#!/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

# from https://stackoverflow.com/questions/287871/how-to-print-colored-text-in-terminal-in-python
class bcolors:
    HEADER = '\033[95m'
    OKBLUE = '\033[94m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'
    FAIL = '\033[91m'
    ENDC = '\033[0m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'

# 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
DC_ITC            = 0x400
DC_BREAKDOWN      = 0x800

def plot_hist(df, title=None):
    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))

    if title:
        plt.suptitle(title)

    if len(df):
        plt.tight_layout()

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in range(0, len(l), n):
        yield l[i:i + n]

def print_warning(msg):
    print(bcolors.FAIL + msg + bcolors.ENDC,file=sys.stderr)

def unwrap(p, delta, axis=-1):
    """
    A modified version of np.unwrap() useful for unwrapping the 50 MHz clock.
    It unwraps discontinuities bigger than delta/2 by delta.

    Example:

        >>> a = np.arange(10) % 5
        >>> a
        array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4])
        >>> unwrap(a,5)
        array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])

    In the case of the 50 MHz clock delta should be 0x7ffffffffff*20.0.
    """
    p = np.asarray(p)
    nd = p.ndim
    dd = np.diff(p, axis=axis)
    slice1 = [slice(None, None)]*nd     # full slices
    slice1[axis] = slice(1, None)
    slice1 = tuple(slice1)
    ddmod = np.mod(dd + delta/2, delta) - delta/2
    np.copyto(ddmod, delta/2, where=(ddmod == -delta/2) & (dd > 0))
    ph_correct = ddmod - dd
    np.copyto(ph_correct, 0, where=abs(dd) < delta/2)
    up = np.array(p, copy=True, dtype='d')
    up[slice1] = p[slice1] + ph_correct.cumsum(axis)
    return up

def unwrap_50_mhz_clock(gtr):
    """
    Unwrap an array with 50 MHz clock times. These times should all be in
    nanoseconds and come from the KEV_GTR variable in the EV bank.

    Note: We assume here that the events are already ordered contiguously by
    GTID, so you shouldn't pass an array with multiple runs!
    """
    return unwrap(gtr,0x7ffffffffff*20.0)

def retrigger_cut(ev):
    """
    Cuts all retrigger events.
    """
    return ev[ev.dt > 500]

def breakdown_follower_cut(ev):
    """
    Cuts all events within 1 second of breakdown events.
    """
    breakdowns = ev[ev.dc & DC_BREAKDOWN != 0]
    return ev[~np.any((ev.gtr.values > breakdowns.gtr.values[:,np.newaxis]) & \
                      (ev.gtr.values < breakdowns.gtr.values[:,np.newaxis] + 1e9),axis=0)]

def flasher_follower_cut(ev):
    """
    Cuts all events within 200 microseconds of flasher events.
    """
    flashers = ev[ev.dc & DC_FLASHER != 0]
    return ev[~np.any((ev.gtr.values > flashers.gtr.values[:,np.newaxis]) & \
                      (ev.gtr.values < flashers.gtr.values[:,np.newaxis] + 200e3),axis=0)]

def muon_follower_cut(ev):
    """
    Cuts all events 200 microseconds after a muon.
    """
    muons = ev[ev.dc & DC_MUON != 0]
    return ev[~np.any((ev.gtr.values > muons.gtr.values[:,np.newaxis]) & \
                      (ev.gtr.values < muons.gtr.values[:,np.newaxis] + 200e3),axis=0)]

def michel_cut(ev):
    """
    Looks for Michel electrons after muons.
    """
    prompt_plus_muons = ev[ev.prompt | ((ev.dc & DC_MUON) != 0)]

    # Michel electrons and neutrons can be any event which is not a prompt
    # event
    follower = ev[~ev.prompt]

    # require Michel events to pass more of the SNO data cleaning cuts
    michel = follower[follower.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ESUM | DC_OWL | DC_OWL_TRIGGER | DC_FTS) == 0]

    michel = michel[michel.nhit >= 100]

    # Accept events which had a muon more than 800 nanoseconds but less
    # than 20 microseconds before them. The 800 nanoseconds cut comes from
    # Richie's thesis. He also mentions that the In Time Channel Spread Cut
    # is very effective at cutting electrons events caused by muons, so I
    # should implement that.
    #
    # Note: We currently don't look across run boundaries. This should be a
    # *very* small effect, and the logic to do so would be very complicated
    # since I would have to deal with 50 MHz clock rollovers, etc.
    #
    # Do a simple python for loop here over the runs since we create
    # temporary arrays with shape (michel.size,prompt_plus_muons.size)
    # which could be too big for the full dataset.
    #
    # There might be some clever way to do this without the loop in Pandas,
    # but I don't know how.
    if prompt_plus_muons.size and michel.size:
        return michel[np.any((michel.gtr.values > prompt_plus_muons.gtr.values[:,np.newaxis] + 800) & \
                             (michel.gtr.values < prompt_plus_muons.gtr.values[:,np.newaxis] + 20e3),axis=0)]
    else:
        return pd.DataFrame(columns=ev.columns)

def atmospheric_events(ev):
    """
    Tags atmospheric events which have a neutron follower.
    """
    prompt = ev[ev.prompt]

