lalinference_pipe_utils.py 146 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
from ligo import segments
from ligo.segments import utils as segmentsUtils
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from glue.ligolw import ligolw, lsctables
from glue.ligolw import utils as ligolw_utils
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import os
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import socket
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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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import math
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from six.moves import range
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from six import next
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from functools import reduce
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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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def findSegmentsToAnalyze(ifo, frametype, state_vector_channel, bits, gpsstart, gpsend):
    """Return list of segments whose data quality is good enough for PE. The data
    quality is examined with statevector in frame files. If frame files do not
    exist, return empty list.

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    Parameters
    ----
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    ifo: string
    frametype: string
    state_vector_channel: string
    bits: list of string
        List of bits. This function extracts the data taken when all of the
        bits in this list are "active" assuming such data is good enough for
        PE.
    gpsstart, gpsend: float
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        GPS period to analyse
    """
    try:
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        from glue.lal import Cache
        from gwdatafind import find_urls
        import gwpy
        from gwpy.timeseries import StateVector
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    except ImportError:
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        print('Unable to import necessary modules. Querying science segments not possible. Please try installing gwdatafind and gwpy')
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        raise
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    # search for frame file and read its statevector channel
    datacache = Cache.from_urls(find_urls(ifo[0], frametype, gpsstart, gpsend))
    if not datacache:
        return gwpy.segments.SegmentList([])
    flags = gwpy.timeseries.StateVector.read(
        datacache, state_vector_channel, start=gpsstart, end=gpsend
    ).to_dqflags()
    # extract segments all of whose bits are active
    segments = flags[bits[0]].active
    for bit in bits:
        segments -= ~flags[bit].active
    return segments
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def guess_url(fslocation):
    """
    Try to work out the web address of a given path
    """
    SERVER="localhost"
    USER=os.environ['USER']
    HOST=socket.getfqdn()
    if 'public_html' in fslocation:
        k='public_html/'
    elif 'WWW' in fslocation:
        k='WWW/'
    elif 'www_html' in fslocation:
        k='www_html/'
    else:
        k=None
    if k is not None:
        (a,b)=fslocation.split(k)
        webpath=os.path.join('~%s'%(USER),b)
        onweb=True
    else:
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        (c,d)=fslocation.split(USER,1)
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        for k in ['public_html','WWW','www_html']:
            trypath=c+os.environ['USER']+'/'+k+d
            #Follow symlinks
            if os.path.realpath(trypath)==os.path.normpath(fslocation):
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                #(a,b)=trypath.split(k)
                webpath=os.path.join('~%s'%(USER),d)
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                onweb=True
                break
            else:
                webpath=fslocation
                onweb=False
    if 'atlas' in HOST:
        url="https://atlas1.atlas.aei.uni-hannover.de/"
    elif 'ligo-wa' in HOST:
        url="https://ldas-jobs.ligo-wa.caltech.edu/"
    elif 'ligo-la' in HOST:
        url="https://ldas-jobs.ligo-la.caltech.edu/"
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    elif 'cit' in HOST or 'caltech' in HOST:
        url="https://ldas-jobs.ligo.caltech.edu/"
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    elif 'uwm' in HOST or 'nemo' in HOST:
        url="https://ldas-jobs.phys.uwm.edu/"
    elif 'phy.syr.edu' in HOST:
        url="https://sugar-jobs.phy.syr.edu/"
    elif 'arcca.cf.ac.uk' in HOST:
        url="https://geo2.arcca.cf.ac.uk/"
    elif 'vulcan' in HOST:
        url="https://galahad.aei.mpg.de/"
    else:
        if onweb:
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            url="http://%s/"%(HOST)
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        else:
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            url=HOST+':'
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    url=url+webpath
    return(url)

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class Event():
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    """
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    Represents a unique event to run on
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    """
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    new_id=itertools.count()
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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):
        self.trig_time=trig_time
        self.injection=SimInspiral
        self.burstinjection=SimBurst
        self.sngltrigger=SnglInspiral
        if timeslide_dict is None:
            self.timeslides={}
        else:
            self.timeslides=timeslide_dict
        self.GID=GID
        self.coinctrigger=CoincInspiral
        if ifos is None:
            self.ifos = []
        else:
            self.ifos = ifos
        self.duration = duration
        self.srate = srate
        self.trigSNR = trigSNR
        self.fhigh = fhigh
        self.horizon_distance = horizon_distance
        if event_id is not None:
            self.event_id=event_id
        else:
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            self.event_id=next(Event.new_id)
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        if self.injection is not None:
            self.trig_time=self.injection.get_end()
            if event_id is None: self.event_id=int(str(self.injection.simulation_id).split(':')[2])
        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])
        if self.sngltrigger is not None:
            self.trig_time=self.sngltrigger.get_end()
            self.event_id=int(str(self.sngltrigger.event_id).split(':')[2])
        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()))
        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 create_events_from_coinc_and_psd(
    coinc_xml_obj, psd_dict, gid=None, threshold_snr=None, flow=20.0, roq=False
):
    """This function calculates seglen, fhigh, srate and horizon distance from
    coinc.xml and psd.xml.gz from GraceDB and create list of Events as input of
    pipeline. This function is based on Chris Pankow's script.

