def load_bbh_trigs(bbhtrigfile, segs):
# Read in the BBh triggers
bbh_trigs = SnglInspiralTable.read(bbhtrigfile)
# Check if BBH triggers have been read in successfully
if not bbh_trigs:
sys.exit("ERROR: No triggers for BBH file: %s" % bbhtrigfile)
else:
print "%d BBH triggers read" % len(bbh_trigs)
#Get the BBH triggers that lie within the valid segment list
bbh_trigs = bbh_trigs.vetoed(segs)
# Sort the bbh_triggers by their end times. This will be useful later
bbh_trigs.sort(key=lambda x: x.end_time + x.end_time_ns * 1.0e-9)
return bbh_trigs
#! /usr/bin/env python
# This is a simple script to check if all the data in the BBH trigger
# files is according to specification. Particularly, that all the triggers
# are from the same detector and that no triggers overlap within a 0.1 second
# window
import sys
import numpy as np
from gwpy.table.lsctables import SnglInspiralTable
triggers = sys.argv[1]
gwdata = SnglInspiralTable.read(triggers)
ifos = np.array(gwdata.getColumnByName('ifo')[:])
same_ifo = np.any(ifos != ifos[0])
if same_ifo:
sys.exit("Error: Not all triggers are from the same detector!")
etimes = np.array(gwdata.getColumnByName('end_time')[:])
etimes_ns = np.array(gwdata.getColumnByName('end_time_ns')[:])
etimes = etimes + etimes_ns * 1.0e-9
etimes = np.sort(etimes)
delta_t = np.diff(etimes)
overlap = np.any(delta_t < 0.1)
if overlap:
help='IFO, H1 or L1')
parser.add_argument('--ranking-statistic', type=str, required=True, default='newsnr',
choices=['snr','newsnr'],help='Ranking statistic, snr or newsnr')
parser.add_argument('--output-file', type=str, required=True,
help='Full path to output file')
parser.add_argument('--central-time',type=float,required=False,
help='Central time to look at')
parser.add_argument('--window',type=float,required=False,default=40.0,
help='Script will find loudest trigger within +/- window seconds')
parser.add_argument('--plot-window',type=float,required=True,default=40.0,
help='Plot will display +/- plot-window seconds around the loudest trigger')
parser.add_argument('--N', type=int, required=False, default=1,
help='Code will plot the Nth loudest trigger')
args = parser.parse_args()
events = SnglInspiralTable.read(args.single_ifo_trigs)
ifo = args.ifo
logging.info('Parsing XML files')
# get SNR of single detector triggers and find index of loudest event
snr = events.get_column('snr')
newsnr = [row.get_new_snr() for row in events]
end_time = events.get_column('end_time')
end_time_ns = events.get_column('end_time_ns')
end_times = np.add(end_time,end_time_ns*10**-9)
if args.ranking_statistic == 'snr':
highest_idx = np.argsort(snr)[-args.N]
trig_time = end_times[highest_idx]
elif args.ranking_statistic == 'newsnr':
#"ASC-AS_B_RF45_Q_PIT_OUT_DQ",
#"SUS-OMC_M1_ISIWIT_T_DQ",
#"PSL-ISS_AOM_DRIVER_MON_OUT_DQ",
#"LSC-PRCL_OUT_DQ"]
ifo = sys.argv[1]
bbhdir = sys.argv[2]
bbhfile= glob.glob(os.path.join(bbhdir, ifo+'*.xml.gz'))[0]
omiccachedir = sys.argv[3]
# Read in the segment file
segments = SegmentList.read('/home/albert.wandui/detchar'+\
'/ER7/jul13/%s_ER7_segments.txt' %ifo)
# Read in the BBH triggers
bbh_trigs = SnglInspiralTable.read(bbhfile)
# We only want the triggers in the given segments
bbh_trigs = bbh_trigs.vetoed(segments)
#bbh_trigs.sort(key=lambda x: x.end_time + x.end_time_ns * 1.0e-9)
print "Read in all the BBH triggers!!!\n"
print "Let's start working on the Omicron triggers...\n"
# ---------------------------------------------------------------------------- #
# Read in all the Omicron caches
# Also get an idea of the speed of the code when reading from cache file vs
# letting vet get the data itself
Nchannels = len(channels)
def get_omicron_triggers(channel, ifo, segments, cachefile):
import numpy as np
from gwpy.table.lsctables import SnglInspiralTable
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.stats as stats
#L1data = np.load('ER7L1triggers.npz', 'r')
#H1data = np.load('ER7H1triggers.npz', 'r')
import sys
fn = sys.argv[1]
gwdata = SnglInspiralTable.read(fn)
endtime =np.array(gwdata.getColumnByName('end_time')[:]) +\
np.array(gwdata.getColumnByName('end_time_ns')[:]) * 1.0e-9
mass1 = np.array(gwdata.getColumnByName('mass1'))
mass2 = np.array(gwdata.getColumnByName('mass2'))
snr = np.array(gwdata.getColumnByName('snr'))
chisq = np.array(gwdata.getColumnByName('chisq'))
mtotal = mass1 + mass2
eta = mass1 * mass2/ mtotal**2
mchirp = eta**(3./5)*mtotal
# Make the boolean arrays for selecting the data that we need.
# Set a threshold on the SNR so that we can focus on the
# fall off at lower SNRs.
min = 5.5
max = np.max(snr)
snr_sel = np.logical_and(snr >= min, snr <max)
mass_sel = np.vstack((np.logical_and(mchirp > 0.0, mchirp <= 5.0),\
