def __init__(self, spectrum, T_eff, logg, z):
'''
Object to generate realistic limb darkening parameters
Parameters
----------
spectrum : TransitCurveGen.Spectrum
Spectrum we want the LD coeffs for
T_eff : tuple
The effective temperature of the host star, given as
(value, uncertainty) pair
logg : tuple
The log_10 of the surface gravity of the host star, with gravity
measured in cm/s2. Should be given as a (value, uncertainty) pair.
z : tuple
The metalicity of the host, given as a (value, uncertainty) pair.
'''
# Make the filters
log_wl = np.log10(spectrum.wavelengths)
delta = log_wl[1] - log_wl[0]
edges = [log_wl[0] - delta] + [wl + delta for wl in log_wl]
edges = np.array(edges)
self.filters = [
BoxcarFilter('{}'.format(i), edges[i], edges[i + 1])
for i in range(len(edges) - 1)
]
print(len(self.filters), len(spectrum.wavelengths))
sc = LDPSetCreator(teff=T_eff, logg=logg, z=z, filters=self.filters)
self.ld_profiles = sc.create_profiles()
class LDTkLDModel(LDModel):
def __init__(self,
pbs: Tuple,
teff: Tuple[float, float],
logg: Tuple[float, float],
z: Tuple[float, float],
samples: int = 500,
frozen: bool = False,
cache: Optional[Union[str, Path]] = None,
dataset: str = 'vis-lowres'):
super().__init__()
self._sc = LDPSetCreator(teff,
logg,
z,
pbs,
cache=cache,
dataset=dataset)
self._ps = self._sc.create_profiles(samples)
self._i = 0
self.npb = len(pbs)
self.nsamples = samples
self.frozen = frozen
self.z = self._ps._z
self.mu = self._ps._mu
self.profiles = self._ps._ldps
self.mean_profiles = self._ps.profile_averages
def thaw(self):
self.frozen = False
def freeze(self):
self.frozen = True
def __call__(self,
mu: ndarray,
x: Optional[ndarray] = None) -> Tuple[ndarray, ndarray]:
npv = 1 if x is None else x.shape[0]
self._i = i = randint(0, self.nsamples)
if self.frozen:
return eval_ldm_frozen(mu, self.mu, self.z, self.mean_profiles,
npv)
else:
return eval_ldm(mu, self.mu, self.z, self.profiles, npv,
self.nsamples)
def _evaluate(self, mu: ndarray, x: ndarray) -> ndarray:
raise NotImplementedError
def _integrate(self, x: ndarray) -> float:
raise NotImplementedError
def ldtk_ldc(lambda_min, lambda_max, Teff, Teff_unc, logg, logg_unc, z, z_unc):
"""
Function to estimate quadratic limb darkening coefficients for a given star
Parameters:
----------
lambda_min: Start wavelength of the bandpass filter.
lambda_max: End wavelength of the bandpass filter.
Teff: Effective temperature of the star.
Teff_unc: Uncertainty in Teff.
logg: Surface gravity of the star.
logg_unc: Uncertainty in logg.
z: Metallicity of the star.
z_unc: Uncertainty in z.
Returns
-------
cq, eq : Each an array giving the 2 quadractic limb darkening parameters and the errors associated with them
"""
from ldtk import LDPSetCreator, BoxcarFilter
# Define your passbands. Boxcar filters useful in transmission spectroscopy
filters = [BoxcarFilter('a', lambda_min, lambda_max)]
sc = LDPSetCreator(
teff=(Teff, Teff_unc), # Define your star, and the code
logg=(logg, logg_unc), # downloads the uncached stellar
z=(z, z_unc), # spectra from the Husser et al.
filters=filters) # FTP server automatically.
ps = sc.create_profiles() # Create the limb darkening profiles
cq, eq = ps.coeffs_qd(do_mc=True) # Estimate quadratic law coefficients
#lnlike = ps.lnlike_qd([[0.45,0.15], # Calculate the quadratic law log
# [0.35,0.10], # likelihood for a set of coefficients
# [0.25,0.05]]) # (returns the joint likelihood)
#lnlike = ps.lnlike_qd([0.25,0.05],flt=0) # Quad. law log L for the first filter
return cq, eq
class BaseLPF(LogPosteriorFunction):
_lpf_name = 'BaseLPF'
def __init__(self, name: str, passbands: list, times: list = None, fluxes: Iterable = None, errors: list = None,
pbids: list = None, covariates: list = None, wnids: list = None, tm: TransitModel = None,
nsamples: tuple = 1, exptimes: tuple = 0., init_data=True, result_dir: Path = None, tref: float = 0.0,
lnlikelihood: str = 'wn'):
"""The base Log Posterior Function class.
The `BaseLPF` class creates the basis for transit light curve analyses using `PyTransit`. This class can be
used in a basic analysis directly, or it can be inherited to create a basis for a more complex analysis.
Parameters
----------
name: str
Name of the log posterior function instance.
passbands: Iterable
List of unique passband names (filters) that the light curves have been observed in.
times: Iterable
List of 1d ndarrays each containing the mid-observation times for a single light curve.
fluxes: Iterable
List of 1d ndarrays each containing the normalized fluxes for a single light curve.
errors: Iterable
List of 1d ndarrays each containing the flux measurement uncertainties for a single light curvel.
pbids: Iterable of ints
List of passband indices mapping each light curve to a single passband.
covariates: Iterable
List of covariates one 2d narray per light curve.
wnids: Iterable of ints
List of noise set indices mapping each light curve to a single noise set.
tm: TransitModel
Transitmodel to use instead of the default model.
nsamples: list[int]
List of supersampling factors. The values should be integers and given one per light curve.
exptimes: list[float]
List of exposure times. The values should be floats with the time given in days.
init_data: bool
Set to `False` to allow the LPF to be initialized without data. This is mainly for debugging.
result_dir: Path, optional
Default saving directory
tref: float, optional
Reference time
"""
self._pre_initialisation()
super().__init__(name=name, result_dir=result_dir)
self.tm = tm or QuadraticModel(klims=(0.01, 0.75), nk=512, nz=512)
self._tref = tref
# Passbands
# ---------
# Passbands should be arranged from blue to red
if isinstance(passbands, (list, tuple, ndarray)):
self.passbands = passbands
else:
self.passbands = [passbands]
self.npb = npb = len(self.passbands)
self.nsamples = None
self.exptimes = None
self.lnlikelihood_type = lnlikelihood.lower()
# Declare high-level objects
# --------------------------
self._lnlikelihood_models = []
self._baseline_models = []
self.ps = None # Parametrisation
self.de = None # Differential evolution optimiser
self.sampler = None # MCMC sampler
self.instrument = None # Instrument
self.ldsc = None # Limb darkening set creator
self.ldps = None # Limb darkening profile set
self.cntm = None # Contamination model
# Declare data arrays and variables
# ---------------------------------
self.nlc: int = 0 # Number of light curves
self.n_noise_blocks: int = 0 # Number of noise blocks
self.noise_ids = None
self.times: list = None # List of time arrays
self.fluxes: list = None # List of flux arrays
self.errors: list = None # List of flux uncertainties
self.covariates: list = None # List of covariates
self.wn: ndarray = None # Array of white noise estimates for each light curve
self.timea: ndarray = None # Array of concatenated times
self.mfluxa: ndarray = None # Array of concatenated model fluxes
self.ofluxa: ndarray = None # Array of concatenated observed fluxes
self.errora: ndarray = None # Array of concatenated model fluxes
self.lcids: ndarray = None # Array of light curve indices for each datapoint
self.pbids: ndarray = None # Array of passband indices for each light curve
self.lcslices: list = None # List of light curve slices
if init_data:
# Set up the observation data
# ---------------------------
self._init_data(times = times, fluxes = fluxes, pbids = pbids, covariates = covariates,
errors = errors, wnids = wnids, nsamples = nsamples, exptimes = exptimes)
# Set up the parametrisation
# --------------------------
self._init_parameters()
# Inititalise the instrument
# --------------------------
self._init_instrument()
self._init_lnlikelihood()
self._init_baseline()
self._post_initialisation()
def _init_data(self, times: Union[List, ndarray], fluxes: Union[List, ndarray], pbids: Union[List, ndarray] = None,
covariates: Union[List, ndarray] = None, errors: Union[List, ndarray] = None, wnids: Union[List, ndarray] = None,
nsamples: Union[int, ndarray, Iterable] = 1, exptimes: Union[float, ndarray, Iterable] = 0.):
if isinstance(times, ndarray) and times.ndim == 1 and times.dtype == float:
times = [times]
if isinstance(fluxes, ndarray) and fluxes.ndim == 1 and fluxes.dtype == float:
fluxes = [fluxes]
if pbids is None:
if self.pbids is None:
self.pbids = zeros(len(fluxes), int)
else:
self.pbids = atleast_1d(pbids).astype('int')
self.nlc = len(times)
self.times = times
self.fluxes = fluxes
self.wn = [nanstd(diff(f)) / sqrt(2) for f in fluxes]
self.timea = concatenate(self.times)
self.ofluxa = concatenate(self.fluxes)
self.mfluxa = zeros_like(self.ofluxa)
self.lcids = concatenate([full(t.size, i) for i, t in enumerate(self.times)])
# TODO: Noise IDs get scrambled when removing transits, fix!!!
if wnids is None:
if self.noise_ids is None:
self.noise_ids = zeros(self.nlc, int)
self.n_noise_blocks = 1
else:
self.noise_ids = asarray(wnids)
self.n_noise_blocks = len(unique(self.noise_ids))
assert self.noise_ids.size == self.nlc, "Need one noise block id per light curve."
assert self.noise_ids.max() == self.n_noise_blocks - 1, "Error initialising noise block ids."
if isscalar(nsamples):
self.nsamples = full(self.nlc, nsamples)
self.exptimes = full(self.nlc, exptimes)
else:
assert (len(nsamples) == self.nlc) and (len(exptimes) == self.nlc)
self.nsamples = asarray(nsamples, 'int')
self.exptimes = asarray(exptimes)
self.tm.set_data(self.timea-self._tref, self.lcids, self.pbids, self.nsamples, self.exptimes)
if errors is None:
self.errors = [full(t.size, nan) for t in self.times]
else:
self.errors = errors
self.errora = concatenate(self.errors)
# Initialise the light curves slices
# ----------------------------------
self.lcslices = []
sstart = 0
for i in range(self.nlc):
s = self.times[i].size
self.lcslices.append(s_[sstart:sstart + s])
sstart += s
# Initialise the covariate arrays, if given
# -----------------------------------------
if covariates is not None:
self.covariates = covariates
for cv in self.covariates:
cv = (cv - cv.mean(0)) / cv.std(0)
#self.ncovs = self.covariates[0].shape[1]
#self.covsize = array([c.size for c in self.covariates])
#self.covstart = concatenate([[0], self.covsize.cumsum()[:-1]])
#self.cova = concatenate(self.covariates)
def _add_lnlikelihood_model(self, lnl):
self._lnlikelihood_models.append(lnl)
def _add_baseline_model(self, blm):
self._baseline_models.append(blm)
def _init_parameters(self):
self.ps = ParameterSet()
self._init_p_orbit()
self._init_p_planet()
self._init_p_limb_darkening()
self._init_p_baseline()
self.ps.freeze()
def _init_p_orbit(self):
"""Orbit parameter initialisation.
"""
porbit = [
GParameter('tc', 'zero_epoch', 'd', N(0.0, 0.1), (-inf, inf)),
GParameter('p', 'period', 'd', N(1.0, 1e-5), (0, inf)),
GParameter('rho', 'stellar_density', 'g/cm^3', U(0.1, 25.0), (0, inf)),
GParameter('b', 'impact_parameter', 'R_s', U(0.0, 1.0), (0, 1))]
self.ps.add_global_block('orbit', porbit)
def _init_p_planet(self):
"""Planet parameter initialisation.
"""
pk2 = [PParameter('k2', 'area_ratio', 'A_s', U(0.05**2, 0.2**2), (0, inf))]
self.ps.add_passband_block('k2', 1, 1, pk2)
self._pid_k2 = repeat(self.ps.blocks[-1].start, self.npb)
self._start_k2 = self.ps.blocks[-1].start
self._sl_k2 = self.ps.blocks[-1].slice
def _init_p_limb_darkening(self):
"""Limb darkening parameter initialisation.
"""
pld = concatenate([
[PParameter(f'q1_{pb}', 'q1 coefficient {pb}', '', U(0, 1), bounds=(0, 1)),
PParameter(f'q2_{pb}', 'q2 coefficient {pb}', '', U(0, 1), bounds=(0, 1))]
for i,pb in enumerate(self.passbands)])
self.ps.add_passband_block('ldc', 2, self.npb, pld)
self._sl_ld = self.ps.blocks[-1].slice
self._start_ld = self.ps.blocks[-1].start
def _init_p_baseline(self):
pass
def _init_p_noise(self):
pass
def _init_instrument(self):
pass
def _pre_initialisation(self):
pass
def _post_initialisation(self):
pass
def _init_lnlikelihood(self):
if self.lnlikelihood_type == 'wn':
self._add_lnlikelihood_model(WNLogLikelihood(self))
elif self.lnlikelihood_type == 'celerite':
self._add_lnlikelihood_model(CeleriteLogLikelihood(self))
else:
raise NotImplementedError
def _init_baseline(self):
pass
def create_pv_population(self, npop=50):
pvp = self.ps.sample_from_prior(npop)
# With LDTk
# ---------
#
# Use LDTk to create the sample if LDTk has been initialised.
#
if self.ldps:
istart = self._start_ld
cms, ces = self.ldps.coeffs_tq()
for i, (cm, ce) in enumerate(zip(cms.flat, ces.flat)):
pvp[:, i + istart] = normal(cm, ce, size=pvp.shape[0])
# No LDTk
# -------
#
# Ensure that the total limb darkening decreases towards
# red passbands.
#
else:
ldsl = self._sl_ld
for i in range(pvp.shape[0]):
pid = argsort(pvp[i, ldsl][::2])[::-1]
pvp[i, ldsl][::2] = pvp[i, ldsl][::2][pid]
pvp[i, ldsl][1::2] = pvp[i, ldsl][1::2][pid]
return pvp
def add_prior(self, prior):
self._additional_log_priors.append(prior)
def baseline(self, pv):
if self._baseline_models:
if pv.ndim == 1:
bl = ones_like(self.timea)
else:
bl = ones((pv.shape[0], self.timea.size))
for blm in self._baseline_models:
bl = blm(pv, bl)
return bl
else:
return 1.
def trends(self, pv):
"""Additive trends"""
return 0.
def transit_model(self, pv, copy=True):
pv = atleast_2d(pv)
ldc = map_ldc(pv[:,self._sl_ld])
zero_epoch = pv[:,0] - self._tref
period = pv[:,1]
smaxis = as_from_rhop(pv[:,2], period)
inclination = i_from_ba(pv[:,3], smaxis)
radius_ratio = sqrt(pv[:,4:5])
return self.tm.evaluate(radius_ratio, ldc, zero_epoch, period, smaxis, inclination)
def flux_model(self, pv):
baseline = self.baseline(pv)
trends = self.trends(pv)
model_flux = self.transit_model(pv)
return baseline * model_flux + trends
def residuals(self, pv):
return self.ofluxa - self.flux_model(pv)
def lnlikelihood(self, pvp):
"""Log likelihood for a 1D or 2D array of model parameters.
Parameters
----------
pvp: ndarray
Either a 1D parameter vector or a 2D parameter array.
Returns
-------
Log likelihood for the given parameter vector(s).
"""
fmodel = self.flux_model(pvp)
if pvp.ndim == 1:
lnl = 0.
else:
lnl = zeros(pvp.shape[0])
for lnlikelihood in self._lnlikelihood_models:
lnl += lnlikelihood(pvp, fmodel)
return lnl
def set_radius_ratio_prior(self, kmin, kmax):
"""Set a uniform prior on all radius ratios."""
for p in self.ps[self._sl_k2]:
p.prior = U(kmin ** 2, kmax ** 2)
def add_t14_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the transit duration.
Parameters
----------
mean: float
Mean of the normal distribution
std: float
Standard deviation of the normal distribution.
"""
def T14(pv):
pv = atleast_2d(pv)
a = as_from_rhop(pv[:, 2], pv[:, 1])
t14 = duration_eccentric(pv[:, 1], sqrt(pv[:, 4]), a, arccos(pv[:, 3] / a), 0, 0, 1)
return norm.logpdf(t14, mean, std)
self._additional_log_priors.append(T14)
def add_as_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the scaled semi-major axis :math:`(a / R_\star)`.
Parameters
----------
mean: float
Mean of the normal distribution.
std: float
Standard deviation of the normal distribution
"""
def as_prior(pv):
a = as_from_rhop(pv[2], pv[1])
return norm.logpdf(a, mean, std)
self._additional_log_priors.append(as_prior)
def add_ldtk_prior(self, teff: tuple, logg: tuple, z: tuple, passbands: tuple,
uncertainty_multiplier: float = 3, **kwargs) -> None:
"""Add a LDTk-based prior on the limb darkening.
Parameters
----------
teff
logg
z
passbands
uncertainty_multiplier
Returns
-------
"""
if 'pbs' in kwargs.keys():
raise DeprecationWarning("The 'pbs' argument has been renamed to 'passbands'")
if isinstance(passbands[0], str):
raise DeprecationWarning(
'Passing passbands by name has been deprecated, they should be now Filter instances.')
self.ldsc = LDPSetCreator(teff, logg, z, list(passbands))
self.ldps = self.ldsc.create_profiles(1000)
self.ldps.resample_linear_z()
self.ldps.set_uncertainty_multiplier(uncertainty_multiplier)
def ldprior(pv):
return self.ldps.lnlike_tq(pv[:, self._sl_ld].reshape([pv.shape[0], -1, 2]))
self._additional_log_priors.append(ldprior)
def remove_outliers(self, sigma=5):
fmodel = squeeze(self.flux_model(self.de.minimum_location))
covariates = [] if self.covariates is not None else None
times, fluxes, lcids, errors = [], [], [], []
for i in range(len(self.times)):
res = self.fluxes[i] - fmodel[self.lcslices[i]]
mask = ~sigma_clip(res, sigma=sigma).mask
times.append(self.times[i][mask])
fluxes.append(self.fluxes[i][mask])
if covariates is not None:
covariates.append(self.covariates[i][mask])
if self.errors is not None:
errors.append(self.errors[i][mask])
self._init_data(times=times, fluxes=fluxes, covariates=self.covariates, pbids=self.pbids,
errors=(errors if self.errors is not None else None), wnids=self.noise_ids,
nsamples=self.nsamples, exptimes=self.exptimes)
def remove_transits(self, tids):
m = ones(len(self.times), bool)
m[tids] = False
self._init_data(self.times[m], self.fluxes[m], self.pbids[m],
self.covariates[m] if self.covariates is not None else None,
self.errors[m], self.noise_ids[m], self.nsamples[m], self.exptimes[m])
self._init_parameters()
def posterior_samples(self, burn: int = 0, thin: int = 1, derived_parameters: bool = True):
df = super().posterior_samples(burn=burn, thin=thin)
if derived_parameters:
for k2c in df.columns[self._sl_k2]:
df[k2c.replace('k2', 'k')] = sqrt(df[k2c])
df['a'] = as_from_rhop(df.rho.values, df.p.values)
df['inc'] = i_from_baew(df.b.values, df.a.values, 0., 0.)
average_ks = sqrt(df.iloc[:, self._sl_k2]).mean(1).values
df['t14'] = d_from_pkaiews(df.p.values, average_ks, df.a.values, df.inc.values, 0., 0., 1, kind=14)
df['t23'] = d_from_pkaiews(df.p.values, average_ks, df.a.values, df.inc.values, 0., 0., 1, kind=23)
return df
def plot_light_curves(self, method='de', ncol: int = 3, width: float = 2., max_samples: int = 1000, figsize=None,
data_alpha=0.5, ylim=None):
nrow = int(ceil(self.nlc / ncol))
if method == 'mcmc':
df = self.posterior_samples(derived_parameters=False)
t0, p = df.tc.median(), df.p.median()
fmodel = self.flux_model(permutation(df.values)[:max_samples])
fmperc = percentile(fmodel, [50, 16, 84, 2.5, 97.5], 0)
else:
fmodel = squeeze(self.flux_model(self.de.minimum_location))
t0, p = self.de.minimum_location[0], self.de.minimum_location[1]
fmperc = None
fig, axs = subplots(nrow, ncol, figsize=figsize, constrained_layout=True, sharey='all', sharex='all',
squeeze=False)
for i in range(self.nlc):
ax = axs.flat[i]
e = epoch(self.times[i].mean(), t0, p)
tc = t0 + e * p
time = self.times[i] - tc
ax.plot(time, self.fluxes[i], '.', alpha=data_alpha)
if method == 'de':
ax.plot(time, fmodel[self.lcslices[i]], 'w', lw=4)
ax.plot(time, fmodel[self.lcslices[i]], 'k', lw=1)
else:
ax.fill_between(time, *fmperc[3:5, self.lcslices[i]], alpha=0.15)
ax.fill_between(time, *fmperc[1:3, self.lcslices[i]], alpha=0.25)
ax.plot(time, fmperc[0, self.lcslices[i]])
setp(ax, xlabel=f'Time - T$_c$ [d]', xlim=(-width / 2 / 24, width / 2 / 24))
setp(axs[:, 0], ylabel='Normalised flux')
if ylim is not None:
setp(axs, ylim=ylim)
for ax in axs.flat[self.nlc:]:
ax.remove()
return fig
def __repr__(self):
return f"Target: {self.name}\nLPF: {self._lpf_name}\n Passbands: {self.passbands}"
def __init__(self,
host_T,
host_logg,
host_z,
filters,
ld_model='quadratic',
n_samples=20000,
do_mc=False,
cache_path=None):
# Sanity checks
if not ld_model in _implemented_ld_models:
raise ValueError('Unrecognised ld_model {}'.format(ld_model))
self.default_model = ld_model
# Set up the filters
#print('Setting up filters')
ldtk_filters = []
for i, f in enumerate(filters):
if isinstance(f[0], Iterable):
# We have been passed a full filter profile, set up
# TabulatedFilter
# Work out if the profile is in percent or fraction - is
# anything bigget than 1?
if np.any(f[1] > 1):
tmf = 1e-2
else:
tmf = 1
ldtk_filters.append(TabulatedFilter(i, f[0], f[1], tmf))
else:
ldtk_filters.append(BoxcarFilter(i, f[0], f[1]))
# Make the set creator, downloading data files if required
if cache_path is not None:
os.makedirs(cache_path, exist_ok=True)
#print('Making LD parameter set creator.')
#print('This may take some time as we may need to download files...')
set_creator = LDPSetCreator(teff=host_T,
logg=host_logg,
z=host_z,
filters=ldtk_filters,
cache=cache_path,
dataset='visir-lowres')
# Get the LD profiles from the set creator
#print('Obtaining LD profiles')
self.profile_set = set_creator.create_profiles(nsamples=n_samples)
# Find the 'best values' for each filter and then find the ratios
# compared to the first.
#print('Finding coefficients and ratios')
self.coeffs = {}
self.ratios = {}
self._power2_available = True
for model in _implemented_ld_models:
try:
self.coeffs[model] = self._extract_best_coeffs(model)
self.ratios[
model] = self.coeffs[model][0] / self.coeffs[model][0][0]
except Exception as e:
print(e)
print(f'Unable to initialise {model} model')
self._power2_available = False
def createldgrid(minmu,
maxmu,
orbp,
ldmodel='nonlinear',
phoenixmin=1e-1,
segmentation=int(10),
verbose=False):
'''
G. ROUDIER: Wrapper around LDTK downloading tools
LDTK: Parviainen et al. https://github.com/hpparvi/ldtk
'''
tstar = orbp['T*']
terr = np.sqrt(abs(orbp['T*_uperr'] * orbp['T*_lowerr']))
fehstar = orbp['FEH*']
feherr = np.sqrt(abs(orbp['FEH*_uperr'] * orbp['FEH*_lowerr']))
loggstar = orbp['LOGG*']
loggerr = np.sqrt(abs(orbp['LOGG*_uperr'] * orbp['LOGG*_lowerr']))
log.warning('>-- Temperature: %s +/- %s', str(tstar), str(terr))
log.warning('>-- Metallicity: %s +/- %s', str(fehstar), str(feherr))
log.warning('>-- Surface Gravity: %s +/- %s', str(loggstar), str(loggerr))
niter = int(len(minmu) / segmentation) + 1
allcl = None
allel = None
out = {}
avmu = [np.mean([mm, xm]) for mm, xm in zip(minmu, maxmu)]
for i in np.arange(niter):
loweri = i * segmentation
upperi = (i + 1) * segmentation
if i == (niter - 1): upperi = len(avmu)
munm = 1e3 * np.array(avmu[loweri:upperi])
munmmin = 1e3 * np.array(minmu[loweri:upperi])
munmmax = 1e3 * np.array(maxmu[loweri:upperi])
filters = [
BoxcarFilter(str(mue), mun, mux)
for mue, mun, mux in zip(munm, munmmin, munmmax)
]
sc = LDPSetCreator(teff=(tstar, terr),
logg=(loggstar, loggerr),
z=(fehstar, feherr),
filters=filters)
ps = sc.create_profiles(nsamples=int(1e4))
itpfail = False
for testprof in ps.profile_averages:
if np.all(~np.isfinite(testprof)): itpfail = True
pass
nfail = 1e0
while itpfail:
nfail *= 2
sc = LDPSetCreator(teff=(tstar, nfail * terr),
logg=(loggstar, loggerr),
z=(fehstar, feherr),
filters=filters)
ps = sc.create_profiles(nsamples=int(1e4))
itpfail = False
for testprof in ps.profile_averages:
if np.all(~np.isfinite(testprof)): itpfail = True
pass
pass
cl, el = ldx(ps.profile_mu,
ps.profile_averages,
ps.profile_uncertainties,
mumin=phoenixmin,
debug=verbose,
model=ldmodel)
if allcl is None: allcl = cl
else: allcl = np.concatenate((allcl, cl), axis=0)
if allel is None: allel = el
else: allel = np.concatenate((allel, el), axis=0)
pass
allel[allel > 1.] = 0.
allel[~np.isfinite(allel)] = 0.
out['MU'] = avmu
out['LD'] = allcl.T
out['ERR'] = allel.T
for i in range(0, len(allcl.T)):
log.warning('>-- LD%s: %s +/- %s', str(int(i)), str(float(allcl.T[i])),
str(float(allel.T[i])))
pass
return out
#g = [(const.G.si * (mass + j) * const.M_sun.si / (((radius +i) * const.R_sun.si)**2)).cgs.value for i in np.linspace(-uradius,uradius,25) for j in np.linspace(-umass,umass,25)]
g = [(const.G.si * (mass + j) * const.M_sun.si / (((radius +i) * const.R_sun.si)**2)).cgs.value for i in [-uradius,uradius] for j in [-umass,umass]]
#Now for LDCs
kep_trans = np.loadtxt('/Users/jlothrin/LDCs/kepler_response_lowres1.txt',delimiter=' ',skiprows=9)
waves = kep_trans[:,0] * 1000.
throughs = kep_trans[:,1]
filters = [ldtk.TabulatedFilter('kepler',waves,throughs)]
sc = LDPSetCreator(teff=[3390,150],logg=[4.9,0.55],z=[-0.25,0.20],filters=filters)
ps = sc.create_profiles(nsamples=500)
plot(ps._mu,ps.profile_averages[0],'k',label='Model Intensity')
plot(ps._mu,ps.profile_averages[0],'ko',label='Sampled Mu')
oldmu = ps._mu
oldprof = ps.profile_averages[0]
ps.set_limb_z_try(sqrt(1-np.min(ps._mu)**2))
qc,qe = ps.coeffs_qd(do_mc=True)
qc,qm = ps.coeffs_qd(do_mc=True,return_cm=True)
nlc,nle = ps.coeffs_nl(do_mc=True)
lnc,lne = ps.coeffs_ln(do_mc=True)
#good_mu = ps_mu[2:]
class M2LPF(BaseLPF):
def __init__(self,
target: str,
photometry: list,
tid: int,
cids: list,
filters: tuple,
aperture_lims: tuple = (0, inf),
use_opencl: bool = False,
n_legendre: int = 0,
use_toi_info=True,
with_transit=True,
with_contamination=False,
radius_ratio: str = 'achromatic'):
assert radius_ratio in ('chromatic', 'achromatic')
self.use_opencl = use_opencl
self.planet = None
self.photometry_frozen = False
self.with_transit = with_transit
self.with_contamination = with_contamination
self.chromatic_transit = radius_ratio == 'chromatic'
self.radius_ratio = radius_ratio
self.n_legendre = n_legendre
self.toi = None
if self.with_transit and 'toi' in target.lower() and use_toi_info:
self.toi = get_toi(float(target.lower().strip('toi')))
# Set photometry
# --------------
self.phs = photometry
self.nph = len(photometry)
# Set the aperture ranges
# -----------------------
self.min_apt = amin = min(max(aperture_lims[0], 0),
photometry[0].flux.aperture.size)
self.max_apt = amax = max(
min(aperture_lims[1], photometry[0].flux.aperture.size), 0)
self.napt = amax - amin
# Target and comparison star IDs
# ------------------------------
self.tid = atleast_1d(tid)
if self.tid.size == 1:
self.tid = tile(self.tid, self.nph)
self.cids = atleast_2d(cids)
if self.cids.shape[0] == 1:
self.cids = tile(self.cids, (self.nph, 1))
assert self.tid.size == self.nph
assert self.cids.shape[0] == self.nph
self.covnames = 'intercept sky airmass xshift yshift entropy'.split()
times = [array(ph.bjd) for ph in photometry]
fluxes = [
array(ph.flux[:, tid, 1]) for tid, ph in zip(self.tid, photometry)
]
fluxes = [f / nanmedian(f) for f in fluxes]
self.apertures = ones(len(times)).astype('int')
self.t0 = floor(times[0].min())
times = [t - self.t0 for t in times]
self._tmin = times[0].min()
self._tmax = times[0].max()
covariates = []
for ph in photometry:
covs = concatenate(
[ones([ph._fmask.sum(), 1]),
array(ph.aux)[:, [1, 3, 4, 5]]], 1)
covariates.append(covs)
self.airmasses = [array(ph.aux[:, 2]) for ph in photometry]
wns = [ones(ph.nframes) for ph in photometry]
if use_opencl:
import pyopencl as cl
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
tm = QuadraticModelCL(klims=(0.005, 0.25),
nk=512,
nz=512,
cl_ctx=ctx,
cl_queue=queue)
else:
tm = QuadraticModel(interpolate=True,
klims=(0.005, 0.25),
nk=512,
nz=512)
super().__init__(target,
filters,
times,
fluxes,
wns,
arange(len(photometry)),
covariates,
arange(len(photometry)),
tm=tm)
self.legendre = [
legvander((t - t.min()) / (0.5 * t.ptp()) - 1,
self.n_legendre)[:, 1:] for t in self.times
]
# Create the target and reference star flux arrays
# ------------------------------------------------
self.ofluxes = [
array(ph.flux[:, self.tid[i], amin:amax + 1] /
ph.flux[:, self.tid[i], amin:amax + 1].median('mjd'))
for i, ph in enumerate(photometry)
]
self.refs = []
for ip, ph in enumerate(photometry):
self.refs.append([
pad(array(ph.flux[:, cid, amin:amax + 1]), ((0, 0), (1, 0)),
mode='constant') for cid in self.cids[ip]
])
self.set_orbit_priors()
def _init_parameters(self):
self.ps = ParameterSet()
if self.with_transit:
self._init_p_orbit()
self._init_p_planet()
self._init_p_limb_darkening()
self._init_p_baseline()
self._init_p_noise()
self.ps.freeze()
def set_orbit_priors(self):
if self.with_transit and self.toi is not None:
tn = round((self.times[0].mean() - (self.toi.epoch[0] - self.t0)) /
self.toi.period[0])
epoch = ufloat(*self.toi.epoch)
period = ufloat(*self.toi.period)
tc = epoch - self.t0 + tn * period
self.set_prior(0, NP(tc.n, tc.s))
self.set_prior(1, NP(*self.toi.period))
self.add_t14_prior(self.toi.duration[0] / 24,
0.5 * self.toi.duration[1] / 24)
def _init_p_planet(self):
"""Planet parameter initialisation.
