# make sure all pulsar have same reference time
tt=[]
for p in psr:
tt.append(np.min(p.toas))
# find reference time
tref = np.min(tt)
# now scale pulsar time
for p in psr:
p.toas -= tref
# get G matrices
for p in psr:
p.G = PALutils.createGmatrix(p.dmatrix)
# run Fp statistic to determine starting frequency
print 'Running initial Fpstat search'
fsearch = np.logspace(-9, -7, 200)
fpstat = np.zeros(len(fsearch))
for ii in range(len(fsearch)):
fpstat[ii] = PALLikelihoods.fpStat(psr, fsearch[ii])
# determine maximum likelihood frequency
fmaxlike = fsearch[np.argmax(fpstat)]
print 'Maximum likelihood from f-stat search = {0}\n'.format(fmaxlike)
# prior ranges
def addInverseCovFromNoiseFile(self, parfile, timfile, noisefile, DMOFF=None, DMXOFF=None, dailyAverage=False):
"""
Add noise covariance matrix after timing model subtraction.
"""
# Check whether the two files exist
if not os.path.isfile(parfile) or not os.path.isfile(timfile):
raise IOError, "Cannot find parfile (%s) or timfile (%s)!" % (parfile, timfile)
assert(self.filename != None), "ERROR: HDF5 file not set!"
# 'a' means: read/write if exists, create otherwise
self.h5file = h5.File(self.filename, 'a')
# Create the data subgroup if it does not exist
if "Data" in self.h5file:
datagroup = self.h5file["Data"]
else:
raise IOError, "Cannot add noise parameters if Data group does not exist!"
# Load pulsar data from the JPL Cython tempo2 library
t2pulsar = t2.tempopulsar(parfile, timfile)
# turn off DMMODEL fitting
if DMOFF is not None:
t2pulsar['DMMODEL'].fit = False
# turn off DMX fitting
if DMXOFF is not None:
DMXFlag = False
print 'Turning off DMX fitting and turning DM fitting on'
for par in t2pulsar.pars:
if 'DMX' in par:
t2pulsar[par].fit = False
t2pulsar['DM'].fit = True
DMXFlag = True
if DMXFlag== False:
print 'NO DMX for pulsar {0}'.format(t2pulsar.name)
# refit 5 times to make sure we are converged
t2pulsar.fit(iters=5)
# Create the pulsar subgroup if it does not exist
if "Pulsars" in datagroup:
pulsarsgroup = datagroup["Pulsars"]
else:
raise IOError, "Cannot add noise parameters if pulsar group does not exist!"
# Look up the name of the pulsar, and see if it exist
if t2pulsar.name in pulsarsgroup:
pass
else:
raise IOError, "%s must already exists in %s to add noise parameters!"\
% (t2pulsar.name, self.filename)
pulsarsgroup = pulsarsgroup[t2pulsar.name]
# first create G matrix from design matrix and toas
designmatrix = np.double(t2pulsar.designmatrix())
toas = np.double(t2pulsar.toas()*86400)
errs = np.double(t2pulsar.toaerrs*1e-6)
# if doing daily averaging
if dailyAverage:
# get average quantities
toas, qmatrix, errs, dmatrix, freqs, bands = PALutils.dailyAverage(t2pulsar)
# construct new daily averaged residuals and designmatrix
toas *= 86400
designmatrix = np.dot(qmatrix, dmatrix)
G = PALutils.createGmatrix(designmatrix)
# create matrix of time lags
tm = PALutils.createTimeLags(toas, toas, round=True)
# now read noise file to get model and parameters
file = open(noisefile,'r')
fH = None
tau = None
DMAmp = None
DMgam = None
for line in file.readlines():
# default parameters for different models other than pure PL
key = line.split()[0]
# get amplitude
if "Amp" == key:
Amp = float(line.split()[-1])
# get spectral index
elif "gam" == key:
gam = float(line.split()[-1])
# get efac
elif "efac" == key:
efac = float(line.split()[-1])
# get quad
elif "equad" == key:
equad = float(line.split()[-1])
# get high frequency cutoff if available
elif "fH" == key:
fH = float(line.split()[-1])
# get correlation time scale if available
elif "tau" == key:
tau = float(line.split()[-1])
# get DM Amplitude if available
elif "DMAmp" == key:
DMAmp = float(line.split()[-1])
# get DM Spectral Index if available
elif "DMgam" == key:
DMgam = float(line.split()[-1])
# cosstruct red and white noise covariance matrices
red = PALutils.createRedNoiseCovarianceMatrix(tm, Amp, gam, fH=fH)
white = PALutils.createWhiteNoiseCovarianceMatrix(errs, efac, equad, tau=tau)
# construct post timing model marginalization covariance matrix
cov = red + white
pcov = np.dot(G.T, np.dot(cov, G))
# finally construct "inverse"
invCov = np.dot(G, np.dot(np.linalg.inv(pcov), G.T))
# create dataset for inverse covariance matrix
pulsarsgroup.create_dataset('invCov', data = invCov)
# create dataset for G matrix
pulsarsgroup.create_dataset('Gmatrix', data = G)
# record noise parameter values
pulsarsgroup.create_dataset('Amp', data = Amp)
pulsarsgroup.create_dataset('gam', data = gam)
pulsarsgroup.create_dataset('efac', data = efac)
pulsarsgroup.create_dataset('equad', data = equad)
if fH is not None:
pulsarsgroup.create_dataset('fH', data = fH)
