def prior(cube, ndim, nparams):
# Prior on "txi" is uniform between -1 and 1. txi is a transform of xi such that xi = abs(txi) so thing is symmetric around 0.
cube[0] = utils.transform_uniform(cube[0], 0., 1.)
# Prior on nightside temperature is uniform from 0 to 3000:
cube[1] = utils.transform_uniform(cube[1], 0., 3000.)
# Prior on delta temperature goes from 0 to 3000 as well:
cube[2] = utils.transform_uniform(cube[1], 0., 3000.)
def prior(cube):
# Prior on "median flux" is uniform:
cube[0] = utils.transform_uniform(cube[0], -2., 2.)
# Pior on the log-jitter term (note this is the log VARIANCE, not sigma); from 0.01 to 100 ppm:
cube[1] = utils.transform_uniform(cube[1], np.log((0.01e-3)**2),
np.log((100e-3)**2))
pcounter = 2
# Prior on coefficients of comparison stars:
if compfilename is not None:
for key in nights:
for i in range(nights[key][3].shape[0]):
cube[pcounter] = utils.transform_uniform(
cube[pcounter], -10, 10)
pcounter += 1
# Prior on kernel maximum variance; from 0.01 to 100 mmag:
cube[pcounter] = utils.transform_loguniform(cube[pcounter],
(0.01 * 1e-3)**2,
(100 * 1e-3)**2)
pcounter = pcounter + 1
# Now priors on the alphas = 1/lambdas; gamma(1,1) = exponential, same as Gibson+:
for key in nights:
for i in range(nights[key][2].shape[0]):
cube[pcounter] = utils.transform_exponential(cube[pcounter])
pcounter += 1
return cube
def prior(cube, ndim, nparams):
# Prior on la0
cube[0] = utils.transform_uniform(cube[0], -np.pi, np.pi)
#cube[0] = utils.transform_uniform(cube[0],-180.,180.)
# Prior on lo0:
#cube[1] = utils.transform_uniform(cube[1],-90.,90.)
cube[1] = utils.transform_uniform(cube[1], -np.pi / 2., np.pi / 2.)
# Log10 of spherical harmonic coefficient:
cube[2] = utils.transform_uniform(cube[2], -6, -2.)
cube[3] = utils.transform_uniform(cube[3], -6, -2.)
def prior(cube, ndim, nparams):
# Prior on "median flux" is uniform:
cube[0] = utils.transform_uniform(cube[0],-2.,2.)
# Pior on the log-jitter term (note this is the log VARIANCE, not sigma); from 1 to 10,000 ppm:
cube[1] = utils.transform_uniform(cube[1],np.log((1e-6)**2),np.log((10000e-6)**2))
# Prior on the planet-to-star radius ratio:
cube[2] = utils.transform_truncated_normal(cube[2],pmean,psd)
# (Transformed) limb-darkening coefficients:
cube[3] = utils.transform_uniform(cube[3],0.,1.)
pcounter = 4
if ld_law != 'linear':
cube[pcounter] = utils.transform_uniform(cube[pcounter],0.,1.)
pcounter = pcounter + 1
# Prior on kernel maximum variance; from 1 to 10,000 ppm:
cube[pcounter] = utils.transform_loguniform(cube[pcounter],(1*1e-6)**2,(10000*1e-6)**2)
pcounter = pcounter + 1
# Now priors on the alphas = 1/lambdas; gamma(1,1) = exponential, same as Gibson+:
for i in range(X.shape[0]):
cube[pcounter] = utils.transform_exponential(cube[pcounter])
pcounter = pcounter + 1
def prior(cube, ndim, nparams):
# Prior on "median flux" is uniform:
cube[0] = utils.transform_uniform(cube[0],0.5,1.5)
# Prior on log-"amplitude" is uniform:
cube[1] = utils.transform_uniform(cube[1],-30,30.)
