def plot_flux_vs_time(self, ax = None):
transits = self.zero_epoch + unique(epoch(self.time, self.zero_epoch, self.period)) * self.period
[ax.axvline(t, ls='--', alpha=0.5, lw=1) for t in transits]
ax.plot(self.time, self.flux)
tb, fb, eb = downsample_time(self.time, self.flux, 1 / 24)
ax.plot(tb, fb, 'k', lw=1)
setp(ax, xlabel=f'Time [BJD]', ylabel='Normalized flux', xlim=self.time[[0,-1]])
def plot_flux_vs_time(self, ax=None):
rpc = percentile(self.flux_raw, [0.5, 99.5, 50])
dpc = percentile(self.flux_detrended, [0.5, 99.5, 50])
d = 1.15
bbox_raw = rpc[-1] + d * (rpc[0] - rpc[-1]), rpc[-1] + d * (rpc[1] - rpc[-1])
bbox_dtr = dpc[-1] + d * (dpc[0] - dpc[-1]), dpc[-1] + d * (dpc[1] - dpc[-1])
offset = d * (rpc[1] - rpc[-1]) - d * (dpc[0] - dpc[-1])
ax.plot(self.time_detrended - self.bjdrefi, self.flux_detrended + 1.2*offset, label='detrended')
ax.plot(self.time_raw - self.bjdrefi, self.flux_raw, label='raw')
if self.zero_epoch:
transits = self.zero_epoch + unique(epoch(self.time, self.zero_epoch, self.period)) * self.period
[ax.axvline(t - self.bjdrefi, ls='--', alpha=0.5, lw=1) for t in transits if
self.time[0] < t < self.time[-1]]
def time2epoch(x):
return (x + self.bjdrefi - self.zero_epoch) / self.period
def epoch2time(x):
return self.zero_epoch - self.bjdrefi + x * self.period
secax = ax.secondary_xaxis('top', functions=(time2epoch, epoch2time))
secax.set_xlabel('Epoch')
secax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.legend(loc='upper right')
ax.autoscale(axis='x', tight=True)
yp_offset = diff(ax.transData.inverted().transform([[0, 0], [0, 40]])[:, 1])
setp(ax, xlabel=f'Time - {self.bjdrefi} [BJD]', ylabel='Normalized flux',
ylim=(bbox_raw[0] - yp_offset, bbox_dtr[1] + offset + yp_offset))
def __call__(self, *args, **kwargs):
self.logger = getLogger(
f"{self.name}:{self.ts.name.lower().replace('_','-')}")
self.logger.info("Running BLS periodogram")
self._periods = linspace(self.ts.pmin, self.ts.pmax, self.ts.nper)
self.bls = BoxLeastSquares(self.ts.time * u.day, self.ts.flux,
self.ts.ferr)
self.result = self.bls.power(self._periods,
self._durations,
objective='snr')
for p in self.ts.masked_periods:
self.result.depth_snr *= maskf(self._periods, p, .1)
self.result.log_likelihood *= maskf(self._periods, p, .1)
i = argmax(self.result.depth_snr)
self.period = self.result.period[i].value
self.snr = self.result.depth_snr[i]
self.duration = self.result.duration[i].value
self.depth = self.result.depth[i]
t0 = self.result.transit_time[i].value
ep = epoch(self.ts.time.min(), t0, self.period)
self.zero_epoch = t0 + ep * self.period
self.ts.update_ephemeris(self.zero_epoch, self.period, self.duration,
self.depth)
self.logger.info(
f"BLS SNR {self.snr:.2f} period {self.period:.2f} d, duration {24*self.duration:.2f} h"
)
def plot_flux_vs_time(self, ax=None):
transits = self.zero_epoch + unique(epoch(self.time, self.zero_epoch, self.period)) * self.period
[ax.axvline(t - self.bjdrefi, ls='--', alpha=0.5, lw=1) for t in transits]
offset1 = -1.05 * (self.trtime - self.fraw).min()
offset2 = offset1 - 1.05 * (self.trposi - self.trtime).min()
offset3 = offset2 - 1.05 * (self.flux - self.trposi).min()
time = self.time - self.bjdrefi
ax.plot(time, self.flux + offset3, label='Detrended flux')
ax.plot(time, self.trposi + offset2, label='Rotation trend')
ax.plot(time, self.trtime + offset1, label='Time trend')
ax.plot(time, self.fraw, label='PDC Flux')
ax.legend(loc='upper right')
ax.autoscale(axis='x', tight=True)
setp(ax, xlabel=f'Time - {self.bjdrefi} [BJD]', ylabel='Normalized flux')
def plot_gb_transits(self,
method='de',
pv: ndarray = None,
figsize: tuple = (14, 2),
axes=None,
ncol: int = 4,
xlim: tuple = None,
ylim: tuple = None):
if pv is None:
if method == 'de':