if tau is not None:
pulsarsgroup.create_dataset('tau', data = tau)
if DMAmp is not None:
pulsarsgroup.create_dataset('DMAmp', data = DMAmp)
if DMgam is not None:
pulsarsgroup.create_dataset('DMgam', data = DMgam)
# Close the hdf5 file
self.h5file.close()
def __init__(self,pulsargroup, addNoise=False, addGmatrix=True):
# loop though keys in pulsargroup and fill in psr attributes that are needed for GW analysis
self.dist = None
self.distErr = None
self.fH = None
for key in pulsargroup:
# look for TOAs
if key == "TOAs":
self.toas = pulsargroup[key].value
# residuals
elif key == "residuals":
self.res = pulsargroup[key].value
# toa error bars
elif key == "toaErr":
self.err = pulsargroup[key].value
# frequencies in Hz
elif key == "freqs":
self.freqs = pulsargroup[key].value
# design matrix
elif key == "designmatrix":
self.dmatrix = pulsargroup[key].value
self.ntoa, self.nfit = self.dmatrix.shape
if addGmatrix:
self.G = PALutils.createGmatrix(self.dmatrix)
# design matrix
elif key == "pname":
self.name = pulsargroup[key].value
# pulsar distance in kpc
elif key == "dist":
self.dist = pulsargroup[key].value
# pulsar distance uncertainty in kpc
elif key == "distErr":
self.distErr = pulsargroup[key].value
# right ascension and declination
elif key == 'tmp_name':
par_names = list(pulsargroup[key].value)
for ct,name in enumerate(par_names):
# right ascension and phi
if name == "RAJ":
self.ra = pulsargroup["tmp_valpost"].value[ct]
self.phi = self.ra
# right ascension
if name == "DECJ":
self.dec = pulsargroup["tmp_valpost"].value[ct]
self.theta = np.pi/2 - self.dec
# inverse covariance matrix
elif key == "invCov":
if addNoise:
self.invCov = pulsargroup[key].value
## noise parameters ##
elif key == "Amp":
self.Amp = pulsargroup[key].value
# red noise spectral
elif key == "gam":
self.gam = pulsargroup[key].value
# efac
elif key == "efac":
self.efac = pulsargroup[key].value
# equad
elif key == "equad":
self.equad = pulsargroup[key].value
# fH
elif key == "fH":
self.fH = pulsargroup[key].value
# pulsar distance uncertainty in kpc
elif key == "distErr":
self.distErr = pulsargroup[key].value
if self.dist is None:
print 'WARNING: No distance info, using d = 1 kpc'
self.dist = 1.0
if self.distErr is None:
print 'WARNING: No distance error info, using sigma_d = 0.1 kpc'
self.distErr = 0.1
def __init__(self, pulsargroup, addNoise=False, addGmatrix=True):
# loop though keys in pulsargroup and fill in psr attributes that are needed for GW analysis
self.dist = None
self.distErr = None
self.fH = None
for key in pulsargroup:
# look for TOAs
if key == "TOAs":
self.toas = pulsargroup[key].value
# residuals
elif key == "residuals":
self.res = pulsargroup[key].value
# toa error bars
elif key == "toaErr":
self.err = pulsargroup[key].value
# frequencies in Hz
elif key == "freqs":
self.freqs = pulsargroup[key].value
# design matrix
elif key == "designmatrix":
self.dmatrix = pulsargroup[key].value
self.ntoa, self.nfit = self.dmatrix.shape
if addGmatrix:
self.G = PALutils.createGmatrix(self.dmatrix)
# design matrix
elif key == "pname":
self.name = pulsargroup[key].value
# pulsar distance in kpc
elif key == "dist":
self.dist = pulsargroup[key].value
# pulsar distance uncertainty in kpc
elif key == "distErr":
self.distErr = pulsargroup[key].value
# right ascension and declination
elif key == 'tmp_name':
par_names = list(pulsargroup[key].value)
for ct, name in enumerate(par_names):
# right ascension and phi
if name == "RAJ":
self.ra = pulsargroup["tmp_valpost"].value[ct]
self.phi = self.ra
# right ascension
if name == "DECJ":
self.dec = pulsargroup["tmp_valpost"].value[ct]
self.theta = np.pi / 2 - self.dec
# inverse covariance matrix
elif key == "invCov":
if addNoise:
self.invCov = pulsargroup[key].value
## noise parameters ##
elif key == "Amp":
self.Amp = pulsargroup[key].value
# red noise spectral
elif key == "gam":
self.gam = pulsargroup[key].value
# efac
elif key == "efac":
self.efac = pulsargroup[key].value
# equad
elif key == "equad":
self.equad = pulsargroup[key].value
elif key == "cequad":
self.cequad = pulsargroup[key].value
# fH
elif key == "fH":
self.fH = pulsargroup[key].value
# pulsar distance uncertainty in kpc
elif key == "distErr":
self.distErr = pulsargroup[key].value
if self.dist is None:
print 'WARNING: No distance info, using d = 1 kpc'
self.dist = 1.0
if self.distErr is None:
print 'WARNING: No distance error info, using sigma_d = 0.1 kpc'
self.distErr = 0.1
def addInverseCovFromNoiseFile(self,
parfile,
timfile,
noisefile,
DMOFF=None,
DMXOFF=None,
dailyAverage=False):
"""
Add noise covariance matrix after timing model subtraction.
