#!/usr/bin/env python

# Copyright (C) 2014 Alex Nitz
#
# 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, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

import sys
import logging, argparse, numpy, itertools
import pycbc
import pycbc.version
from pycbc import vetoes, psd, waveform, events, strain, scheme, fft, DYN_RANGE_FAC
from pycbc.vetoes.sgchisq import SingleDetSGChisq
from pycbc.filter import MatchedFilterControl, make_frequency_series
from pycbc.types import TimeSeries, FrequencySeries, zeros, float32, complex64
import pycbc.fft.fftw, pycbc.version
import pycbc.opt
import pycbc.weave
import pycbc.inject
import time

last_progress_update = -1.0

def update_progress(p,u,n):
    """ updates a file 'progress.txt' with a value 0 .. 1.0 when enough (filtering) progress was made
    """
    global last_progress_update
    if p > last_progress_update + u:
        f = open(n,"w")
        if f:
            f.write("%.4f" % p)
            f.close()
        last_progress_update = p

tstart = time.time()

parser = argparse.ArgumentParser(usage='',
    description="Find single detector gravitational-wave triggers.")

parser.add_argument('--version', action=pycbc.version.Version)
parser.add_argument("-V", "--verbose", action="store_true",
                  help="print extra debugging information", default=False )
parser.add_argument("--update-progress",
                  help="updates a file 'progress.txt' with a value 0 .. 1.0 when this amount of (filtering) progress was made",
                  type=float, default=0)
parser.add_argument("--update-progress-file",
                  help="name of the file to write the amount of (filtering) progress to",
                  type=str, default="progress.txt")
parser.add_argument("--output", type=str, help="FIXME: ADD")
parser.add_argument("--bank-file", type=str, help="FIXME: ADD")
parser.add_argument("--snr-threshold",
                  help="SNR threshold for trigger generation", type=float)
parser.add_argument("--newsnr-threshold", type=float, metavar='THRESHOLD',
                    help="Cut triggers with NewSNR less than THRESHOLD")
parser.add_argument("--low-frequency-cutoff", type=float,
                  help="The low frequency cutoff to use for filtering (Hz)")
parser.add_argument("--enable-bank-start-frequency", action='store_true',
                  help="Read the starting frequency of template waveforms"
                       " from the template bank.")
parser.add_argument("--max-template-length", type=float,
                  help="The maximum length of a template is seconds. The "
                       "starting frequency of the template is modified to "
                       "ensure the proper length")
# add approximant arg
pycbc.waveform.bank.add_approximant_arg(parser)
parser.add_argument("--order", type=int,
                  help="The integer half-PN order at which to generate"
                       " the approximant. Default is -1 which indicates to use"
                       " approximant defined default.", default=-1,
                       choices = numpy.arange(-1, 9, 1))
taper_choices = ["start","end","startend"]
parser.add_argument("--taper-template", choices=taper_choices,
                    help="For time-domain approximants, taper the start and/or"
                    " end of the waveform before FFTing.")
parser.add_argument("--cluster-method", choices=["template", "window"],
                    help="FIXME: ADD")
parser.add_argument("--cluster-function", choices=["findchirp", "symmetric"],
                    help="How to cluster together triggers within a window. "
                    "'findchirp' uses a forward sliding window; 'symmetric' "
                    "will compare each window to the one before and after, keeping "
                    "only a local maximum.", default="findchirp")
parser.add_argument("--cluster-window", type=float, default = -1,
                    help="Length of clustering window in seconds."
                    " Set to 0 to disable clustering.")
parser.add_argument("--maximization-interval", type=float, default=0,
                    help="Maximize triggers over the template bank (ms)")
parser.add_argument("--bank-veto-bank-file", type=str, help="FIXME: ADD")
parser.add_argument("--chisq-snr-threshold", type=float,
                    help="Minimum SNR to calculate the power chisq")
parser.add_argument("--chisq-bins", default=0, help=
                    "Number of frequency bins to use for power chisq. Specify"
                    " an integer for a constant number of bins, or a function "
                    "of template attributes.  Math functions are "
                    "allowed, ex. "
                    "'10./math.sqrt((params.mass1+params.mass2)/100.)'. "
                    "Non-integer values will be rounded down.")
parser.add_argument("--chisq-threshold", type=float, default=0,
                    help="FIXME: ADD")
parser.add_argument("--chisq-delta", type=float, default=0, help="FIXME: ADD")
parser.add_argument("--autochi-number-points", type=int, default=0,
                    help="The number of points to use, in both directions if"
                         "doing a two-sided auto-chisq, to calculate the"
                         "auto-chisq statistic.")
parser.add_argument("--autochi-stride", type=int, default=0,
                    help="The gap, in sample points, between the points at"
                         "which to calculate auto-chisq.")
parser.add_argument("--autochi-two-phase", action="store_true",
                    default=False,
                    help="If given auto-chisq will be calculated by testing "
                         "against both phases of the SNR time-series. "
                         "If not given, only the phase matching the trigger "
                         "will be used.")
parser.add_argument("--autochi-onesided", action='store', default=None,
                    choices=['left','right'],
                    help="Decide whether to calculate auto-chisq using"
                         "points on both sides of the trigger or only on one"
                         "side. If not given points on both sides will be"
                         "used. If given, with either 'left' or 'right',"
                         "only points on that side (right = forward in time,"
                         "left = back in time) will be used.")
parser.add_argument("--autochi-reverse-template", action="store_true",
                    default=False,
                    help="If given, time-reverse the template before"
                         "calculating the auto-chisq statistic. This will"
                         "come at additional computational cost as the SNR"
                         "time-series will need recomputing for the time-"
                         "reversed template.")
parser.add_argument("--autochi-max-valued", action="store_true",
                    default=False,
                    help="If given, store only the maximum value of the auto-"
                         "chisq over all points tested. A disadvantage of this "
                         "is that the mean value will not be known "
                         "analytically.")
parser.add_argument("--autochi-max-valued-dof", action="store", metavar="INT",
                    type=int,
                    help="If using --autochi-max-valued this value denotes "
                         "the pre-calculated mean value that will be stored "
                         "as the auto-chisq degrees-of-freedom value.")
parser.add_argument("--downsample-factor", type=int,
                    help="Factor that determines the interval between the "
                         "initial SNR sampling. If not set (or 1) no sparse sample "
                         "is created, and the standard full SNR is calculated.", default=1)
parser.add_argument("--upsample-threshold", type=float,
                    help="The fraction of the SNR threshold to check the sparse SNR sample.")
parser.add_argument("--upsample-method", choices=["pruned_fft"],
                    help="The method to find the SNR points between the sparse SNR sample.",
                    default='pruned_fft')
parser.add_argument("--user-tag", type=str, metavar="TAG", help="""
                    This is used to identify FULL_DATA jobs for
                    compatibility with pipedown post-processing.
                    Option will be removed when no longer needed.""")
parser.add_argument("--keep-loudest-interval", type=float,
                    help="Window in seconds to maximize triggers over bank")
parser.add_argument("--keep-loudest-num", type=int,
                    help="Number of triggers to keep from each maximization interval")
parser.add_argument("--gpu-callback-method", default='none')
parser.add_argument("--use-compressed-waveforms", action="store_true", default=False,
                    help='Use compressed waveforms from the bank file.')
parser.add_argument("--waveform-decompression-method", action='store', default=None,
                    help='Method to be used decompress waveforms from the bank file.')

