lalinference_pipe_utils.py 96.5 KB
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#flow DAG Class definitions for LALInference Pipeline
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# (C) 2012 John Veitch, Vivien Raymond, Kiersten Ruisard, Kan Wang
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import itertools
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import glue
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from glue import pipeline,segmentsUtils,segments
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import os
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import socket
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from lalapps import inspiralutils
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import uuid
import ast
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import pdb
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import string
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from math import floor,ceil,log,pow
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import sys
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import random
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from itertools import permutations
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import shutil
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import numpy as np
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# We use the GLUE pipeline utilities to construct classes for each
# type of job. Each class has inputs and outputs, which are used to
# join together types of jobs into a DAG.

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class Event():
  """
  Represents a unique event to run on
  """
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  new_id=itertools.count().next
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  def __init__(self,trig_time=None,SimInspiral=None,SimBurst=None,SnglInspiral=None,CoincInspiral=None,event_id=None,timeslide_dict=None,GID=None,ifos=None, duration=None,srate=None,trigSNR=None,fhigh=None,horizon_distance=None):
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    self.trig_time=trig_time
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    self.injection=SimInspiral
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    self.burstinjection=SimBurst
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    self.sngltrigger=SnglInspiral
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    if timeslide_dict is None:
      self.timeslides={}
    else:
      self.timeslides=timeslide_dict
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    self.GID=GID
    self.coinctrigger=CoincInspiral
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    if ifos is None:
      self.ifos = []
    else:
      self.ifos = ifos
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    self.duration = duration
    self.srate = srate
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    self.trigSNR = trigSNR
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    self.fhigh = fhigh
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    self.horizon_distance = horizon_distance
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    if event_id is not None:
        self.event_id=event_id
    else:
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        self.event_id=Event.new_id()
    if self.injection is not None:
        self.trig_time=self.injection.get_end()
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        if event_id is None: self.event_id=int(str(self.injection.simulation_id).split(':')[2])
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    if self.burstinjection is not None:
        self.trig_time=self.burstinjection.get_end()
        if event_id is None: self.event_id=int(str(self.burstinjection.simulation_id).split(':')[2])
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    if self.sngltrigger is not None:
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        self.trig_time=self.sngltrigger.get_end()
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        self.event_id=int(str(self.sngltrigger.event_id).split(':')[2])
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    if self.coinctrigger is not None:
        self.trig_time=self.coinctrigger.end_time + 1.0e-9 * self.coinctrigger.end_time_ns
    if self.GID is not None:
        self.event_id=int(''.join(i for i in self.GID if i.isdigit()))
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    self.engine_opts={}
  def set_engine_option(self,opt,val):
    """
    Can set event-specific options for the engine nodes
    using this option, e.g. ev.set_engine_option('time-min','1083759273')
    """
    self.engine_opts[opt]=val
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dummyCacheNames=['LALLIGO','LALVirgo','LALAdLIGO','LALAdVirgo']
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def readLValert(threshold_snr=None,gid=None,flow=40.0,gracedb="gracedb",basepath="./",downloadpsd=True):
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  """
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  Parse LV alert file, containing coinc, sngl, coinc_event_map.
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  and create a list of Events as input for pipeline
  Based on Chris Pankow's script
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  """
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  output=[]
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  from glue.ligolw import utils
  from glue.ligolw import lsctables
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  from glue.ligolw import ligolw
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  class PSDContentHandler(ligolw.LIGOLWContentHandler):
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    pass