    Parameters
    ----------
    coinc_xml_obj: glue.ligolw.ligolw.Document
        file object of coinc.xml
    psd_dict: dictionary of REAL8FrequencySeries
        PSDs of all the ifos
    threshold_snr: float
        snr threshold for detection
    flow: float
        lower frequecy cutoff for overlap calculation
    roq: bool
        Whether the run uses ROQ or not
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    """
    output=[]
    from lal import series as lalseries
    import lal
    from lalsimulation import SimInspiralChirpTimeBound, GetApproximantFromString, IMRPhenomDGetPeakFreq
    from ligo.gracedb.rest import GraceDb, HTTPError
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    try:
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        from gstlal import reference_psd
    except ImportError:
        reference_psd = None
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    try:
        from gwpy.frequencyseries import FrequencySeries
        from gwpy.astro import inspiral_range
    except ImportError:
        inspiral_range = None
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    coinc_events = lsctables.CoincInspiralTable.get_table(coinc_xml_obj)
    sngl_event_idx = dict((row.event_id, row) for row in lsctables.SnglInspiralTable.get_table(coinc_xml_obj))
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    ifos = sorted(coinc_events[0].instruments)
    trigSNR = coinc_events[0].snr
    # Parse PSD
    srate_psdfile=16384
    fhigh=None
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    if psd_dict is not None:
        psd = list(psd_dict.values())[0]
        srate_psdfile = pow(
            2.0, ceil(log(psd.f0 + psd.deltaF * (psd.data.length - 1), 2))
        ) * 2
    coinc_map = lsctables.CoincMapTable.get_table(coinc_xml_obj)
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    for coinc in coinc_events:
        these_sngls = [sngl_event_idx[c.event_id] for c in coinc_map if c.coinc_event_id == coinc.coinc_event_id]
        dur=[]
        srate=[]
        horizon_distance=[]
        for e in these_sngls:
            if roq==False:
                chirplen = SimInspiralChirpTimeBound(flow, e.mass1 * lal.MSUN_SI, e.mass2 * lal.MSUN_SI, 0.0, 0.0)
                fstop = IMRPhenomDGetPeakFreq(e.mass1, e.mass2, 0.0, 0.0)
                dur.append(pow(2.0, ceil( log(max(8.0, chirplen + 2.0), 2) ) ) )
                srate.append(pow(2.0, ceil( log(fstop, 2) ) ) * 2)
            # determine horizon distance
            if threshold_snr is not None:
                if e.eff_distance is not None and not math.isnan(e.eff_distance):
                    if e.snr > threshold_snr:
                        horizon_distance.append(e.eff_distance * e.snr / threshold_snr)
                    else:
                        horizon_distance.append(2 * e.eff_distance)
                else:
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                    if psd_dict is not None:
                        # Calculate horizon distance from psd to determine
                        # upper limit of distance prior.
                        psd = psd_dict[e.ifo]
                        # If roq is not used, fstop has not been calculated up
                        # to this point.
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                        if not roq==False:
                            fstop = IMRPhenomDGetPeakFreq(e.mass1, e.mass2, 0.0, 0.0)
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                        # reference_psd.HorizonDistance is more precise
                        # calculator of horizon distance than
                        # gwpy.astro.inspiral_range.
                        if reference_psd is not None:
                            HorizonDistanceObj = reference_psd.HorizonDistance(
                                f_min = flow, f_max = fstop, delta_f = 1.0 / 32.0,
                                m1 = e.mass1, m2 = e.mass2
                            )
                            horizon_distance.append(
                                HorizonDistanceObj(psd, snr = threshold_snr)[0]
                            )
                        # If reference_psd is not available, use
                        # gwpy.astro.inspiral_range.
                        elif inspiral_range is not None:
                            gwpy_psd = FrequencySeries(
                                psd.data.data, f0 = psd.f0, df = psd.deltaF
                            )
                            try:
                                horizon_distance.append(
                                    inspiral_range(
                                        gwpy_psd, threshold_snr, e.mass1, e.mass2,
                                        flow, fstop, True
                                    ).value
                                )
                            # If flow of psd is lower than f_ISCO, inspiral_range
                            # raises IndexError. In this case, nothing is
                            # appended to horizon_distance.
                            except IndexError:
                                pass
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        if srate:
            if max(srate)<srate_psdfile:
                srate = max(srate)
            else:
                srate = srate_psdfile
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                if psd_dict is not None:
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                    fhigh = srate_psdfile/2.0 * 0.95 # Because of the drop-off near Nyquist of the PSD from gstlal
        else:
            srate = None
        if dur:
            duration = max(dur)
        else:
            duration = None
        horizon_distance = max(horizon_distance) if len(horizon_distance) > 0 else None
        ev=Event(CoincInspiral=coinc, GID=gid, ifos = ifos, duration = duration, 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))
    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):
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        raise Exception('Unable to open input file: %s'%(database_filename))
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    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:
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        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):
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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) \
    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) \
            WHERE coinc_event.time_slide_id=='time_slide:time_slide_id:10049'\
            "
    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_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
    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 \
                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 time_slide\
                on (time_slide.time_slide_id == coinc_event.time_slide_id and time_slide.instrument==sngl_inspiral.ifo)\
                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'"
    joinstr = ' and '
    if gpsstart is not None:
        get_coincs=get_coincs+ joinstr + ' coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1e-9 > %f'%(gpsstart)
    if gpsend is not None:
        get_coincs=get_coincs+ joinstr+' coinc_inspiral.end_time+coinc_inspiral.end_time_ns*1e-9 <%f'%(gpsend)
    if max_cfar!=-1:
        get_coincs=get_coincs+joinstr+' coinc_inspiral.combined_far < %f'%(max_cfar)
    db_out=database_connection.cursor().execute(get_coincs)
    # Timeslide functionality requires obsolete pylal - will be removed
    import pylal
    from pylal import SnglInspiralUtils
    extra={}
    for (sngl_time, slide, ifo, coinc_id, snr, chisq, cfar) in db_out:
        coinc_id=int(coinc_id.split(":")[-1])
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        seg=list(filter(lambda seg:sngl_time in seg,seglist))[0]
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        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}
    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 mkdirs(path):
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    """
    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):
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    if name=='lalinferencenest':
        return LALInferenceNestNode
    if name=='lalinferenceburst':
        return LALInferenceBurstNode
    if name=='lalinferencemcmc':
        return LALInferenceMCMCNode
    if name=='lalinferencedatadump':
        return LALInferenceDataDumpNode
    if name=='bayeswavepsd':
        return BayesWavePSDNode
    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']
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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:
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        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):
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    """
    Get a psd.xml.gz file and:
    1) Reads it
    2) Checks the psd file contains all the IFO we want to analyze
    3) Writes down the PSDs 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 PSD will be written to
      (end_time): trigtime for this event. Will be used a part of the PSD file name
    """
    try:
        from lal import series as lalseries
    except ImportError:
        print("ERROR, cannot import lal.series in bppu/get_xml_psds()\n")
        raise

