def myloglike(cube, ndim, nparams):
efac = cube[0]
equad = 10**cube[1]
gam = cube[2]
A = 10**cube[3]
fs = np.zeros(args.ss)
for ii in range(args.ss):
fs[ii] = 10**cube[4 + ii]
rho2 = np.zeros(args.ss)
for ii in range(args.ss):
rho2[ii] = cube[ii + 4 + args.ss]
# check to make sure frequencies are ordered
ordered = np.all([fs[ii] < fs[ii + 1] for ii in range(args.ss - 1)])
if ordered:
F1 = list(
PALutils.createfourierdesignmatrix(psr.toas, args.nmodes).T)
tmp, f = PALutils.createfourierdesignmatrix(psr.toas,
args.nmodes,
freq=True)
for ii in range(args.ss):
F1.append(np.cos(2 * np.pi * fs[ii] * psr.toas))
F1.append(np.sin(2 * np.pi * fs[ii] * psr.toas))
F = np.array(F1).T
F = np.dot(proj, F)
# compute rho from A and gam# compute total time span of data
Tspan = psr.toas.max() - psr.toas.min()
# get power spectrum coefficients
f1yr = 1 / 3.16e7
rho = list(
np.log10(A**2 / 12 / np.pi**2 * f1yr**(gam - 3) * f**(-gam) /
Tspan))
# compute total rho
for ii in range(args.ss):
rho.append(rho2[ii])
loglike = PALLikelihoods.lentatiMarginalizedLike(
psr, F, s, np.array(rho), efac**2, equad)
else:
loglike = -np.inf
#print efac, rho, loglike
return loglike
def myloglike(cube, ndim, nparams):
efac = cube[0]
equad = 10**cube[1]
gam = cube[2]
A = 10**cube[3]
fs = np.zeros(args.ss)
for ii in range(args.ss):
fs[ii] = 10**cube[4+ii]
rho2 = np.zeros(args.ss)
for ii in range(args.ss):
rho2[ii] = cube[ii+4+args.ss]
# check to make sure frequencies are ordered
ordered = np.all([fs[ii] < fs[ii+1] for ii in range(args.ss-1)])
if ordered:
F1 = list(PALutils.createfourierdesignmatrix(psr.toas, args.nmodes).T)
tmp, f = PALutils.createfourierdesignmatrix(psr.toas, args.nmodes, freq=True)
for ii in range(args.ss):
F1.append(np.cos(2*np.pi*fs[ii]*psr.toas))
F1.append(np.sin(2*np.pi*fs[ii]*psr.toas))
F = np.array(F1).T
F = np.dot(proj, F)
# compute rho from A and gam# compute total time span of data
Tspan = psr.toas.max() - psr.toas.min()
# get power spectrum coefficients
f1yr = 1/3.16e7
rho = list(np.log10(A**2/12/np.pi**2 * f1yr**(gam-3) * f**(-gam)/Tspan))
# compute total rho
for ii in range(args.ss):
rho.append(rho2[ii])
loglike = PALLikelihoods.lentatiMarginalizedLike(psr, F, s, np.array(rho), efac**2, equad)
else:
loglike = -np.inf
#print efac, rho, loglike
return loglike
def myloglike(cube, ndim, nparams):
efac = cube[0]
equad = 10**cube[1]
fs = np.zeros(args.ss)
for ii in range(args.ss):
fs[ii] = 10**cube[2+ii]
rho = np.zeros(args.nmodes+args.ss)
for ii in range(args.nmodes+args.ss):
rho[ii] = cube[ii+2+args.ss]
F1 = list(PALutils.createfourierdesignmatrix(psr.toas, args.nmodes).T)
for ii in range(args.ss):
F1.append(np.cos(2*np.pi*fs[ii]*psr.toas))
F1.append(np.sin(2*np.pi*fs[ii]*psr.toas))
F = np.array(F1).T
F = np.dot(proj, F)
loglike = PALLikelihoods.lentatiMarginalizedLike(psr, F, s, rho, efac, equad)
#print efac, rho, loglike
return loglike
if args.null == False:
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 Tmax
Tmax = np.max([p.toas.max() - p.toas.min() for p in psr])
# initialize fourier design matrix
F = [PALutils.createfourierdesignmatrix(p.toas, args.nmodes, Tspan=Tmax) for p in psr]
f = []
for ct, p in enumerate(psr):
T = p.toas.max() - p.toas.min()
f.append(np.linspace(1 / T, args.nmodes / T, args.nmodes))
