# FIXME: This is turning into a mess
if opts.output_file:
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__)
result_dict = {"mass1": opts.mass1, "mass2": opts.mass2, "event_duration": numpy.sqrt(var)/res, "ttotal": sampler.ntotal}
if opts.spin1z is not None or sngl_inspiral_table:
result_dict["spin1z"] = opts.spin1z or 0.0
if opts.spin2z is not None or sngl_inspiral_table:
result_dict["spin2z"] = opts.spin2z or 0.0
if opts.eff_lambda is not None:
result_dict["psi0"] = opts.eff_lambda
if opts.deff_lambda is not None:
result_dict["psi3"] = opts.deff_lambda
xmlutils.append_likelihood_result_to_xmldoc(xmldoc, numpy.log(res), neff=neff, **result_dict)
utils.write_filename(xmldoc, opts.output_file, gz=opts.output_file.endswith(".gz"))
if opts.save_samples:
samples = sampler._rvs
samples["distance"] = samples["distance"]
if not opts.time_marginalization:
samples["t_ref"] += float(fiducial_epoch)
else:
samples["t_ref"] = float(fiducial_epoch)*numpy.ones(len(sampler._rvs["psi"]))
samples["polarization"] = samples["psi"]
samples["coa_phase"] = samples["phi_orb"]
if ("declination", "right_ascension") in sampler.params:
samples["latitude"], samples["longitude"] = samples[("declination", "right_ascension")]
else:
samples["latitude"] = samples["declination"]
samples["longitude"] = samples["right_ascension"]
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__)
if opts.save_samples:
samples = sampler._rvs
# FIXME: Does sim insp do kpc or mpc
samples["distance"] = samples["distance"]
if not opts.time_marginalization:
samples["t_ref"] += float(fiducial_epoch)
else:
samples["t_ref"] = float(fiducial_epoch)*numpy.ones(len(sampler._rvs["psi"]))
samples["polarization"] = samples["psi"]
samples["coa_phase"] = samples["phi_orb"]
if ("declination", "right_ascension") in sampler.params:
samples["latitude"], samples["longitude"] = samples[("declination", "right_ascension")]
else:
samples["latitude"] = samples["declination"]
samples["longitude"] = samples["right_ascension"]
samples["loglikelihood"] = numpy.log(samples["integrand"])
samples["mass1"] = numpy.ones(samples["psi"].shape)*opts.mass1
samples["mass2"] = numpy.ones(samples["psi"].shape)*opts.mass2
if opts.eff_lambda is not None or opts.deff_lambda is not None:
# FIXME: the column mapping isn't working right, we need to fix that
# rather than give these weird names
samples["psi0"] = numpy.ones(samples["psi"].shape)*opts.eff_lambda
samples["psi3"] = numpy.ones(samples["psi"].shape)*(opts.deff_lambda or 0)
xmlutils.append_samples_to_xmldoc(xmldoc, samples)
# FIXME: likelihood or loglikehood
# FIXME: How to encode variance?
xmlutils.append_likelihood_result_to_xmldoc(xmldoc, numpy.log(res), neff=neff, **{"mass1": opts.mass1, "mass2": opts.mass2, "event_duration": numpy.sqrt(var)/res, "ttotal": sampler.ntotal})
utils.write_filename(xmldoc, opts.output_file, gz=opts.output_file.endswith(".gz"))
"mass1": opts.mass1,
"mass2": opts.mass2,
"event_duration": numpy.sqrt(var) / res,
"ttotal": sampler.ntotal
}
if opts.spin1z is not None or sngl_inspiral_table:
result_dict["spin1z"] = opts.spin1z or 0.0
if opts.spin2z is not None or sngl_inspiral_table:
result_dict["spin2z"] = opts.spin2z or 0.0
if opts.eff_lambda is not None:
result_dict["psi0"] = opts.eff_lambda
if opts.deff_lambda is not None:
result_dict["psi3"] = opts.deff_lambda
xmlutils.append_likelihood_result_to_xmldoc(xmldoc,
numpy.log(res),
neff=neff,
**result_dict)
utils.write_filename(xmldoc,
opts.output_file,
gz=opts.output_file.endswith(".gz"))
if opts.save_samples:
samples = sampler._rvs
samples["distance"] = samples["distance"]
if not opts.time_marginalization:
samples["t_ref"] += float(fiducial_epoch)
else:
samples["t_ref"] = float(fiducial_epoch) * numpy.ones(
len(sampler._rvs["psi"]))
samples["polarization"] = samples["psi"]
samples["coa_phase"] = samples["phi_orb"]
if ("declination", "right_ascension") in sampler.params:
samples["longitude"] = samples["right_ascension"]
samples["loglikelihood"] = numpy.log(samples["integrand"])
samples["mass1"] = numpy.ones(samples["psi"].shape) * opts.mass1
samples["mass2"] = numpy.ones(samples["psi"].shape) * opts.mass2
if opts.eff_lambda is not None or opts.deff_lambda is not None:
# FIXME: the column mapping isn't working right, we need to fix that
# rather than give these weird names
samples["psi0"] = numpy.ones(
samples["psi"].shape) * opts.eff_lambda
samples["psi3"] = numpy.ones(
samples["psi"].shape) * (opts.deff_lambda or 0)
xmlutils.append_samples_to_xmldoc(xmldoc, samples)
# FIXME: likelihood or loglikehood
# FIXME: How to encode variance?
xmlutils.append_likelihood_result_to_xmldoc(xmldoc,
numpy.log(res),
neff=neff,
**{
"mass1":
opts.mass1,
"mass2":
opts.mass2,
"event_duration":
numpy.sqrt(var) / res,
"ttotal":
sampler.ntotal
})
utils.write_filename(xmldoc,
opts.output_file,
gz=opts.output_file.endswith(".gz"))