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  lsctables.use_in(PSDContentHandler)
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  from glue.ligolw import param
  from glue.ligolw import array
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  import subprocess
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  from lal import series as lalseries
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  from subprocess import Popen, PIPE
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  cwd=os.getcwd()
  os.chdir(basepath)
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  print "%s download %s coinc.xml"%(gracedb,gid)
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  subprocess.call([gracedb,"download", gid ,"coinc.xml"])
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  xmldoc=utils.load_filename("coinc.xml",contenthandler = PSDContentHandler)
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  coinctable = lsctables.CoincInspiralTable.get_table(xmldoc)
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  coinc_events = [event for event in coinctable]
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  sngltable = lsctables.SnglInspiralTable.get_table(xmldoc)
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  sngl_events = [event for event in sngltable]
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  #Issues to identify IFO with good data that did not produce a trigger
  #search_summary = lsctables.getTablesByType(xmldoc, lsctables.SearchSummaryTable)[0]
  #ifos = search_summary[0].ifos.split(",")
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  #coinc_table = lsctables.getTablesByType(xmldoc, lsctables.CoincTable)[0]
  #ifos = coinc_table[0].instruments.split(",")
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  trigSNR = 2.0*coinctable[0].snr #The factor of 2.0 is because detection pipelines recover SNR lower than PE can recover.
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  # Parse PSD
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  srate_psdfile=16384
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  ifos=None
  if downloadpsd:
    print "gracedb download %s psd.xml.gz" % gid
    subprocess.call([gracedb,"download", gid ,"psd.xml.gz"])
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    xmlpsd = lalseries.read_psd_xmldoc(utils.load_filename('psd.xml.gz',contenthandler = lalseries.PSDContentHandler))
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    # Note: This finds the active IFOs by looking for available PSDs
    # Is there another way of getting this info?
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    ifos = xmlpsd.keys()
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  psdasciidic=None
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  fhigh=None
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  if os.path.exists("psd.xml.gz"):
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    psdasciidic=get_xml_psds(os.path.realpath("./psd.xml.gz"),ifos,os.path.realpath('./PSDs'),end_time=None)
    combine=np.loadtxt(psdasciidic[psdasciidic.keys()[0]])
    srate_psdfile = pow(2.0, ceil( log(float(combine[-1][0]), 2) ) ) * 2
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  else:
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    print "Failed to gracedb download %s psd.xml.gz. lalinference will estimate the psd itself." % gid
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  # Logic for template duration and sample rate disabled
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  coinc_map = lsctables.CoincMapTable.get_table(xmldoc)
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  for coinc in coinc_events:
    these_sngls = [e for e in sngl_events if e.event_id in [c.event_id for c in coinc_map if c.coinc_event_id == coinc.coinc_event_id] ]
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    dur=[]
    srate=[]
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    horizon_distance=[]
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    for e in these_sngls:
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      # Review: Replace this with a call to LALSimulation function at some point
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      p=Popen(["lalapps_chirplen","--flow",str(flow),"-m1",str(e.mass1),"-m2",str(e.mass2)],stdout=PIPE, stderr=PIPE, stdin=PIPE)
      strlen = p.stdout.read()
      dur.append(pow(2.0, ceil( log(max(8.0,float(strlen.splitlines()[2].split()[5]) + 2.0), 2) ) ) )
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      srate.append(pow(2.0, ceil( log(float(strlen.splitlines()[1].split()[5]), 2) ) ) * 2 )
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      snr = e.snr
      eff_dist = e.eff_distance
      if threshold_snr is not None:
          if snr > threshold_snr:
              horizon_distance.append(eff_dist * snr/threshold_snr)
          else:
              horizon_distance.append(2 * eff_dist)
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    if max(srate)<srate_psdfile:
      srate = max(srate)
    else:
      srate = srate_psdfile
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      fhigh = srate_psdfile/2.0 * 0.95 # Because of the drop-off near Nyquist of the PSD from gstlal
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    horizon_distance = max(horizon_distance) if len(horizon_distance) > 0 else None
    ev=Event(CoincInspiral=coinc, GID=gid, ifos = ifos, duration = max(dur), srate = srate,
             trigSNR = trigSNR, fhigh = fhigh, horizon_distance=horizon_distance)
    output.append(ev)
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  print "Found %d coinc events in table." % len(coinc_events)
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  os.chdir(cwd)
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  return output