"""
if self.radius_ratio == 'achromatic':
pk2 = [
PParameter('k2', 'area_ratio', 'A_s', UP(0.005**2, 0.25**2),
(0.005**2, 0.25**2))
]
self.ps.add_passband_block('k2', 1, 1, pk2)
self._pid_k2 = repeat(self.ps.blocks[-1].start, self.npb)
else:
pk2 = [
PParameter(f'k2_{pb}', f'area_ratio {pb}', 'A_s',
UP(0.005**2, 0.25**2), (0.005**2, 0.25**2))
for pb in self.passbands
]
self.ps.add_passband_block('k2', 1, self.npb, pk2)
self._pid_k2 = arange(self.npb) + self.ps.blocks[-1].start
self._start_k2 = self.ps.blocks[-1].start
self._sl_k2 = self.ps.blocks[-1].slice
if self.with_contamination:
pcn = [
PParameter('cnt_ref', 'Reference contamination', '',
UP(0., 1.), (0., 1.))
]
pcn.extend([
PParameter(f'cnt_{pb}', 'contamination', '', UP(-1., 1.),
(-1., 1.)) for pb in self.passbands[1:]
])
self.ps.add_passband_block('contamination', 1, self.npb, pcn)
self._pid_cn = arange(self.ps.blocks[-1].start,
self.ps.blocks[-1].stop)
self._sl_cn = self.ps.blocks[-1].slice
def _init_p_baseline(self):
c = []
for ilc in range(self.nlc):
c.append(
LParameter(f'ci_{ilc:d}',
'intercept_{ilc:d}',
'',
NP(1.0, 0.03),
bounds=(0.5, 1.5)))
c.append(
LParameter(f'cs_{ilc:d}',
'sky_{ilc:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
c.append(
LParameter(f'ca_{ilc:d}',
'airmass_{ilc:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
c.append(
LParameter(f'cx_{ilc:d}',
'xshift_{ilc:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
c.append(
LParameter(f'cy_{ilc:d}',
'yshift_{ilc:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
c.append(
LParameter(f'ce_{ilc:d}',
'entropy_{ilc:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
self.ps.add_lightcurve_block('ccoef', 6, self.nlc, c)
self._sl_ccoef = self.ps.blocks[-1].slice
self._start_ccoef = self.ps.blocks[-1].start
if self.n_legendre > 0:
c = []
for ilc in range(self.nlc):
for ilg in range(self.n_legendre):
c.append(
LParameter(f'leg_{ilc:d}_{ilg:d}',
f'legendre__{ilc:d}_{ilg:d}',
'',
NP(0.0, 0.01),
bounds=(-0.5, 0.5)))
self.ps.add_lightcurve_block('legendre_polynomials',
self.n_legendre, self.nlc, c)
self._sl_leg = self.ps.blocks[-1].slice
self._start_leg = self.ps.blocks[-1].start
if not self.photometry_frozen:
c = []
for ilc in range(self.nlc):
c.append(
LParameter(f'tap_{ilc:d}',
f'target_aperture__{ilc:d}',
'',
UP(0.0, 0.999),
bounds=(0.0, 0.999)))
self.ps.add_lightcurve_block('apertures', 1, self.nlc, c)
self._sl_tap = self.ps.blocks[-1].slice
self._start_tap = self.ps.blocks[-1].start
c = []
for ilc in range(self.nlc):
for irf in range(self.cids.shape[1]):
c.append(
LParameter(f'ref_{irf:d}_{ilc:d}',
f'comparison_star_{irf:d}_{ilc:d}',
'',
UP(0.0, 0.999),
bounds=(0.0, 0.999)))
self.ps.add_lightcurve_block('rstars', self.cids.shape[1],
self.nlc, c)
self._sl_ref = self.ps.blocks[-1].slice
self._start_ref = self.ps.blocks[-1].start
def _init_p_noise(self):
"""Noise parameter initialisation.
"""
pns = [
LParameter('loge_{:d}'.format(i),
'log10_error_{:d}'.format(i),
'',
UP(-3, 0),
bounds=(-3, 0)) for i in range(self.n_noise_blocks)
]
self.ps.add_lightcurve_block('log_err', 1, self.n_noise_blocks, pns)
self._sl_err = self.ps.blocks[-1].slice
self._start_err = self.ps.blocks[-1].start
def _init_instrument(self):
self.instrument = Instrument('MuSCAT2',
[sdss_g, sdss_r, sdss_i, sdss_z])
self.cm = SMContamination(self.instrument, "i'")
def create_pv_population(self, npop=50):
pvp = self.ps.sample_from_prior(npop)
if self.with_transit:
#for sl in self.ps.blocks[1].slices:
# pvp[:,sl] = uniform(0.01**2, 0.25**2, size=(npop, 1))
if self.with_contamination:
p = pvp[:, self._sl_cn]
p[:, 1] = uniform(size=npop)
p[:, 1:] = normal(0, 0.2, size=(npop, self.npb - 1))
p[:, 1:] = clip(p[:, 0][:, newaxis] + p[:, 1:], 0.001,
0.999) - p[:, 0][:, newaxis]
# With LDTk
# ---------
#
# Use LDTk to create the sample if LDTk has been initialised.
#
if self.ldps:
istart = self._start_ld
cms, ces = self.ldps.coeffs_tq()
for i, (cm, ce) in enumerate(zip(cms.flat, ces.flat)):
pvp[:, i + istart] = normal(cm, ce, size=pvp.shape[0])
# No LDTk
# -------
#
# Ensure that the total limb darkening decreases towards
# red passbands.
#
else:
pvv = uniform(size=(npop, 2 * self.npb))
pvv[:, ::2] = sort(pvv[:, ::2], 1)[:, ::-1]
pvv[:, 1::2] = sort(pvv[:, 1::2], 1)[:, ::-1]
pvp[:, self._sl_ld] = pvv
#for i in range(pvp.shape[0]):
# pid = argsort(pvp[i, ldsl][::2])[::-1]
# pvp[i, ldsl][::2] = pvp[i, ldsl][::2][pid]
# pvp[i, ldsl][1::2] = pvp[i, ldsl][1::2][pid]
# Estimate white noise from the data
# ----------------------------------
for i in range(self.nlc):
wn = diff(self.ofluxa).std() / sqrt(2)
pvp[:,
self._start_err] = log10(uniform(0.5 * wn, 2 * wn, size=npop))
return pvp
def target_apertures(self, pv):
pv = atleast_2d(pv)
p = floor(clip(pv[:, self._sl_tap], 0., 0.999) *
(self.napt)).astype('int')
return squeeze(p)
def reference_apertures(self, pv):
pv = atleast_2d(pv)
p = floor(clip(pv[:, self._sl_ref], 0., 0.999) *
(self.napt + 1)).astype('int')
return squeeze(p)
def set_ofluxa(self, pv):
self.ofluxa[:] = self.relative_flux(pv)
def freeze_photometry(self, pv=None):
pv = pv if pv is not None else self.de.minimum_location
ps_orig = self.ps
self._original_population = pvp = self.de.population.copy()
self._frozen_population = delete(
pvp, s_[self._sl_tap.start:self._sl_ref.stop], 1)
self.taps = self.target_apertures(pv)
self.raps = self.reference_apertures(pv)
self._target_flux = self.target_flux(pv)
self._reference_flux = self.reference_flux(pv)
self.ofluxa[:] = self.relative_flux(pv)
self.photometry_frozen = True
self._init_parameters()
if self.with_transit:
for i in range(self._start_ld):
self.ps[i].prior = ps_orig[i].prior
self.ps[i].bounds = ps_orig[i].bounds
self.de = DiffEvol(self.lnposterior,
clip(self.ps.bounds, -1, 1),
self.de.n_pop,
maximize=True,
vectorize=True)
self.de._population[:, :] = self._frozen_population.copy()
self.de._fitness[:] = self.lnposterior(self._frozen_population)
def transit_model(self, pv, copy=True):
if self.with_transit:
pv = atleast_2d(pv)
if self.chromatic_transit:
mean_ar = pv[:, self._sl_k2].mean(1)
pvv = zeros((pv.shape[0], pv.shape[1] - self.npb + 1))
pvv[:, :4] = pv[:, :4]
pvv[:, 4] = mean_ar
pvv[:, 5:] = pv[:, 4 + self.npb:]
pvp = map_pv(pvv)
ldc = map_ldc(pvv[:, 5:5 + 2 * self.npb])
flux = self.tm.evaluate_pv(pvp, ldc, copy)
rel_ar = pv[:, self._sl_k2] / mean_ar[:, newaxis]
flux = change_depth(rel_ar, flux, self.lcids, self.pbids)
else:
flux = super().transit_model(pv, copy)
if self.with_contamination:
p = pv[:, self._sl_cn]
pv_cnt = zeros((pv.shape[0], self.npb))
pv_cnt[:, 0] = p[:, 0]
pv_cnt[:, 1:] = p[:, 1:] + p[:, 0][:, newaxis]
bad = any(pv_cnt < 0.0, 1)
flux = contaminate(flux, pv_cnt, self.lcids, self.pbids)
flux[bad, 0] = inf
return flux
else:
return 1.
def flux_model(self, pv):
baseline = self.baseline(pv)
trends = self.trends(pv)
model_flux = self.transit_model(pv, copy=True)
return baseline * model_flux + trends
def relative_flux(self, pv):
return self.target_flux(pv) / self.reference_flux(pv)
def target_flux(self, pv):
pv = atleast_2d(pv)
p = floor(clip(pv[:, self._sl_tap], 0., 0.999) *
self.napt).astype('int')
off = zeros((p.shape[0], self.timea.size))
for i, sl in enumerate(self.lcslices):
off[:, sl] = self.ofluxes[i][:, p[:, i]].T
return squeeze(off)
def reference_flux(self, pv):
pv = atleast_2d(pv)
p = floor(clip(pv[:, self._sl_ref], 0., 0.999) * self.napt +
1).astype('int')
r = zeros((pv.shape[0], self.ofluxa.size))
nref = self.cids.shape[1]
for ipb, sl in enumerate(self.lcslices):
for i in range(nref):
r[:, sl] += self.refs[ipb][i][:, p[:, ipb * nref + i]].T
r[:, sl] = r[:, sl] / median(r[:, sl], 1)[:, newaxis]
return squeeze(where(isfinite(r), r, inf))
def extinction(self, pv):
pv = atleast_2d(pv)
ext = zeros((pv.shape[0], self.timea.size))
for i, sl in enumerate(self.lcslices):
st = self._start_ccoef + i * 6
ext[:, sl] = exp(-pv[:, st + 2] * self.airmasses[i][:, newaxis]).T
ext[:, sl] /= mean(ext[:, sl], 1)[:, newaxis]
return squeeze(ext)
def baseline(self, pv):
pv = atleast_2d(pv)
bl = zeros((pv.shape[0], self.timea.size))
for i, sl in enumerate(self.lcslices):
st = self._start_ccoef + i * 6
p = pv[:, st:st + 6]
#bl[:, sl] = (self.covariates[i] @ p[:,[0,1,3,4,5]].T).T
bl[:,
sl] = (self.covariates[i][:, [0, 2, 3]] @ p[:, [0, 3, 4]].T).T
if self.n_legendre > 0:
for i, sl in enumerate(self.lcslices):
st = self._start_leg + i * self.n_legendre
p = pv[:, st:st + self.n_legendre]
bl[:, sl] += (self.legendre[i] @ p.T).T
bl = bl * self.extinction(pv)
return bl
def lnlikelihood(self, pv):
flux_m = self.flux_model(pv)
wn = 10**(atleast_2d(pv)[:, self._sl_err])
if self.photometry_frozen:
return lnlike_logistic_v1d(self.ofluxa, flux_m, wn, self.lcids)
else:
return lnlike_logistic_v(self.relative_flux(pv), flux_m, wn,
self.lcids)
def ldprior(self, pv):
ld = pv[:, self._sl_ld]
#lds = ld[:,::2] + ld[:, 1::2]
#return where(any(diff(lds, 1) > 0., 1), -inf, 0.)
return where(
any(diff(ld[:, ::2], 1) > 0., 1)
| any(diff(ld[:, 1::2], 1) > 0., 1), -inf, 0)
def inside_obs_prior(self, pv):
return where(transit_inside_obs(pv, self._tmin, self._tmax, 20.), 0.,
-inf)
def lnprior(self, pv):
pv = atleast_2d(pv)
lnp = self.ps.lnprior(pv)
if self.with_transit:
lnp += self.additional_priors(pv) + self.ldprior(pv)
#lnp += self.ldprior(pv) + self.inside_obs_prior(pv) + self.additional_priors(pv)
return lnp
def add_t14_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the transit duration.
Parameters
----------
mean
std
Returns
-------
"""
def T14(pv):
pv = atleast_2d(pv)
a = as_from_rhop(pv[:, 2], pv[:, 1])
t14 = duration_eccentric(pv[:, 1], sqrt(pv[:, 4]), a,
arccos(pv[:, 3] / a), 0, 0, 1)
return norm.logpdf(t14, mean, std)
self.lnpriors.append(T14)
def add_ldtk_prior(
self,
teff: tuple,
logg: tuple,
z: tuple,
uncertainty_multiplier: float = 3,
pbs: tuple = ('g', 'r', 'i', 'z')
) -> None:
"""Add a LDTk-based prior on the limb darkening.