"""
# Check whether the two files exist
if not os.path.isfile(parfile) or not os.path.isfile(timfile):
raise IOError, "Cannot find parfile (%s) or timfile (%s)!" % (
parfile, timfile)
assert (self.filename != None), "ERROR: HDF5 file not set!"
# 'a' means: read/write if exists, create otherwise
self.h5file = h5.File(self.filename, 'a')
# Create the data subgroup if it does not exist
if "Data" in self.h5file:
datagroup = self.h5file["Data"]
else:
raise IOError, "Cannot add noise parameters if Data group does not exist!"
# Load pulsar data from the JPL Cython tempo2 library
t2pulsar = t2.tempopulsar(parfile, timfile)
# turn off DMMODEL fitting
if DMOFF is not None:
t2pulsar['DMMODEL'].fit = False
# turn off DMX fitting
if DMXOFF is not None:
DMXFlag = False
print 'Turning off DMX fitting and turning DM fitting on'
for par in t2pulsar.pars:
if 'DMX' in par:
t2pulsar[par].fit = False
t2pulsar['DM'].fit = True
DMXFlag = True
if DMXFlag == False:
print 'NO DMX for pulsar {0}'.format(t2pulsar.name)
# refit 5 times to make sure we are converged
t2pulsar.fit(iters=5)
# Create the pulsar subgroup if it does not exist
if "Pulsars" in datagroup:
pulsarsgroup = datagroup["Pulsars"]
else:
raise IOError, "Cannot add noise parameters if pulsar group does not exist!"
# Look up the name of the pulsar, and see if it exist
if t2pulsar.name in pulsarsgroup:
pass
else:
raise IOError, "%s must already exists in %s to add noise parameters!"\
% (t2pulsar.name, self.filename)
pulsarsgroup = pulsarsgroup[t2pulsar.name]
# first create G matrix from design matrix and toas
designmatrix = np.double(t2pulsar.designmatrix())
toas = np.double(t2pulsar.toas() * 86400)
errs = np.double(t2pulsar.toaerrs * 1e-6)
# if doing daily averaging
if dailyAverage:
# get average quantities
toas, qmatrix, errs, dmatrix, freqs, bands = PALutils.dailyAverage(
t2pulsar)
# construct new daily averaged residuals and designmatrix
toas *= 86400
designmatrix = np.dot(qmatrix, dmatrix)
G = PALutils.createGmatrix(designmatrix)
# create matrix of time lags
tm = PALutils.createTimeLags(toas, toas, round=True)
# now read noise file to get model and parameters
file = open(noisefile, 'r')
fH = None
tau = None
DMAmp = None
DMgam = None
for line in file.readlines():
# default parameters for different models other than pure PL
key = line.split()[0]
# get amplitude
if "Amp" == key:
Amp = float(line.split()[-1])
# get spectral index
elif "gam" == key:
gam = float(line.split()[-1])
# get efac
elif "efac" == key:
efac = float(line.split()[-1])
# get quad
elif "equad" == key:
equad = float(line.split()[-1])
# get high frequency cutoff if available
elif "fH" == key:
fH = float(line.split()[-1])
# get correlation time scale if available
elif "tau" == key:
tau = float(line.split()[-1])
# get DM Amplitude if available
elif "DMAmp" == key:
DMAmp = float(line.split()[-1])
# get DM Spectral Index if available
elif "DMgam" == key:
DMgam = float(line.split()[-1])
# cosstruct red and white noise covariance matrices
red = PALutils.createRedNoiseCovarianceMatrix(tm, Amp, gam, fH=fH)
white = PALutils.createWhiteNoiseCovarianceMatrix(errs,
efac,
equad,
tau=tau)
# construct post timing model marginalization covariance matrix
cov = red + white
pcov = np.dot(G.T, np.dot(cov, G))
# finally construct "inverse"
invCov = np.dot(G, np.dot(np.linalg.inv(pcov), G.T))