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def open_pipedown_database(database_filename,tmp_space):
    """
    Open the connection to the pipedown database
    """
    if not os.access(database_filename,os.R_OK):
	raise Exception('Unable to open input file: %s'%(database_filename))
    from glue.ligolw import dbtables
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    import sqlite3
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    working_filename=dbtables.get_connection_filename(database_filename,tmp_path=tmp_space)
    connection = sqlite3.connect(working_filename)
    if tmp_space:
	dbtables.set_temp_store_directory(connection,tmp_space)
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    #dbtables.DBTable_set_connection(connection)
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    return (connection,working_filename)
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def get_zerolag_lloid(database_connection, dumpfile=None, gpsstart=None, gpsend=None, max_cfar=-1, min_cfar=-1):
	"""
	Returns a list of Event objects
	from pipedown data base. Can dump some stats to dumpfile if given,
	and filter by gpsstart and gpsend to reduce the nunmber or specify
	max_cfar to select by combined FAR
	"""
	output={}
	if gpsstart is not None: gpsstart=float(gpsstart)
	if gpsend is not None: gpsend=float(gpsend)
	# Get coincs
	get_coincs = "SELECT sngl_inspiral.end_time+sngl_inspiral.end_time_ns*1e-9,sngl_inspiral.ifo,coinc_event.coinc_event_id,sngl_inspiral.snr,sngl_inspiral.chisq,coinc_inspiral.combined_far \
		FROM sngl_inspiral join coinc_event_map on (coinc_event_map.table_name=='sngl_inspiral' and coinc_event_map.event_id ==\
		sngl_inspiral.event_id) join coinc_event on (coinc_event.coinc_event_id==coinc_event_map.coinc_event_id) \
		join coinc_inspiral on (coinc_event.coinc_event_id==coinc_inspiral.coinc_event_id) \
        WHERE coinc_event.time_slide_id=='time_slide:time_slide_id:1'\
		"
	if gpsstart is not None:
		get_coincs=get_coincs+' and coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1.0e-9 > %f'%(gpsstart)
	if gpsend is not None:
		get_coincs=get_coincs+' and coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1.0e-9 < %f'%(gpsend)
	if max_cfar !=-1:
		get_coincs=get_coincs+' and coinc_inspiral.combined_far < %f'%(max_cfar)
	if min_cfar != -1:
		get_coincs=get_coincs+' and coinc_inspiral.combined_far > %f'%(min_cfar)
	db_out=database_connection.cursor().execute(get_coincs)
    	extra={}
	for (sngl_time, ifo, coinc_id, snr, chisq, cfar) in db_out:
      		coinc_id=int(coinc_id.split(":")[-1])
	  	if not coinc_id in output.keys():
			output[coinc_id]=Event(trig_time=sngl_time,timeslide_dict={},event_id=int(coinc_id))
			extra[coinc_id]={}
		output[coinc_id].timeslides[ifo]=0
		output[coinc_id].ifos.append(ifo)
		extra[coinc_id][ifo]={'snr':snr,'chisq':chisq,'cfar':cfar}
	if dumpfile is not None:
		fh=open(dumpfile,'w')
		for co in output.keys():
			for ifo in output[co].ifos:
				fh.write('%s %s %s %s %s %s %s\n'%(str(co),ifo,str(output[co].trig_time),str(output[co].timeslides[ifo]),str(extra[co][ifo]['snr']),str(extra[co][ifo]['chisq']),str(extra[co][ifo]['cfar'])))
		fh.close()
	return output.values()