Parameters
----------
teff
logg
z
uncertainty_multiplier
pbs
Returns
-------
"""
fs = {
n: f
for n, f in zip('g r i z'.split(), (sdss_g, sdss_r, sdss_i,
sdss_z))
}
filters = [fs[k] for k in pbs]
self.ldsc = LDPSetCreator(teff, logg, z, filters)
self.ldps = self.ldsc.create_profiles(1000)
self.ldps.resample_linear_z()
self.ldps.set_uncertainty_multiplier(uncertainty_multiplier)
def ldprior(pv):
pv = atleast_2d(pv)
lnl = zeros(pv.shape[0])
for i in range(pv.shape[0]):
lnl[i] = self.ldps.lnlike_tq(pv[i, self._sl_ld])
return lnl
self.lnpriors.append(ldprior)
def plot_light_curves(self,
model: str = 'de',
figsize: tuple = (13, 8),
fig=None,
gridspec=None):
if fig is None:
fig = figure(figsize=figsize, constrained_layout=True)
gs = dict(height_ratios=(0.5, 2, 2, 1))
if gridspec:
gs.update(gridspec)
axs = fig.subplots(4, self.nlc, sharey='row', gridspec_kw=gs)
if model == 'de':
pv = self.de.minimum_location
err = 10**pv[self._sl_err]
if not self.photometry_frozen:
self.set_ofluxa(pv)
elif model == 'mc':
fc = array(self.posterior_samples(include_ldc=True))
pv = permutation(fc)[:300]
err = 10**median(pv[:, self._sl_err], 0)
if not self.photometry_frozen:
self.set_ofluxa(median(pv, 0))
else:
raise NotImplementedError(
"Light curve plotting `model` needs to be either `de` or `mc`")
ps = [50, 16, 84]
if self.with_transit:
tm = percentile(atleast_2d(self.transit_model(pv)), ps, 0)
else:
tm = percentile(atleast_2d(ones(self.timea.size)), ps, 0)
fm = percentile(atleast_2d(self.flux_model(pv)), ps, 0)
bl = percentile(atleast_2d(self.baseline(pv)), ps, 0)
t0 = self.t0
for i, sl in enumerate(self.lcslices):
t = self.timea[sl]
axs[1, i].plot(t, self.ofluxa[sl], '.', alpha=0.5)
axs[1, i].plot(t, fm[0][sl], 'k', lw=2)
axs[2, i].plot(t, self.ofluxa[sl] / bl[0][sl], '.', alpha=0.5)
if model == 'mc':
axs[2, i].fill_between(t,
tm[1][sl],
tm[2][sl],
facecolor='darkblue',
alpha=0.25)
axs[2, i].plot(t, tm[0][sl], 'k', lw=2)
axs[3, i].plot(t, self.ofluxa[sl] - fm[0][sl], '.', alpha=0.5)
res = self.ofluxa[sl] - fm[0][sl]
x = linspace(-4 * err, 4 * err)
axs[0, i].hist(1e3 * res, 'auto', density=True, alpha=0.5)
axs[0, i].plot(1e3 * x,
logistic(0, 1e3 * err[i]).pdf(1e3 * x), 'k')
axs[0,
i].text(0.05,
0.95,
f"$\sigma$ = {(1e3 * err[i] * pi / sqrt(3)):5.2f} ppt",
transform=axs[0, i].transAxes,
va='top')
[
ax.set_title(f"MuSCAT2 {t}", size='large')
for ax, t in zip(axs[0], self.passbands)
]
[setp(ax.get_xticklabels(), visible=False) for ax in axs[1:3].flat]
setp(axs[1, 0], ylabel='Transit + Systematics')
setp(axs[2, 0], ylabel='Transit - Systematics')
setp(axs[3, 0], ylabel='Residuals')
setp(axs[3, :], xlabel=f'Time - {self.t0:9.0f} [BJD]')
setp(axs[0, :], xlabel='Residual [ppt]', yticks=[])
[sb.despine(ax=ax, offset=5, left=True) for ax in axs[0]]
return fig, axs
def plot_posteriors(self,
figsize: tuple = (13, 5),
fig=None,
gridspec=None):
if fig is None:
fig = figure(figsize=figsize, constrained_layout=True)
axs = fig.subplots(2, 3, gridspec_kw=gridspec)
df = self.posterior_samples(include_ldc=True)
df = df.iloc[:, :5].copy()
df['k'] = sqrt(df.k2)
names = 'stellar density, impact parameter, transit depth, radius ratio'.split(
', ')
# Transit centre
# --------------
p = self.ps.priors[0]
t0 = floor(df.tc.mean())
trange = p.mean - t0 - 3 * p.std, p.mean - t0 + 3 * p.std
x = linspace(*trange)
axs[0, 0].hist(df.tc - t0,
50,
density=True,
range=trange,
alpha=0.5,
edgecolor='k',
histtype='stepfilled')
axs[0, 0].set_xlabel(f'Transit center - {t0:9.0f} [BJD]')
axs[0, 0].fill_between(x,
exp(p.logpdf(x + t0)),
alpha=0.5,
edgecolor='k')
# Period
# ------
p = self.ps.priors[1]
trange = p.mean - 3 * p.std, p.mean + 3 * p.std
x = linspace(*trange)
axs[0, 1].hist(df.pr,
50,
density=True,
range=trange,
alpha=0.5,
edgecolor='k',
histtype='stepfilled')
axs[0, 1].set_xlabel('Period [days]')
axs[0, 1].fill_between(x, exp(p.logpdf(x)), alpha=0.5, edgecolor='k')
setp(axs[0, 1], xticks=df.pr.quantile([0.05, 0.5, 0.95]))
# Rest without priors
# -------------------
for i, ax in enumerate(axs.flat[2:]):
ax.hist(df.iloc[:, i + 2],
50,
density=True,
alpha=0.5,
edgecolor='k',
histtype='stepfilled')
ax.set_xlabel(names[i])
# TFOP Transit depth estimates
axs[1, 1].axvline(1e-6 * self.toi.depth[0], c='0.5', lw=2)
axs[1, 2].axvline(sqrt(1e-6 * self.toi.depth[0]), c='0.5', lw=2)
setp(axs, yticks=[])
return fig, axs
def plot_chains(self, pids=(0, 1, 2, 3, 4)):
fig, axs = subplots(len(pids),
1,
figsize=(13, 10),
constrained_layout=True,
sharex='all')
x = arange(self.sampler.chain.shape[1])
for i, (pid, ax) in enumerate(zip(pids, axs)):
pes = percentile(self.sampler.chain[:, :, pid],
[50, 16, 84, 0.5, 99.5], 0)
ax.fill_between(x, pes[3], pes[4])
ax.fill_between(x, pes[1], pes[2])
ax.plot(pes[0], 'k')
setp(ax, ylabel=self.ps.names[pid])
setp(axs, xlim=(0, x[-1]))
def plot_running_mean(self,
figsize=(13, 5),
errors=True,
combine=False,
remove_baseline=True,
ylim=None,
npt=100,
width_min=10):
pv = self.de.minimum_location
rflux = self.relative_flux(pv)
if remove_baseline:
rflux /= squeeze(self.baseline(pv))
if combine:
bt, bf, be = running_mean(self.timea, rflux, npt, width_min)
fig, axs = subplots(figsize=figsize, constrained_layout=True)
if errors:
axs.errorbar(bt, bf, be, drawstyle='steps-mid', c='k')
else:
axs.plot(bt, bf, drawstyle='steps-mid', c='k')
axs.fill_between(bt,
bf - 3 * be,
bf + 3 * be,
alpha=0.2,
step='mid')
else:
rfluxes = [rflux[sl] for sl in self.lcslices]
fig, axs = subplots(1,
self.nlc,
figsize=figsize,
constrained_layout=True,
sharey='all')
for i, ax in enumerate(axs):
bt, bf, be = running_mean(self.times[i], rfluxes[i], npt,
width_min)
if errors:
ax.errorbar(bt, bf, be, drawstyle='steps-mid', c='k')
else:
ax.plot(bt, bf, drawstyle='steps-mid', c='k')
if ylim:
setp(axs, ylim=ylim)
class WithTLCs(TransmissionSpectrum):
"""Transmission spectrum object that also contains the light curves (needed for initial fits, not for later plotting.)"""
def __init__(self, cube, maskname='defaultMask', **kwargs):
TransmissionSpectrum.__init__(self, cube, **kwargs)
# load the light curves associate with this transmission spectrum
self.constructBins()
self.speak('its name is {0}'.format(self))
# manage the mask, and create a default dummy mask if need be
self.maskname = maskname
self.applyMask()
def constructBins(self):
'''Populate the transmission spectrum bins with light curves.'''
self.speak("constructing the bins of {0}".format(self))
# what files exist?
possibleTLCs = glob.glob(self.rawlightcurvedirectory + '*.lightcurve')
self.speak('there are {0} .lightcurve files in {1}'.format(
len(possibleTLCs), self.rawlightcurvedirectory))
# do we have to make the light curve files from the spectroscopic cube?
if len(possibleTLCs) == 0:
self.speak("creating .lightcurve(s) from the spectroscopic cube")
# bin and save the light curves for this binsize
self.cube.makeLCs()
# return the list of light curve filenames now
possibleTLCs = glob.glob(self.rawlightcurvedirectory +
'*.lightcurve')
# make sure at least one TLC exists
assert (len(possibleTLCs) > 0)
# pull out the ????to???? strings from the .lightcurve filenames
chunks = []
for file in possibleTLCs:
chunks.append(
(file.split('/')[-1].split('.lightcurve')[0]).split('_')[-1])
# populate bins (and their central wavelengths)
bins = []
wavelengths = []
for trimmed in chunks:
# figure out the boundaries of this bin
left = np.int(trimmed.split('to')[0])
right = np.int(trimmed.split('to')[-1])
# create a wavelength bin (but don't load its lightcurve yet)
bins.append(WavelengthBin(self, left=left, right=right))
wavelengths.append(bins[-1].wavelength)
# assign the bins to this spectrum, sorted by wavelength
self.bins = np.array(bins)[np.argsort(wavelengths)]
self.wavelengths = np.array(wavelengths)[np.argsort(wavelengths)]
# loop over the bins, and load the light curve for each
self.loadLCs()
# read the first bin (necessary for setting up initial conditions for fits)
self.speak(
"spectrum contains {0} bins covering {1} to {2}{3} at {4}{3} resolution"
.format(len(self.bins), self.bins[0].left / self.unit,
self.bins[-1].right / self.unit, self.unitstring,
self.binsize / self.unit))
def loadLCs(self):
'''Loop over bins, load their light curves.'''
self.speak('loading all the light curves for all the bins' '')
for b in self.bins:
b.readTLC()
@property
def nBins(self):
"""Return the number of bins in this spectrum."""
try:
return self._nBins
except:
self._nBins = len(self.bins)
return self._nBins
@property
def nTimes(self):
"""Return the *maximum* number of times among any of the lightcurves associated with this spectrum."""
try:
return self._nTimes
except:
# figure out the maximum number of times there are
self._nTimes = 0
for i in range(self.nBins):
b = self.bins[i]
try:
b.tlc.flux
except:
b.readTLC()
l = len(b.tlc.flux)
# update the maximum nTimes, and the most densely populated times (necessary for interpolation)
if l > self._nTimes:
self._nTimes = l
return self._nTimes
def toArray(self, key):
'''Create an image array of a TLC variable.'''
# create an empty array
a = np.zeros((self.nBins, self.nTimes))
# we can't guarantee the tlcs all have the exact same timestamps
# because some data points might have been thrown out, so we have
# to do the interpolation at least once (we can store interpolation
# indices inside the bin object to speed this in the future)
# loop over the bins
for i in range(self.nBins):
# pick out this bin's TLC
tlc = self.bins[i].tlc
try:
a[i, :] = tlc.__dict__[key]
except KeyError:
a[i, :] = tlc.externalvariables[key]
return a
#def interpolationindices(self, tlc):
# # define the interpolation indices for this TLC
# try:
# return tlc.interpolationindices
# except:
# # interpolate from times to indices
# interpolation = scipy.interpolate.interp1d(self.themostdenselypopulatedtimes, np.arange(self.nTimes), bounds_error=True, kind='nearest')
# tlc.interpolationindices = interpolation(tlc.bjd).astype(np.int)
# return tlc.interpolationindices
def createMask(self, empty=False, afterfastfit=False):
if empty:
maskname = 'defaultMask'
mask = np.array(self.toArray('bad')).astype(np.byte)
elif afterfastfit:
maskname = 'trimOutliers'
mask = np.array(self.toArray('bad')).astype(np.byte)
else:
a = raw_input("What would you like to call this mask?\n ")
maskname = a.replace(" ", "")
keys = ['peak_', 'sky_', 'width_', 'centroid_']
# one column for light curves, one for target, one for comparison
nColumns = 3
nRows = len(keys)
plt.figure('masking')
ip = craftroom.displays.iplot.iplot(nRows, nColumns)
kw = dict(cmap='gray',
interpolation='nearest',
aspect='auto',
vmin=None,
vmax=None)
mask = np.array(self.toArray('bad')).astype(np.byte)
nBins = len(self.bins)
threshold = 4
# identify outliers
try:
for i in range(nBins):
residuals = self.bins[i].tlc.residuals()
noise = np.std(residuals)
bad = np.abs(residuals) > threshold * noise
self.speak(
" in bin {0}, {1} points exceeded {2} x {3}".format(
i, np.sum(bad), threshold, noise))
mask[i, :] = mask[
i, :] | bad * self.bins[0].tlc.flags['outlier']
except:
pass
# identify "saturated"
#saturationthreshold = 1.8e6
#saturated = (self.toArray('peak_target') > saturationthreshold) | (self.toArray('peak_target') > saturationthreshold)
#print " {0} total points were over {1} in their peak counts".format(np.sum(saturated), saturationthreshold)
#mask = mask | saturated*self.bins[0].tlc.flags['saturation']
# set up the axes (this can be slow, so do it once)
ax = ip.subplot(0, 0, name='flux')
sub = {}
sub['sharex'], sub['sharey'] = ip.axes['flux'], ip.axes['flux']
ax = ip.subplot(1, 0, name='instrumental', **sub)
ax = ip.subplot(2, 0, name='corrected', **sub)
ax = ip.subplot(3, 0, name='residuals', **sub)
for i in range(len(keys)):
key = keys[i] + 'target'
ax = ip.subplot(i, 1, name=key, **sub)
key = keys[i] + 'star01'
ax = ip.subplot(i, 2, name=key, **sub)
keepgoing = True
while keepgoing:
# clear the axes again
for k in ip.axes.keys():
ip.axes[k].cla()
# plot the initial light curve
flux = self.toArray('flux')
kw['vmin'], kw['vmax'] = 0.98, 1.02
ip.axes['flux'].imshow(flux, **kw)
ip.axes['flux'].set_title('flux')
# plot the instrumental correction
instrumental = np.zeros_like(flux)
residuals = np.zeros_like(flux)
try:
for i in range(nBins):
instrumental[i, :] = self.bins[i].tm.instrument_model()
residuals[i, :] = self.bins[i].tlc.residuals()
except:
pass
ip.axes['instrumental'].imshow(instrumental, **kw)
ip.axes['instrumental'].set_title('instrumental')
ip.axes['corrected'].imshow(flux / instrumental, **kw)
ip.axes['corrected'].set_title('corrected')
kw['vmin'], kw['vmax'] = 0.98 - 1, 1.02 - 1
ip.axes['residuals'].imshow(residuals, **kw)
ip.axes['residuals'].set_title('residuals')
# plot target diagnostics
for i in range(len(keys)):
key = keys[i] + 'target'
ax = ip.axes[key]
ax.set_title(key)
array = self.toArray(key)
kw['vmin'], kw['vmax'] = None, None
ax.imshow(array, **kw)
# plot comparison diagnostics
for i in range(len(keys)):
key = keys[i] + 'star01'
ax = ip.axes[key]
ax.set_title(key)
array = self.toArray(key)
kw['vmin'], kw['vmax'] = None, None
ax.imshow(array, **kw)
# zoom out a tiny bit to make selection easier
for k in ip.axes.keys():
y, x = self.toArray('flux').shape
ip.axes[k].set_xlim(0 - x / 20, x + x / 20)
ip.axes[k].set_ylim(0 - y / 20, y + y / 20)
masked = np.ma.masked_where(mask == 0, mask)
my_cmap = copy.copy(plt.cm.get_cmap(
'autumn')) # get a copy of the gray color map
my_cmap.set_bad(alpha=0)
for k in ip.axes.keys():
ax = ip.axes[k]
ax.imshow(masked,
cmap=my_cmap,
alpha=0.5,
aspect='auto',
interpolation='nearest')
plt.draw()
unreasonableanswer = True
while unreasonableanswer:
answer = raw_input(
"What would you like to do? [a]dd masking, [s]ubtract masking, [r]efit using this mask, [f]inish?\n (I'd like to) "
)
unreasonableanswer = False
if 'a' in answer:
self.speak(
" Click at the two corners of a box you'd like to mask.\n"
)
clicks = ip.getMouseClicks(2)
rows = (clicks[0].ydata, clicks[1].ydata)
top = np.int(np.max(rows))
bottom = np.maximum(np.int(np.min(rows)), 0)
columns = (clicks[0].xdata, clicks[1].xdata)
right = np.int(np.max(columns))
left = np.maximum(np.int(np.min(columns)), 0)
mask[bottom:top, left:right] = mask[
bottom:top,
left:right] | self.bins[0].tlc.flags['custom']
elif 's' in answer:
self.speak(
" Click at the two corners of a box you'd like to unmask.\n"
)
clicks = ip.getMouseClicks(2)
rows = np.round((clicks[0].ydata, clicks[1].ydata))
top = np.int(np.max(rows))
bottom = np.maximum(np.int(np.min(rows)), 0)
columns = np.round((clicks[0].xdata, clicks[1].xdata))
right = np.int(np.max(columns))
left = np.maximum(np.int(np.min(columns)), 0)
mask[bottom:top, left:right] -= mask[
bottom:top,
left:right] & self.bins[0].tlc.flags['custom']
elif 'r' in answer:
self.speak("Okay, refitting. It may take a while!")
self.mask = mask
self.applyMask(maskname=maskname)
self.fitRigid()
elif 'f' in answer:
keepgoing = False
else:
unreasonableanswer = True
self.speak(
" I'm sorry, I didn't quite understand that.")