    # Michel electrons and neutrons can be any event which is not a prompt
    # event
    follower = ev[~ev.prompt]

    ev['atm'] = np.zeros(len(ev),dtype=np.bool)

    if prompt.size and follower.size:
        # neutron followers have to obey stricter set of data cleaning cuts
        neutron = follower[follower.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ESUM | DC_OWL | DC_OWL_TRIGGER | DC_FTS) == 0]
        neutron = neutron[~np.isnan(neutron.ftp_x) & ~np.isnan(neutron.rsp_energy)]
        r = np.sqrt(neutron.ftp_x**2 + neutron.ftp_y**2 + neutron.ftp_z**2)
        neutron = neutron[r < AV_RADIUS]
        neutron = neutron[neutron.rsp_energy > 4.0]

        # neutron events accepted after 20 microseconds and before 250 ms (50 ms during salt)
        ev.loc[ev.prompt,'atm'] = np.any((neutron.gtr.values > prompt.gtr.values[:,np.newaxis] + 20e3) & \
                                         (neutron.gtr.values < prompt.gtr.values[:,np.newaxis] + 250e6),axis=1)

    return ev

def gtid_sort(ev, first_gtid):
    """
    Adds 0x1000000 to the gtid_sort column for all gtids before the first gtid
    in a run, which should be passed as a dictionary. This column can then be
    used to sort the events sequentially.

    This function should be passed to ev.groupby('run').apply(). We use this
    idiom instead of just looping over the groupby results since groupby()
    makes a copy of the dataframe, i.e.

        for run, ev_run in ev.groupby('run'):
            ev_run.loc[ev_run.gtid < first_gtid[run],'gtid_sort'] += 0x1000000

    would produce a SettingWithCopyWarning, so instead we use:

        ev = ev.groupby('run',as_index=False).apply(gtid_sort,first_gtid=first_gtid)

    which doesn't have this problem.
    """
    # see https://stackoverflow.com/questions/32460593/including-the-group-name-in-the-apply-function-pandas-python
    run = ev.name

    if run not in first_gtid:
        print_warning("No RHDR bank for run %i! Assuming first event is the first GTID." % run)
        first_gtid[run] = ev.gtid[0]

    ev.loc[ev.gtid < first_gtid[run],'gtid_sort'] += 0x1000000

    return ev

if __name__ == '__main__':
    import argparse
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import sys
    import h5py

    parser = argparse.ArgumentParser("plot fit results")
    parser.add_argument("filenames", nargs='+', help="input files")
    args = parser.parse_args()

    ev = pd.concat([pd.read_hdf(filename, "ev") for filename in args.filenames],ignore_index=True)
    fits = pd.concat([pd.read_hdf(filename, "fits") for filename in args.filenames],ignore_index=True)
    rhdr = pd.concat([pd.read_hdf(filename, "rhdr") for filename in args.filenames],ignore_index=True)

    first_gtid = rhdr.set_index('run').to_dict()['first_gtid']

    # First, remove junk events since orphans won't have a 50 MHz clock and so
    # could screw up the 50 MHz clock unwrapping
    ev = ev[ev.dc & DC_JUNK == 0]

    # We need the events to be in time order here in order to calculate the
    # delta t between events. It's not obvious exactly how to do this. You
    # could sort by GTID, but that wraps around. Similarly we can't sort by the
    # 50 MHz clock because it also wraps around. Finally, I'm hesitant to sort
    # by the 10 MHz clock since it can be unreliable.
    #
    # Update: Phil proposed a clever way to get the events in order using the
    # GTID:
    #
    # > The GTID rollover should be easy to handle because there should never
    # > be two identical GTID's in a run. So if you order the events by GTID,
    # > you can assume that events with GTID's that come logically before the
    # > first GTID in the run must have occurred after the other events.
    #
    # Therefore, we can just add 0x1000000 to all GTIDs before the first GTID
    # in the event and sort on that. We get the first GTID from the RHDR bank.
    ev['gtid_sort'] = ev['gtid'].copy()

    ev = ev.groupby('run',as_index=False).apply(gtid_sort,first_gtid=first_gtid).reset_index(level=0,drop=True)

    ev = ev.sort_values(by=['run','gtid_sort'],kind='mergesort')

    for run, ev_run in ev.groupby('run'):
        # Warn about 50 MHz clock jumps since they could indicate that the
        # events aren't in order.
        dt = np.diff(ev_run.gtr)
        if np.count_nonzero((np.abs(dt) > 1e9) & (dt > -0x7ffffffffff*20.0/2)):
            print_warning("Warning: %i 50 MHz clock jumps in run %i. Are the events in order?" % \
                (np.count_nonzero((np.abs(dt) > 1e9) & (dt > -0x7ffffffffff*20.0/2)),run))