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def get_zerolag_pipedown(database_connection, dumpfile=None, gpsstart=None, gpsend=None, max_cfar=-1, min_cfar=-1):
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	"""
	Returns a list of Event objects
	from pipedown data base. Can dump some stats to dumpfile if given,
	and filter by gpsstart and gpsend to reduce the nunmber or specify
	max_cfar to select by combined FAR
	"""
	output={}
	if gpsstart is not None: gpsstart=float(gpsstart)
	if gpsend is not None: gpsend=float(gpsend)
	# Get coincs
	get_coincs = "SELECT sngl_inspiral.end_time+sngl_inspiral.end_time_ns*1e-9,sngl_inspiral.ifo,coinc_event.coinc_event_id,sngl_inspiral.snr,sngl_inspiral.chisq,coinc_inspiral.combined_far \
		FROM sngl_inspiral join coinc_event_map on (coinc_event_map.table_name=='sngl_inspiral' and coinc_event_map.event_id ==\
		sngl_inspiral.event_id) join coinc_event on (coinc_event.coinc_event_id==coinc_event_map.coinc_event_id) \
		join coinc_inspiral on (coinc_event.coinc_event_id==coinc_inspiral.coinc_event_id) \
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		WHERE coinc_event.time_slide_id=='time_slide:time_slide_id:10049'\
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		"
	if gpsstart is not None:
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		get_coincs=get_coincs+' and coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1.0e-9 > %f'%(gpsstart)
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	if gpsend is not None:
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		get_coincs=get_coincs+' and coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1.0e-9 < %f'%(gpsend)
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	if max_cfar !=-1:
		get_coincs=get_coincs+' and coinc_inspiral.combined_far < %f'%(max_cfar)
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	if min_cfar != -1:
		get_coincs=get_coincs+' and coinc_inspiral.combined_far > %f'%(min_cfar)
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	db_out=database_connection.cursor().execute(get_coincs)
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    	extra={}
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	for (sngl_time, ifo, coinc_id, snr, chisq, cfar) in db_out:
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      		coinc_id=int(coinc_id.split(":")[-1])
	  	if not coinc_id in output.keys():
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			output[coinc_id]=Event(trig_time=sngl_time,timeslide_dict={},event_id=int(coinc_id))
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			extra[coinc_id]={}
		output[coinc_id].timeslides[ifo]=0
		output[coinc_id].ifos.append(ifo)
		extra[coinc_id][ifo]={'snr':snr,'chisq':chisq,'cfar':cfar}
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	if dumpfile is not None:
		fh=open(dumpfile,'w')
		for co in output.keys():
			for ifo in output[co].ifos:
				fh.write('%s %s %s %s %s %s %s\n'%(str(co),ifo,str(output[co].trig_time),str(output[co].timeslides[ifo]),str(extra[co][ifo]['snr']),str(extra[co][ifo]['chisq']),str(extra[co][ifo]['cfar'])))
		fh.close()
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	return output.values()
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def get_timeslides_pipedown(database_connection, dumpfile=None, gpsstart=None, gpsend=None, max_cfar=-1):
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	"""
	Returns a list of Event objects
	with times and timeslide offsets
	"""
	output={}
	if gpsstart is not None: gpsstart=float(gpsstart)
	if gpsend is not None: gpsend=float(gpsend)
	db_segments=[]
	sql_seg_query="SELECT search_summary.out_start_time, search_summary.out_end_time from search_summary join process on process.process_id==search_summary.process_id where process.program=='thinca'"
	db_out = database_connection.cursor().execute(sql_seg_query)
	for d in db_out:
		if d not in db_segments:
			db_segments.append(d)
	seglist=segments.segmentlist([segments.segment(d[0],d[1]) for d in db_segments])
	db_out_saved=[]
	# Get coincidences
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	get_coincs="SELECT sngl_inspiral.end_time+sngl_inspiral.end_time_ns*1e-9,time_slide.offset,sngl_inspiral.ifo,coinc_event.coinc_event_id,sngl_inspiral.snr,sngl_inspiral.chisq,coinc_inspiral.combined_far \
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		    FROM sngl_inspiral join coinc_event_map on (coinc_event_map.table_name == 'sngl_inspiral' and coinc_event_map.event_id \
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		    == sngl_inspiral.event_id) join coinc_event on (coinc_event.coinc_event_id==coinc_event_map.coinc_event_id) join time_slide\
		    on (time_slide.time_slide_id == coinc_event.time_slide_id and time_slide.instrument==sngl_inspiral.ifo)\
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		    join coinc_inspiral on (coinc_inspiral.coinc_event_id==coinc_event.coinc_event_id) where coinc_event.time_slide_id!='time_slide:time_slide_id:10049'"
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	joinstr = ' and '
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	if gpsstart is not None:
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		get_coincs=get_coincs+ joinstr + ' coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1e-9 > %f'%(gpsstart)
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	if gpsend is not None:
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		get_coincs=get_coincs+ joinstr+' coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1e-9 <%f'%(gpsend)
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	if max_cfar!=-1:
		get_coincs=get_coincs+joinstr+' coinc_inspiral.combined_far < %f'%(max_cfar)
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	db_out=database_connection.cursor().execute(get_coincs)
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	from pylal import SnglInspiralUtils
	extra={}
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	for (sngl_time, slide, ifo, coinc_id, snr, chisq, cfar) in db_out:
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		coinc_id=int(coinc_id.split(":")[-1])
		seg=filter(lambda seg:sngl_time in seg,seglist)[0]
		slid_time = SnglInspiralUtils.slideTimeOnRing(sngl_time,slide,seg)
		if not coinc_id in output.keys():
			output[coinc_id]=Event(trig_time=slid_time,timeslide_dict={},event_id=int(coinc_id))
			extra[coinc_id]={}
		output[coinc_id].timeslides[ifo]=slid_time-sngl_time
		output[coinc_id].ifos.append(ifo)
		extra[coinc_id][ifo]={'snr':snr,'chisq':chisq,'cfar':cfar}
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	if dumpfile is not None:
		fh=open(dumpfile,'w')
		for co in output.keys():
			for ifo in output[co].ifos:
				fh.write('%s %s %s %s %s %s %s\n'%(str(co),ifo,str(output[co].trig_time),str(output[co].timeslides[ifo]),str(extra[co][ifo]['snr']),str(extra[co][ifo]['chisq']),str(extra[co][ifo]['cfar'])))
		fh.close()
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	return output.values()
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def mkdirs(path):
  """
  Helper function. Make the given directory, creating intermediate
  dirs if necessary, and don't complain about it already existing.
  """
  if os.access(path,os.W_OK) and os.path.isdir(path): return
  else: os.makedirs(path)