# set the mask and maskname attributes
self.maskname = maskname
self.mask = mask
# save the mask to the mask directory
self.saveMask()
# loop through the light curves and apply the mask to each
self.applyMask(maskname=maskname)
def saveMask(self):
"Save the masking array for this particular mask."
self.speak('saving mask "{0}" to {1}'.format(self.maskname,
self.maskdirectory))
np.save(self.maskdirectory + 'mask.npy', self.mask)
def applyMask(self, maskname=None):
"Load a mask from this mask directory and apply it to the light curves."
# update the maskname attribute to the desired one
if maskname is not None:
self.maskname = maskname
# load the mask from the masking directory (probably slows things down a bit, but ensure everything links up)
try:
self.mask = np.load(self.maskdirectory + 'mask.npy')
self.speak('loaded mask from {0}'.format(self.maskdirectory))
except:
self.speak(
"couldn't load requested mask '{0}', so reverting to default".
format(self.maskname))
self.createMask(empty=True)
# loop through all the bins, and apply the mask to the individual lightcurves
nBins = len(self.bins)
for i in range(nBins):
b = self.bins[i]
try:
b.tlc
except:
b.readTLC()
b.tlc.bad = self.mask[i, :]
def fit(self, planet, star, instrument, plot=True):
'''Take an input planet, star, and instrument; and do a fit across all wavelength bins.'''
self.rp_over_rs = np.zeros(len(self.bins))
self.uncertainty = np.zeros_like(self.rp_over_rs)
self.wavelengths = np.zeros_like(self.rp_over_rs)
#for i in range(len(self.bins)):
# b = self.bins[i]
# b.fit(planet, star, instrument, plot=plot)
# self.rp_over_rs[i] = b.tm.planet.rp_over_rs.value
## self.wavelengths[i] = b.wavelength
def load(self, method='lm'):
'''Load light curves and fits for this transmission spectrum.'''
self.wavelengths = np.zeros(len(self.bins))
self.fitted, self.uncertainty = {}, {}
for i in np.arange(len(self.bins)):
# select an individual bin
bin = self.bins[i]
# load its TLC and TM
bin.load()
# load (or create) the fit
if method == 'mcmc':
bin.tm.slowfit(plot=False, remake=False)
else:
bin.tm.fastfit(plot=False, remake=False)
# loop over the parameters, and store them in the spectrum
for parameter in bin.tm.fitter.pdf.parameters:
key = parameter.name
try:
self.fitted[key][i] = parameter.value
self.uncertainty[key][i] = parameter.uncertainty
except KeyError:
self.fitted[key] = np.full(len(self.bins), None)
self.uncertainty[key] = np.full(len(self.bins), None)
self.fitted[key][i] = parameter.value
self.uncertainty[key][i] = parameter.uncertainty
self.wavelengths[i] = bin.wavelength
def setupInitialConditions(self):
self.initial = {}
self.initial['planet'] = Planet(J=0.0, \
k=0.107509268533, \
rs_over_a =0.136854018274, \
b =0.143228040337, \
q=0.0, \
period=3.95023867775, \
t0=2456416.39659, \
dt = -0.000109927092499, \
esinw=0.0, \
ecosw=0.0)
self.initial['star'] = Star(u1=0.47,
u2=0.33,
temperature=6170.0,
logg=4.27,
metallicity=0.26)
self.initial['instrument'] = Instrument(self.bins[0].tlc, order=2)
self.speak('using LDTk to estimate limb-darkening coefficients')
self.ldtk_filename = os.path.join(self.binningdirectory,
'ldtk_coefs.npy')
try:
self.ldtk_coefs, self.ldtk_coefuncertainties = np.load(
self.ldtk_filename)
self.speak('loaded LD coefficients from {}'.format(
self.ldtk_filename))
except IOError:
# create some filters for the limb-darkening
self.ldtk_filters = [
BoxcarFilter(b.identifier, b.left / 10, b.right / 10)
for b in self.bins
]
# set up the profile creator
self.ldtk_sc = LDPSetCreator(
teff=(6170, 80), # Define your star, and the code
logg=(4.27, 0.07), # downloads the uncached stellar
z=(0.26, 0.15), # spectra from the Husser et al.
filters=self.ldtk_filters) # FTP server automatically.
self.ldtk_profiles = self.ldtk_sc.create_profiles()
self.ldtk_coefs, self.ldtk_coefuncertainties = self.ldtk_profiles.coeffs_qd(
do_mc=False)
np.save(self.ldtk_filename,
(self.ldtk_coefs, self.ldtk_coefuncertainties))
self.speak('saved new LD coefficients to {}'.format(
self.ldtk_filename))
# initialize a limb-darkening object
#self.ld = LD.LD(temperature=self.initial['star'].temperature.value, gravity=self.initial['star'].logg.value, metallicity=self.initial['star'].metallicity.value, directory = self.binningdirectory, plot=True)
def setupFit(self,
label='fixedGeometry',
maskname='defaultMask',
remake=True):
# set the label to the kind of fit
self.label = label
self.applyMask(maskname)
# make sure some initial conditions are set
self.setupInitialConditions()
# pull out the initial planet, star, and instrument
p, s, i = self.initial['planet'], self.initial['star'], self.initial[
'instrument']
instrumentallimits = [-0.05, 0.05]
# modify these according to what kind of a fit we want to use
if label == 'fixedGeometry' or label == 'floatingLD':
# float the radius ratio
p.k.float(limits=[0.05, 0.15])
# a constant baseline
i.C.float(value=1.0, limits=[0.5, 1.5])
# instrument rotator angle (seems to matter!)
i.rotatore_tothe1.float(value=0.002, limits=instrumentallimits)
# width of the whole spectrum, and the width in the wavelength range
i.width_target_tothe1.float(value=0.002, limits=instrumentallimits)
i.dwidth_target_tothe1.float(value=0.002,
limits=instrumentallimits)
# cross-dispersion centroid of the whole spectrum, and in the wavelength range
i.centroid_target_tothe1.float(value=0.002,
limits=instrumentallimits)
i.dcentroid_target_tothe1.float(value=0.002,
limits=instrumentallimits)
# applied wavelength offset
i.shift_target_tothe1.float(value=0.002, limits=instrumentallimits)
# the sky brightness in
i.sky_target_tothe1.float(value=0.002, limits=instrumentallimits)
i.peak_target_tothe1.float(value=0.002, limits=instrumentallimits)
# allow the limbdarkening to float [a prior for each bin will be set later]
if label == 'floatingLD':
s.u1.float(value=s.u1.value, limits=[0.0, 1.0])
s.u2.float(value=s.u2.value, limits=[0.0, 1.0])
else:
s.u1.fix(value=s.u1.value)
s.u2.fix(value=s.u2.value)
return
if label == 'floatingGeometry':
p.rs_over_a.float(value=0.14, limits=[0.0, 0.3], shrink=1000.0)
p.k.float(limits=[0.05, 0.15])
p.b.float(value=0.8, limits=[0.0, 1.0], shrink=1000.0)
p.dt.float(limits=np.array([-0.01, 0.01]))
i.C.float(value=1.0, limits=[0.9, 1.1])
i.rotatore_tothe1.float(value=0.002, limits=instrumentallimits)
i.width_target_tothe1.float(value=0.002, limits=instrumentallimits)
i.centroid_target_tothe1.float(value=0.002,
limits=instrumentallimits)
i.shift_target_tothe1.float(value=0.002, limits=instrumentallimits)
i.dwidth_target_tothe1.float(value=0.002,
limits=instrumentallimits)
i.dcentroid_target_tothe1.float(value=0.002,
limits=instrumentallimits)
i.sky_target_tothe1.float(value=0.002, limits=instrumentallimits)
i.peak_target_tothe1.float(value=0.002, limits=instrumentallimits)
return
assert (False)
def psi(self):
"""Quick wrapper to spit out a tuple of the initial conditions."""
try:
return self.initial['planet'], self.initial['star'], self.initial[
'instrument']
except AttributeError:
self.setupFit()
return self.initial['planet'], self.initial['star'], self.initial[
'instrument']
def fitBins(self,
label='fixedGeometry',
maskname='defaultMask',
remake=False,
slow=False,
plot=False,
**kw):
self.speak('about to fit {0} bins with:')
for k in locals().keys():
self.speak(' {0} = {1}'.format(k, locals()[k]))
#self.input('are you okay with that?')
self.setupFit(label=label, maskname=maskname, remake=True)
if label == 'floatingLD':
kw['ldpriors'] = False
assert (self.label == label)
for b in self.bins:
b.fit(plot=plot,
slow=slow,
remake=remake,
label=label,
maskname=maskname,
**kw)
assert (self.label == label)
class LDTKHandler:
'''
The LDTKHandler provides an easy way to interface ldtk with TransitFit.
Parameters
----------
host_T : tuple
The effective temperature of the host star, in Kelvin, given as a
(value, uncertainty) pair.
host_logg : tuple
The log_10 of the surface gravity of the host star, with gravity
measured in cm/s2. Should be given as a (value, uncertainty) pair.
host_z : tuple
The metalicity of the host, given as a (value, uncertainty) pair.
filters : array_like
The set of filters, given in [low, high] limits for the wavelengths
with the wavelengths given in nanometers if a uniform filter is to
be used, or [[wavelength...], [transmission]] if a fully-defined
profile is being used. The ordering of the filters should
correspond to the filter_idx parameter used elsewhere.
ld_method : str, optional
The model of limb darkening to use. Allowed values are 'linear',
'quadratic', 'squareroot', 'power2', and 'nonlinear'. Default is
'quadratic'.
n_samples : int, optional
The number of limb darkening profiles to create. Passed to
ldtk.LDPSetCreator.create_profiles(). Default is 20000.
do_mc : bool, optional
If True, will use MCMC to estimate coefficient uncertainties more
accurately. Default is False.
cache_path : str, optional
This is the path to cache LDTK files to. If not specified, will
default to the LDTK default
'''
def __init__(self,
host_T,
host_logg,
host_z,
filters,
ld_model='quadratic',
n_samples=20000,
do_mc=False,
cache_path=None):
# Sanity checks
if not ld_model in _implemented_ld_models:
raise ValueError('Unrecognised ld_model {}'.format(ld_model))
self.default_model = ld_model
# Set up the filters
#print('Setting up filters')
ldtk_filters = []
for i, f in enumerate(filters):
if isinstance(f[0], Iterable):
# We have been passed a full filter profile, set up
# TabulatedFilter
# Work out if the profile is in percent or fraction - is
# anything bigget than 1?
if np.any(f[1] > 1):
tmf = 1e-2
else:
tmf = 1
ldtk_filters.append(TabulatedFilter(i, f[0], f[1], tmf))
else:
ldtk_filters.append(BoxcarFilter(i, f[0], f[1]))
# Make the set creator, downloading data files if required
if cache_path is not None:
os.makedirs(cache_path, exist_ok=True)
#print('Making LD parameter set creator.')
#print('This may take some time as we may need to download files...')
self.set_creator = LDPSetCreator(teff=host_T,
logg=host_logg,
z=host_z,
filters=ldtk_filters,
cache=cache_path)
# Get the LD profiles from the set creator
#print('Obtaining LD profiles')
self.profile_set = self.set_creator.create_profiles(nsamples=n_samples)
# Find the 'best values' for each filter and then find the ratios
# compared to the first.
#print('Finding coefficients and ratios')
self.coeffs = {}
self.ratios = {}
self._power2_available = True
for model in _implemented_ld_models:
try:
self.coeffs[model] = self._extract_best_coeffs(model)
self.ratios[
model] = self.coeffs[model][0] / self.coeffs[model][0][0]
except Exception as e:
print(e)
print(f'Unable to initialise {model} model')
self._power2_available = False
def estimate_values(self, ld0_values, ld_model):
'''
If given a set of LD param values for filter 0, will estimate the LD
parameters for all filters based on the ratios between the best values
found in initialisation.
Parameters
----------
ld1_values : float or array_like
The LD parameters for filter 0
ld_model : str
The limb darkening model to use
Returns
-------
all_ld_values : array_like, shape (n_filters, n_coeffs)
The estimated limb darkening parameters
'''
if ld_model == 'power2' and not self._power2_available:
raise ValueError(
'power2 model is not available. If you want to use this, please use the development version of ldtk available on https://github.com/hpparvi/ldtk, rather than the pypi version.'
)
return ld0_values * self.ratios[ld_model]
def _extract_best_coeffs(self, ld_model):
'''
Extracts the best values for a limb darkening model for the filters
Parameters
----------
ld_model : str
The limb darkening model to obtain the values for.
Returns
-------
coeffs : array_like, shape (n_filters, n_coeffs)
The coefficients for each filter
err : array_like, shape (n_filters, n_coeffs)
The uncertainty on each of the coefficients
'''
if ld_model == 'linear':
coeff, err = self.profile_set.coeffs_ln()
elif ld_model == 'quadratic':
coeff, err = self.profile_set.coeffs_qd()
elif ld_model == 'nonlinear':
coeff, err = self.profile_set.coeffs_nl()
elif ld_model == 'power2':
if not self._power2_available:
raise ValueError(
'power2 model is not available. If you want to use this, please use the development version of ldtk available on https://github.com/hpparvi/ldtk, rather than the pypi version.'
)
coeff, err = self.profile_set.coeffs_p2()
elif ld_model == 'squareroot':
coeff, err = self.profile_set.coeffs_sq()
else:
raise ValueError('Unrecognised ld_model {}'.format(ld_model))
return coeff, err
def lnlike(self, coeffs, ld_model):
'''
Evaluates the log likelihood for a set of coefficients
Parameters
----------
coeffs : array_like, shape (n_filters, n_coeffs)
The coefficients to evaluate the log likelihood for.
ld_model : str, optional
The model to use. Defaults to self.default_model
'''
if ld_model == 'linear':
return self.profile_set.lnlike_ln(coeffs)
if ld_model == 'quadratic':
return self.profile_set.lnlike_qd(coeffs)
if ld_model == 'nonlinear':
return self.profile_set.lnlike_nl(coeffs)
if ld_model == 'power2':
if not self._power2_available:
raise ValueError(
'power2 model is not available. If you want to use this, please use the development version of ldtk available on https://github.com/hpparvi/ldtk, rather than the pypi version.'
)
return self.profile_set.lnlike_p2(coeffs)
if ld_model == 'squareroot':
return self.profile_set.lnlike_sq(coeffs)
raise ValueError('Unrecognised ld_model {}'.format(ld_model))
class BaseLPF:
_lpf_name = 'BaseLPF'
def __init__(self, name: str, passbands: list, times: list = None, fluxes: list = None, errors: list = None,
pbids: list = None, covariates: list = None, wnids: list = None, tm: TransitModel = None,
nsamples: tuple = 1, exptimes: tuple = 0., init_data=True, result_dir: Path = None):
"""The base Log Posterior Function class.
The `BaseLPF` class creates the basis for transit light curve analyses using `PyTransit`. This class can be
used in a basic analysis directly, or it can be inherited to create a basis for a more complex analysis.