    out={}
    if not os.path.isdir(outpath):
        os.makedirs(outpath)
    if end_time is not None:
        time=repr(float(end_time))
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    else:
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        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')
        # Check we don't already have that ascii (e.g. because we are running parallel runs of the save event
        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

    # 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)
    xmlpsd =  lalseries.read_psd_xmldoc(ligolw_utils.load_filename(psdxml,contenthandler = lalseries.PSDContentHandler))
    # Check the psd file contains all the IFOs we want to analize
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    for ifo in ifos:
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        if not ifo in xmlpsd:
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            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
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        path_to_ascii_psd=os.path.join(outpath,instrument+'_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
        if os.path.isfile(path_to_ascii_psd):
            continue
        # get data for the IFO
        ifodata=xmlpsd[instrument]
        #check data is not empty
        if ifodata is None:
            continue
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        # write down PSD into an ascii file
        combine = np.c_[ifodata.f0 + np.arange(ifodata.data.length) * ifodata.deltaF, ifodata.data.data]
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        np.savetxt(path_to_ascii_psd,combine)
        # set node.psds dictionary with the path to the ascii files
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        ifo=instrument
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        out[ifo]=os.path.join(outpath,ifo+'_psd_'+time+'.txt')
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    return out
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def get_trigger_chirpmass(coinc_xml_obj):
    coinc_events = lsctables.CoincInspiralTable.get_table(coinc_xml_obj)
    sngl_event_idx = dict((row.event_id, row) for row in lsctables.SnglInspiralTable.get_table(coinc_xml_obj))
    coinc_map = lsctables.CoincMapTable.get_table(coinc_xml_obj)
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    mass1 = []
    mass2 = []
    for coinc in coinc_events:
        these_sngls = [sngl_event_idx[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.) )

    return mchirp
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def get_roq_mchirp_priors(path, roq_paths, roq_params, key, coinc_xml_obj=None, sim_inspiral=None):
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    ## XML and GID cannot be given at the same time
    ## sim_inspiral must already point at the right row
    mc_priors = {}