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def chooseEngineNode(name):
  if name=='lalinferencenest':
    return LALInferenceNestNode
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  if name=='lalinferenceburst':
    return LALInferenceBurstNode
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  if name=='lalinferencemcmc':
    return LALInferenceMCMCNode
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  if name=='lalinferencebambi' or name=='lalinferencebambimpi':
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    return LALInferenceBAMBINode
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  if name=='lalinferencedatadump':
    return LALInferenceDataDumpNode
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  return EngineNode

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def get_engine_name(cp):
    name=cp.get('analysis','engine')
    if name=='random':
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        engine_list=['lalinferencenest','lalinferencemcmc','lalinferencebambimpi']
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        if cp.has_option('input','gid'):
            gid=cp.get('input','gid')
            engine_number=int(''.join(i for i in gid if i.isdigit())) % 2
        else:
            engine_number=random.randint(0,1)
        return engine_list[engine_number]
    else:
        return name


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def scan_timefile(timefile):
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    import re
    p=re.compile('[\d.]+')
    times=[]
    timefilehandle=open(timefile,'r')
    for time in timefilehandle:
      if not p.match(time):
	continue
      if float(time) in times:
	print 'Skipping duplicate time %s'%(time)
	continue
      print 'Read time %s'%(time)
      times.append(float(time))
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    timefilehandle.close()
    return times
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def get_xml_psds(psdxml,ifos,outpath,end_time=None):
  """
  Get a psd.xml.gz file and:
  1) Reads it
  2) Converts PSD (10e-44) -> ASD ( ~10e-22)
  3) Checks the psd file contains all the IFO we want to analyze
  4) Writes down the ASDs into an ascii file for each IFO in psd.xml.gz. The name of the file contains the trigtime (if given) and the ifo name.
  Input:
    psdxml: psd.xml.gz file
    ifos: list of ifos used for the analysis
    outpath: path where the ascii ASD will be written to
    (end_time): trigtime for this event. Will be used a part of the ASD file name
  """
  lal=1
  from glue.ligolw import utils
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  try:
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    #from pylal import series
    from lal import series as series
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    lal=0
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  except ImportError:
    print "ERROR, cannot import pylal.series in bppu/get_xml_psds()\n"
    exit(1)
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  out={}
  if not os.path.isdir(outpath):
    os.makedirs(outpath)
  if end_time is not None:
    time=repr(float(end_time))
  else:
    time=''
  #check we don't already have ALL the psd files #
  got_all=1
  for ifo in ifos:
    path_to_ascii_psd=os.path.join(outpath,ifo+'_psd_'+time+'.txt')
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    # Check we don't already have that ascii (e.g. because we are running parallel runs of the save event
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    if os.path.isfile(path_to_ascii_psd):
      got_all*=1
    else:
      got_all*=0
  if got_all==1:
    #print "Already have PSD files. Nothing to do...\n"
    for ifo in ifos:
      out[ifo]=os.path.join(outpath,ifo+'_psd_'+time+'.txt')
    return out
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  # We need to convert the PSD for one or more IFOS. Open the file
  if not os.path.isfile(psdxml):
    print "ERROR: impossible to open the psd file %s. Exiting...\n"%psdxml
    sys.exit(1)
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  xmlpsd =  series.read_psd_xmldoc(utils.load_filename(psdxml,contenthandler = series.PSDContentHandler))
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  # Check the psd file contains all the IFOs we want to analize
  for ifo in ifos:
    if not ifo in [i.encode('ascii') for i in xmlpsd.keys()]:
      print "ERROR. The PSD for the ifo %s does not seem to be contained in %s\n"%(ifo,psdxml)
      sys.exit(1)
  #loop over ifos in psd xml file
  for instrument in xmlpsd.keys():
    #name of the ascii file we are going to write the PSD into
    path_to_ascii_psd=os.path.join(outpath,instrument.encode('ascii')+'_psd_'+time+'.txt')
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    # Check we don't already have that ascii (e.g. because we are running parallel runs of the save event
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    if os.path.isfile(path_to_ascii_psd):
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      continue
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    # get data for the IFO
    ifodata=xmlpsd[instrument]
    #check data is not empty
    if ifodata is None:
      continue
    # we have data. Get psd array
    if lal==0:
      #pylal stores the series in ifodata.data
      data=ifodata
    else:
      # lal stores it in ifodata.data.data
      data=ifodata.data
    # Fill a two columns array of (freq, psd) and save it in the ascii file
    f0=ifodata.f0
    deltaF=ifodata.deltaF
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    combine=[]
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    for i in np.arange(len(data.data.data)) :
      combine.append([f0+i*deltaF,np.sqrt(data.data.data[i])])
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    np.savetxt(path_to_ascii_psd,combine)
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    ifo=instrument.encode('ascii')
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    # set node.psds dictionary with the path to the ascii files
    out[ifo]=os.path.join(outpath,ifo+'_psd_'+time+'.txt')
  return out
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def get_trigger_chirpmass(gid=None,gracedb="gracedb"):
  from glue.ligolw import lsctables
  from glue.ligolw import ligolw
  from glue.ligolw import utils
  class PSDContentHandler(ligolw.LIGOLWContentHandler):
    pass
  lsctables.use_in(PSDContentHandler)
  import subprocess

  cwd=os.getcwd()
  subprocess.call([gracedb,"download", gid ,"coinc.xml"])
  xmldoc=utils.load_filename("coinc.xml",contenthandler = PSDContentHandler)
  coinctable = lsctables.CoincInspiralTable.get_table(xmldoc)
  coinc_events = [event for event in coinctable]
  sngltable = lsctables.SnglInspiralTable.get_table(xmldoc)
  sngl_events = [event for event in sngltable]
  coinc_map = lsctables.CoincMapTable.get_table(xmldoc)
  mass1 = []
  mass2 = []
  for coinc in coinc_events:
    these_sngls = [e for e in sngl_events if e.event_id in [c.event_id for c in coinc_map if c.coinc_event_id == coinc.coinc_event_id] ]
    for e in these_sngls:
      mass1.append(e.mass1)
      mass2.append(e.mass2)
  # check that trigger masses are identical in each IFO    
  assert len(set(mass1)) == 1
  assert len(set(mass2)) == 1

  mchirp = (mass1[0]*mass2[0])**(3./5.) / ( (mass1[0] + mass2[0])**(1./5.) ) 
  os.remove("coinc.xml")

  return mchirp

def get_roq_mchirp_priors(path, roq_paths, roq_params, key, gid):
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  mc_priors = {}

  for roq in roq_paths:
    params=os.path.join(path,roq,'params.dat')
    roq_params[roq]=np.genfromtxt(params,names=True)
    mc_priors[roq]=[float(roq_params[roq]['chirpmassmin']),float(roq_params[roq]['chirpmassmax'])]
  ordered_roq_paths=[item[0] for item in sorted(roq_params.items(), key=key)][::-1]
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  # below is to construct non-overlapping mc priors for multiple roq mass-bin runs
  '''i=0
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  for roq in ordered_roq_paths:
    if i>0:
      # change min, just set to the max of the previous one since we have already aligned it in the previous iteration of this loop
      #mc_priors[roq][0]+= (mc_priors[roq_lengths[i-1]][1]-mc_priors[roq][0])/2.
      mc_priors[roq][0]=mc_priors[ordered_roq_paths[i-1]][1]
    if i<len(roq_paths)-1:
      mc_priors[roq][1]-= (mc_priors[roq][1]- mc_priors[ordered_roq_paths[i+1]][0])/2.
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    i+=1'''
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  if gid is not None:
  	trigger_mchirp = get_trigger_chirpmass(gid)
  else:
	trigger_mchirp = None
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  return mc_priors, trigger_mchirp