Parameters
----------
name: str
Name of the log posterior function instance.
passbands: iterable
List of unique passband names (filters) that the light curves have been observed in.
times: iterable
List of 1d ndarrays each containing the mid-observation times for a single light curve.
fluxes: iterable
List of 1d ndarrays each containing the normalized fluxes for a single light curve.
errors: iterable
List of 1d ndarrays each containing the flux measurement uncertainties for a single light curvel.
pbids: iterable of ints
List of passband indices mapping each light curve to a single passband.
covariates: iterable
List of covariates one 2d narray per light curve.
wnids: iterable of ints
List of noise set indices mapping each light curve to a single noise set.
tm: TransitModel
Transitmodel to use instead of the default model.
nsamples: list[int]
List of supersampling factors. The values should be integers and given one per light curve.
exptimes: list[float]
List of exposure times. The values should be floats with the time given in days.
init_data: bool
Set to `False` to allow the LPF to be initialized without data. This is mainly for debugging.
result_dir: Path
Default saving directory
"""
self._pre_initialisation()
self.tm = tm or QuadraticModel(klims=(0.01, 0.75), nk=512, nz=512)
# LPF name
# --------
self.name = name
self.result_dir = result_dir
# Passbands
# ---------
# Should be arranged from blue to red
if isinstance(passbands, (list, tuple, ndarray)):
self.passbands = passbands
else:
self.passbands = [passbands]
self.npb = npb = len(self.passbands)
self.nsamples = None
self.exptimes = None
# Declare high-level objects
# --------------------------
self.ps = None # Parametrisation
self.de = None # Differential evolution optimiser
self.sampler = None # MCMC sampler
self.instrument = None # Instrument
self.ldsc = None # Limb darkening set creator
self.ldps = None # Limb darkening profile set
self.cntm = None # Contamination model
# Declare data arrays and variables
# ---------------------------------
self.nlc: int = 0 # Number of light curves
self.n_noise_blocks: int = 0 # Number of noise blocks
self.noise_ids = None
self.times: list = None # List of time arrays
self.fluxes: list = None # List of flux arrays
self.errors: list = None # List of flux uncertainties
self.covariates: list = None # List of covariates
self.wn: ndarray = None # Array of white noise estimates for each light curve
self.timea: ndarray = None # Array of concatenated times
self.mfluxa: ndarray = None # Array of concatenated model fluxes
self.ofluxa: ndarray = None # Array of concatenated observed fluxes
self.errora: ndarray = None # Array of concatenated model fluxes
self.lcids: ndarray = None # Array of light curve indices for each datapoint
self.pbids: ndarray = None # Array of passband indices for each light curve
self.lcslices: list = None # List of light curve slices
self._local_minimization = None
# Initialise the additional lnprior list
# --------------------------------------
self.lnpriors = []
if init_data:
# Set up the observation data
# ---------------------------
self._init_data(times = times, fluxes = fluxes, pbids = pbids, covariates = covariates,
errors = errors, wnids = wnids, nsamples = nsamples, exptimes = exptimes)
# Set up the parametrisation
# --------------------------
self._init_parameters()
# Inititalise the instrument
# --------------------------
self._init_instrument()
self._post_initialisation()
def _init_data(self, times, fluxes, pbids=None, covariates=None, errors=None, wnids = None, nsamples=1, exptimes=0.):
if isinstance(times, ndarray) and times.ndim == 1 and times.dtype == float:
times = [times]
if isinstance(fluxes, ndarray) and fluxes.ndim == 1 and fluxes.dtype == float:
fluxes = [fluxes]
if pbids is None:
if self.pbids is None:
self.pbids = zeros(len(fluxes), int)
else:
self.pbids = atleast_1d(pbids).astype('int')
self.nlc = len(times)
self.times = times
self.fluxes = fluxes
self.wn = [nanstd(diff(f)) / sqrt(2) for f in fluxes]
self.timea = concatenate(self.times)
self.ofluxa = concatenate(self.fluxes)
self.mfluxa = zeros_like(self.ofluxa)
self.lcids = concatenate([full(t.size, i) for i, t in enumerate(self.times)])
# TODO: Noise IDs get scrambled when removing transits, fix!!!
if wnids is None:
if self.noise_ids is None:
self.noise_ids = zeros(self.nlc, int)
self.n_noise_blocks = 1
else:
self.noise_ids = asarray(wnids)
self.n_noise_blocks = len(unique(self.noise_ids))
assert self.noise_ids.size == self.nlc, "Need one noise block id per light curve."
assert self.noise_ids.max() == self.n_noise_blocks - 1, "Error initialising noise block ids."
if isscalar(nsamples):
self.nsamples = full(self.nlc, nsamples)
self.exptimes = full(self.nlc, exptimes)
else:
assert (len(nsamples) == self.nlc) and (len(exptimes) == self.nlc)
self.nsamples = asarray(nsamples, 'int')
self.exptimes = asarray(exptimes)
self.tm.set_data(self.timea, self.lcids, self.pbids, self.nsamples, self.exptimes)
if errors is None:
self.errors = array([full(t.size, nan) for t in self.times])
else:
self.errors = asarray(errors)
self.errora = concatenate(self.errors)
# Initialise the light curves slices
# ----------------------------------
self.lcslices = []
sstart = 0
for i in range(self.nlc):
s = self.times[i].size
self.lcslices.append(s_[sstart:sstart + s])
sstart += s
# Initialise the covariate arrays, if given
# -----------------------------------------
if covariates is not None:
self.covariates = covariates
for cv in self.covariates:
cv = (cv - cv.mean(0)) / cv.std(0)
#self.ncovs = self.covariates[0].shape[1]
#self.covsize = array([c.size for c in self.covariates])
#self.covstart = concatenate([[0], self.covsize.cumsum()[:-1]])
#self.cova = concatenate(self.covariates)
def print_parameters(self, columns: int = 2):
columns = max(1, columns)
for i, p in enumerate(self.ps):
print(p.__repr__(), end=('\n' if i % columns == columns - 1 else '\t'))
def _init_parameters(self):
self.ps = ParameterSet()
self._init_p_orbit()
self._init_p_planet()
self._init_p_limb_darkening()
self._init_p_baseline()
self._init_p_noise()
self.ps.freeze()
def _init_p_orbit(self):
"""Orbit parameter initialisation.
"""
porbit = [
GParameter('tc', 'zero_epoch', 'd', N(0.0, 0.1), (-inf, inf)),
GParameter('p', 'period', 'd', N(1.0, 1e-5), (0, inf)),
GParameter('rho', 'stellar_density', 'g/cm^3', U(0.1, 25.0), (0, inf)),
GParameter('b', 'impact_parameter', 'R_s', U(0.0, 1.0), (0, 1))]
self.ps.add_global_block('orbit', porbit)
def _init_p_planet(self):
"""Planet parameter initialisation.
"""
pk2 = [PParameter('k2', 'area_ratio', 'A_s', GM(0.1), (0.01**2, 0.75**2))]
self.ps.add_passband_block('k2', 1, 1, pk2)
self._pid_k2 = repeat(self.ps.blocks[-1].start, self.npb)
self._start_k2 = self.ps.blocks[-1].start
self._sl_k2 = self.ps.blocks[-1].slice
def _init_p_limb_darkening(self):
"""Limb darkening parameter initialisation.
"""
pld = concatenate([
[PParameter('q1_{:d}'.format(i), 'q1_coefficient', '', U(0, 1), bounds=(0, 1)),
PParameter('q2_{:d}'.format(i), 'q2_coefficient', '', U(0, 1), bounds=(0, 1))]
for i in range(self.npb)])
self.ps.add_passband_block('ldc', 2, self.npb, pld)
self._sl_ld = self.ps.blocks[-1].slice
self._start_ld = self.ps.blocks[-1].start
def _init_p_baseline(self):
"""Baseline parameter initialisation.
"""
self._sl_bl = None
def _init_p_noise(self):
"""Noise parameter initialisation.
"""
pns = [LParameter('loge_{:d}'.format(i), 'log10_error_{:d}'.format(i), '', U(-4, 0), bounds=(-4, 0)) for i in range(self.n_noise_blocks)]
self.ps.add_lightcurve_block('log_err', 1, self.n_noise_blocks, pns)
self._sl_err = self.ps.blocks[-1].slice
self._start_err = self.ps.blocks[-1].start
def _init_instrument(self):
pass
def _pre_initialisation(self):
pass
def _post_initialisation(self):
pass
def create_pv_population(self, npop=50):
pvp = self.ps.sample_from_prior(npop)
for sl in self.ps.blocks[1].slices:
pvp[:,sl] = uniform(0.01**2, 0.25**2, size=(npop, 1))
# With LDTk
# ---------
#
# Use LDTk to create the sample if LDTk has been initialised.
#
if self.ldps:
istart = self._start_ld
cms, ces = self.ldps.coeffs_tq()
for i, (cm, ce) in enumerate(zip(cms.flat, ces.flat)):
pvp[:, i + istart] = normal(cm, ce, size=pvp.shape[0])
# No LDTk
# -------
#
# Ensure that the total limb darkening decreases towards
# red passbands.
#
else:
ldsl = self._sl_ld
for i in range(pvp.shape[0]):
pid = argsort(pvp[i, ldsl][::2])[::-1]
pvp[i, ldsl][::2] = pvp[i, ldsl][::2][pid]
pvp[i, ldsl][1::2] = pvp[i, ldsl][1::2][pid]
# Estimate white noise from the data
# ----------------------------------
for i in range(self.nlc):
wn = diff(self.ofluxa).std() / sqrt(2)
pvp[:, self._start_err] = log10(uniform(0.5*wn, 2*wn, size=npop))
return pvp
def baseline(self, pv):
"""Multiplicative baseline"""
return 1.
def trends(self, pv):
"""Additive trends"""
return 0.
def transit_model(self, pv, copy=True):
pv = atleast_2d(pv)
pvp = map_pv(pv)
ldc = map_ldc(pv[:,self._sl_ld])
flux = self.tm.evaluate_pv(pvp, ldc, copy)
return flux
def flux_model(self, pv):
baseline = self.baseline(pv)
trends = self.trends(pv)
model_flux = self.transit_model(pv)
return baseline * model_flux + trends
def residuals(self, pv):
return self.ofluxa - self.flux_model(pv)
def set_prior(self, parameter, prior, *nargs) -> None:
if isinstance(parameter, str):
descriptions = self.ps.descriptions
names = self.ps.names
if parameter in descriptions:
parameter = descriptions.index(parameter)
elif parameter in names:
parameter = names.index(parameter)
else:
params = ', '.join([f"{ln} ({sn})" for ln, sn in zip(self.ps.descriptions, self.ps.names)])
raise ValueError(f'Parameter "{parameter}" not found from the parameter set: {params}')
if isinstance(prior, str):
if prior.lower() in ['n', 'np', 'normal']:
prior = N(nargs[0], nargs[1])
elif prior.lower() in ['u', 'up', 'uniform']:
prior = U(nargs[0], nargs[1])
else:
raise ValueError(f'Unknown prior "{prior}". Allowed values are (N)ormal and (U)niform.')
self.ps[parameter].prior = prior
def set_radius_ratio_prior(self, kmin, kmax):
for p in self.ps[self._sl_k2]:
p.prior = U(kmin ** 2, kmax ** 2)
p.bounds = [kmin ** 2, kmax ** 2]
self.ps.thaw()
self.ps.freeze()
def add_t14_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the transit duration.
Parameters
----------
mean: float
Mean of the normal distribution
std: float
Standard deviation of the normal distribution.
"""
def T14(pv):
pv = atleast_2d(pv)
a = as_from_rhop(pv[:, 2], pv[:, 1])
t14 = duration_eccentric(pv[:, 1], sqrt(pv[:, 4]), a, arccos(pv[:, 3] / a), 0, 0, 1)
return norm.logpdf(t14, mean, std)
self.lnpriors.append(T14)
def add_as_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the scaled semi-major axis :math:`(a / R_\star)`.
Parameters
----------
mean: float
Mean of the normal distribution.
std: float
Standard deviation of the normal distribution
"""
def as_prior(pv):
a = as_from_rhop(pv[2], pv[1])
return norm.logpdf(a, mean, std)
self.lnpriors.append(as_prior)
def add_ldtk_prior(self, teff: tuple, logg: tuple, z: tuple, passbands: tuple,
uncertainty_multiplier: float = 3, **kwargs) -> None:
"""Add a LDTk-based prior on the limb darkening.
Parameters
----------
teff
logg
z
passbands
uncertainty_multiplier
Returns
-------
"""
if 'pbs' in kwargs.keys():
raise DeprecationWarning("The 'pbs' argument has been renamed to 'passbands'")
if isinstance(passbands[0], str):
raise DeprecationWarning(
'Passing passbands by name has been deprecated, they should be now Filter instances.')
self.ldsc = LDPSetCreator(teff, logg, z, list(passbands))
self.ldps = self.ldsc.create_profiles(1000)
self.ldps.resample_linear_z()
self.ldps.set_uncertainty_multiplier(uncertainty_multiplier)
def ldprior(pv):
return self.ldps.lnlike_tq(pv[:, self._sl_ld].reshape([pv.shape[0], -1, 2]))
self.lnpriors.append(ldprior)
def remove_outliers(self, sigma=5):
fmodel = squeeze(self.flux_model(self.de.minimum_location))
covariates = [] if self.covariates is not None else None
times, fluxes, lcids, errors = [], [], [], []
for i in range(len(self.times)):
res = self.fluxes[i] - fmodel[self.lcslices[i]]
mask = ~sigma_clip(res, sigma=sigma).mask
times.append(self.times[i][mask])
fluxes.append(self.fluxes[i][mask])
if covariates is not None:
covariates.append(self.covariates[i][mask])
if self.errors is not None:
errors.append(self.errors[i][mask])
self._init_data(times=times, fluxes=fluxes, covariates=self.covariates, pbids=self.pbids,
errors=(errors if self.errors is not None else None), wnids=self.noise_ids,
nsamples=self.nsamples, exptimes=self.exptimes)
def remove_transits(self, tids):
m = ones(len(self.times), bool)
m[tids] = False
self._init_data(self.times[m], self.fluxes[m], self.pbids[m],
self.covariates[m] if self.covariates is not None else None,
self.errors[m], self.noise_ids[m], self.nsamples[m], self.exptimes[m])
self._init_parameters()
def lnprior(self, pv: ndarray) -> Union[Iterable,float]:
"""Log prior density for a 1D or 2D array of model parameters.
Parameters
----------
pv: ndarray
Either a 1D parameter vector or a 2D parameter array.
Returns
-------
Log prior density for the given parameter vector(s).
"""
return self.ps.lnprior(pv) + self.additional_priors(pv)
def additional_priors(self, pv):
"""Additional priors."""
pv = atleast_2d(pv)
return sum([f(pv) for f in self.lnpriors], 0)
def lnlikelihood(self, pv):
"""Log likelihood for a 1D or 2D array of model parameters.
Parameters
----------
pv: ndarray
Either a 1D parameter vector or a 2D parameter array.
Returns
-------
Log likelihood for the given parameter vector(s).
