#!/usr/bin/python
""" Make a table of found injection information
"""
import argparse, h5py, numpy, pycbc.results, pycbc.detector, sys
from pycbc.types import MultiDetOptionAction
import pycbc.pnutils, pycbc.events
import pycbc.version

parser = argparse.ArgumentParser()
parser.add_argument("--version", action="version", version=pycbc.version.git_verbose_msg)
parser.add_argument('--injection-file', help='HDF File containing the matched injections')
parser.add_argument('--single-trigger-files', nargs='*', help="HDF format single detector trigger files", action=MultiDetOptionAction)
parser.add_argument('--verbose', action='count')
parser.add_argument('--show-missed', action='store_true')
parser.add_argument('--output-file')
args = parser.parse_args()

f = h5py.File(args.injection_file)
inj = f['injections']
found_cols, found_names, found_formats = [], [], []

if args.show_missed:
    title = "Missed Injections"
    idx = f['missed/after_vetoes'][:]
else:
    title = "Found Injections"
    found = f['found_after_vetoes']
    idx = found['injection_index'][:]
    tdiff = (found['time1'][:] - found['time2'][:]) * 1000
    tdiff_str = '%s - %s time (ms)' % (f.attrs['detector_1'], f.attrs['detector_2'])
    
    found_cols = [tdiff, found['stat'], found['ifar'], found['ifar_exc']]
    found_names = [tdiff_str, 'Ranking Stat.', 'Inc. IFAR (yrs)', 'Exc. IFAR']
    found_formats =  ['##.##', '##.##', '##', '##']

    ids = {f.attrs['detector_1']: found['trigger_id1'][:],
           f.attrs['detector_2']: found['trigger_id2'][:],}

    if args.single_trigger_files:
        for ifo in args.single_trigger_files:
            f = h5py.File(args.single_trigger_files[ifo], 'r')[ifo]
            i = ids[ifo]
            snr = f['snr'][:][i]
            chisq = f['chisq'][:][i] / (2 * f['chisq_dof'][:][i] - 2)
            newsnr = pycbc.events.newsnr(snr, chisq)
            
            found_names += ["%s SNR" % ifo, "CHISQ", "NewSNR"]
            found_cols += [snr, chisq, newsnr]
            found_formats += ['##.##', '##.##', '##.##']
          
dec_dist = numpy.maximum(inj['eff_dist_h'][:][idx], inj['eff_dist_l'][:][idx])
m1, m2 = inj['mass1'][:][idx], inj['mass2'][:][idx]
mchirp, eta = pycbc.pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
dec_chirp_dist = pycbc.pnutils.chirp_distance(dec_dist, mchirp)

columns = [dec_chirp_dist, inj['end_time'][:][idx], m1, m2, mchirp, eta,
           inj['spin1x'][:][idx], inj['spin1y'][:][idx], inj['spin1z'][:][idx],
           inj['spin2x'][:][idx], inj['spin2y'][:][idx], inj['spin2z'][:][idx],
           inj['distance'][:][idx],
           inj['eff_dist_h'][:][idx], inj['eff_dist_l'][:][idx], 
           ] + found_cols
         
names = ['DChirp Dist', 'Inj Time', 'Mass1', 'Mass2', 'Mchirp', 'Eta',
         's1x', 's1y', 's1z',
         's2x', 's2y', 's2z', 
         'Dist',
         'Eff Dist (H)', '(L)',
        ] + found_names
format_strings = ['##.##', '##.##', '##.##', '##.##', '##.##', '##.##',
                  '##.##', '##.##', '##.##',
                  '##.##', '##.##', '##.##',
                  '##.##',
                  '##.##', '##.##'
                 ] + found_formats

html_table = pycbc.results.table(columns, names, 
                                 format_strings=format_strings, 
                                 page_size=20)
                                 
kwds = { 'title' : title, 
        'caption' : "A table of %s and their coincident statistic information." % title.lower(),
        'cmd' :' '.join(sys.argv), }
pycbc.results.save_fig_with_metadata(str(html_table), args.output_file, **kwds)