def makeSkyMap(samples, lmax, nside=16, psrs=None, cmap='YlOrBr'):
# number of pixels total
npix = hp.nside2npix(nside)
# initialize theta and phi map coordinantes
skypos=[]
for ii in range(npix):
skypos.append(np.array(hp.pix2ang(nside,ii)))
skypos = np.array(skypos)
harmvals = PALutils.SetupSkymapPlottingGrid(lmax,skypos)
pwr = []
for ii in range(len(samples)):
samples_tot = np.append(2.*np.sqrt(np.pi), samples[ii])
gwp = PALutils.GWpower(samples_tot, harmvals)
pwr.append(gwp)
pwr = np.array(pwr)
pwr = np.mean(pwr, axis=0)
ax = plt.subplot(111, projection='astro mollweide')
ax.grid()
plot.outline_text(ax)
plot.healpix_heatmap(pwr, cmap=cmap)
plt.colorbar(orientation='horizontal')
# add pulsars locations
if np.all(psrs):
ax.plot(psrs[:,0], psrs[:,1], '*', color='w', markersize=6, mew=1, mec='w')
return pwr
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']:
def readPulsar(self, psr, psrname, start_time=None, end_time=None):
psr.name = str(psrname)
psr.base_ephem = str(
self.getData(psrname, 'base_ephem', required=False))
# Read the content of the par/tim files in a string
psr.parfile_content = str(
self.getData(psrname, 'parfile', required=False))
psr.timfile_content = str(
self.getData(psrname, 'timfile', required=False))
# Read the timing model parameter descriptions
psr.ptmdescription = map(str, self.getData(psrname, 'tmp_name'))
psr.ptmpars = np.array(self.getData(psrname, 'tmp_valpost'))
psr.ptmparerrs = np.array(self.getData(psrname, 'tmp_errpost'))
psr.flags = np.array(
map(str, self.getData(psrname, 'efacequad_freq', 'Flags')))
psr.tobsflags = np.array(
map(float, self.getData(psrname, 'tobs_all', 'Flags')))
try:
psr.bwflags = np.array(
map(float, self.getData(psrname, 'bwflags', 'Flags')))
except IOError:
psr.bwflags = np.ones(len(psr.flags)) * 4.0
psr.setptmdescription = map(str, self.getData(psrname, 'set_name'))
psr.setptmpars = np.array(self.getData(psrname, 'set_valpost'))
# add this for frequency dependent terms
#TODO: should eventually change psr.flags to a dictionary
psr.fflags = np.array(
map(str, self.getData(psrname, 'efacequad_freq', 'Flags')))
# Read the position of the pulsar
rajind = np.flatnonzero(np.array(psr.setptmdescription) == 'RAJ')
decjind = np.flatnonzero(np.array(psr.setptmdescription) == 'DECJ')
# look for ecliptic coordinates
if len(rajind) == 0 and len(decjind) == 0:
#print 'Could not fine RAJ or DECJ. Looking for ecliptic coords...'
elongind = np.flatnonzero(
np.array(psr.setptmdescription) == 'ELONG')
elatind = np.flatnonzero(np.array(psr.setptmdescription) == 'ELAT')
elong = np.array(self.getData(psrname, 'set_valpost'))[elongind]
elat = np.array(self.getData(psrname, 'set_valpost'))[elatind]
# convert via pyephem
#print elong, elat
try:
ec = pyephem.Ecliptic(elong, elat)
# check for B name
if 'B' in psr.name:
epoch = '1950'
else:
epoch = '2000'
eq = pyephem.Equatorial(ec, epoch=epoch)
psr.raj = np.double([eq.ra])
psr.decj = np.double([eq.dec])
except TypeError:
print 'WARNING: Cannot find sky location coordinates.' \
'Setting to 0.'