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    if coinc_xml_obj is not None and sim_inspiral is not None:
        print("Error in get_roq_mchirp_priors, cannot use both coinc.xml and sim_inspiral\n")
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        sys.exit(1)

    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]
    # below is to construct non-overlapping mc priors for multiple roq mass-bin runs
    '''i=0
    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.
      i+=1'''
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    if coinc_xml_obj is not None:
        trigger_mchirp = get_trigger_chirpmass(coinc_xml_obj)
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    elif sim_inspiral is not None:
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        trigger_mchirp = sim_inspiral.mchirp
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    else:
        trigger_mchirp = None
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    return mc_priors, trigger_mchirp
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def get_roq_component_mass_priors(path, roq_paths, roq_params, key, coinc_xml_obj=None, sim_inspiral=None):
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    ## coinc_xml_obj and sim_inspiral cannot be given at the same time
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    ## sim_inspiral must already point at the right row
    m1_priors = {}
    m2_priors = {}
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    if coinc_xml_obj is not None and sim_inspiral is not None:
        print("Error in get_roq_mchirp_priors, cannot use both coinc.xml and sim_inspiral\n")
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        sys.exit(1)
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    for roq in roq_paths:
        params=os.path.join(path,roq,'params.dat')
        roq_params[roq]=np.genfromtxt(params,names=True)
        m1_priors[roq]=[float(roq_params[roq]['mass1min']),float(roq_params[roq]['mass1max'])]
        m2_priors[roq]=[float(roq_params[roq]['mass2min']),float(roq_params[roq]['mass2max'])]
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    if coinc_xml_obj is not None:
        trigger_mchirp = get_trigger_chirpmass(coinc_xml_obj)
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    elif sim_inspiral is not None:
        trigger_mchirp = sim_inspiral.mchirp
    else:
        trigger_mchirp = None
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    return m1_priors, m2_priors, trigger_mchirp
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def get_roq_mass_freq_scale_factor(mc_priors, trigger_mchirp, force_flow=None):
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    mc_priors_keys_list = list(mc_priors.keys())
    mc_priors_keys_int = [int(seglen[:-1]) for seglen in mc_priors_keys_list]
    roq_min = mc_priors_keys_list[np.argmin(mc_priors_keys_int)]
    roq_max = mc_priors_keys_list[np.argmax(mc_priors_keys_int)]
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    mc_max = mc_priors[roq_min][1]
    mc_min = mc_priors[roq_max][0]
    scale_factor = 1.
    if force_flow == None and trigger_mchirp != None:
        if trigger_mchirp >= mc_max:
            scale_factor = 2.**(floor(trigger_mchirp/mc_max))
        if trigger_mchirp <= mc_min:
            scale_factor = (2./3.2)**(ceil(trigger_mchirp/mc_min))
    elif force_flow != None:
        scale_factor = 20./force_flow
    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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def mchirp_from_components(m1, m2):
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    return (m1*m2)**(3.0/5.0) / (m1+m2)**(1.0/5.0)
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def Query_ROQ_Bounds_Type(path, roq_paths):
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    # Assume that parametrization of ROQ bounds is independent of seglen; just look at first one
    import numpy as np
    roq = roq_paths[0]
    params = os.path.join(path,roq,'params.dat')
    roq_params0 = np.genfromtxt(params,names=True)
    roq_names_set = set(roq_params0.dtype.names)
    component_mass_bounds_set = set(['mass1min', 'mass1max', 'mass2min', 'mass2max'])
    chirp_mass_q_bounds_set = set(['chirpmassmin', 'chirpmassmax', 'qmin', 'qmax'])
    if roq_names_set.issuperset(component_mass_bounds_set):
        roq_bounds = 'component_mass'
    elif roq_names_set.issuperset(chirp_mass_q_bounds_set):
        roq_bounds = 'chirp_mass_q'
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    else:
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        print('Invalid bounds for ROQ. Ether (m1,m2) or (mc,q) bounds are supported.')
        sys.exit(1)
    return roq_bounds
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class LALInferencePipelineDAG(pipeline.CondorDAG):
    def __init__(self,cp,dax=False,site='local'):
        self.subfiles=[]
        self.config=cp
        self.engine=get_engine_name(cp)
        self.EngineNode=chooseEngineNode(self.engine)
        self.site=site
        if cp.has_option('paths','basedir'):
            self.basepath=cp.get('paths','basedir')
        else:
            self.basepath=os.getcwd()
            print('No basepath specified, using current directory: %s'%(self.basepath))
        mkdirs(self.basepath)
        print("Generating LALInference DAG in {0}".format(self.basepath))
        if dax:
            os.chdir(self.basepath)
        self.posteriorpath=os.path.join(self.basepath,'posterior_samples')
        mkdirs(self.posteriorpath)
        daglogdir=cp.get('paths','daglogdir')
