def get_found_param(injfile, bankfile, trigfile, param, ifo):
"""
Translates some popular trigger parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
bankfile: hdf5 File object or None
Template bank file
trigfile: hdf5 File object or None
Single-detector trigger file
param: string
Parameter to be calculated for the recovered triggers
ifo: string or None
Standard ifo name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
foundtmp = injfile["found_after_vetoes/template_id"][:]
if trigfile is not None:
# get the name of the ifo in the injection file, eg "detector_1"
# and the integer from that name
ifolabel = [name for name, val in injfile.attrs.items() if \
"detector" in name and val == ifo][0]
foundtrg = injfile["found_after_vetoes/trigger_id" + ifolabel[-1]]
if bankfile is not None and param in bankfile.keys():
return bankfile[param][:][foundtmp]
elif trigfile is not None and param in trigfile[ifo].keys():
return trigfile[ifo][param][:][foundtrg]
else:
b = bankfile
found_param_dict = {
"mtotal": (b['mass1'][:] + b['mass2'][:])[foundtmp],
"mchirp":
pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[0][foundtmp],
"eta":
pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[1][foundtmp],
"effective_spin":
pnutils.phenomb_chi(b['mass1'][:], b['mass2'][:], b['spin1z'][:],
b['spin2z'][:])[foundtmp]
}
return found_param_dict[param]
def get_found_param(injfile, bankfile, trigfile, param, ifo):
"""
Translates some popular trigger parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
bankfile: hdf5 File object or None
Template bank file
trigfile: hdf5 File object or None
Single-detector trigger file
param: string
Parameter to be calculated for the recovered triggers
ifo: string or None
Standard ifo name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
foundtmp = injfile["found_after_vetoes/template_id"][:]
if trigfile is not None:
# get the name of the ifo in the injection file, eg "detector_1"
# and the integer from that name
ifolabel = [name for name, val in injfile.attrs.items() if \
"detector" in name and val == ifo][0]
foundtrg = injfile["found_after_vetoes/trigger_id" + ifolabel[-1]]
if bankfile is not None and param in bankfile.keys():
return bankfile[param][:][foundtmp]
elif trigfile is not None and param in trigfile[ifo].keys():
return trigfile[ifo][param][:][foundtrg]
else:
b = bankfile
found_param_dict = {
"mtotal" : (b['mass1'][:] + b['mass2'][:])[foundtmp],
"mchirp" : pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[0][foundtmp],
"eta" : pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[1][foundtmp],
"effective_spin" : pnutils.phenomb_chi(b['mass1'][:],
b['mass2'][:],
b['spin1z'][:],
b['spin2z'][:])[foundtmp]
}
return found_param_dict[param]
def get_inj_param(injfile, param, ifo):
"""
Translates some popular injection parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
param: string
Parameter to be calculated for the injected signals
ifo: string
Standard detector name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
det = pycbc.detector.Detector(ifo)
time_delay = numpy.vectorize(#lambda dec, ra, t :
det.time_delay_from_earth_center)#(dec, ra, t))
inj = injfile["injections"]
if param in inj.keys():
return inj["injections/"+param]
inj_param_dict = {
"mtotal" : inj['mass1'][:] + inj['mass2'][:],
"mchirp" : pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:],
inj['mass2'][:])[0],
"eta" : pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:],
inj['mass2'][:])[1],
"effective_spin" : pnutils.phenomb_chi(inj['mass1'][:],
inj['mass2'][:],
inj['spin1z'][:],
inj['spin2z'][:]),
"end_time_"+ifo[0].lower() :
inj['end_time'][:] + time_delay(inj['longitude'][:],
inj['latitude'][:],
inj['end_time'][:]),
}
return inj_param_dict[param]
def get_inj_param(injfile, param, ifo):
"""
Translates some popular injection parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
param: string
Parameter to be calculated for the injected signals
ifo: string
Standard detector name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
det = pycbc.detector.Detector(ifo)
time_delay = numpy.vectorize( #lambda dec, ra, t :
det.time_delay_from_earth_center) #(dec, ra, t))
inj = injfile["injections"]
if param in inj.keys():
return inj["injections/" + param]
inj_param_dict = {
"mtotal":
inj['mass1'][:] + inj['mass2'][:],
"mchirp":
pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:], inj['mass2'][:])[0],
"eta":
pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:], inj['mass2'][:])[1],
"effective_spin":
pnutils.phenomb_chi(inj['mass1'][:], inj['mass2'][:], inj['spin1z'][:],
inj['spin2z'][:]),
"end_time_" + ifo[0].lower():
inj['end_time'][:] + time_delay(
inj['longitude'][:], inj['latitude'][:], inj['end_time'][:]),
}
return inj_param_dict[param]
def effective_spin(self):
# FIXME assumes aligned spins
return pnutils.phenomb_chi(self.mass1, self.mass2, self.spin1z, self.spin2z)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Match Calculation & plotting
#
print "Extracting and generating waveform"
mass = simulations.simulations[sim_number]['Mmin30Hz']
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Common params
mass1, mass2 = pnutils.mtotal_eta_to_mass1_mass2(mass,
simulations.simulations[sim_number]['eta'])
# Estimate ffinal
chi = pnutils.phenomb_chi(mass1, mass2,
simulations.simulations[sim_number]['spin1z'],simulations.simulations[sim_number]['spin2z'])
ffinal = pnutils.get_final_freq(approx, mass1, mass2,
simulations.simulations[sim_number]['spin1z'],simulations.simulations[sim_number]['spin2z'])
#upp_bound = ffinal
#upp_bound = 1.5*ffinal
upp_bound = 0.5/delta_t
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# NUMERICAL RELATIVITY
# --- Generate the polarisations
hplus_NR, hcross_NR = nrbu.get_wf_pols(
simulations.simulations[sim_number]['wavefile'], mass, inclination=inc,
delta_t=delta_t, f_lower=30.0001, distance=distance)
def effective_spin(self):
# FIXME assumes aligned spins
return pnutils.phenomb_chi(self.mass1, self.mass2, self.spin1z,
self.spin2z)
SeffdotL[s] = sim['SeffdotL']
SeffcrossL[s] = sim['SeffcrossL']
SdotL[s] = sim['theta_SdotL']
theta_SdotL[s] = sim['theta_SdotL']
for n in xrange(config.nsampls):
chirp_masses[s,n] = masses[s,n] * sim['eta']**(3./5)
mass1, mass2 = \
pnutils.mchirp_eta_to_mass1_mass2(np.median(chirp_masses[s,:]),
sim['eta'])
chieff[s] = pnutils.phenomb_chi(mass1, mass2, sim['spin1z'],
sim['spin2z'])
median_chirp_masses = np.median(chirp_masses, axis=1)
std_chirp_masses = np.std(chirp_masses, axis=1)
matchsort = np.argsort(median_matches)
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print "Summary for %s"%args[0]
print ""
print " * Highest (median) Match: %f +/- %f"%(median_matches[matchsort][-1],
std_matches[matchsort][-1])
print " * Waveform: %s"%(
simulations_goodmatch[matchsort][-1]['wavefile'].split('/')[-1])
print " * mass ratio: %f"%(mass_ratios[matchsort][-1])
print " * total mass: %f +/- %f"%(median_masses[matchsort][-1],
std_masses[matchsort][-1])