#! /usr/bin/env python
#
# USAGE
# python util_TestXMLParameterStorage.py ; ligolw_print -t sim_inspiral -c alpha5 output.xml.gz
import RIFT.lalsimutils as lalsimutils
import numpy as np
import lal
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--output-file", default="output")
opts = parser.parse_args()
P_list_in = []
for i in np.arange(5):
P_list_in.append(lalsimutils.ChooseWaveformParams(m1=lal.MSUN_SI *
(i + 1)))
P_list_in[-1].lambda1 = 3 * i + 1
P_list_in[-1].lambda2 = 3 * i + 2
P_list_in[-1].approx = 3 * i + 2
P_list_in[-1].print_params()
lalsimutils.ChooseWaveformParams_array_to_xml(P_list_in,
fname=opts.output_file)
print(" ------ ")
P_list_out = lalsimutils.xml_to_ChooseWaveformParams_array(opts.output_file +
".xml.gz")
for P in P_list_out:
P.print_params()
def PopulatePrototypeSignal(opts):
approxSignal = lalsim.GetApproximantFromString(opts.approx)
approxTemplate = approxSignal
ampO = opts.amporder # sets which modes to include in the physical signal
Lmax = opts.Lmax # sets which modes to include
fref = opts.fref
fminWavesSignal = opts.fmin_Template # too long can be a memory and time hog, particularly at 16 kHz
fminSNR = opts.fmin_SNR
fSample = opts.srate
Psig = None
# Read in *coincidence* XML (overridden by injection, if present)
if opts.coinc:
xmldoc = utils.load_filename(opts.coinc)
coinc_table = table.get_table(xmldoc,
lsctables.CoincInspiralTable.tableName)
assert len(coinc_table) == 1
coinc_row = coinc_table[0]
# Populate the SNR sequence and mass sequence
sngl_inspiral_table = table.get_table(
xmldoc, lsctables.SnglInspiralTable.tableName)
m1, m2 = None, None
for sngl_row in sngl_inspiral_table:
# NOTE: gstlal is exact match, but other pipelines may not be
assert m1 is None or (sngl_row.mass1 == m1
and sngl_row.mass2 == m2)
m1, m2 = sngl_row.mass1, sngl_row.mass2
m1 = m1 * lal.MSUN_SI
m2 = m2 * lal.MSUN_SI
# Create a 'best recovered signal'
Psig = lalsimutils.ChooseWaveformParams(
m1=m1,
m2=m2,
approx=approxSignal,
fmin=fminWavesSignal,
dist=factored_likelihood.distMpcRef * 1e6 *
lal.PC_SI, # default distance
fref=fref,
ampO=ampO
) # FIXME: Parameter mapping from trigger space to search space
# Read in *injection* XML
if opts.inj:
Psig = lalsimutils.xml_to_ChooseWaveformParams_array(str(opts.inj))[
opts.event_id] # Load in the physical parameters of the injection.
Psig.deltaT = 1. / fSample # needed if we will generate fake data from the injection xml *by this program, internally*
timeWaveform = lalsimutils.estimateWaveformDuration(Psig)
Psig.deltaF = 1. / lalsimutils.nextPow2(
opts.seglen
) # Frequency binning needs to account for target segment length
if not Psig and opts.channel_name: # If data loaded but no signal generated
if (not opts.template_mass1) or (not opts.template_mass2) or (
not opts.force_gps_time):
print(
" CANCEL: Specifying parameters via m1, m2, and time on the command line "
)
Psig = lalsimutils.ChooseWaveformParams(
approx=approxSignal,
fmin=fminWavesSignal,
dist=factored_likelihood.distMpcRef * 1e6 *
lal.PC_SI, # default distance
fref=fref)
Psig.m1 = lal.MSUN_SI * opts.template_mass1
Psig.m2 = lal.MSUN_SI * opts.template_mass2
Psig.tref = lal.LIGOTimeGPS(
0.000000000) # Initialize as GPSTime object
Psig.tref += opts.force_gps_time # Pass value of float into it
if not (Psig):
m1 = 4 * lal.MSUN_SI
m2 = 3 * lal.MSUN_SI
Psig = lalsimutils.ChooseWaveformParams(m1=m1,
m2=m2,
fmin=fminWavesSignal,
fref=fref,
approx=approxSignal,
ampO=ampO)
# Use forced parameters, if provided
if opts.template_mass1:
Psig.m1 = opts.template_mass1 * lal.LAL_MSUN_SI
if opts.template_mass2:
Psig.m2 = opts.template_mass2 * lal.LAL_MSUN_SI
return Psig
npts_out = np.sum(indx_ok)
new_samples = np.recarray((npts_out, ), dtype=samples.dtype)
for name in samples.dtype.names:
new_samples[name] = samples[name][indx_ok]
samples = new_samples
# Save samples
posterior_list.append(samples)
# Continue ... rest not used at present
continue
# Populate a P_list with the samples, so I can perform efficient conversion for plots
# note only the DETECTOR frame properties are stored here
P_list = []
P = lalsimutils.ChooseWaveformParams()
for indx in np.arange(len(samples["m1"])):
P.m1 = samples["m1"][indx] * lal.MSUN_SI
P.m2 = samples["m2"][indx] * lal.MSUN_SI
P.s1x = samples["a1x"][indx]
P.s1y = samples["a1y"][indx]
P.s1z = samples["a1z"][indx]
P.s2x = samples["a2x"][indx]
P.s2y = samples["a2y"][indx]
P.s2z = samples["a2z"][indx]
if "lnL" in samples.keys():
P.lnL = samples["lnL"][indx] # creates a new field !
else:
P.lnL = -1
# Populate other parameters as needed ...
P_list.append(P)
eff_fisher_psd = getattr(lalsim, opts.fisher_psd) # --fisher-psd SimNoisePSDaLIGOZeroDetHighPower now
analyticPSD_Q=True
else:
print(" Importing PSD file ", opts.psd_file)
eff_fisher_psd = lalsimutils.load_resample_and_clean_psd(opts.psd_file, 'H1', 1./opts.seglen)
analyticPSD_Q = False
print(" Loading samples ")
samples_in = np.genfromtxt(opts.fname_lalinference,names=True)
print(" Done loading samples ")
deltaT = 1./4096
T_window =int(opts.seglen)
# Create overlap
P=lalsimutils.ChooseWaveformParams()
P.deltaT = deltaT
P.deltaF = 1./T_window
P.fmin = samples_in["flow"][0]
P.approx=lalsim.SpinTaylorT2
P.print_params()
print(lalsimutils.estimateDeltaF(P), P.deltaF)
if P.deltaF > lalsimutils.estimateDeltaF(P):
sys.exit(0)
hfBase = lalsimutils.complex_hoff(P) # Does not matter what this is.
print(" Done generating waveform")
IP = lalsimutils.CreateCompatibleComplexOverlap(hfBase,analyticPSD_Q=analyticPSD_Q,psd=eff_fisher_psd,fMax=opts.fmax,interpolate_max=True)
print(" Done populating inner product")
# Similar code: convert_output_format_inference2ile