        mkdirs(daglogdir)
        self.daglogfile=os.path.join(daglogdir,'lalinference_pipeline-'+str(uuid.uuid1())+'.log')
        super(LALInferencePipelineDAG,self).__init__(self.daglogfile,dax=dax)
        if cp.has_option('paths','cachedir'):
            self.cachepath=cp.get('paths','cachedir')
        else:
            self.cachepath=os.path.join(self.basepath,'caches')
        mkdirs(self.cachepath)
        if cp.has_option('paths','logdir'):
            self.logpath=cp.get('paths','logdir')
        else:
            self.logpath=os.path.join(self.basepath,'log')
        mkdirs(self.logpath)
        if cp.has_option('analysis','ifos'):
            self.ifos=ast.literal_eval(cp.get('analysis','ifos'))
        else:
            self.ifos=['H1','L1','V1']
        self.segments={}
        if cp.has_option('datafind','veto-categories'):
            self.veto_categories=cp.get('datafind','veto-categories')
        else: self.veto_categories=[]
        for ifo in self.ifos:
            self.segments[ifo]=[]
        self.computeroqweightsnode={}
        self.bayeslinenode={}
        self.bayeswavepsdnode={}
        self.dq={}
        self.frtypes=ast.literal_eval(cp.get('datafind','types'))
        self.channels=ast.literal_eval(cp.get('data','channels'))
        self.use_available_data=False
        self.webdir=cp.get('paths','webdir')
        if cp.has_option('analysis','dataseed'):
            self.dataseed=cp.getint('analysis','dataseed')
        else:
            self.dataseed=None
        # Set up necessary job files.
        self.prenodes={}
        self.datafind_job = pipeline.LSCDataFindJob(self.cachepath,self.logpath,self.config,dax=self.is_dax())
        self.datafind_job.add_opt('url-type','file')
        # If running on OSG use its datafind server
        if cp.has_option('analysis','osg') and cp.getboolean('analysis','osg'):
            self.datafind_job.add_opt('server','datafind.ligo.org')
        if cp.has_option('condor','accounting_group'):
            self.datafind_job.add_condor_cmd('accounting_group',cp.get('condor','accounting_group'))
        if cp.has_option('condor','accounting_group_user'):
            self.datafind_job.add_condor_cmd('accounting_group_user',cp.get('condor','accounting_group_user'))
        self.datafind_job.set_sub_file(os.path.abspath(os.path.join(self.basepath,'datafind.sub')))
        self.preengine_job = EngineJob(self.config, os.path.join(self.basepath,'prelalinference.sub'),self.logpath,engine='lalinferencedatadump',ispreengine=True,dax=self.is_dax())
        self.preengine_job.set_grid_site('local')
        self.preengine_job.set_universe('vanilla')
        if self.config.getboolean('analysis','roq'):
            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')
        self.bayeswavepsd_job={}
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        if self.config.has_option('condor','bayeswave'):
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            for ifo in self.ifos:
                self.bayeswavepsd_job[ifo] = BayesWavePSDJob(self.config,os.path.join(self.basepath,'bayeswavepsd_%s.sub'%(ifo)),self.logpath,dax=self.is_dax())
                self.bayeswavepsd_job[ifo].set_grid_site('local')
        # Need to create a job file for each IFO combination
        self.engine_jobs={}
        ifocombos=[]
        for N in range(1,len(self.ifos)+1):
            for a in permutations(self.ifos,N):
                ifocombos.append(a)
        for ifos in ifocombos:
            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)
        self.results_page_job = ResultsPageJob(self.config,os.path.join(self.basepath,'resultspage.sub'),self.logpath,dax=self.is_dax())
        self.results_page_job.set_grid_site('local')
        self.cotest_results_page_job = ResultsPageJob(self.config,os.path.join(self.basepath,'resultspagecoherent.sub'),self.logpath,dax=self.is_dax())
        self.cotest_results_page_job.set_grid_site('local')
        if self.engine=='lalinferencemcmc':
            self.combine_job = CombineMCMCJob(self.config,os.path.join(self.basepath,'combine_files.sub'),self.logpath,dax=self.is_dax())
            self.combine_job.set_grid_site('local')
            self.merge_job = MergeJob(self.config,os.path.join(self.basepath,'merge_runs.sub'),self.logpath,dax=self.is_dax(),engine='mcmc')
            self.merge_job.set_grid_site('local')
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        else:
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            self.merge_job = MergeJob(self.config,os.path.join(self.basepath,'merge_runs.sub'),self.logpath,dax=self.is_dax(),engine='nest')
            self.merge_job.set_grid_site('local')
        self.coherence_test_job = CoherenceTestJob(self.config,os.path.join(self.basepath,'coherence_test.sub'),self.logpath,dax=self.is_dax())
        self.coherence_test_job.set_grid_site('local')
        self.gracedbjob = GraceDBJob(self.config,os.path.join(self.basepath,'gracedb.sub'),self.logpath,dax=self.is_dax())
        self.gracedbjob.set_grid_site('local')
        self.mapjob = SkyMapJob(cp, os.path.join(self.basepath,'skymap.sub'), self.logpath)
        self.plotmapjob = PlotSkyMapJob(cp, os.path.join(self.basepath,'plotskymap.sub'),self.logpath)
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        self.postruninfojob=PostRunInfoJob(self.config,os.path.join(self.basepath,'postrungdbinfo.sub'),self.logpath,dax=self.is_dax())
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        # Process the input to build list of analyses to do
        self.events=self.setup_from_inputs()