# Add options groups
psd.insert_psd_option_group(parser)
strain.insert_strain_option_group(parser)
strain.StrainSegments.insert_segment_option_group(parser)
scheme.insert_processing_option_group(parser)
fft.insert_fft_option_group(parser)
pycbc.opt.insert_optimization_option_group(parser)
pycbc.weave.insert_weave_option_group(parser)
pycbc.inject.insert_injfilterrejector_option_group(parser)
SingleDetSGChisq.insert_option_group(parser)
opt = parser.parse_args()

# Check that the values returned for the options make sense
psd.verify_psd_options(opt, parser)
strain.verify_strain_options(opt, parser)
strain.StrainSegments.verify_segment_options(opt, parser)
scheme.verify_processing_options(opt, parser)
fft.verify_fft_options(opt,parser)
pycbc.opt.verify_optimization_options(opt, parser)
pycbc.weave.verify_weave_options(opt, parser)

pycbc.init_logging(opt.verbose)

inj_filter_rejector = pycbc.inject.InjFilterRejector.from_cli(opt)
ctx = scheme.from_cli(opt)
gwstrain = strain.from_cli(opt, dyn_range_fac=DYN_RANGE_FAC,
                           inj_filter_rejector=inj_filter_rejector)
strain_segments = strain.StrainSegments.from_cli(opt, gwstrain)


with ctx:
    fft.from_cli(opt)

    flow = opt.low_frequency_cutoff
    flen = strain_segments.freq_len
    tlen = strain_segments.time_len
    delta_f = strain_segments.delta_f


    logging.info("Making frequency-domain data segments")
    segments = strain_segments.fourier_segments()
    psd.associate_psds_to_segments(opt, segments, gwstrain, flen, delta_f,
                  flow, dyn_range_factor=DYN_RANGE_FAC, precision='single')