"""
flux_m = self.flux_model(pv)
wn = 10**(atleast_2d(pv)[:,self._sl_err])
return lnlike_normal_v(self.ofluxa, flux_m, wn, self.noise_ids, self.lcids)
def lnposterior(self, pv):
lnp = self.lnprior(pv) + self.lnlikelihood(pv)
return where(isfinite(lnp), lnp, -inf)
def __call__(self, pv):
return self.lnposterior(pv)
def optimize_global(self, niter=200, npop=50, population=None, label='Global optimisation', leave=False,
plot_convergence: bool = True, use_tqdm: bool = True):
if self.de is None:
self.de = DiffEvol(self.lnposterior, clip(self.ps.bounds, -1, 1), npop, maximize=True, vectorize=True)
if population is None:
self.de._population[:, :] = self.create_pv_population(npop)
else:
self.de._population[:,:] = population
for _ in tqdm(self.de(niter), total=niter, desc=label, leave=leave, disable=(not use_tqdm)):
pass
if plot_convergence:
fig, axs = subplots(1, 5, figsize=(13, 2), constrained_layout=True)
rfit = self.de._fitness
mfit = isfinite(rfit)
if hasattr(self, '_old_de_fitness'):
m = isfinite(self._old_de_fitness)
axs[0].hist(-self._old_de_fitness[m], facecolor='midnightblue', bins=25, alpha=0.25)
axs[0].hist(-rfit[mfit], facecolor='midnightblue', bins=25)
for i, ax in zip([0, 2, 3, 4], axs[1:]):
if hasattr(self, '_old_de_fitness'):
m = isfinite(self._old_de_fitness)
ax.plot(self._old_de_population[m, i], -self._old_de_fitness[m], 'kx', alpha=0.25)
ax.plot(self.de.population[mfit, i], -rfit[mfit], 'k.')
ax.set_xlabel(self.ps.descriptions[i])
setp(axs, yticks=[])
setp(axs[1], ylabel='Log posterior')
setp(axs[0], xlabel='Log posterior')
sb.despine(fig, offset=5)
self._old_de_population = self.de.population.copy()
self._old_de_fitness = self.de._fitness.copy()
def optimize_local(self, pv0=None, method='powell'):
if pv0 is None:
if self.de is not None:
pv0 = self.de.minimum_location
else:
pv0 = self.ps.mean_pv
pv0[self._sl_err] = log10(self.wn)
res = minimize(lambda pv: -self.lnposterior(pv), pv0, method=method)
self._local_minimization = res
def sample_mcmc(self, niter: int = 500, thin: int = 5, repeats: int = 1, npop: int = None, population=None,
label='MCMC sampling', reset=True, leave=True, save=False, use_tqdm: bool = True):
if save and self.result_dir is None:
raise ValueError('The MCMC sampler is set to save the results, but the result directory is not set.')
if self.sampler is None:
if population is not None:
pop0 = population
elif hasattr(self, '_local_minimization') and self._local_minimization is not None:
pop0 = multivariate_normal(self._local_minimization.x, diag(full(len(self.ps), 0.001 ** 2)), size=npop)
elif self.de is not None:
pop0 = self.de.population.copy()
else:
raise ValueError('Sample MCMC needs an initial population.')
self.sampler = EnsembleSampler(pop0.shape[0], pop0.shape[1], self.lnposterior, vectorize=True)
else:
pop0 = self.sampler.chain[:,-1,:].copy()
for i in tqdm(range(repeats), desc='MCMC sampling', disable=(not use_tqdm)):
if reset or i > 0:
self.sampler.reset()
for _ in tqdm(self.sampler.sample(pop0, iterations=niter, thin=thin), total=niter,
desc='Run {:d}/{:d}'.format(i+1, repeats), leave=False, disable=(not use_tqdm)):
pass
if save:
self.save(self.result_dir)
pop0 = self.sampler.chain[:,-1,:].copy()
def posterior_samples(self, burn: int = 0, thin: int = 1, derived_parameters: bool = True):
fc = self.sampler.chain[:, burn::thin, :].reshape([-1, self.de.n_par])
df = pd.DataFrame(fc, columns=self.ps.names)
if derived_parameters:
for k2c in df.columns[self._sl_k2]:
df[k2c.replace('k2', 'k')] = sqrt(df[k2c])
df['a'] = as_from_rhop(df.rho.values, df.p.values)
df['inc'] = i_from_baew(df.b.values, df.a.values, 0., 0.)
average_ks = sqrt(df.iloc[:, self._sl_k2]).mean(1).values
df['t14'] = d_from_pkaiews(df.p.values, average_ks, df.a.values, df.inc.values, 0., 0., 1)
return df
def plot_mcmc_chains(self, pid: int=0, alpha: float=0.1, thin: int=1, ax=None):
fig, ax = (None, ax) if ax is not None else subplots()
ax.plot(self.sampler.chain[:, ::thin, pid].T, 'k', alpha=alpha)
fig.tight_layout()
return fig
def save(self, save_path: Path = '.'):
save_path = Path(save_path)
if self.de:
de = xa.DataArray(self.de.population, dims='pvector name'.split(), coords={'name': self.ps.names})
else:
de = None
if self.sampler is not None:
mc = xa.DataArray(self.sampler.chain, dims='pvector step name'.split(),
coords={'name': self.ps.names}, attrs={'ndim': self.de.n_par, 'npop': self.de.n_pop})
else:
mc = None
ds = xa.Dataset(data_vars={'de_population_lm': de, 'lm_mcmc': mc},
attrs={'created': strftime('%Y-%m-%d %H:%M:%S'), 'target': self.name})
ds.to_netcdf(save_path.joinpath(f'{self.name}.nc'))
def plot_light_curves(self, method='de', ncol: int = 3, width: float = 2., max_samples: int = 1000, figsize=None,
data_alpha=0.5, ylim=None):
nrow = int(ceil(self.nlc / ncol))
if method == 'mcmc':
df = self.posterior_samples(derived_parameters=False)
t0, p = df.tc.median(), df.p.median()
fmodel = self.flux_model(permutation(df.values)[:max_samples])
fmperc = percentile(fmodel, [50, 16, 84, 2.5, 97.5], 0)
else:
fmodel = squeeze(self.flux_model(self.de.minimum_location))
t0, p = self.de.minimum_location[0], self.de.minimum_location[1]
fmperc = None
fig, axs = subplots(nrow, ncol, figsize=figsize, constrained_layout=True, sharey='all', sharex='all',
squeeze=False)
for i in range(self.nlc):
ax = axs.flat[i]
e = epoch(self.times[i].mean(), t0, p)
tc = t0 + e * p
time = self.times[i] - tc
ax.plot(time, self.fluxes[i], '.', alpha=data_alpha)
if method == 'de':
ax.plot(time, fmodel[self.lcslices[i]], 'w', lw=4)
ax.plot(time, fmodel[self.lcslices[i]], 'k', lw=1)
else:
ax.fill_between(time, *fmperc[3:5, self.lcslices[i]], alpha=0.15)
ax.fill_between(time, *fmperc[1:3, self.lcslices[i]], alpha=0.25)
ax.plot(time, fmperc[0, self.lcslices[i]])
setp(ax, xlabel=f'Time - T$_c$ [d]', xlim=(-width / 2 / 24, width / 2 / 24))
setp(axs[:, 0], ylabel='Normalised flux')
if ylim is not None:
setp(axs, ylim=ylim)
for ax in axs.flat[self.nlc:]:
ax.remove()
return fig
def __repr__(self):
return f"Target: {self.name}\nLPF: {self._lpf_name}\n Passbands: {self.passbands}"
class BaseLPF:
_lpf_name = 'base'
def __init__(self, name: str, passbands: list, times: list = None, fluxes: list = None, errors: list = None,
pbids: list = None, covariates: list = None, tm: TransitModel = None,
nsamples: tuple = 1, exptimes: tuple = 0.):
self.tm = tm or QuadraticModel(klims=(0.01, 0.75), nk=512, nz=512)
# LPF name
# --------
self.name = name
# Passbands
# ---------
# Should be arranged from blue to red
if isinstance(passbands, (list, tuple, ndarray)):
self.passbands = passbands
else:
self.passbands = [passbands]
self.npb = npb = len(self.passbands)
self.nsamples = None
self.exptimes = None
# Declare high-level objects
# --------------------------
self.ps = None # Parametrisation
self.de = None # Differential evolution optimiser
self.sampler = None # MCMC sampler
self.instrument = None # Instrument
self.ldsc = None # Limb darkening set creator
self.ldps = None # Limb darkening profile set
self.cntm = None # Contamination model
# Declare data arrays and variables
# ---------------------------------
self.nlc: int = 0 # Number of light curves
self.times: list = None # List of time arrays
self.fluxes: list = None # List of flux arrays
self.errors: list = None # List of flux uncertainties
self.covariates: list = None # List of covariates
self.wn: ndarray = None # Array of white noise estimates for each light curve
self.timea: ndarray = None # Array of concatenated times
self.mfluxa: ndarray = None # Array of concatenated model fluxes
self.ofluxa: ndarray = None # Array of concatenated observed fluxes
self.errora: ndarray = None # Array of concatenated model fluxes
self.lcids: ndarray = None # Array of light curve indices for each datapoint
self.pbids: ndarray = None # Array of passband indices for each light curve
self.lcslices: list = None # List of light curve slices
# Set up the observation data
# ---------------------------
if times is not None and fluxes is not None and pbids is not None:
self._init_data(times, fluxes, pbids, covariates, errors, nsamples, exptimes)
# Setup parametrisation
# =====================
self._init_parameters()
# Initialise the additional lnprior list
# --------------------------------------
self.lnpriors = []
# Initialise the temporary arrays
# -------------------------------
self._zpv = zeros(6)
self._tuv = zeros((npb, 2))
self._zeros = zeros(npb)
self._ones = ones(npb)
# Inititalise the instrument
self._init_instrument()
if times is not None:
self._bad_fluxes = [ones_like(t) for t in self.times]
else:
self._bad_fluxes = None
def _init_data(self, times, fluxes, pbids, covariates=None, errors=None, nsamples=1, exptimes=0.):
if isinstance(times, ndarray) and times.dtype == float:
times = [times]
if isinstance(fluxes, ndarray) and fluxes.dtype == float:
fluxes = [fluxes]
self.nlc = len(times)
self.times = asarray(times)
self.fluxes = asarray(fluxes)
self.pbids = asarray(pbids)
self.wn = [diff(f).std() / sqrt(2) for f in fluxes]
self.timea = concatenate(self.times)
self.ofluxa = concatenate(self.fluxes)
self.mfluxa = zeros_like(self.ofluxa)
self.pbids = atleast_1d(pbids).astype('int')
self.lcids = concatenate([full(t.size, i) for i, t in enumerate(self.times)])
if isscalar(nsamples):
self.nsamples = full(self.nlc, nsamples)
self.exptimes = full(self.nlc, exptimes)
else:
assert (len(nsamples) == self.nlc) and (len(exptimes) == self.nlc)
self.nsamples = asarray(nsamples, 'int')
self.exptimes = asarray(exptimes)
self.tm.set_data(self.timea, self.lcids, self.pbids, self.nsamples, self.exptimes)
if errors is None:
self.errors = array([full(t.size, nan) for t in self.times])
else:
self.errors = asarray(errors)
self.errora = concatenate(self.errors)
# Initialise the light curves slices
# ----------------------------------
self.lcslices = []
sstart = 0
for i in range(self.nlc):
s = self.times[i].size
self.lcslices.append(s_[sstart:sstart + s])
sstart += s
# Initialise the covariate arrays, if given
# -----------------------------------------
if covariates is not None:
self.covariates = covariates
for cv in self.covariates:
cv[:, 1:] = (cv[:, 1:] - cv[:, 1:].mean(0)) / cv[:, 1:].ptp(0)
self.ncovs = self.covariates[0].shape[1]
self.covsize = array([c.size for c in self.covariates])
self.covstart = concatenate([[0], self.covsize.cumsum()[:-1]])
self.cova = concatenate(self.covariates)
def _init_parameters(self):
self.ps = ParameterSet()
self._init_p_orbit()
self._init_p_planet()
self._init_p_limb_darkening()
self._init_p_baseline()
self._init_p_noise()
self.ps.freeze()
def _init_p_orbit(self):
"""Orbit parameter initialisation.
"""
porbit = [
GParameter('tc', 'zero_epoch', 'd', N(0.0, 0.1), (-inf, inf)),
GParameter('pr', 'period', 'd', N(1.0, 1e-5), (0, inf)),
GParameter('rho', 'stellar_density', 'g/cm^3', U(0.1, 25.0), (0, inf)),
GParameter('b', 'impact_parameter', 'R_s', U(0.0, 1.0), (0, 1))]
self.ps.add_global_block('orbit', porbit)
def _init_p_planet(self):
"""Planet parameter initialisation.
"""
pk2 = [PParameter('k2', 'area_ratio', 'A_s', GM(0.1), (0.01**2, 0.55**2))]
self.ps.add_passband_block('k2', 1, 1, pk2)
self._pid_k2 = repeat(self.ps.blocks[-1].start, self.npb)
self._start_k2 = self.ps.blocks[-1].start
self._sl_k2 = self.ps.blocks[-1].slice
def _init_p_limb_darkening(self):
"""Limb darkening parameter initialisation.
"""
pld = concatenate([
[PParameter('q1_{:d}'.format(i), 'q1_coefficient', '', U(0, 1), bounds=(0, 1)),
PParameter('q2_{:d}'.format(i), 'q2_coefficient', '', U(0, 1), bounds=(0, 1))]
for i in range(self.npb)])
self.ps.add_passband_block('ldc', 2, self.npb, pld)
self._sl_ld = self.ps.blocks[-1].slice
self._start_ld = self.ps.blocks[-1].start
def _init_p_baseline(self):
"""Baseline parameter initialisation.
"""
pass
def _init_p_noise(self):
"""Noise parameter initialisation.
"""
pns = [LParameter('lne_{:d}'.format(i), 'log_error_{:d}'.format(i), '', U(-8, -0), bounds=(-8, -0)) for i in range(self.nlc)]
self.ps.add_lightcurve_block('log_err', 1, self.nlc, pns)
self._sl_err = self.ps.blocks[-1].slice
self._start_err = self.ps.blocks[-1].start
def _init_instrument(self):
pass
def create_pv_population(self, npop=50):
pvp = self.ps.sample_from_prior(npop)
for sl in self.ps.blocks[1].slices:
pvp[:,sl] = uniform(0.01**2, 0.25**2, size=(npop, 1))
# With LDTk
# ---------
#
# Use LDTk to create the sample if LDTk has been initialised.
#
if self.ldps:
istart = self._start_ld
cms, ces = self.ldps.coeffs_tq()
for i, (cm, ce) in enumerate(zip(cms.flat, ces.flat)):
pvp[:, i + istart] = normal(cm, ce, size=pvp.shape[0])
# No LDTk
# -------
#
# Ensure that the total limb darkening decreases towards
# red passbands.
#
else:
ldsl = self._sl_ld
for i in range(pvp.shape[0]):
pid = argsort(pvp[i, ldsl][::2])[::-1]
pvp[i, ldsl][::2] = pvp[i, ldsl][::2][pid]
pvp[i, ldsl][1::2] = pvp[i, ldsl][1::2][pid]
# Estimate white noise from the data
# ----------------------------------
for i in range(self.nlc):
wn = diff(self.ofluxa).std() / sqrt(2)
pvp[:, self._start_err] = log10(uniform(0.5*wn, 2*wn, size=npop))
return pvp
def baseline(self, pv):
"""Multiplicative baseline"""
return 1.
def trends(self, pv):
"""Additive trends"""
return 0.
def transit_model(self, pv):
pv = atleast_2d(pv)
pvp = map_pv(pv)
ldc = map_ldc(pv[:,self._sl_ld])
flux = self.tm.evaluate_pv(pvp, ldc)
return flux
def flux_model(self, pv):
baseline = self.baseline(pv)
trends = self.trends(pv)
model_flux = self.transit_model(pv)
return baseline * model_flux + trends
def residuals(self, pv):
return self.ofluxa - self.flux_model(pv)
def set_prior(self, pid: int, prior) -> None:
self.ps[pid].prior = prior
def add_t14_prior(self, mean: float, std: float) -> None:
"""Add a normal prior on the transit duration.
Parameters
----------
mean
std
Returns
-------
"""
def T14(pv):
a = as_from_rhop(pv[2], pv[1])
t14 = duration_eccentric(pv[1], sqrt(pv[4]), a, mt.acos(pv[3] / a), 0, 0, 1)
return norm.logpdf(t14, mean, std)
self.lnpriors.append(T14)
def add_as_prior(self, mean: float, std: float) -> None:
"""Add a prior on the scaled semi-major axis
Parameters
----------
mean
std
Returns
-------
"""
def as_prior(pv):
a = as_from_rhop(pv[2], pv[1])
return norm.logpdf(a, mean, std)
self.lnpriors.append(as_prior)
def add_ldtk_prior(self, teff: tuple, logg: tuple, z: tuple,
uncertainty_multiplier: float = 3,
pbs: tuple = ('g', 'r', 'i', 'z')) -> None:
"""Add a LDTk-based prior on the limb darkening.