        # Sanity checking
        if len(self.events)==0:
            print('No input events found, please check your config if you expect some events')
        self.times=[e.trig_time for e in self.events]

        # Set up the segments
        if not (self.config.has_option('input','gps-start-time') and self.config.has_option('input','gps-end-time')) and len(self.times)>0:
            (mintime,maxtime)=self.get_required_data(self.times)
            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))))
        self.add_science_segments()

        # Save the final configuration that is being used
        # first to the run dir
        conffilename=os.path.join(self.basepath,'config.ini')
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        with open(conffilename,'w') as conffile:
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            self.config.write(conffile)
        if self.config.has_option('paths','webdir'):
            mkdirs(self.config.get('paths','webdir'))
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            with open(os.path.join(self.config.get('paths','webdir'),'config.ini'),'w') as conffile:
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                self.config.write(conffile)

        # Generate the DAG according to the config given
        for event in self.events: self.add_full_analysis(event)
        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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            if self.config.has_option('condor','gdbinfo') and self.config.has_option('analysis','ugid') and self.config.getboolean('analysis','upload-to-gracedb'):
                self.add_gracedb_info_node(None,event.GID,analysis='LIB',issky=True)

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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(os.path.join(self.basepath,self.dagfilename))
        if self.is_dax():
            self.set_dax_file(self.dagfilename)

    def add_full_analysis(self,event):
        if self.engine=='lalinferencenest' or  self.engine=='lalinferenceburst':
            result=self.add_full_analysis_lalinferencenest(event)
        elif self.engine=='lalinferencemcmc':
            result=self.add_full_analysis_lalinferencemcmc(event)
        else:
            raise Exception('Unknown engine {0}'.format(self.engine))
        return result
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    def create_frame_pfn_file(self):
        """
        Create a pegasus cache file name, uses inspiralutils
        """
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        from lalapps import inspiralutils as iu
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        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):
        """
        Calculate the data that will be needed to process all events
        """
        #psdlength = self.config.getint('input','max-psd-length')
        padding=self.config.getint('input','padding')
        if self.config.has_option('engine','seglen') or self.config.has_option('lalinference','seglen'):
            if self.config.has_option('engine','seglen'):
                seglen = int(np.ceil(self.config.getfloat('engine','seglen')))
            if self.config.has_option('lalinference','seglen'):
                seglen = self.config.getint('lalinference','seglen')

            if os.path.isfile(os.path.join(self.basepath,'psd.xml.gz')) or self.config.has_option('condor','bayesline') or self.config.has_option('condor','bayeswave'):
                psdlength = 0
                padding = 0
                self.config.set('input','padding',str(padding))
                if self.config.has_option('condor','bayeswave'):
                    if (np.log2(seglen)%1):
                        seglen = np.power(2., np.ceil(np.log2(seglen)))
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            else:
                psdlength = 32*seglen
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        else:
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            seglen = max(e.duration for e in self.events)
            if os.path.isfile(os.path.join(self.basepath,'psd.xml.gz')) or self.config.has_option('condor','bayesline') or self.config.has_option('condor','bayeswave'):
                psdlength = 0
                padding = 0
                self.config.set('input','padding',str(padding))
                if self.config.has_option('condor','bayeswave'):
                    if (np.log2(seglen)%1):
                        seglen = np.power(2., np.ceil(np.log2(seglen)))
            else:
                psdlength = 32*seglen
        # Assume that the data interval is (end_time - seglen -padding , end_time + psdlength +padding )
        # -> change to (trig_time - seglen - padding - psdlength + 2 , trig_time + padding + 2) to estimate the psd before the trigger for online follow-up.
        # Also require padding before start time
        return (min(times)-padding-seglen-psdlength+2,max(times)+padding+2)

    def setup_from_times(self,times):
        """
        Generate a DAG from a list of times
        """
        for time in self.times:
            self.add_full_analysis(Event(trig_time=time))

