def init_os_common(dataset, h5file, psrlist, nf, noisedir=None, noVaryNoise=False,
incJitterEquad=True, incEquad=True, outputfile=None):
model = PALmodels.PTAmodels(h5file, pulsars=psrlist)
# make model dictionary, can play with number of frequencies here
# Note: Tmax=0 means it will use the largest time span in the array to set the frequency bin size
fullmodel = model.makeModelDict(incRedNoise=True, incEquad=incEquad, incJitterEquad=incJitterEquad,
likfunc='mark9', incGWB=True, incEphemMixture=False, nfreqs=nf, Tmax=0)
# loop through pulsars and fix white noise parameters to values in file
if noisedir is not None:
for ct, p in enumerate(model.psr):
d = np.genfromtxt(noisedir + p.name + '_noise.txt', dtype='S42')
pars = d[:,0]
vals = np.array([float(d[ii,2]) for ii in range(d.shape[0])])
sigs = [psig for psig in fullmodel['signals'] if psig['pulsarind'] == ct]
sigs = PALutils.fixNoiseValues(sigs, vals, pars, bvary=False, verbose=False)
# turn back on red-noise parameters (will use later when drawing from posteriors)
for sig in fullmodel['signals']:
if sig['corr'] == 'single' and sig['stype'] == 'powerlaw':
sig['bvary'][1] = True
sig['bvary'][0] = True
if noVaryNoise:
nflags = ['efac', 'equad', 'jitter', 'jitter_equad']
for sig in fullmodel['signals']:
if sig['stype'] in nflags:
sig['bvary'][0] = False
# intialize mdoel
model.initModel(fullmodel, memsave=True, write='no')
# start parameters off at initial values (i.e the fixed values red in above)
p0 = model.initParameters(fixpstart=True)
# essentially turn off GWB component (again we will do something different when drawing from full PTA posterior)
p0[-2] = -19
# call once to fix white noise
xi, rho, sig, Opt, Sig = model.opt_stat_mark9(p0)
if outputfile is not None:
f = open(outputfile, 'w')
f.write('Opt {0}\n'.format(Opt))
f.write('Sig {0}\n'.format(Sig))
f.write('SNR {0}\n'.format(Opt/Sig))
f.close()
return model
sig['pstart']))
#for sig in fullmodel['signals']:
# if sig['stype'] == 'jitter_equad':
# if sig['flagvalue'] == 'J1741+1351-430_ASP':
# sig['bvary'][0] = False
if args.fixNoise:
noisedir = args.noisedir
for ct, p in enumerate(model.psr):
d = np.genfromtxt(noisedir + p.name + '_noise.txt', dtype='S42')
pars = d[:,0]
vals = np.array([float(d[ii,1]) for ii in range(d.shape[0])])
sigs = [psig for psig in fullmodel['signals'] if psig['pulsarind'] == ct]
sigs = PALutils.fixNoiseValues(sigs, vals, pars, bvary=False, verbose=True)
# turn red noise back on
if args.fixNoise and args.fixWhite:
print('Turning on red noise and only fixing white noise')
for sig in fullmodel['signals']:
if sig['corr'] == 'single' and sig['stype'] == 'powerlaw':
sig['bvary'][1] = True
sig['bvary'][0] = True
# write JSON file
if not args.incTimingModel:
model.writeModelToFile(fullmodel, args.outDir + '/model.json')
# check for single efacs
separateJitter=False, separateJitterEquadByFreq=True,
incEquad=incEquad, incJitter=False, incJitterEquad=incJitterEquad,
incGWB=False, nfreqs=50, ndmfreqs=50, Tmax=0,
incBWM=incBWM, compression='None', likfunc=likfunc)
if args.noisedir is None:
noisedir = outdir + '/noisefiles/'
else:
noisedir = args.noisedir + '/'
for ct, p in enumerate(model.psr):
d = np.genfromtxt(noisedir + p.name + '_noise.txt', dtype='S42')
pars = d[:,0]
vals = np.array([float(d[ii,1]) for ii in range(d.shape[0])])
sigs = [psig for psig in fullmodel['signals'] if psig['pulsarind'] == ct]
sigs = PALutils.fixNoiseValues(sigs, vals, pars, bvary=False, verbose=False)
# turn red noise back on
if args.pipeline == 'BWM':
print 'Turning on red noise and only fixing white noise'
for sig in fullmodel['signals']:
if sig['corr'] == 'single' and sig['stype'] == 'powerlaw':
sig['bvary'][1] = True
sig['bvary'][0] = True
model.initModel(fullmodel, memsave=True, fromFile=False, verbose=False, write='no')
# set initial parameters
p0 = model.initParameters(fixpstart=True, startEfacAtOne=False)
if args.pta in ['nanograv5', 'nanograv9']:
