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diff --git a/utils/plot-michels b/utils/plot-michels
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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 the fit results for stopping muons and Michels. To run it just
+run:
+
+    $ ./plot-muon [list of fit results]
+
+Currently it will plot energy distributions for external muons, stopping muons,
+and michel electrons.
+"""
+from __future__ import print_function, division
+import numpy as np
+from scipy.stats import iqr, poisson
+from scipy.stats import iqr, norm, beta
+from sddm.stats import *
+
+particle_id = {20: 'e', 22: 'u'}
+
+def print_particle_probs(data):
+    n = [len(data[data.id == id]) for id in (20,22,2020,2022,2222)]
+
+    alpha = np.ones_like(n) + n
+
+    mode = dirichlet_mode(alpha)
+    std = np.sqrt(dirichlet.var(alpha))
+
+    for i, id in enumerate((20,22,2020,2022,2222)):
+        particle_id_str = ''.join([particle_id[int(''.join(x))] for x in grouper(str(id),2)])
+        print("P(%s) = %.2f +/- %.2f" % (particle_id_str,mode[i]*100,std[i]*100))
+
+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("--mc", nargs='+', required=True, help="atmospheric MC files")
+    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 = []
+    rhdr = pd.concat([read_hdf(filename, "rhdr").assign(filename=filename) for filename in args.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))
+    ev = pd.concat(evs)
+    ev_mc = get_events(args.mc, merge_fits=True)
+
+    # Drop events without fits
+    ev = ev[~np.isnan(ev.fmin)]
+    ev_mc = ev_mc[~np.isnan(ev_mc.fmin)]
+
+    ev = ev.reset_index()
+    ev_mc = ev_mc.reset_index()
+
+    # Set all prompt events in the MC to be muons
+    ev_mc.loc[ev_mc.prompt & ev_mc.filename.str.contains("cosmic"),'muon'] = True
+
+    # First, do basic data cleaning which is done for all events.
+    ev = ev[ev.signal | ev.muon]
+    ev_mc = ev_mc[ev_mc.signal | ev_mc.muon]
+
+    # 00-orphan cut
+    ev = ev[(ev.gtid & 0xff) != 0]
+    ev_mc = ev_mc[(ev_mc.gtid & 0xff) != 0]
+
+    # 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.muon]
+    muons_mc = ev_mc[ev_mc.muon]
+
+    # 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]
+    michel_mc = ev_mc[ev_mc.michel]
+
+    # remove events 200 microseconds after a muon
+    ev = ev.groupby('run',group_keys=False).apply(muon_follower_cut)
+
+    ev = ev[ev.psi < 6]
+    ev_mc = ev_mc[ev_mc.psi < 6]
+
+    handles = [Line2D([0], [0], color='C0'),
+               Line2D([0], [0], color='C1')]
+    labels = ('Data','Monte Carlo')
+
+    # For the Michel energy plot, we only look at events where the
+    # corresponding muon had less than 2500 nhit.  The reason for only looking
+    # at Michel electrons from muons with less than 2500 nhit is because there
+    # is significant ringing and afterpulsing after a large muon which can
+    # cause the reconstruction to overestimate the energy.
+    michel_low_nhit = michel[michel.muon_gtid.isin(ev.gtid.values) & (michel.muon_nhit < 2500)]
+    michel_low_nhit_mc = michel_mc[michel_mc.muon_gtid.isin(ev_mc.gtid.values) & (michel_mc.muon_nhit < 2500)]
+
+    print("Particle ID probability for Michel electrons:")
+    print("Data")
+    print_particle_probs(michel_low_nhit)
+    print("Monte Carlo")
+    print_particle_probs(michel_low_nhit_mc)
+
+    fig = plt.figure()
+    plot_hist2_data_mc(michel_low_nhit,michel_low_nhit_mc)
+    despine(fig,trim=True)
+    fig.legend(handles,labels,loc='upper right')
+    if args.save:
+        plt.savefig("michel_electrons.pdf")
+        plt.savefig("michel_electrons.eps")
+    else:
+        plt.suptitle("Michel Electrons")
+
+    fig = plt.figure()
+    bins = np.linspace(0,100,41)
+    plt.hist(michel_low_nhit.ke.values, bins=bins, histtype='step', color='C0', label="Data")
+    plt.hist(michel_low_nhit_mc.ke.values, weights=np.tile(len(michel_low_nhit)/len(michel_low_nhit_mc),len(michel_low_nhit_mc.ke.values)), bins=bins, histtype='step', color='C1', label="Monte Carlo")
+    hist = np.histogram(michel_low_nhit.ke.values,bins=bins)[0]
+    hist_mc = np.histogram(michel_low_nhit_mc.ke.values,bins=bins)[0]
+    if hist_mc.sum() > 0:
+        norm = hist.sum()/hist_mc.sum()
+    else:
+        norm = 1.0
+    p = get_multinomial_prob(hist,hist_mc,norm)
+    plt.text(0.95,0.95,"p = %.2f" % p,horizontalalignment='right',verticalalignment='top',transform=plt.gca().transAxes)
+    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()