    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
        """
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        events=[]
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        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))
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        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
        """
        events=[]
        gpsstart=None
        gpsend=None
        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','coinc-xml','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])
        # If no input events given, just return an empty list (e.g. for PP pipeline)
        if ReadInputFromList!=1 and (gpsstart is None or gpsend is None):
            return []
        # Review: Clean up this section
        if self.config.has_option('input','events'):
            selected_events=self.config.get('input','events')
            print('Selected events %s'%(str(selected_events)))

            if selected_events=='all':
                selected_events=None
            else:
                selected_events=self.select_events()
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        else:
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            selected_events=None

        if(self.config.has_option('engine','correlatedGaussianLikelihood') or
           self.config.has_option('engine','bimodalGaussianLikelihood') or
           self.config.has_option('engine','rosenbrockLikelihood')):
            analytic_test = True
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        else:
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            analytic_test = False

        # No input file given, analyse the entire time stretch between gpsstart and gpsend
        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))
            if self.config.has_option('engine','seglen'):
                seglen=self.config.getfloat('engine','seglen')
            else:
                print('ERROR: seglen must be specified in [engine] section when running without input file')
                sys.exit(1)
            if(self.config.has_option('input','segment-overlap')):
                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):
                ev=Event(trig_time=t+seglen-2)
                ev.set_engine_option('segment-start',str(t-overlap))
                if not analytic_test:
                    ev.set_engine_option('time-min',str(t))
                tMax=t + seglen - overlap
                if tMax>=gpsend:
                    tMax=gpsend
                if not analytic_test:
                    ev.set_engine_option('time-max',str(tMax))
                events.append(ev)
                t=tMax
            return events

        # ASCII list of GPS times
        if self.config.has_option('input','gps-time-file'):
            times=scan_timefile(self.config.get('input','gps-time-file'))
            if self.config.has_option('input','timeslides-ascii'):
            # The timeslides-ascii files contains one row per trigtime, and a column per IFO
            # Note: the IFO order is the same given in the ifos section of the [analysis] tag
                print("Reading timeslides from ascii file. Columns order is understood as follow:")
                for this_ifo,ifo in enumerate(self.ifos):
                    print("Column %d"%this_ifo + "= %s "%(ifo))
                dest=self.config.get('input','timeslides-ascii')
                if not os.path.isfile(dest):
                    print("ERROR the ascii file %s containing the timeslides does not exist\n"%dest)
                    exit(1)
                else:
                    from numpy import loadtxt
                    data=loadtxt(dest).reshape(-1,len(self.ifos))
                    if len(self.ifos)!= len(data[0,:]):
                        print("ERROR: ascii timeslide file must contain a column for each IFO used in the analysis!\n")
                        exit(1)
                    if len(times)!=len(data[:,0]):
                        print('ERROR: ascii timeslide must contain a row for each trigtime. Exiting...\n')
                        exit(1)
                    timeslides={}
                    for this_time,time in enumerate(times):
                        timeslides[this_time]={}
                        for this_ifo,ifo in enumerate(self.ifos):
                            timeslides[this_time][ifo]=data[this_time,this_ifo]
                events=[Event(trig_time=time,timeslide_dict=timeslides[i_time]) for i_time,time in enumerate(times)]
            else:
                events=[Event(trig_time=time) for time in times]
        # Siminspiral Table
        if self.config.has_option('input','injection-file'):
            injTable = lsctables.SimInspiralTable.get_table(
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                              ligolw_utils.load_filename(self.config.get('input','injection-file'),
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                                                  contenthandler=lsctables.use_in(ligolw.LIGOLWContentHandler)) )
            events=[Event(SimInspiral=inj) for inj in injTable]
            self.add_pfn_cache([create_pfn_tuple(self.config.get('input','injection-file'))])
        # SimBurst Table
        if self.config.has_option('input','burst-injection-file'):
            injfile=self.config.get('input','burst-injection-file')
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            injTable=lsctables.SimBurstTable.get_table(ligolw_utils.load_filename(injfile,contenthandler = lsctables.use_in(LIGOLWContentHandler)))
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            events=[Event(SimBurst=inj) for inj in injTable]
            self.add_pfn_cache([create_pfn_tuple(self.config.get('input','burst-injection-file'))])
        # LVAlert CoincInspiral Table
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        gid = None
        if self.config.has_option('input','gid') or self.config.has_option('input', 'coinc-xml'):
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            flow=20.0
            if self.config.has_option('lalinference','flow'):
                flow=min(ast.literal_eval(self.config.get('lalinference','flow')).values())
            threshold_snr = None
            if not self.config.has_option('engine','distance-max') and self.config.has_option('input','threshold-snr'):
                threshold_snr=self.config.getfloat('input','threshold-snr')
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            # get coinc object and psd object
            from lal import series as lalseries
            psd_file_obj = None
            if self.config.has_option('input', 'gid'):
                from ligo.gracedb.rest import GraceDb, HTTPError
                gid = self.config.get('input', 'gid')
                if self.config.has_option('analysis','service-url'):
                    client = GraceDb(
                        service_url=self.config.get('analysis', 'service-url')
                    )
                else:
                    client = GraceDb()
                print("Download %s coinc.xml" % gid)
                coinc_file_obj = client.files(gid, "coinc.xml")
                try:
                    downloadpsd = (not self.config.getboolean('input','ignore-gracedb-psd'))
                except:
                    downloadpsd = True
                if downloadpsd:
                    print("Download %s psd.xml.gz" % gid)
                    try:
                        psd_file_obj = client.files(gid, "psd.xml.gz")
                    except HTTPError:
                        print("Failed to download %s psd.xml.gz. lalinference will estimate the psd itself." % gid)
            else:
                coinc_file_obj = open(self.config.get('input', 'coinc-xml'), "rb")
                try:
                    psd_file_obj =  open(self.config.get('input', 'psd-xml-gz'), "rb")
                except:
                    print("lalinference will estimate the psd itself.")

