# if opts.fmin_template2:
# fmin = np.min([fmin, opts.fmin_template2])
fmaxSNR = 1700
analyticPSD_Q = True
psd_dict = {}
ifo_list = []
psd = lalsim.SimNoisePSDaLIGOZeroDetHighPower
if opts.psd_file:
analyticPSD_Q = False
for inst, psdf in map(lambda c: c.split("="), opts.psd_file):
if opts.verbose:
print("Reading PSD for instrument %s from %s" % (inst, psdf))
psd_dict[inst] = lalsimutils.load_resample_and_clean_psd(
psdf, inst, df)
ifo_list = psd_dict.keys()
elif opts.psd and hasattr(lalsim, opts.psd):
ifo_list = ['H1', 'L1']
psd = getattr(lalsim, opts.psd)
fNyq = opts.srate / 2.
IP = None
IP_list = {}
if opts.maximize:
for ifo in ifo_list:
IP_list[ifo] = lalsimutils.ComplexOverlap(fNyq=fNyq,
deltaF=df,
analyticPSD_Q=analyticPSD_Q,
print(" Fisher in matrix form is ", mtx)
# make distance function
dist_here = make_distance_for_fisher(mtx, param_names)
elif opts.approx:
if not opts.psd_file:
#eff_fisher_psd = eval(opts.fisher_psd)
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
###
### 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,
for ifo in ifo_list:
try:
channel = ifo + ":FAKE-STRAIN"
data_dict[ifo] = lalsimutils.frame_data_to_non_herm_hoff(
opts.cache, channel)
fSample = len(data_dict[ifo].data.data) * data_dict[ifo].deltaF
df = data_dict[ifo].deltaF
if not (ifo in psd_name):
print(ifo, " analytic psd ", opts.psd)
analyticPSD_Q = True
psd_dict[ifo] = eval(opts.psd)
else:
analyticPSD_Q = False
print("Reading PSD for instrument %s from %s" %
(ifo, psd_name[ifo]))
psd_dict[ifo] = lalsimutils.load_resample_and_clean_psd(
psd_name[ifo], ifo, df)
IP = lalsimutils.ComplexIP(fLow=fminSNR,
fNyq=fSample / 2,
deltaF=df,
psd=psd_dict[ifo],
fMax=fmaxSNR,
analyticPSD_Q=analyticPSD_Q)
rhoDet = rho_dict[ifo] = IP.norm(data_dict[ifo])
print(ifo, rho_dict[ifo])
rho2Net += rhoDet * rhoDet
if opts.plot_sanity:
fvals = lalsimutils.evaluate_fvals(data_dict[ifo])
plt.plot(
fvals,
np.power(np.abs(data_dict[ifo].data.data), 2) *
IP.weights2side)