Parameters
----------
teff
logg
z
uncertainty_multiplier
pbs
Returns
-------
"""
fs = {n: f for n, f in zip('g r i z'.split(), (sdss_g, sdss_r, sdss_i, sdss_z))}
filters = [fs[k] for k in pbs]
self.ldsc = LDPSetCreator(teff, logg, z, filters)
self.ldps = self.ldsc.create_profiles(1000)
self.ldps.resample_linear_z()
self.ldps.set_uncertainty_multiplier(uncertainty_multiplier)
def ldprior(pv):
return self.ldps.lnlike_tq(pv[self._sl_ld])
self.lnpriors.append(ldprior)
def remove_outliers(self, sigma=5):
fmodel = self.flux_model(self.de.minimum_location)
times, fluxes, pbids, errors = [], [], [], []
for i in range(len(self.times)):
res = self.fluxes[i] - fmodel[i]
mask = ~sigma_clip(res, sigma=sigma).mask
times.append(self.times[i][mask])
fluxes.append(self.fluxes[i][mask])
if self.errors is not None:
errors.append(self.errors[i][mask])
self._init_data(times, fluxes, self.pbids, (errors if self.errors is not None else None))
def remove_transits(self, tids):
m = ones(len(self.times), bool)
m[tids] = False
self._init_data(self.times[m], self.fluxes[m], self.pbids[m],
self.covariates[m] if self.covariates is not None else None,
self.errors[m], self.nsamples[m], self.exptimes[m])
self._init_parameters()
def lnprior(self, pv):
return self.ps.lnprior(pv) + self.additional_priors(pv)
def additional_priors(self, pv):
"""Additional priors."""
pv = atleast_2d(pv)
return sum([f(pv) for f in self.lnpriors], 0)
def lnlikelihood(self, pv):
flux_m = self.flux_model(pv)
wn = 10**(atleast_2d(pv)[:,self._sl_err])
return lnlike_normal_v(self.ofluxa, flux_m, wn, self.lcids)
def lnposterior(self, pv):
lnp = self.lnprior(pv) + self.lnlikelihood(pv)
return where(isfinite(lnp), lnp, -inf)
def __call__(self, pv):
return self.lnposterior(pv)
def optimize_global(self, niter=200, npop=50, population=None, label='Global optimisation', leave=False):
if self.de is None:
self.de = DiffEvol(self.lnposterior, clip(self.ps.bounds, -1, 1), npop, maximize=True, vectorize=True)
if population is None:
self.de._population[:, :] = self.create_pv_population(npop)
else:
self.de._population[:,:] = population
for _ in tqdm(self.de(niter), total=niter, desc=label, leave=leave):
pass
def sample_mcmc(self, niter=500, thin=5, label='MCMC sampling', reset=False, leave=True):
if self.sampler is None:
self.sampler = EnsembleSampler(self.de.n_pop, self.de.n_par, self.lnposterior, vectorize=True)
pop0 = self.de.population
else:
pop0 = self.sampler.chain[:,-1,:].copy()
if reset:
self.sampler.reset()
for _ in tqdm(self.sampler.sample(pop0, iterations=niter, thin=thin), total=niter, desc=label, leave=False):
pass
def posterior_samples(self, burn: int=0, thin: int=1, include_ldc: bool=False):
ldstart = self._sl_ld.start
fc = self.sampler.chain[:, burn::thin, :].reshape([-1, self.de.n_par])
d = fc if include_ldc else fc[:, :ldstart]
n = self.ps.names if include_ldc else self.ps.names[:ldstart]
return pd.DataFrame(d, columns=n)
def plot_mcmc_chains(self, pid: int=0, alpha: float=0.1, thin: int=1, ax=None):
fig, ax = (None, ax) if ax is not None else subplots()
ax.plot(self.sampler.chain[:, ::thin, pid].T, 'k', alpha=alpha)
fig.tight_layout()
return fig
def __repr__(self):
s = f"""Target: {self.name}
LPF: {self._lpf_name}
Passbands: {self.passbands}"""
return s
def ldc_calc(star=None,filt=None,teff=None,logg=None,metal=None,binstart=500,binend=550,model='non-linear',compare=False,rescaled=True,plt=True):
import numpy as np
from scipy.interpolate import interp1d
import ldtk
from ldtk import (LDPSetCreator, BoxcarFilter,TabulatedFilter)
from scipy.optimize import curve_fit
#Import filters
if filt == 'g430l':
fil = np.loadtxt('/Users/jlothrin/Desktop/G430L.csv',delimiter=',',skiprows=1)
if filt == 'g750l':
fil = np.loadtxt('/Users/jlothrin/Desktop/G750L.csv',delimiter=',',skiprows=1)
if filt == 'wfc3141':
fil = np.loadtxt('/Users/jlothrin/Desktop/wfc3141.csv',delimiter=',',skiprows=1)
if filt == 'IRAC_4_5':
fil = np.loadtxt('/Users/jlothrin/Desktop/IRAC_4_5.csv',delimiter=',',skiprows=1)
waves = fil[:,0]
if filt[0] is 'g':
waves = fil[:,0] / 10.
if filt == 'IRAC_4_5':
waves = fil[:,0]*1000.
throughs = fil[:,1]
f = interp1d(waves,throughs)
waves_hires = np.linspace(min(waves),max(waves),500,endpoint=True)
throughs_hires = f(waves_hires)
#EVERYTHING MUST BE IN NM
lower_wave = binstart
upper_wave = binend
w = [waves_hires[i] for i in range(0,len(waves_hires)) if waves_hires[i] > lower_wave if waves_hires[i] < upper_wave]
t = [throughs_hires[i] for i in range(0,len(waves_hires)) if waves_hires[i] > lower_wave if waves_hires[i] < upper_wave]
#filters = [BoxcarFilter('a',450,550), BoxcarFilter('b',650,750), BoxcarFilter('c',850,950)]
#filters = [BoxcarFilter('a',650,750)]
filters = [ldtk.TabulatedFilter('stis',w,t)]
if star == '55cnc':
sc = LDPSetCreator([5250, 100],[4.50, 0.10],[0.25, 0.05],filters)
teff = 5250
logg = 4.50
metal = 0.25
#sc = LDPSetCreator([6443,75],[4.76,0.09],[-0.08,0.05],filters)
if star == 'wasp31':
sc = LDPSetCreator([6250,50],[4.5,0.09],[-0.2,0.05],filters)
teff = 6250
logg = 4.5
metal = -0.2
if star == 'gj436':
sc = LDPSetCreator([3416,100],[4.843,0.018],[0.02,0.2],filters)
teff = 3416
logg = 4.843
metal = 0.02
if star == 'gj3470':
sc = LDPSetCreator([3652,50],[4.78,0.12],[0.18,0.08],filters)
teff = 3652
logg = 4.78
metal = 0.18
if star == 'hd97658':
sc = LDPSetCreator([5217,33],[4.583,0.054],[-0.26,0.03],filters)
teff = 5217
logg = 4.583
metal = -0.26
if star == 'k218':
sc = LDPSetCreator([3503,60],[4.863,0.13],[0.09,0.09],filters)
teff = 3503
logg = 4.863
metal = 0.09
if star == 'k138':
sc = LDPSetCreator([3841,50],[4.886,0.01],[-0.280,0.1],filters)
teff = 3841
logg = 4.886
metal = -0.280
if star == 'wasp17':
sc = LDPSetCreator([6666,30],[4.26,0.06],[-0.04,0.03],filters)
teff = 6666
logg = 4.26
metal = -0.04
if star is None:
sc = LDPSetCreator([teff,50],[logg,0.1],[metal,0.1],filters)
ps = sc.create_profiles(nsamples=100)
qc,qe = ps.coeffs_qd(do_mc=False)
nlc,nle = ps.coeffs_nl(do_mc=False)
lnc,lne = ps.coeffs_ln(do_mc=False)
print nlc
print nle
def nl_func(x,y1,y2,y3,y4):
out = 1.-(y1*(1.-(x**(1./2.)))+y2*(1.-(x**(2./2.)))+y3*(1.-(x**(3./2.)))+y4*(1.-(x**(4./2.))))
params = [y1,y2,y3,y4]
bad = [1 for i in range(0,len(params)) if abs(params[i]) > 1.]
# if np.sum(bad) > 0:
# out = out+1000.
return out
fit= curve_fit(nl_func,ps._mu,ps.profile_averages[0],[0.2,0.9,-0.2,-0.03])
if plt is True:
figure()
plot(ps._mu,ps.profile_averages[0],label='Model Intensity')
plot(ps._mu,ps.profile_averages[0],'bo',label='Sampled Mu')
#plot(ps._mu,1-qc[0,0]*(1-ps._mu)-qc[0,1]*(1-ps._mu)**2,'g--',label = 'Quad Fit')
#plot(ps._mu,1-nlc[0,0]*(1-((ps._mu**(1./2.)))+nlc[0,1]*(1-ps._mu)+nlc[0,2]*(1-(ps._mu**(3./2.)))+nlc[0,3]*(1-(ps._mu**(2.)))),'r',label = 'Non-Linear Fit')
plot(ps._mu,1-lnc[0,0]*(1-ps._mu),'y',label='Linear Fit')
plot(ps._mu,nl_func(ps._mu,fit[0][0],fit[0][1],fit[0][2],fit[0][3]),'g',label = 'My non-linear fit')
plot(ps._mu,nl_func(ps._mu,nlc[0][0],nlc[0][1],nlc[0][2],nlc[0][3]),'--k',label='Non-Linear Fit')
#sing 450-460
#plot(ps._mu,nl_func(ps._mu,0.1778,1.1859,-0.7235,0.1887),'k',label='Sing+2013')
#sing 460-470
#plot(ps._mu,nl_func(ps._mu,0.2136,0.8688,-0.1927,-0.0317),'k',label='Sing+2013')
new_mu = [(x-min(ps._mu))/(max(ps._mu)-min(ps._mu)) for x in ps._mu]
refit= curve_fit(nl_func,new_mu,ps.profile_averages[0],[0.2,0.7,-0.2,-0.03])
if plt is True:
plot(new_mu,ps.profile_averages[0],label='Rescaled Model Intensity')
plot(new_mu,nl_func(np.asarray(new_mu),refit[0][0],refit[0][1],refit[0][2],refit[0][3]),'b',label = 'My rescaled non-linear fit')
#my model for 55 cancri
if compare is True:
good_mu_old = [0.1313,0.19335,0.2554,0.31745,0.3795,0.44155,0.5036,0.56565,0.6277,0.68975,0.7518,0.8135,0.876,0.93795,1.0]
one_sum_norm_old = [ 0.37541731740693157, 0.46238495959398135, 0.52611395484286205, 0.57920999647415661, 0.62658004547351620, 0.67049257831603259, 0.71208564917444228, 0.75195835479518414,
0.79045298237117534 , 0.82776743852276846 , 0.86401545614819741, 0.89927622441607569 , 0.93360250587690652 , 0.96703568174637822, 1.0000000000000000]
good_mu = [0.1313,0.19335,0.2554,0.31745,0.3795,0.44155,0.5036,0.56565,0.6277,0.68975,0.7518,0.8135,0.876,0.93795,1.0]
one_sum_norm = [0.38759735798258849, 0.46854567224818039, 0.52910322497992557, 0.58049544322307434, 0.62695066358655005, 0.67038264597838637, 0.71174173136349628, 0.75153440209321387,
0.79003806657404141, 0.82741678251146478, 0.86376146640146356, 0.89913775107728544 , 0.93359035990132300, 0.96715566457928104 , 1.0000000000000000]
if plt is True:
plot(good_mu,one_sum_norm,'r--',label='Josh model')
plot(good_mu,one_sum_norm,'ro',label='Josh Sampled Mu')
plot(good_mu_old,one_sum_norm_old,'k--',label='Josh model old')
plot(good_mu_old,one_sum_norm_old,'ko',label='Josh Sampled Mu old')
#ylim([0.,1.0])
#legend(loc='lower right')
#ylabel('Normalized Intensity')
#xlabel('Mu')
#title('LDTK - Teff = 5250 logg = 4.33, z = 0.25 - 650-750 nm')
#text(0.4,0.4,'Q1 = '+str(qc[0,0]))
#text(0.4,0.36,'Q2 = '+str(qc[0,1]))
if plt is True:
legend(loc='lower right')
ylabel('Normalized Intensity')
xlabel('Mu')
title('LDTK - Teff = '+str(teff)+', logg = '+str(logg)+', z = '+str(metal)+' - STIS - '+str(lower_wave)+'-'+str(upper_wave)+' nm')
if rescaled is True:
out = refit[0]
if rescaled is False:
out = fit[0]
#pdb.set_trace()
return out,nle
def ldc_calc_k2(star=None,teff=None,tu=None,logg=None,lu=None,metal=None,metu=None,out_file=None,pickle_file=None,make_txt=True,make_fig=True):
import numpy as np
from scipy.interpolate import interp1d
import ldtk
from ldtk import (LDPSetCreator, BoxcarFilter,TabulatedFilter)
from scipy.optimize import curve_fit
kep_trans = np.loadtxt('/Users/jlothrin/LDCs/kepler_response_lowres1.csv',delimiter=',',skiprows=9)
waves = kep_trans[:,0] * 1000.
throughs = kep_trans[:,1]
filters = [ldtk.TabulatedFilter('kepler',waves,throughs)]
#filters = [BoxcarFilter('a',650,750)]
sc = LDPSetCreator(teff=[teff,tu],logg=[logg,lu],z=[metal,metu],filters=filters)
ps = sc.create_profiles(nsamples=100)
#plot(ps._mu,ps.profile_averages[0],'k',label='Model Intensity')
#plot(ps._mu,ps.profile_averages[0],'ko',label='Sampled Mu')
oldmu = ps._mu
oldprof = ps.profile_averages[0]
ps.set_limb_z_try(sqrt(1-np.min(ps._mu)**2))
qc,qe = ps.coeffs_qd(do_mc=True)
qc,qm = ps.coeffs_qd(do_mc=True,return_cm=True)
nlc,nle = ps.coeffs_nl(do_mc=True)
lnc,lne = ps.coeffs_ln(do_mc=True)
if make_txt is True:
text_file = open(out_file,'w')
text_file.write(star + '\n \n')
text_file.write('LINEAR: 1 - coeff*(1-mu) \n')
text_file.write('Linear Coefficient: %s \n' % lnc[0,0])
text_file.write('Linear Coefficient Uncertainty: %s \n \n' % lne[0])
text_file.write('QUADRATIC: 1 - coeff1*(1-mu)-coeff2*(1-mu)**2 \n')
text_file.write('Quadratic Coefficients: %s %f \n' % (qc[0,0],qc[0,1]))
text_file.write('Quadratic Coefficients Uncertainty: %s %f \n' % (qe[0,0],qe[0,1]))
text_file.close()
#good_mu = ps_mu[2:]
lw = 2.0
ms = 3
fig0 = figure()
title(star)
xlabel('mu')
ylabel('I')
ylim([0,1.0])
plot(oldmu,oldprof,'r',label='Model Intensity',linewidth=lw)
plot(oldmu,oldprof,'ro',label='Sampled Mu')
plot(ps._mu,ps.profile_averages[0],'k',label='Rescaled Model Intensity',linewidth=lw)
plot(ps._mu,ps.profile_averages[0],'ko',label='Rescaled Sampled Mu')
plot(ps._mu,1-lnc[0,0]*(1-ps._mu),'y',label='Linear Fit',linewidth=lw)
plot(ps._mu,1-qc[0,0]*(1-ps._mu)-qc[0,1]*(1-ps._mu)**2,'g--',label = 'Quad Fit',linewidth=lw)
plot(ps._mu,ps.profile_averages[0]+ps.profile_uncertainties[0],'k:',label='Intensity Uncertainties')
plot(ps._mu,ps.profile_averages[0]-ps.profile_uncertainties[0],'k:')
#plot(new_mu,oldps,'r',label='Shifted')
#plot(new_mu,oldps,'ro',label='Shifted')
legend(loc='lower right')
if make_fig is True:
savefig(star+'ldc.png', format='png')
close(fig0)
fig1 = figure()
title(star)
xlabel('z')
ylabel('I')
plot(ps._z,ps.profile_averages[0],label='Model Intensity')
plot(ps._z,ps.profile_averages[0],'bo',label='Sampled z')
plot(ps._z,1-lnc[0,0]*(1-ps._mu),'y',label='Linear Fit')
plot(ps._z,1-qc[0,0]*(1-ps._mu)-qc[0,1]*(1-ps._mu)**2,'g--',label = 'Quad Fit')
#plot(new_mu,oldps,'r',label='Shifted')
legend(loc='lower right')
close(fig1)
variables = {'sc':sc,'ps':ps,'qc':qc,'qe':qe,'qm':qm,'lnc':lnc,'lne':lne}
if pickle_file is not None:
pickle.dump(variables,open(pickle_file,'wb'),protocol=2)
return lnc, lne, qc, qe