# pre compute diagonalized efac + equad white noise model
SS = []
proj = []
SS = []
print "Pre-Computing white noise covariances"
for ct, p in enumerate(psr):
efac = np.dot(p.G.T, np.dot(np.diag(p.err ** 2), p.G))
equad = np.dot(p.G.T, p.G)
L = np.linalg.cholesky(equad)
L = []
for ct, p in enumerate(psr):
Amp = p.Amp
gam = p.gam
efac = p.efac
equad = p.equad
try:
cequad = p.cequad
except AttributeError:
cequad = 0
avetoas, U = PALutils.exploderMatrix(p.toas)
Tspan = p.toas.max()-p.toas.min()
F, f = PALutils.createfourierdesignmatrix(p.toas, 10, freq=True, Tspan=Tspan)
f1yr = 1/3.16e7
rho = (Amp**2/12/np.pi**2 * f1yr**(gam-3) * f**(-gam)/Tspan)
tmp = np.zeros(20)
tmp[0::2] = rho
tmp[1::2] = rho
phi = np.diag(tmp)
white = PALutils.createWhiteNoiseCovarianceMatrix(p.err, efac**2, equad)
cequad_mat = cequad**2 * np.dot(U,U.T)
red = np.dot(F, np.dot(phi, F.T))
for p in psr:
print 'Pulsar {0} has {1} ns weighted rms'.format(
p.name,
p.rms() * 1e9)
npsr = len(psr)
pfile.close()
# get Tmax
Tmax = np.max([p.toas.max() - p.toas.min() for p in psr])
# initialize fourier design matrix
F = [
PALutils.createfourierdesignmatrix(p.toas, args.nmodes, Tspan=Tmax)
for p in psr
]
if args.powerlaw:
tmp, f = PALutils.createfourierdesignmatrix(p.toas,
args.nmodes,
Tspan=Tmax,
freq=True)
# get G matrices
for p in psr:
p.G = PALutils.createGmatrix(p.dmatrix)
# pre compute diagonalized efac + equad white noise model
Diag = []
if args.null == False:
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 Tmax
Tmax = np.max([p.toas.max() - p.toas.min() for p in psr])
# initialize fourier design matrix
F = [PALutils.createfourierdesignmatrix(p.toas, args.nmodes, Tspan=Tmax) for p in psr]
f = []
for ct, p in enumerate(psr):
T = p.toas.max() - p.toas.min()
f.append(np.linspace(1/T, args.nmodes/T, args.nmodes))
# pre compute diagonalized efac + equad white noise model
SS = []
proj = []
SS = []
print 'Pre-Computing white noise covariances'
for ct, p in enumerate(psr):
efac = np.dot(p.G.T, np.dot(np.diag(p.err**2), p.G))
equad = np.dot(p.G.T, p.G)
L = np.linalg.cholesky(equad)
L = []
for ct, p in enumerate(psr):
Amp = p.Amp
gam = p.gam
efac = p.efac
equad = p.equad
try:
cequad = p.cequad
except AttributeError:
cequad = 0
avetoas, U = PALutils.exploderMatrix(p.toas)
Tspan = p.toas.max() - p.toas.min()
F, f = PALutils.createfourierdesignmatrix(p.toas,
10,
freq=True,
Tspan=Tspan)
f1yr = 1 / 3.16e7
rho = (Amp**2 / 12 / np.pi**2 * f1yr**(gam - 3) * f**(-gam) / Tspan)
tmp = np.zeros(20)
tmp[0::2] = rho
tmp[1::2] = rho
phi = np.diag(tmp)
white = PALutils.createWhiteNoiseCovarianceMatrix(p.err, efac**2, equad)
cequad_mat = cequad**2 * np.dot(U, U.T)
pass
print 'Reading in HDF5 file'
# 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)
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))
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 Tmax
Tmax = np.max([p.toas.max() - p.toas.min() for p in psr])
# initialize fourier design matrix
F = [PALutils.createfourierdesignmatrix(p.toas, args.nmodes, Tspan=Tmax) for p in psr]
if args.powerlaw:
tmp, f = PALutils.createfourierdesignmatrix(p.toas, args.nmodes, Tspan=Tmax, freq=True)
# 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])
pass
print 'Reading in HDF5 file'
# 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)