# Prior on log-"timescale" is also uniform:
cube[2] = utils.transform_uniform(cube[2],-30,30)
# Prior on log-period:
cube[3] = utils.transform_uniform(cube[3],min_timescale,max_timescale)
# Prior on the log-factor:
cube[4] = utils.transform_uniform(cube[4],-30.,30.)
# Pior on the log-jitter term:
cube[5] = utils.transform_uniform(cube[5],-100.,30.)
def prior(cube, ndim, nparams):
# Prior on GP lamplitude:
cube[0] = utils.transform_uniform(cube[0], -10, 10)
# Prior on GP llengthscale:
cube[1] = utils.transform_uniform(cube[1], -10, 10)
def prior(cube):
# Prior on "median flux" is uniform:
cube[0] = utils.transform_uniform(cube[0], -2., 2.)
# Pior on the log-jitter term (note this is the log VARIANCE, not sigma); from 0.01 to 100 ppm:
cube[1] = utils.transform_uniform(cube[1], np.log((0.01e-3)**2),
np.log((100e-3)**2))
# Prior on t0:
if t0mean is None:
cube[2] = utils.transform_uniform(cube[2], np.min(t), np.max(t))
else:
cube[2] = utils.transform_normal(cube[2], t0mean, t0sd)
# Prior on Period:
if Pmean is None:
cube[3] = utils.transform_loguniform(cube[3], 0.1, 1000.)
else:
cube[3] = utils.transform_normal(cube[3], Pmean, Psd)
# Prior on planet-to-star radius ratio:
if pmean is None:
cube[4] = utils.transform_uniform(cube[4], 0, 1)
else:
cube[4] = utils.transform_truncated_normal(cube[4], pmean, psd)
# Prior on a/Rs:
if amean is None:
cube[5] = utils.transform_uniform(cube[5], 0.1, 300.)
else:
cube[5] = utils.transform_normal(cube[5], amean, asd)
# Prior on impact parameter:
if bmean is None:
cube[6] = utils.transform_uniform(cube[6], 0, 2.)
else:
cube[6] = utils.transform_truncated_normal(cube[6],
bmean,
bsd,
a=0.,
b=2.)
# Prior either on the linear LD or the transformed first two-parameter law LD (q1):
cube[7] = utils.transform_uniform(cube[7], 0, 1.)
pcounter = 8
# (Transformed) limb-darkening coefficient for two-parameter laws (q2):
if ld_law != 'linear':
cube[pcounter] = utils.transform_uniform(cube[pcounter], 0, 1.)
pcounter += 1
if not circular:
if eccmean is None:
cube[pcounter] = utils.transform_uniform(cube[pcounter], 0, 1.)
else:
cube[pcounter] = utils.transform_truncated_normal(cube[pcounter],
eccmean,
eccsd,
a=0.,
b=1.)
pcounter += 1
if omegamean is None:
cube[pcounter] = utils.transform_uniform(cube[pcounter], 0, 360.)
else:
cube[pcounter] = utils.transform_truncated_normal(cube[pcounter],
omegamean,
omegasd,
a=0.,
b=360.)
pcounter += 1
# Prior on coefficients of comparison stars:
if compfilename is not None:
for key in nights:
for i in range(nights[key][3].shape[0]):
cube[pcounter] = utils.transform_uniform(
cube[pcounter], -10, 10)
pcounter += 1
# Prior on kernel maximum variance; from 0.01 to 100 mmag:
cube[pcounter] = utils.transform_loguniform(cube[pcounter],
(0.01 * 1e-3)**2,
(100 * 1e-3)**2)
pcounter = pcounter + 1
# Now priors on the alphas = 1/lambdas; gamma(1,1) = exponential, same as Gibson+:
for key in nights:
for i in range(nights[key][2].shape[0]):
cube[pcounter] = utils.transform_exponential(cube[pcounter])
pcounter += 1
return cube
def prior(cube, ndim, nparams):
# Prior on Temperature of spot:
cube[0] = utils.transform_uniform(cube[0], 4000, 6500)
# Prior on temperature of WASP-19:
cube[1] = utils.transform_normal(cube[1], 5460, 90)