psr.raj = np.array([0.0])
psr.decj = np.array([0.0])
else:
psr.raj = np.array(self.getData(psrname, 'set_valpost'))[rajind]
psr.decj = np.array(self.getData(psrname, 'set_valpost'))[decjind]
psr.theta = np.pi / 2 - psr.decj
psr.phi = psr.raj
# period of pulsar
perind = np.flatnonzero(np.array(psr.ptmdescription) == 'F0')
psr.period = 1 / np.array(self.getData(psrname, 'tmp_valpost'))[perind]
# pulsar distance and uncertainty
psr.pdist = np.double(self.getData(psrname, 'pdist'))
psr.pdistErr = np.double(self.getData(psrname, 'pdistErr'))
# ppdm
psr.ppdm = np.array(self.getData(psrname, 'ppdm', 'Flags'))
psr.ppdmerr = np.array(self.getData(psrname, 'ppdme', 'Flags'))
if np.all(psr.ppdm != 0):
dmin = psr.ppdmerr == 0
psr.ppdmerr[dmin] = 1e10
# Obtain residuals, TOAs, etc.
psr.toas = np.array(self.getData(psrname, 'TOAs'))
psr.toaerrs = np.array(self.getData(psrname, 'toaErr'))
psr.residuals = np.array(self.getData(psrname, 'postfitRes'))
psr.detresiduals = np.array(self.getData(psrname, 'postfitRes'))
psr.freqs = np.array(self.getData(psrname, 'freq'))
psr.Mmat = np.array(self.getData(psrname, 'designmatrix'))
# filter data based on start and end times
if start_time is not None or end_time is not None:
if start_time is None:
start_time = psr.toas.min() / 86400
if end_time is None:
end_time = psr.toas.max() / 86400
print 'Filtering TOAs between {0} and {1} for PSR {2}'.format(
start_time, end_time, psr.name)
ind = np.logical_and(psr.toas / 86400 >= start_time,
psr.toas / 86400 <= end_time)
psr.toas = psr.toas[ind]
psr.toaerrs = psr.toaerrs[ind]
psr.residuals = psr.residuals[ind]
psr.detresiduals = psr.detresiduals[ind]
psr.freqs = psr.freqs[ind]
psr.flags = psr.flags[ind]
psr.fflags = psr.fflags[ind]
psr.tobsflags = psr.tobsflags[ind]
psr.bwflags = psr.bwflags[ind]
# design matrix
print 'Design Matrix Shape pre-filter:', psr.Mmat.shape
psr.Mmat = psr.Mmat[ind, :]
dind = psr.Mmat.sum(axis=0) == 0
psr.Mmat = psr.Mmat[:, ~dind]
print 'Design Matrix Shape post-filter:', psr.Mmat.shape
# fitted parameters
psr.ptmdescription = list(np.array(psr.ptmdescription)[~dind])
psr.ptmpars = psr.ptmpars[~dind]
psr.ptmparerrs = psr.ptmparerrs[~dind]
# get number of epochs (i.e 1 s window)
try:
(avetoas, aveflags,
Umat) = PALutils.exploderMatrixNoSingles(psr.toas,
np.array(psr.flags),
dt=1)
psr.nepoch = len(avetoas)
except IndexError:
pass
def readPulsar(self, psr, psrname, start_time=None, end_time=None):
psr.name = str(psrname)
# Read the content of the par/tim files in a string
psr.parfile_content = str(self.getData(psrname, 'parfile', required=False))
psr.timfile_content = str(self.getData(psrname, 'timfile', required=False))
# Read the timing model parameter descriptions
psr.ptmdescription = map(str, self.getData(psrname, 'tmp_name'))
psr.ptmpars = np.array(self.getData(psrname, 'tmp_valpost'))
psr.ptmparerrs = np.array(self.getData(psrname, 'tmp_errpost'))
psr.flags = np.array(map(str, self.getData(psrname, 'efacequad_freq', 'Flags')))
psr.tobsflags = np.array(map(float, self.getData(psrname, 'tobs_all', 'Flags')))
try:
psr.bwflags = np.array(map(float, self.getData(psrname, 'bwflags', 'Flags')))
except IOError:
psr.bwflags = np.ones(len(psr.flags)) * 4.0
psr.setptmdescription = map(str, self.getData(psrname, 'set_name'))
psr.setptmpars = np.array(self.getData(psrname, 'set_valpost'))
# add this for frequency dependent terms
#TODO: should eventually change psr.flags to a dictionary
psr.fflags = np.array(map(str, self.getData(psrname, 'efacequad_freq', 'Flags')))
# Read the position of the pulsar
rajind = np.flatnonzero(np.array(psr.setptmdescription) == 'RAJ')
decjind = np.flatnonzero(np.array(psr.setptmdescription) == 'DECJ')
# look for ecliptic coordinates
if len(rajind) == 0 and len(decjind) == 0:
#print 'Could not fine RAJ or DECJ. Looking for ecliptic coords...'
