Source code for pycbc.tmpltbank.bank_output_utils

import numpy
from lal import PI, MTSUN_SI, TWOPI, GAMMA
from ligo.lw import ligolw, lsctables, utils as ligolw_utils
from pycbc import pnutils
from pycbc.tmpltbank.lambda_mapping import ethinca_order_from_string
from pycbc.io.ligolw import (
    return_empty_sngl, return_search_summary, create_process_table
)


[docs]def convert_to_sngl_inspiral_table(params, proc_id): ''' Convert a list of m1,m2,spin1z,spin2z values into a basic sngl_inspiral table with mass and spin parameters populated and event IDs assigned Parameters ----------- params : iterable Each entry in the params iterable should be a sequence of [mass1, mass2, spin1z, spin2z] in that order proc_id : int Process ID to add to each row of the sngl_inspiral table Returns ---------- SnglInspiralTable Bank of templates in SnglInspiralTable format ''' sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable) col_names = ['mass1','mass2','spin1z','spin2z'] for values in params: tmplt = return_empty_sngl() tmplt.process_id = proc_id for colname, value in zip(col_names, values): setattr(tmplt, colname, value) tmplt.mtotal, tmplt.eta = pnutils.mass1_mass2_to_mtotal_eta( tmplt.mass1, tmplt.mass2) tmplt.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta( tmplt.mass1, tmplt.mass2) tmplt.template_duration = 0 # FIXME tmplt.event_id = sngl_inspiral_table.get_next_id() sngl_inspiral_table.append(tmplt) return sngl_inspiral_table
[docs]def calculate_ethinca_metric_comps(metricParams, ethincaParams, mass1, mass2, spin1z=0., spin2z=0., full_ethinca=True): """ Calculate the Gamma components needed to use the ethinca metric. At present this outputs the standard TaylorF2 metric over the end time and chirp times \tau_0 and \tau_3. A desirable upgrade might be to use the \chi coordinates [defined WHERE?] for metric distance instead of \tau_0 and \tau_3. The lower frequency cutoff is currently hard-coded to be the same as the bank layout options fLow and f0 (which must be the same as each other). Parameters ----------- metricParams : metricParameters instance Structure holding all the options for construction of the metric and the eigenvalues, eigenvectors and covariance matrix needed to manipulate the space. ethincaParams : ethincaParameters instance Structure holding options relevant to the ethinca metric computation. mass1 : float Mass of the heavier body in the considered template. mass2 : float Mass of the lighter body in the considered template. spin1z : float (optional, default=0) Spin of the heavier body in the considered template. spin2z : float (optional, default=0) Spin of the lighter body in the considered template. full_ethinca : boolean (optional, default=True) If True calculate the ethinca components in all 3 directions (mass1, mass2 and time). If False calculate only the time component (which is stored in Gamma0). Returns -------- fMax_theor : float Value of the upper frequency cutoff given by the template parameters and the cutoff formula requested. gammaVals : numpy_array Array holding 6 independent metric components in (end_time, tau_0, tau_3) coordinates to be stored in the Gamma0-5 slots of a SnglInspiral object. """ if (float(spin1z) != 0. or float(spin2z) != 0.) and full_ethinca: raise NotImplementedError("Ethinca cannot at present be calculated " "for nonzero component spins!") f0 = metricParams.f0 if f0 != metricParams.fLow: raise ValueError("If calculating ethinca the bank f0 value must be " "equal to f-low!") if ethincaParams.fLow is not