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def get_roq_mass_freq_scale_factor(mc_priors, trigger_mchirp):
  mc_max = mc_priors['4s'][1]
  mc_min = mc_priors['128s'][0]
  scale_factor = 1
  if trigger_mchirp >= mc_max: 
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  	scale_factor = 2**(floor(trigger_mchirp/mc_max))
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  if trigger_mchirp <= mc_min:
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	scale_factor = 1./2**(ceil(trigger_mchirp/mc_min))
  return scale_factor
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def create_pfn_tuple(filename,protocol='file://',site='local'):
    return( (os.path.basename(filename),protocol+os.path.abspath(filename),site) )
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class LALInferencePipelineDAG(pipeline.CondorDAG):
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  def __init__(self,cp,dax=False,first_dag=True,previous_dag=None,site='local'):
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    self.subfiles=[]
    self.config=cp
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    self.engine=get_engine_name(cp)
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    self.EngineNode=chooseEngineNode(self.engine)
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    self.site=site
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    if cp.has_option('paths','basedir'):
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      self.basepath=cp.get('paths','basedir')
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    else:
      self.basepath=os.getcwd()
      print 'No basepath specified, using current directory: %s'%(self.basepath)
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    mkdirs(self.basepath)
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    if dax:
        os.chdir(self.basepath)
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    self.posteriorpath=os.path.join(self.basepath,'posterior_samples')
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    mkdirs(self.posteriorpath)
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    if first_dag:
      daglogdir=cp.get('paths','daglogdir')
      mkdirs(daglogdir)
      self.daglogfile=os.path.join(daglogdir,'lalinference_pipeline-'+str(uuid.uuid1())+'.log')
      pipeline.CondorDAG.__init__(self,self.daglogfile,dax=dax)
    elif not first_dag and previous_dag is not None:
      daglogdir=cp.get('paths','daglogdir')
      mkdirs(daglogdir)
      self.daglogfile=os.path.join(daglogdir,'lalinference_pipeline-'+str(uuid.uuid1())+'.log')
      pipeline.CondorDAG.__init__(self,self.daglogfile,dax=dax)
      for node in previous_dag.get_nodes():
        self.add_node(node)
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    if cp.has_option('paths','cachedir'):
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      self.cachepath=cp.get('paths','cachedir')
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    else:
      self.cachepath=os.path.join(self.basepath,'caches')
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    mkdirs(self.cachepath)
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    if cp.has_option('paths','logdir'):
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      self.logpath=cp.get('paths','logdir')
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    else:
      self.logpath=os.path.join(self.basepath,'log')
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    mkdirs(self.logpath)
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    if cp.has_option('analysis','ifos'):
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      self.ifos=ast.literal_eval(cp.get('analysis','ifos'))
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    else:
      self.ifos=['H1','L1','V1']
    self.segments={}
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    if cp.has_option('datafind','veto-categories'):
      self.veto_categories=cp.get('datafind','veto-categories')
    else: self.veto_categories=[]
    for ifo in self.ifos:
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      self.segments[ifo]=[]
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    self.computeroqweightsnodes={}
    self.bayeslinenodes={}
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    self.dq={}
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    self.frtypes=ast.literal_eval(cp.get('datafind','types'))
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    self.channels=ast.literal_eval(cp.get('data','channels'))
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    self.use_available_data=False
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    self.webdir=cp.get('paths','webdir')
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    if cp.has_option('analysis','dataseed'):
      self.dataseed=cp.getint('analysis','dataseed')
    else:
      self.dataseed=None
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    # Set up necessary job files.
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    self.prenodes={}
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    self.datafind_job = pipeline.LSCDataFindJob(self.cachepath,self.logpath,self.config,dax=self.is_dax())
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    self.datafind_job.add_opt('url-type','file')
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    if cp.has_option('analysis','accounting_group'):
      self.datafind_job.add_condor_cmd('accounting_group',cp.get('analysis','accounting_group'))
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    if cp.has_option('analysis','accounting_group_user'):
      self.datafind_job.add_condor_cmd('accounting_group_user',cp.get('analysis','accounting_group_user'))
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    self.datafind_job.set_sub_file(os.path.abspath(os.path.join(self.basepath,'datafind.sub')))