    # storage for values and types to be passed to event manager
    out_types = {
        'time_index'     : int,
        'snr'            : complex64,
        'chisq'          : float32,
        'chisq_dof'      : int,
        'bank_chisq'     : float32,
        'bank_chisq_dof' : int,
        'cont_chisq'     : float32,
                }
    out_types.update(SingleDetSGChisq.returns)
    out_vals = {key: None for key in out_types}
    names = sorted(out_vals.keys())

    if len(strain_segments.segment_slices) == 0:
        logging.info("--filter-inj-only specified and no injections in analysis time")
        event_mgr = events.EventManager(
              opt, names, [out_types[n] for n in names], psd=None,
              gating_info=gwstrain.gating_info)
        event_mgr.finalize_template_events()
        event_mgr.write_events(opt.output)
        logging.info("Finished")
        sys.exit(0)

    # FIXME: Maybe we should use the PSD corresponding to each trigger
    event_mgr = events.EventManager(
            opt, names, [out_types[n] for n in names], psd=segments[0].psd,
            gating_info=gwstrain.gating_info)

    template_mem = zeros(tlen, dtype = complex64)
    cluster_window = int(opt.cluster_window * gwstrain.sample_rate)

    if opt.cluster_window == 0.0:
        use_cluster = False
    else:
        use_cluster = True

    if hasattr(ctx, "num_threads"):
            ncores = ctx.num_threads
    else:
            ncores = 1


    matched_filter = MatchedFilterControl(opt.low_frequency_cutoff, None,
                                   opt.snr_threshold, tlen, delta_f, complex64,
                                   segments, template_mem, use_cluster,
                                   downsample_factor=opt.downsample_factor,
                                   upsample_threshold=opt.upsample_threshold,
                                   upsample_method=opt.upsample_method,
                                   gpu_callback_method=opt.gpu_callback_method,
                                   cluster_function=opt.cluster_function)

    bank_chisq = vetoes.SingleDetBankVeto(opt.bank_veto_bank_file,
                                          flen, delta_f, flow, complex64,
                                          phase_order=opt.order,
                                          approximant=opt.approximant)

    power_chisq = vetoes.SingleDetPowerChisq(opt.chisq_bins, opt.chisq_snr_threshold)

    autochisq = vetoes.SingleDetAutoChisq(opt.autochi_stride,
                                 opt.autochi_number_points,
                                 onesided=opt.autochi_onesided,
                                 twophase=opt.autochi_two_phase,
                                 reverse_template=opt.autochi_reverse_template,
                                 take_maximum_value=opt.autochi_max_valued,
                                 maximal_value_dof=opt.autochi_max_valued_dof)

    logging.info("Overwhitening frequency-domain data segments")
    for seg in segments:
        seg /= seg.psd

    logging.info("Read in template bank")
    bank = waveform.FilterBank(opt.bank_file, flen, delta_f,
        low_frequency_cutoff=None if opt.enable_bank_start_frequency else flow,
        dtype=complex64, phase_order=opt.order,
        taper=opt.taper_template, approximant=opt.approximant,
        out=template_mem, max_template_length=opt.max_template_length,
        enable_compressed_waveforms=True if opt.use_compressed_waveforms else False,
        waveform_decompression_method=
        opt.waveform_decompression_method if opt.use_compressed_waveforms else None)

    sg_chisq = SingleDetSGChisq.from_cli(opt, bank, opt.chisq_bins)

    ntemplates = len(bank)
    nfilters = 0

    logging.info("Full template bank size: %s", ntemplates)
    bank.template_thinning(inj_filter_rejector)
    if not len(bank) == ntemplates:
        logging.info("Template bank size after thinning: %s", len(bank))

    tsetup = time.time() - tstart

    # Note: in the class-based approach used now, 'template' is not explicitly used
    # within the loop.  Rather, the iteration simply fills the memory specifed in
    # the 'template_mem' argument to MatchedFilterControl with the next template
    # from the bank.
    for t_num in xrange(len(bank)):
        tmplt_generated = False