None and ( ethincaParams.fLow != metricParams.fLow): raise NotImplementedError("An ethinca metric f-low different from the" " bank metric f-low is not supported!") twicePNOrder = ethinca_order_from_string(ethincaParams.pnOrder) piFl = PI * f0 totalMass, eta = pnutils.mass1_mass2_to_mtotal_eta(mass1, mass2) totalMass = totalMass * MTSUN_SI v0cube = totalMass*piFl v0 = v0cube**(1./3.) # Get theoretical cutoff frequency and work out the closest # frequency for which moments were calculated fMax_theor = pnutils.frequency_cutoff_from_name( ethincaParams.cutoff, mass1, mass2, spin1z, spin2z) fMaxes = list(metricParams.moments['J4'].keys()) fMaxIdx = abs(numpy.array(fMaxes,dtype=float) - fMax_theor).argmin() fMax = fMaxes[fMaxIdx] # Set the appropriate moments Js = numpy.zeros([18,3],dtype=float) for i in range(18): Js[i,0] = metricParams.moments['J%d'%(i)][fMax] Js[i,1] = metricParams.moments['log%d'%(i)][fMax] Js[i,2] = metricParams.moments['loglog%d'%(i)][fMax] # Compute the time-dependent metric term. two_pi_flower_sq = TWOPI * f0 * TWOPI * f0 gammaVals = numpy.zeros([6],dtype=float) gammaVals[0] = 0.5 * two_pi_flower_sq * \ ( Js[(1,0)] - (Js[(4,0)]*Js[(4,0)]) ) # If mass terms not required stop here if not full_ethinca: return fMax_theor, gammaVals # 3pN is a mess, so split it into pieces a0 = 11583231236531/200286535680 - 5*PI*PI - 107*GAMMA/14 a1 = (-15737765635/130056192 + 2255*PI*PI/512)*eta a2 = (76055/73728)*eta*eta a3 = (-127825/55296)*eta*eta*eta alog = numpy.log(4*v0) # Log terms are tricky - be careful # Get the Psi coefficients Psi = [{},{}] #Psi = numpy.zeros([2,8,2],dtype=float) Psi[0][0,0] = 3/5 Psi[0][2,0] = (743/756 + 11*eta/3)*v0*v0 Psi[0][3,0] = 0. Psi[0][4,0] = (-3058673/508032 + 5429*eta/504 + 617*eta*eta/24)\ *v0cube*v0 Psi[0][5,1] = (-7729*PI/126)*v0cube*v0*v0/3 Psi[0][6,0] = (128/15)*(-3*a0 - a1 + a2 + 3*a3 + 107*(1+3*alog)/14)\ *v0cube*v0cube Psi[0][6,1] = (6848/35)*v0cube*v0cube/3 Psi[0][7,0] = (-15419335/63504 - 75703*eta/756)*PI*v0cube*v0cube*v0 Psi[1][0,0] = 0. Psi[1][2,0] = (3715/12096 - 55*eta/96)/PI/v0; Psi[1][3,0] = -3/2 Psi[1][4,0] = (15293365/4064256 - 27145*eta/16128 - 3085*eta*eta/384)\ *v0/PI Psi[1][5,1] = (193225/8064)*v0*v0/3 Psi[1][6,0] = (4/PI)*(2*a0 + a1/3 - 4*a2/3 - 3*a3 -107*(1+6*alog)/42)\ *v0cube Psi[1][6,1] = (-428/PI/7)*v0cube/3 Psi[1][7,0] = (77096675/1161216 + 378515*eta/24192 + 74045*eta*eta/8064)\ *v0cube*v0 # Set the appropriate moments Js = numpy.zeros([18,3],dtype=float) for i in range(18): Js[i,0] = metricParams.moments['J%d'%(i)][fMax] Js[i,1] = metricParams.moments['log%d'%(i)][fMax] Js[i,2] = metricParams.moments['loglog%d'%(i)][fMax] # Calculate the g matrix PNterms = [(0,0),(2,0),(3,0),(4,0),(5,1),(6,0),(6,1),(7,0)] PNterms = [term for term in PNterms if term[0] <= twicePNOrder] # Now can compute the mass-dependent gamma values for m in [0, 1]: for k in PNterms: gammaVals[1+m] += 0.5 * two_pi_flower_sq * Psi[m][k] * \ ( Js[(9-k[0],k[1])] - Js[(12-k[0],k[1])] * Js[(4,0)] ) g = numpy.zeros([2,2],dtype=float) for (m,n) in [(0,0),(0,1),(1,1)]: for k in PNterms: for l in PNterms: g[m,n] += Psi[m][k] * Psi[n][l] * \ ( Js[(17-k[0]-l[0], k[1]+l[1])] - Js[(12-k[0],k[1])] * Js[(12-l[0],l[1])] ) g[m,n] = 0.5 * two_pi_flower_sq * g[m,n] g[n,m] = g[m,n] gammaVals[3] = g[0,0] gammaVals[4] = g[0,1] gammaVals[5] = g[1,1] return fMax_theor, gammaVals