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    self.preengine_job = EngineJob(self.config, os.path.join(self.basepath,'prelalinference.sub'),self.logpath,engine='lalinferencedatadump',ispreengine=True,dax=self.is_dax())
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    self.preengine_job.set_grid_site('local')
    self.preengine_job.set_universe('vanilla')
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    if self.config.has_option('condor','computeroqweights'):
      self.computeroqweights_job = ROMJob(self.config,os.path.join(self.basepath,'computeroqweights.sub'),self.logpath,dax=self.is_dax())
      self.computeroqweights_job.set_grid_site('local')
    if self.config.has_option('condor','bayesline'):
      self.bayesline_job = BayesLineJob(self.config,os.path.join(self.basepath,'bayesline.sub'),self.logpath,dax=self.is_dax())
      self.bayesline_job.set_grid_site('local')
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    # Need to create a job file for each IFO combination
    self.engine_jobs={}
    ifocombos=[]
    for N in range(1,len(self.ifos)+1):
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        for a in permutations(self.ifos,N):
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            ifocombos.append(a)
    for ifos in ifocombos:
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        self.engine_jobs[ifos] = EngineJob(self.config, os.path.join(self.basepath,'engine_%s.sub'%(reduce(lambda x,y:x+y, map(str,ifos)))),self.logpath,engine=self.engine,dax=self.is_dax(), site=site)
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    self.results_page_job = ResultsPageJob(self.config,os.path.join(self.basepath,'resultspage.sub'),self.logpath,dax=self.is_dax())
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    self.results_page_job.set_grid_site('local')
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    self.cotest_results_page_job = ResultsPageJob(self.config,os.path.join(self.basepath,'resultspagecoherent.sub'),self.logpath,dax=self.is_dax())
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    self.cotest_results_page_job.set_grid_site('local')
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    self.merge_job = MergeNSJob(self.config,os.path.join(self.basepath,'merge_runs.sub'),self.logpath,dax=self.is_dax())
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    self.merge_job.set_grid_site('local')
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    self.coherence_test_job = CoherenceTestJob(self.config,os.path.join(self.basepath,'coherence_test.sub'),self.logpath,dax=self.is_dax())
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    self.coherence_test_job.set_grid_site('local')
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    self.gracedbjob = GraceDBJob(self.config,os.path.join(self.basepath,'gracedb.sub'),self.logpath,dax=self.is_dax())
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    self.gracedbjob.set_grid_site('local')
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    # Process the input to build list of analyses to do
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    self.events=self.setup_from_inputs()
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    # Sanity checking
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    if len(self.events)==0:
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      print 'No input events found, please check your config if you expect some events'
    self.times=[e.trig_time for e in self.events]
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    # Set up the segments
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    if not (self.config.has_option('input','gps-start-time') and self.config.has_option('input','gps-end-time')) and len(self.times)>0:
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      (mintime,maxtime)=self.get_required_data(self.times)
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      if not self.config.has_option('input','gps-start-time'):
        self.config.set('input','gps-start-time',str(int(floor(mintime))))
      if not self.config.has_option('input','gps-end-time'):
        self.config.set('input','gps-end-time',str(int(ceil(maxtime))))
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    self.add_science_segments()
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    # Save the final configuration that is being used
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    # first to the run dir
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    conffilename=os.path.join(self.basepath,'config.ini')
    with open(conffilename,'wb') as conffile:
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      self.config.write(conffile)
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    if self.config.has_option('paths','webdir'):
      mkdirs(self.config.get('paths','webdir'))
      with open(os.path.join(self.config.get('paths','webdir'),'config.ini'),'wb') as conffile:
        self.config.write(conffile)
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    # Generate the DAG according to the config given
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    for event in self.events: self.add_full_analysis(event)
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    self.add_skyarea_followup()
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    if self.config.has_option('analysis','upload-to-gracedb'):
      if self.config.getboolean('analysis','upload-to-gracedb'):
        self.add_gracedb_FITSskymap_upload(self.events[0],engine=self.engine)
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    self.dagfilename="lalinference_%s-%s"%(self.config.get('input','gps-start-time'),self.config.get('input','gps-end-time'))
    self.set_dag_file(self.dagfilename)
    if self.is_dax():
      self.set_dax_file(self.dagfilename)