elongind = np.flatnonzero(np.array(psr.setptmdescription) == 'ELONG')
elatind = np.flatnonzero(np.array(psr.setptmdescription) == 'ELAT')
elong = np.array(self.getData(psrname, 'set_valpost'))[elongind]
elat = np.array(self.getData(psrname, 'set_valpost'))[elatind]
# convert via pyephem
#print elong, elat
try:
ec = ephem.Ecliptic(elong, elat)
# check for B name
if 'B' in psr.name:
epoch = '1950'
else:
epoch = '2000'
eq = ephem.Equatorial(ec, epoch=epoch)
psr.raj = np.double([eq.ra])
psr.decj = np.double([eq.dec])
except TypeError:
print 'WARNING: Cannot find sky location coordinates.' \
'Setting to 0.'
psr.raj = np.array([0.0])
psr.decj = np.array([0.0])
else:
psr.raj = np.array(self.getData(psrname, 'set_valpost'))[rajind]
psr.decj = np.array(self.getData(psrname, 'set_valpost'))[decjind]
psr.theta = np.pi/2 - psr.decj
psr.phi = psr.raj
# period of pulsar
perind = np.flatnonzero(np.array(psr.ptmdescription) == 'F0')
psr.period = 1/np.array(self.getData(psrname, 'tmp_valpost'))[perind]
# pulsar distance and uncertainty
psr.pdist = np.double(self.getData(psrname, 'pdist'))
psr.pdistErr = np.double(self.getData(psrname, 'pdistErr'))
# ppdm
psr.ppdm = np.array(self.getData(psrname, 'ppdm', 'Flags'))
psr.ppdmerr = np.array(self.getData(psrname, 'ppdme', 'Flags'))
if np.all(psr.ppdm!=0):
dmin = psr.ppdmerr == 0
psr.ppdmerr[dmin] = np.min(psr.ppdmerr[~dmin])
# Obtain residuals, TOAs, etc.
psr.toas = np.array(self.getData(psrname, 'TOAs'))
psr.toaerrs = np.array(self.getData(psrname, 'toaErr'))
psr.residuals = np.array(self.getData(psrname, 'postfitRes'))
psr.detresiduals = np.array(self.getData(psrname, 'postfitRes'))
psr.freqs = np.array(self.getData(psrname, 'freq'))
psr.Mmat = np.array(self.getData(psrname, 'designmatrix'))
# filter data based on start and end times
if start_time is not None or end_time is not None:
if start_time is None:
start_time = psr.toas.min()/86400
if end_time is None:
end_time = psr.toas.max()/86400
print 'Filtering TOAs between {0} and {1} for PSR {2}'.format(
start_time, end_time, psr.name)
ind = np.logical_and(psr.toas/86400>=start_time, psr.toas/86400<=end_time)
psr.toas = psr.toas[ind]
psr.toaerrs = psr.toaerrs[ind]
psr.residuals = psr.residuals[ind]
psr.detresiduals = psr.detresiduals[ind]
psr.freqs = psr.freqs[ind]
psr.flags = psr.flags[ind]
psr.fflags = psr.fflags[ind]
psr.tobsflags = psr.tobsflags[ind]
psr.bwflags = psr.bwflags[ind]
# design matrix
print 'Design Matrix Shape pre-filter:', psr.Mmat.shape
psr.Mmat = psr.Mmat[ind,:]
dind = psr.Mmat.sum(axis=0) == 0
psr.Mmat = psr.Mmat[:,~dind]
print 'Design Matrix Shape post-filter:', psr.Mmat.shape
# fitted parameters
psr.ptmdescription = list(np.array(psr.ptmdescription)[~dind])
psr.ptmpars = psr.ptmpars[~dind]
psr.ptmparerrs = psr.ptmparerrs[~dind]
# get number of epochs (i.e 1 s window)
try:
(avetoas, aveflags, Umat) = PALutils.exploderMatrixNoSingles(
psr.toas, np.array(psr.flags), dt=1)
psr.nepoch = len(avetoas)
except IndexError:
pass
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