[docs]def output_sngl_inspiral_table(outputFile, tempBank, metricParams, ethincaParams, programName="", optDict = None, outdoc=None): """ Function that converts the information produced by the various PyCBC bank generation codes into a valid LIGOLW XML file containing a sngl_inspiral table and outputs to file. Parameters ----------- outputFile : string Name of the file that the bank will be written to tempBank : iterable Each entry in the tempBank iterable should be a sequence of [mass1,mass2,spin1z,spin2z] in that order. metricParams : metricParameters instance Structure holding all the options for construction of the metric and the eigenvalues, eigenvectors and covariance matrix needed to manipulate the space. ethincaParams: {ethincaParameters instance, None} Structure holding options relevant to the ethinca metric computation including the upper frequency cutoff to be used for filtering. NOTE: The computation is currently only valid for non-spinning systems and uses the TaylorF2 approximant. programName (key-word-argument) : string Name of the executable that has been run optDict (key-word argument) : dictionary Dictionary of the command line arguments passed to the program outdoc (key-word argument) : ligolw xml document If given add template bank to this representation of a xml document and write to disk. If not given create a new document. """ if optDict is None: optDict = {} if outdoc is None: outdoc = ligolw.Document() outdoc.appendChild(ligolw.LIGO_LW()) # get IFO to put in search summary table ifos = [] if 'channel_name' in optDict.keys(): if optDict['channel_name'] is not None: ifos = [optDict['channel_name'][0:2]] proc = create_process_table( outdoc, program_name=programName, detectors=ifos, options=optDict ) proc_id = proc.process_id sngl_inspiral_table = convert_to_sngl_inspiral_table(tempBank, proc_id) # Calculate Gamma components if needed if ethincaParams is not None: if ethincaParams.doEthinca: for sngl in sngl_inspiral_table: # Set tau_0 and tau_3 values needed for the calculation of # ethinca metric distances (sngl.tau0,sngl.tau3) = pnutils.mass1_mass2_to_tau0_tau3( sngl.mass1, sngl.mass2, metricParams.f0) fMax_theor, GammaVals = calculate_ethinca_metric_comps( metricParams, ethincaParams, sngl.mass1, sngl.mass2, spin1z=sngl.spin1z, spin2z=sngl.spin2z, full_ethinca=ethincaParams.full_ethinca) # assign the upper frequency cutoff and Gamma0-5 values sngl.f_final = fMax_theor for i in range(len(GammaVals)): setattr(sngl, "Gamma"+str(i), GammaVals[i]) # If Gamma metric components are not wanted, assign f_final from an # upper frequency cutoff specified in ethincaParams elif ethincaParams.cutoff is not None: for sngl in sngl_inspiral_table: sngl.f_final = pnutils.frequency_cutoff_from_name( ethincaParams.cutoff, sngl.mass1, sngl.mass2, sngl.spin1z, sngl.spin2z) # set per-template low-frequency cutoff if 'f_low_column' in optDict and 'f_low' in optDict and \ optDict['f_low_column'] is not None: for sngl in sngl_inspiral_table: setattr(sngl, optDict['f_low_column'], optDict['f_low']) outdoc.childNodes[0].appendChild(sngl_inspiral_table) # get times to put in search summary table start_time = 0 end_time = 0 if 'gps_start_time' in optDict.keys() and 'gps_end_time' in optDict.keys(): start_time = optDict['gps_start_time'] end_time = optDict['gps_end_time'] # make search summary table search_summary_table = lsctables.New(lsctables.SearchSummaryTable) search_summary = return_search_summary( start_time, end_time, len(sngl_inspiral_table), ifos ) search_summary_table.append(search_summary) outdoc.childNodes[0].appendChild(search_summary_table) # write the xml doc to disk ligolw_utils.write_filename(outdoc, outputFile)