def eval_overlap(grid,P_list, IP,indx):
global opts
# if opts.verbose:
# print " Evaluating for ", indx
global use_external_EOB
global Lmax
global opts
P2 = P_list[indx]
T_here = 1./IP.deltaF
P2.deltaF=1./T_here
# P2.print_params()
if not opts.skip_overlap:
if not use_external_EOB:
hf2 = lalsimutils.complex_hoff(P2)
else:
print(" Waiting for EOB waveform ....", indx, " with duration ", T_here)
wfP = eobwf.WaveformModeCatalog(P2,lmax=Lmax) # only include l=2 for us.
hf2 = wfP.complex_hoff(force_T=T_here)
nm2 = IP.norm(hf2); hf2.data.data *= 1./nm2
# if opts.verbose:
# print " Waveform normalized for ", indx
ip_val = IP.ip(hfBase,hf2)
line_out = []
line_out = list(grid[indx])
if not opts.skip_overlap:
line_out.append(ip_val)
else:
line_out.append(-1)
if opts.verbose:
print(" Answer ", indx, line_out)
return line_out
def ip_here(x):
PT.assign_param(param_now,x)
PT.deltaF = IP.deltaF
hf_now = lalsimutils.complex_hoff(PT)
nm_now = IP.norm(hf_now)
val = IP.ip(hfBase,hf_now)/nm_now
if opts.verbose:
print(param_now, x, val)
return val - opts.match_value
def my_distance(P1, P2):
global IP
global opts
P1.approx = P2.approx = lalsim.GetApproximantFromString(opts.approx)
P1.fmin = P2.fmin = opts.fmin
P1.deltaF = P2.deltaF = 1. / T_window
# if opts.verbose:
# print " ---> Inside distance function < "
# P1.print_params()
# P2.print_params()
dist = 1e5
try:
hF1 = lalsimutils.complex_hoff(P1)
hF2 = lalsimutils.complex_hoff(P2)
rho1 = IP.norm(hF1)
rho2 = IP.norm(hF2)
dist = 1 - np.abs(IP.ip(hF1, hF2) / rho1 / rho2)
except:
print(" Distance evaluation failure ")
if np.isnan(dist):
return 1e5
return dist
def eval_overlap(grid, P_list, IP, indx):
# if opts.verbose:
# print " Evaluating for ", indx
global Lmax
global opts
P2 = P_list[indx]
# P2.print_params()
line_out = []
line_out = list(grid[indx])
if not opts.skip_overlap:
T_here = 1. / IP.deltaF
P2.deltaF = 1. / T_here
hf2 = lalsimutils.complex_hoff(P2)
nm2 = IP.norm(hf2)
hf2.data.data *= 1. / nm2
ip_val = IP.ip(hfBase, hf2)
line_out.append(ip_val)
else:
line_out.append(-1)
if opts.verbose:
print(" Answer ", indx, line_out)
return line_out
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
###
### Create the inner product function, etc needed (distance =match)
###
P = lalsimutils.ChooseWaveformParams()
P.m1 = P.m2 = 50 * lal.MSUN_SI
P.approx = lalsim.GetApproximantFromString(opts.approx)
P.deltaT = 1. / srate
P.deltaF = 1. / opts.seglen
hfBase = lalsimutils.complex_hoff(P)
IP = lalsimutils.CreateCompatibleComplexOverlap(
hfBase,
analyticPSD_Q=analyticPSD_Q,
psd=eff_fisher_psd,
fMax=opts.fmax,
interpolate_max=True)
def my_distance(P1, P2):
global IP
global opts
P1.approx = P2.approx = lalsim.GetApproximantFromString(opts.approx)
P1.fmin = P2.fmin = opts.fmin
P1.deltaF = P2.deltaF = 1. / T_window
# if opts.verbose:
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
fac_reduce=1
for indx in np.arange(len(samples_in["m1"])):
# print indx
###
### Define the system
###
m1 = samples_in["m1"][indx]*lal.MSUN_SI
m2 = samples_in["m2"][indx]*lal.MSUN_SI
d = samples_in["distance"][indx]*lal.PC_SI*1e6