# create dataset for inverse covariance matrix
pulsarsgroup.create_dataset('invCov', data=invCov)
# create dataset for G matrix
pulsarsgroup.create_dataset('Gmatrix', data=G)
# record noise parameter values
pulsarsgroup.create_dataset('Amp', data=Amp)
pulsarsgroup.create_dataset('gam', data=gam)
pulsarsgroup.create_dataset('efac', data=efac)
pulsarsgroup.create_dataset('equad', data=equad)
if fH is not None:
pulsarsgroup.create_dataset('fH', data=fH)
if tau is not None:
pulsarsgroup.create_dataset('tau', data=tau)
if DMAmp is not None:
pulsarsgroup.create_dataset('DMAmp', data=DMAmp)
if DMgam is not None:
pulsarsgroup.create_dataset('DMgam', data=DMgam)
# Close the hdf5 file
self.h5file.close()
# make sure all pulsar have same reference time
tt=[]
for p in psr:
tt.append(np.min(p.toas))
# find reference time
tref = np.min(tt)
# now scale pulsar time
for p in psr:
p.toas -= tref
# get G matrices
for p in psr:
p.G = PALutils.createGmatrix(p.dmatrix)
# run Fp statistic to determine starting frequency
if args.freq is None:
print 'Running initial Fpstat search'
fsearch = np.logspace(-9, -7, 1000)
fpstat = np.zeros(len(fsearch))
for ii in range(len(fsearch)):
fpstat[ii] = PALLikelihoods.fpStat(psr, fsearch[ii])
# determine maximum likelihood frequency
fmaxlike = fsearch[np.argmax(fpstat)]
print 'Maximum likelihood from f-stat search = {0}\n'.format(fmaxlike)
# get determinant of covariance matrix for use in likelihood
logdetTerm = []
# initialize fourier design matrix
if args.nmodes != 0:
F, f = PALutils.createfourierdesignmatrix(psr.toas, args.nmodes, freq=True)
Tspan = psr.toas.max() - psr.toas.min()
##fsred = np.array([1.599558028614668e-07, 5.116818355403073e-08]) # 1855
#fsred = np.array([9.549925860214369e-08]) # 1909
#fsred = np.array([1/Tspan, 9.772372209558111e-08]) # 1909
#
#F = np.zeros((psr.ntoa, 2*len(fsred)))
#F[:,0::2] = np.cos(2*np.pi*np.outer(psr.toas, fsred))
#F[:,1::2] = np.sin(2*np.pi*np.outer(psr.toas, fsred))
# get G matrices
psr.G = PALutils.createGmatrix(psr.dmatrix)
# pre compute diagonalized efac + equad white noise model
efac = np.dot(psr.G.T, np.dot(np.diag(psr.err**2), psr.G))
equad = np.dot(psr.G.T, psr.G)
L = np.linalg.cholesky(equad)
Linv = np.linalg.inv(L)
sand = np.dot(Linv, np.dot(efac, Linv.T))
u, s, v = np.linalg.svd(sand)
proj = np.dot(u.T, np.dot(Linv, psr.G.T))
# project residuals onto new basis
psr.res = np.dot(proj, psr.res)
if args.nmodes != 0 and args.single == False:
print 'Projecting F matrix'
# import hdf5 file
pfile = h5.File(args.h5file, 'r')
# define the pulsargroup
pulsargroup = pfile['Data']['Pulsars'][args.pname]
# fill in pulsar class
psr = PALpulsarInit.pulsar(pulsargroup, addGmatrix=True)
# initialize fourier design matrix
if args.nmodes != 0:
F, f = PALutils.createfourierdesignmatrix(psr.toas, args.nmodes, freq=True)
# get G matrices
psr.G = PALutils.createGmatrix(psr.dmatrix)
# pre compute diagonalized efac + equad white noise model
efac = np.dot(psr.G.T, np.dot(np.diag(psr.err**2), psr.G))
equad = np.dot(psr.G.T, psr.G)
L = np.linalg.cholesky(equad)
Linv = np.linalg.inv(L)
sand = np.dot(Linv, np.dot(efac, Linv.T))
u,s,v = np.linalg.svd(sand)
proj = np.dot(u.T, np.dot(Linv, psr.G.T))
# project residuals onto new basis
psr.res = np.dot(proj, psr.res)
if args.nmodes != 0:
F = np.dot(proj, F)