    # unwrap the 50 MHz clock within each run
    ev.gtr = ev.groupby(['run'],as_index=False)['gtr'].transform(unwrap_50_mhz_clock)

    for run, ev_run in ev.groupby('run'):
        # Warn about GTID jumps since we could be missing a potential flasher
        # and/or breakdown, and we need all the events in order to do a
        # retrigger cut
        if np.count_nonzero(np.diff(ev_run.gtid) != 1):
            print_warning("Warning: %i GTID jumps in run %i" % (np.count_nonzero(np.diff(ev_run.gtid) != 1),run))

    # calculate the time difference between each event and the previous event
    # so we can tag retrigger events
    ev['dt'] = ev.groupby(['run'],as_index=False)['gtr'].transform(lambda x: np.concatenate(([1e9],np.diff(x.values))))

    # 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.
    #
    # FIXME: Test this since query() is new to pandas
    fits = fits.query('not (n > 1 and ((id1 == 20 and energy1 < 20)  or (id2 == 20 and energy2 < 20)  or (id3 == 20 and energy3 < 20)))')
    fits = fits.query('not (n > 1 and ((id2 == 22 and energy1 < 200) or (id2 == 22 and energy2 < 200) or (id3 == 22 and energy3 < 200)))')

    # 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.
    fits['w'] = fits['n']*np.log(0.1*0.001) + np.log(fits['energy1']) + fits['n']*np.log(1e-4/(4*np.pi))

    # Note: we index on the left hand site with loc to avoid a copy error
    #
    # See https://www.dataquest.io/blog/settingwithcopywarning/
    fits.loc[fits['n'] > 1, 'w'] += np.log(fits[fits['n'] > 1]['energy2'])
    fits.loc[fits['n'] > 2, 'w'] += np.log(fits[fits['n'] > 2]['energy3'])

    fits['fmin'] = fits['fmin'] - fits['w']

    fits['psi'] /= fits.merge(ev,on=['run','gtid'])['nhit']
    fits['ke'] = fits['energy1']
    fits['id'] = fits['id1']
    fits.loc[fits['n'] == 2, 'id'] = fits['id1']*100 + fits['id2']
    fits.loc[fits['n'] == 3, 'id'] = fits['id1']*10000 + fits['id2']*100 + fits['id3']
    fits['theta'] = fits['theta1']

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

    # Now, select prompt events.
    #
    # We define a prompt event here as any event with an NHIT > 100 and whose
    # previous > 100 nhit event was more than 250 ms ago
    #
    # Note: It's important we do this *before* applying the data cleaning cuts
    # since otherwise we may have a prompt event identified only after the
    # cuts.
    #
    # For example, suppose there was a breakdown and for whatever reason
    # the *second* event after the breakdown didn't get tagged correctly. If we
    # apply the data cleaning cuts first and then tag prompt events then this
    # event will get tagged as a prompt event.
    ev['prompt'] = (ev.nhit >= 100)
    ev.loc[ev.prompt,'prompt'] &= np.concatenate(([True],np.diff(ev[ev.prompt].gtr.values) > 250e6))

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

    # flasher follower cut
    ev = ev.groupby('run',as_index=False).apply(flasher_follower_cut).reset_index(level=0,drop=True)

    # breakdown follower cut
    ev = ev.groupby('run',as_index=False).apply(breakdown_follower_cut).reset_index(level=0,drop=True)

    # retrigger cut
    ev = ev.groupby('run',as_index=False).apply(retrigger_cut).reset_index(level=0,drop=True)

    # 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.groupby('run',as_index=False).apply(michel_cut).reset_index(level=0,drop=True)

    # Tag atmospheric events.
    #
    # Note: We don't cut atmospheric events or muons yet because we still need
    # all the events in order to apply the muon follower cut.
    ev = ev.groupby('run',as_index=False).apply(atmospheric_events).reset_index(level=0,drop=True)

    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',as_index=False).apply(muon_follower_cut).reset_index(level=0,drop=True)

    # 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]

    # require r < 6 meters
    prompt = prompt[np.sqrt(prompt.x.values**2 + prompt.y.values**2 + prompt.z.values**2) < AV_RADIUS]
    atm = atm[np.sqrt(atm.x.values**2 + atm.y.values**2 + atm.z.values**2) < AV_RADIUS]

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

    # Note: Need to design and apply a psi based cut here

    plt.figure()
    plot_hist(prompt,"Without Neutron Follower")
    plt.figure()
    plot_hist(atm,"With Neutron Follower")
    plt.figure()
    plot_hist(michel_best_fit,"Michel Electrons")
    plt.figure()
    plot_hist(muon_best_fit,"External Muons")

    # Plot the energy and angular distribution for external muons
    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')
    plt.xlabel(r"$\cos(\theta)$")
    plt.suptitle("Muons")

    # For the Michel energy plot, we only look at the single particle electron
    # fit
    michel = michel[michel.id == 20]

    plt.figure()
    plt.hist(michel.ke.values, bins=np.linspace(20,100,100), histtype='step', label="My 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")
    plt.xlabel("Energy (MeV)")
    plt.title("Michel Electrons")
    plt.legend()
    plt.show()