            # write down the objects to files
            coinc_xml_obj = ligolw_utils.load_fileobj(
                coinc_file_obj,
                contenthandler = lsctables.use_in(ligolw.LIGOLWContentHandler)
            )[0]
            ligolw_utils.write_filename(
                coinc_xml_obj, os.path.join(self.basepath, "coinc.xml")
            )
            if psd_file_obj is not None:
                path_to_psd = os.path.join(self.basepath, "psd.xml.gz")
                psd_xml_obj = ligolw_utils.load_fileobj(
                    psd_file_obj,
                    contenthandler = lalseries.PSDContentHandler
                )[0]
                psd_dict = lalseries.read_psd_xmldoc(psd_xml_obj)
                ligolw_utils.write_filename(psd_xml_obj, path_to_psd, gz = True)
                ifos = sorted(
                    lsctables.CoincInspiralTable.get_table(
                        coinc_xml_obj
                    )[0].instruments
                )
                get_xml_psds(
                    os.path.realpath(path_to_psd), ifos,
                    os.path.realpath(os.path.join(self.basepath, "PSDs")),
                    end_time=None
                )
            else:
                psd_dict = None

            events = create_events_from_coinc_and_psd(
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                         coinc_xml_obj, psd_dict, gid, threshold_snr=threshold_snr, flow=flow,
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                         roq=self.config.getboolean('analysis','roq')
                     )

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        # pipedown-database
        if self.config.has_option('input','gstlal-db'):
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            queryfunc=get_zerolag_lloid
            dbname=self.config.get('input','gstlal-db')
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        elif self.config.has_option('input','pipedown-db'):
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            queryfunc=get_zerolag_pipedown
            dbname=self.config.get('input','pipedown-db')
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        else: dbname=None
        if dbname:
            db_connection = open_pipedown_database(dbname,None)[0]
            # Timeslides
            if self.config.has_option('input','time-slide-dump'):
                timeslidedump=self.config.get('input','time-slide-dump')
            else:
                timeslidedump=None
            if self.config.has_option('input','min-cfar'):
                mincfar=self.config.getfloat('input','min-cfar')
            else:
                mincfar=-1
            if self.config.has_option('input','max-cfar'):
                maxcfar=self.config.getfloat('input','max-cfar')
            else:
                maxcfar=-1
            if self.config.get('input','timeslides').lower()=='true':
                events=get_timeslides_pipedown(db_connection, gpsstart=gpsstart, gpsend=gpsend,dumpfile=timeslidedump,max_cfar=maxcfar)
            else:
                events=queryfunc(db_connection, gpsstart=gpsstart, gpsend=gpsend, dumpfile=timeslidedump,max_cfar=maxcfar,min_cfar=mincfar)
        if(selected_events is not None):
            used_events=[]
            for i in selected_events:
                e=events[i]
                e.event_id=i
                used_events.append(e)
            events=used_events
        if gpsstart is not None:
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            events = list(filter(lambda e: not e.trig_time<gpsstart, events))
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        if gpsend is not None:
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            events = list(filter(lambda e: not e.trig_time>gpsend, events))
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        return events
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