("External Muons")

    # Plot the energy and angular distribution for external muons
    fig = plt.figure()
    plt.subplot(2,1,1)
    plt.hist(muons.ke.values, bins=np.logspace(3,7,100), histtype='step')
    plt.xlabel("Energy (MeV)")
    plt.gca().set_xscale("log")
    plt.subplot(2,1,2)
    plt.hist(np.cos(muons.theta.values), bins=np.linspace(-1,1,100), histtype='step')
    despine(fig,trim=True)
    plt.xlabel(r"$\cos(\theta)$")
    plt.tight_layout()
    if args.save:
        plt.savefig("muon_energy_cos_theta.pdf")
        plt.savefig("muon_energy_cos_theta.eps")
    else:
        plt.suptitle("Muons")

    # For the Michel energy plot, we only look at the single particle electron
    # fit
    michel = michel[michel.id == 20].sort_values('fmin').groupby(['run','gtid'],as_index=False).nth(0).reset_index(level=0,drop=True)

    stopping_muons = pd.merge(muons,michel,left_on=['run','gtid'],right_on=['run','muon_gtid'],suffixes=('','_michel'))

    if len(stopping_muons):
        # project muon to PSUP
        stopping_muons['dx'] = stopping_muons.apply(get_dx,axis=1)
        # energy based on distance travelled
        stopping_muons['T_dx'] = dx_to_energy(stopping_muons.dx)
        stopping_muons['dT'] = stopping_muons['energy1'] - stopping_muons['T_dx']

        fig = plt.figure()
        plt.hist((stopping_muons['energy1']-stopping_muons['T_dx'])*100/stopping_muons['T_dx'], bins=np.linspace(-100,100,200), histtype='step')
        despine(fig,trim=True)
        plt.xlabel("Fractional energy difference (\%)")
        plt.title("Fractional energy difference for Stopping Muons")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_fractional_energy_difference.pdf")
            plt.savefig("stopping_muon_fractional_energy_difference.eps")
        else:
            plt.title("Stopping Muon Fractional Energy Difference")

        # 100 bins between 50 MeV and 10 GeV
        bins = np.arange(50,10000,1000)

        pd_bins = pd.cut(stopping_muons['energy1'],bins)

        T = (bins[1:] + bins[:-1])/2

        dT = stopping_muons.groupby(pd_bins)['dT'].agg(['mean','sem','std',std_err,median,median_err,iqr_std,iqr_std_err])

        fig = plt.figure()
        plt.errorbar(T,dT['median']*100/T,yerr=dT['median_err']*100/T)
        despine(fig,trim=True)
        plt.xlabel("Kinetic Energy (MeV)")
        plt.ylabel(r"Energy bias (\%)")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_energy_bias.pdf")
            plt.savefig("stopping_muon_energy_bias.eps")
        else:
            plt.title("Stopping Muon Energy Bias")

        fig = plt.figure()
        plt.errorbar(T,dT['iqr_std']*100/T,yerr=dT['iqr_std_err']*100/T)
        despine(fig,trim=True)
        plt.xlabel("Kinetic Energy (MeV)")
        plt.ylabel(r"Energy resolution (\%)")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_energy_resolution.pdf")
            plt.savefig("stopping_muon_energy_resolution.eps")
        else:
            plt.title("Stopping Muon Energy Resolution")

    fig = plt.figure()
    bins=np.linspace(0,100,100)
    plt.hist(michel.ke.values, bins=bins, histtype='step', label="Dark Matter Fitter")
    if michel.size:
        plt.hist(michel[~np.isnan(michel.rsp_energy.values)].rsp_energy.values, bins=np.linspace(20,100,100), histtype='step',label="RSP")
    x = np.linspace(0,100,1000)
    y = michel_spectrum(x)
    y /= np.trapz(y,x=x)
    N = len(michel)
    plt.plot(x, N*y*(bins[1]-bins[0]), ls='--', color='k', label="Michel Spectrum")
    despine(fig,trim=True)
    plt.xlabel("Energy (MeV)")
    plt.tight_layout()
    plt.legend()
    if args.save:
        plt.savefig("michel_electrons_ke.pdf")
        plt.savefig("michel_electrons_ke.eps")
    else:
        plt.title("Michel Electrons")
    plt.show()
Age	Commit message (Collapse)	Author
2011-07-19	removed STL pmt models; pmt models are now built by calling rotate_extrude() ↵	Anthony LaTorre
	on a profile of the PMT model (see build_pmt() in solids/pmts.py). triangle intersection now allows one of the two coefficients multiplying the vectors which span the triangle to float slightly negative (up to -EPSILON; EPSILON is defined in src/intersect.h) in order to eliminate rays passing through the line between two triangles. cleaned up a lot of unused code. pulled duplicate code in view() and render() into functions in view.py. in order to allow view.py and render.py to search pre-defined geometries, solids, meshes, etc. without requiring them to be pre-built, pre-defined geometries, solids, meshes, etc. should be returned by a function tagged by the decorator @buildable(identifier) defined in view.py, where identifier is a string used to identify the object as an argument to either view.py or render.py. optical materials and surfaces are now defined in optics.py. added an image directory to save cool screenshots.
2011-07-10	added a hybrid monte carlo ray tracing rendering algorithm	Anthony LaTorre

2011-06-21	Fix PMT models to work with OpenSCAD, add reduced models cut just	Stan Seibert
	behind the PMT equator to save memory. Note: To repair a PMT produced by Sketchup, run Netfabb, start repair mode, and run the "Remove degenerate faces" action, and apply repair. Now you can export the part and OpenSCAD will be happy.
2011-06-19	reverse the face orientation on all triangles in the stl files exported from ↵	Anthony LaTorre
	sketchup.
2011-06-17	visually tested optics code. added models of the inner and outer meshes for ↵	Anthony LaTorre
	the 12" hamamatsu and sno pmts. ratdb.py is able to parse ratdb files. chromaticity.py provides a function to map wavelength -> rgb color. lbne detector model now includes an outer black cylinder and pmts with a glass layer and photocathode/reflective surfaces.
2011-06-12	added some fun models; added some untested code to implement absorption, ↵	Anthony LaTorre
	scattering, reflection, and refraction
2011-06-06	Thin lens with radius of curvature of 5 units, and thickness of 2 units.	Stan Seibert

2011-06-01	STL models for SNO, 10 inch and 12 inch PMTs in normal and high-resolution.	Stan Seibert

2011-05-26	cleanup	Anthony LaTorre

2011-05-20	fixed 12 inch hamamastu pmt mesh	Anthony LaTorre

2011-05-16	added 12 inch hamamatsu pmt model	Anthony LaTorre

2011-05-15	added a detector folder to put detector geometries	Anthony LaTorre

2011-05-13	add a miniature lbne model	Anthony LaTorre

2011-05-07	tie fighter	Anthony LaTorre

2011-05-06	triangle mesh intersection, stl reader, stl model of a lego minifigure	Anthony LaTorre