  def add_skyarea_followup(self):
    # Add skyarea jobs if the executable is given
    # Do one for each results page for now
    if self.config.has_option('condor','skyarea'):
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      self.skyareajob=SkyAreaJob(self.config,os.path.join(self.basepath,'skyarea.sub'),self.logpath,dax=self.is_dax())
      respagenodes=filter(lambda x: isinstance(x,ResultsPageNode) ,self.get_nodes())
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      if self.engine=='lalinferenceburst':
          prefix='LIB_'
      else:
          prefix='LALInference_'
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      for p in respagenodes:
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          skyareanode=SkyAreaNode(self.skyareajob,prefix=prefix)
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          skyareanode.add_resultspage_parent(p)
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          skyareanode.set_ifos(p.ifos)
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          self.add_node(skyareanode)
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  def add_full_analysis(self,event):
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    if self.engine=='lalinferencenest' or  self.engine=='lalinferenceburst':
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      result=self.add_full_analysis_lalinferencenest(event)
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    elif self.engine=='lalinferencemcmc':
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      result=self.add_full_analysis_lalinferencemcmc(event)
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    elif self.engine=='lalinferencebambi' or self.engine=='lalinferencebambimpi':
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      result=self.add_full_analysis_lalinferencebambi(event)
    return result

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  def create_frame_pfn_file(self):
    """
    Create a pegasus cache file name, uses inspiralutils
    """
    import inspiralutils as iu
    gpsstart=self.config.get('input','gps-start-time')
    gpsend=self.config.get('input','gps-end-time')
    pfnfile=iu.create_frame_pfn_file(self.frtypes,gpsstart,gpsend)
    return pfnfile
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  def get_required_data(self,times):
    """
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    Calculate the data that will be needed to process all events
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    """
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    #psdlength = self.config.getint('input','max-psd-length')
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    padding=self.config.getint('input','padding')
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    if self.config.has_option('engine','seglen') or self.config.has_option('lalinference','seglen'):
      if self.config.has_option('engine','seglen'):
        seglen = self.config.getint('engine','seglen')
      if self.config.has_option('lalinference','seglen'):
        seglen = self.config.getint('lalinference','seglen')

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      if os.path.isfile(os.path.join(self.basepath,'psd.xml.gz')):
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        psdlength = 0
      else:
        psdlength = 32*seglen
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    else:
      seglen = max(e.duration for e in self.events)
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      if os.path.isfile(os.path.join(self.basepath,'psd.xml.gz')):
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        psdlength = 0
      else:
        psdlength = 32*seglen
    # Assume that the data interval is (end_time - seglen -padding , end_time + psdlength +padding )
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    # -> change to (trig_time - seglen - padding - psdlength + 2 , trig_time + padding + 2) to estimate the psd before the trigger for online follow-up.
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    # Also require padding before start time
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    return (min(times)-padding-seglen-psdlength+2,max(times)+padding+2)
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  def setup_from_times(self,times):
    """
    Generate a DAG from a list of times
    """
    for time in self.times:
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      self.add_full_analysis(Event(trig_time=time))
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  def select_events(self):
    """
    Read events from the config parser. Understands both ranges and comma separated events, or combinations
    eg. events=[0,1,5:10,21] adds to the analysis the events: 0,1,5,6,7,8,9,10 and 21
    """
    events=[]
    times=[]
    raw_events=self.config.get('input','events').replace('[','').replace(']','').split(',')
    for raw_event in raw_events:
        if ':' in raw_event:
            limits=raw_event.split(':')
            if len(limits) != 2:
                print "Error: in event config option; ':' must separate two numbers."
                exit(0)
            low=int(limits[0])
            high=int(limits[1])
            if low>high:
                events.extend(range(int(high),int(low)+1))
            elif high>low:
                events.extend(range(int(low),int(high)+1))
        else:
            events.append(int(raw_event))
    return events

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  def setup_from_inputs(self):
    """
    Scan the list of inputs, i.e.
    gps-time-file, injection-file, sngl-inspiral-file, coinc-inspiral-file, pipedown-database
    in the [input] section of the ini file.
    And process the events found therein
    """
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    events=[]
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    gpsstart=None
    gpsend=None
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    if self.config.has_option('input','gps-start-time'):
      gpsstart=self.config.getfloat('input','gps-start-time')
    if self.config.has_option('input','gps-end-time'):
      gpsend=self.config.getfloat('input','gps-end-time')
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    inputnames=['gps-time-file','burst-injection-file','injection-file','sngl-inspiral-file','coinc-inspiral-file','pipedown-db','gid','gstlal-db']
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    ReadInputFromList=sum([ 1 if self.config.has_option('input',name) else 0 for name in inputnames])
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    # If no input events given, just return an empty list (e.g. for PP pipeline)
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    if ReadInputFromList!=1 and (gpsstart is None or gpsend is None):
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        return []
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    # Review: Clean up this section
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    if self.config.has_option('input','events'):
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      selected_events=self.config.get('input','events')
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      print 'Selected events %s'%(str(selected_events))
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      if selected_events=='all':
          selected_events=None
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      else:
          selected_events=self.select_events()
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    else:
        selected_events=None
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    # No input file given, analyse the entire time stretch between gpsstart and gpsend
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    if self.config.has_option('input','analyse-all-time') and self.config.getboolean('input','analyse-all-time')==True:
        print 'Setting up for analysis of continuous time stretch %f - %f'%(gpsstart,gpsend)
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        seglen=self.config.getfloat('engine','seglen')
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        if(self.config.has_option('input','segment-overlap')):
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          overlap=self.config.getfloat('input','segment-overlap')
        else:
          overlap=32.;
        if(overlap>seglen):
          print 'ERROR: segment-overlap is greater than seglen'
          sys.exit(1)
        # Now divide gpsstart - gpsend into jobs of seglen - overlap length
        t=gpsstart
        events=[]
        while(t<gpsend):
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            ev=Event(trig_time=t+seglen-2)
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            ev.set_engine_option('segment-start',str(t-overlap))
            ev.set_engine_option('time-min',str(t))
            tMax=t + seglen - overlap
            if tMax>=gpsend:
                tMax=gpsend
            ev.set_engine_option('time-max',str(tMax))
            events.append(ev)
            t=tMax