def to_sngl(self):
# All numerical values are initiated as 0 and all strings as ''
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.flow)
row.template_duration = self.dur
row.spin1x = self.spin1x
row.spin1y = self.spin1y
row.spin1z = self.spin1z
row.spin2x = 0
row.spin2y = 0
row.spin2z = 0
row.alpha1 = self.theta
row.alpha2 = self.phi
row.alpha3 = self.iota
row.alpha4 = self.psi
row.alpha5 = self.orb_phase
row.sigmasq = self.sigmasq
if self.bank.flow_column:
setattr(row, self.bank.flow_column, self.flow)
return row
def to_sngl(self):
# All numerical values are initiated as 0 and all strings as ''
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.flow)
row.template_duration = self.dur
row.spin1x = self.spin1x
row.spin1y = self.spin1y
row.spin1z = self.spin1z
row.spin2x = 0
row.spin2y = 0
row.spin2z = 0
row.alpha1 = self.theta
row.alpha2 = self.phi
row.alpha3 = self.iota
row.alpha4 = self.psi
row.alpha5 = self.orb_phase
row.sigmasq = self.sigmasq
if self.bank.flow_column:
setattr(row, self.bank.flow_column, self.flow)
return row
def to_sngl(self):
# note that we use the C version; this causes all numerical values to be initiated
# as 0 and all strings to be '', which is nice
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.bank.flow)
row.f_final = self._f_final
row.template_duration = self._dur
row.sigmasq = self.sigmasq
return row
def to_sngl(self):
# All numerical values are initiated as 0 and all strings as ''
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.bank.flow)
row.f_final = self.f_final
row.template_duration = self._dur
row.spin1z = self.spin1z
row.spin2z = self.spin2z
row.sigmasq = self.sigmasq
return row
def to_sngl(self):
# All numerical values are initiated as 0 and all strings as ''
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.bank.flow)
row.f_final = self.f_final
row.template_duration = self._dur
row.spin1z = self.spin1z
row.spin2z = self.spin2z
row.sigmasq = self.sigmasq
return row
def to_sngl(self):
# note that we use the C version; this causes all numerical
# values to be initiated as 0 and all strings to be '', which
# is nice
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.bank.flow)
row.f_final = self.f_final
row.template_duration = self._dur
row.spin1z = self.spin1z
row.spin2z = self.spin2z
row.sigmasq = self.sigmasq
return row
def to_sngl(self):
# note that we use the C version; this causes all numerical values to
# be initiated as 0 and all strings to be '', which is nice
row = SnglInspiralTable()
row.mass1 = self.m1
row.mass2 = self.m2
row.mtotal = self.m1 + self.m2
row.mchirp = self._mchirp
row.eta = row.mass1 * row.mass2 / (row.mtotal * row.mtotal)
row.tau0, row.tau3 = m1m2_to_tau0tau3(self.m1, self.m2, self.bank.flow)
row.f_final = self.f_final
row.template_duration = self._dur
row.spin1x = self.spin1x
row.spin1y = self.spin1y
row.spin1z = self.spin1z
row.spin2x = 0
row.spin2y = 0
row.spin2z = 0
row.alpha1 = self.theta
row.alpha2 = self.phi
row.alpha3 = self.iota
row.alpha4 = self.psi
row.sigmasq = self.sigmasq
return row
# get other useful quantities
pid = find_process_id(h5file["/process"], u"lalapps_cbc_sbank_sim")
flow = float(find_process_param(pparams, pid, u"--flow"))
# derived quantities
# NB: Named fields in dtypes (see ligolw_table_to_array in lalapps_cbc_sbank_sim)
# allow dict-like access to columns: e.g. arr["mass1"].
min_match = match[int(floor(len(match) * 0.9))]
inj_M = inj_arr["mass1"] + inj_arr["mass2"] #total mass
inj_eta = inj_arr["mass1"] * inj_arr["mass2"] / (inj_arr["mass1"] +
inj_arr["mass2"])**2
tmplt_M = tmplt_arr["mass1"] + tmplt_arr["mass2"]
tmplt_eta = tmplt_arr["mass1"] * tmplt_arr["mass2"] / (tmplt_arr["mass1"] +
tmplt_arr["mass2"])**2
inj_tau0, inj_tau3 = m1m2_to_tau0tau3(inj_arr["mass1"], inj_arr["mass2"], flow)
tmplt_tau0, tmplt_tau3 = m1m2_to_tau0tau3(tmplt_arr["mass1"],
tmplt_arr["mass2"], flow)
tmplt_mchirp = compute_mchirp(tmplt_arr["mass1"], tmplt_arr["mass2"])
inj_mchirp = compute_mchirp(inj_arr["mass1"], inj_arr["mass2"])
#
# compute effective/reduced spin parameter for templates
#
if tmplt_approx == "TaylorF2RedSpin":
tmplt_chi_label = "\chi_\mathrm{red}"
tmplt_chi = [
SimInspiralTaylorF2ReducedSpinComputeChi(float(row["mass1"]),
float(row["mass2"]),
float(row["spin1z"]),
float(row["spin2z"]))
inj_approx = find_process_param(pparams, pid, u"--injection-approx"),
tmplt_approx = find_process_param(pparams, pid, u"--template-approx")
# get other useful quantities
pid = find_process_id(h5file["/process"], u"lalapps_cbc_sbank_sim")
flow = float(find_process_param(pparams, pid, u"--flow"))
# derived quantities
# NB: Named fields in dtypes (see ligolw_table_to_array in lalapps_cbc_sbank_sim)
# allow dict-like access to columns: e.g. arr["mass1"].
min_match = match[int(floor(len(match)*0.9))]
inj_M = inj_arr["mass1"] + inj_arr["mass2"] #total mass
inj_eta = inj_arr["mass1"] * inj_arr["mass2"] / (inj_arr["mass1"] + inj_arr["mass2"])**2
tmplt_M = tmplt_arr["mass1"] + tmplt_arr["mass2"]
tmplt_eta = tmplt_arr["mass1"] * tmplt_arr["mass2"] / (tmplt_arr["mass1"] + tmplt_arr["mass2"])**2
inj_tau0, inj_tau3 = m1m2_to_tau0tau3(inj_arr["mass1"], inj_arr["mass2"], flow)
tmplt_tau0, tmplt_tau3 = m1m2_to_tau0tau3(tmplt_arr["mass1"], tmplt_arr["mass2"], flow)
tmplt_mchirp = compute_mchirp(tmplt_arr["mass1"], tmplt_arr["mass2"])
inj_mchirp = compute_mchirp(inj_arr["mass1"], inj_arr["mass2"])
#
# compute effective/reduced spin parameter for templates
#
if tmplt_approx == "TaylorF2RedSpin":
tmplt_chi_label = "\chi_\mathrm{red}"
tmplt_chi = [SimInspiralTaylorF2ReducedSpinComputeChi(float(row["mass1"]), float(row["mass2"]), float(row["spin1z"]), float(row["spin2z"])) for row in tmplt_arr]
else: # default to effective spin
tmplt_chi_label = "\chi_\mathrm{eff}"
tmplt_chi = [SimIMRPhenomBComputeChi(float(row["mass1"]), float(row["mass2"]), float(row["spin1z"]), float(row["spin2z"])) for row in tmplt_arr]
# FIXME hack