        for s_num, stilde in enumerate(segments):
            # Filter check checks the 'inj_filter_rejector' options to
            # determine whether
            # to filter this template/segment if injections are present.
            if not inj_filter_rejector.template_segment_checker(
                    bank, t_num, stilde, opt.gps_start_time):
                continue
            if not tmplt_generated:
                template = bank[t_num]
                event_mgr.new_template(tmplt=template.params,
                    sigmasq=template.sigmasq(segments[0].psd))
                tmplt_generated = True

            if opt.cluster_method == "window":
                cluster_window = int(opt.cluster_window * gwstrain.sample_rate)
            if opt.cluster_method == "template":
                cluster_window = \
                    int(template.chirp_length * gwstrain.sample_rate)

            if opt.update_progress:
                update_progress((t_num + (s_num / float(len(segments))) ) / len(bank),
                                opt.update_progress, opt.update_progress_file)
            logging.info("Filtering template %d/%d segment %d/%d" %
                         (t_num + 1, len(bank), s_num + 1, len(segments)))

            nfilters = nfilters + 1
            snr, norm, corr, idx, snrv = \
               matched_filter.matched_filter_and_cluster(s_num,
                                                         template.sigmasq(stilde.psd),
                                                         cluster_window,
                                                         epoch=stilde._epoch)

            if not len(idx):
                continue

            out_vals['bank_chisq'], out_vals['bank_chisq_dof'] = \
                  bank_chisq.values(template, stilde.psd, stilde, snrv, norm,
                                    idx+stilde.analyze.start)

            out_vals['chisq'], out_vals['chisq_dof'] = \
                  power_chisq.values(corr, snrv, norm, stilde.psd,
                                     idx+stilde.analyze.start, template)

            out_vals['sg_chisq'] = sg_chisq.values(stilde, template, stilde.psd,
                                          snrv, norm,
                                          out_vals['chisq'],
                                          out_vals['chisq_dof'],
                                          idx+stilde.analyze.start)

            out_vals['cont_chisq'] = \
                  autochisq.values(snr, idx+stilde.analyze.start, template,
                                   stilde.psd, norm, stilde=stilde,
                                   low_frequency_cutoff=flow)

            idx += stilde.cumulative_index

            out_vals['time_index'] = idx
            out_vals['snr'] = snrv * norm

            event_mgr.add_template_events(names, [out_vals[n] for n in names])

        event_mgr.cluster_template_events("time_index", "snr", cluster_window)
        event_mgr.finalize_template_events()

logging.info("Found %s triggers" % str(len(event_mgr.events)))

if opt.chisq_threshold and opt.chisq_bins:
    logging.info("Removing triggers with poor chisq")
    event_mgr.chisq_threshold(opt.chisq_threshold, opt.chisq_bins,
                              opt.chisq_delta)
    logging.info("%d remaining triggers" % len(event_mgr.events))

if opt.newsnr_threshold and opt.chisq_bins:
    logging.info("Removing triggers with NewSNR below threshold")
    event_mgr.newsnr_threshold(opt.newsnr_threshold)
    logging.info("%d remaining triggers" % len(event_mgr.events))

if opt.keep_loudest_interval:
    logging.info("Removing triggers that are not within the top %s loudest"
                 " of a %s second interval" % (opt.keep_loudest_num,
                                               opt.keep_loudest_interval))
    event_mgr.keep_loudest_in_interval(opt.keep_loudest_interval * opt.sample_rate,
                                       opt.keep_loudest_num)
    logging.info("%d remaining triggers" % len(event_mgr.events))

if opt.injection_window and hasattr(gwstrain, 'injections'):
    logging.info("Keeping triggers within %s seconds of injection" % opt.injection_window)
    event_mgr.keep_near_injection(opt.injection_window, gwstrain.injections)
    logging.info("%d remaining triggers" % len(event_mgr.events))

if opt.maximization_interval:
    logging.info("Maximizing triggers over %s ms window" % opt.maximization_interval)
    window = int(opt.maximization_interval * gwstrain.sample_rate / 1000)
    event_mgr.maximize_over_bank("time_index", "snr", window)
    logging.info("%d remaining triggers" % len(event_mgr.events))

tstop = time.time()
run_time = tstop - tstart
event_mgr.save_performance(ncores, nfilters, ntemplates, run_time, tsetup)

logging.info("Writing out triggers")
event_mgr.write_events(opt.output)

if opt.fftw_output_float_wisdom_file:
    fft.fftw.export_single_wisdom_to_filename(opt.fftw_output_float_wisdom_file)

if opt.fftw_output_double_wisdom_file:
    fft.fftw.export_double_wisdom_to_filename(opt.fftw_output_double_wisdom_file)

logging.info("Finished")