pv = self.de.minimum_location
else:
raise NotImplementedError
nlc = self.nlc - self._stess
nrow = int(floor(nlc / ncol))
if axes is None:
fig, axs = subplots(nrow,
ncol,
figsize=figsize,
constrained_layout=True,
sharex='all',
sharey='all',
squeeze=False)
else:
fig, axs = None, axes
[ax.autoscale(enable=True, axis='x', tight=True) for ax in axs.flat]
fmodel = squeeze(self.flux_model(pv))
etess = self._stess
t0, p = self.de.minimum_location[[0, 1]]
for i, ax in enumerate(axs.T.flat):
t = self.times[etess + i]
e = epoch(t.mean(), t0, p)
tc = t0 + e * p
tt = 24 * (t - tc)
ax.plot(tt, self.fluxes[etess + i], 'k.', alpha=0.2)
ax.plot(tt, fmodel[self.lcslices[etess + i]], 'k')
setp(axs, xlim=xlim, ylim=ylim)
setp(axs[-1, :], xlabel='Time - T$_c$ [h]')
setp(axs[:, 0], ylabel='Normalised flux')
return fig
def plot_m2_transits(self, figsize=(14, 5)):
fig, axs = subplots(3, 4, figsize=figsize, constrained_layout=True, sharex='all', sharey='all')
fmodel = squeeze(self.flux_model(self.de.population))[self.de.minimum_index]
etess = self._stess
t0, p = self.de.minimum_location[[0,1]]
for i, ax in enumerate(axs.T.flat):
t = self.times[etess + i]
e = epoch(t.mean(), t0, p)
tc = t0 + e * p
ax.plot(t - tc, self.fluxes[etess + i], 'k.', alpha=0.2)
ax.plot(t - tc, fmodel[self.lcslices[etess + i]], 'k')
setp(ax, xlim=(-0.045, 0.045))
setp(axs, ylim=(0.92, 1.05))
setp(axs[-1, :], xlabel='Time [BJD]')
setp(axs[:, 0], ylabel='Normalised flux')
return fig
def _compute_indices(self):
self.qids = unique(self.qidarr)
self.tids = unique(self.tidarr)
self.nt = len(self.tids)
self.npt = self.time.size
self.qslices = [
slice(*where(self.qidarr == qid)[0][[0, -1]] + [0, 1])
for qid in self.qids
]
self.qsldict = {
qid: slice(*where(self.qidarr == qid)[0][[0, -1]] + [0, 1])
for qid in self.qids
}
for i, tid in enumerate(self.tids):
self.tidarr[self.tidarr == tid] = i
self.tids = unique(self.tidarr)
self.tslices = [
slice(*where(self.tidarr == tid)[0][[0, -1]] + [0, 1])
for tid in self.tids
]
self.orbit_n = array(
[epoch(t.mean(), self.t0, self.p) for t in self.time_per_transit])
def __call__(self, npop: int = 40, de_niter: int = 1000, mcmc_niter: int = 200, mcmc_repeats: int = 3, initialize_only: bool = False):
self.logger = getLogger(f"{self.name}:{self.ts.name.lower().replace('_','-')}")
self.logger.info(f"Fitting {self.mode} transits")
self.ts.transit_fits[self.mode] = self
epochs = epoch(self.ts.time, self.ts.zero_epoch, self.ts.period)
if self.mode == 'all':
mask = ones(self.ts.time.size, bool)
elif self.mode == 'even':
mask = epochs % 2 == 0
elif self.mode == 'odd':
mask = epochs % 2 == 1
else:
raise NotImplementedError
mask &= abs(self.ts.phase - 0.5*self.ts.period) < 4 * 0.5 * self.ts.duration
self.ts.transit_fit_masks[self.mode] = self.mask = mask
self.epochs = epochs = epochs[mask]
self.time = self.ts.time[mask]
self.fobs = self.ts.flux[mask]
tref = floor(self.time.min())
tm = QuadraticModelCL(klims=(0.01, 0.60)) if self.use_opencl else QuadraticModel(interpolate=False)
self.lpf = lpf = SearchLPF(times=self.time, fluxes=self.fobs, epochs=epochs, tm=tm,
nsamples=self.nsamples, exptimes=self.exptime, tref=tref)
# TODO: V-shaped transits are not always modelled well. Need to set smarter priors (or starting population)
# for the impact parameter and stellar density.
lpf.set_prior('rho', 'UP', 0.01, 25)
if self.mode == 'all':
d = min(self.ts.depth, 0.75)
lpf.set_prior('tc', 'NP', self.ts.zero_epoch, 0.01)
lpf.set_prior('p', 'NP', self.ts.period, 0.001)
lpf.set_prior('k2', 'UP', max(0.01**2, 0.5*d), min(max(0.08**2, 4*d), 0.75**2))
else:
pr = self.ts.tf_all.parameters
lpf.set_prior('tc', 'NP', pr.tc.med, 5*pr.tc.err)
lpf.set_prior('p', 'NP', pr.p.med, pr.p.err)
lpf.set_prior('k2', 'UP', max(0.01**2, 0.5 * pr.k2.med), max(0.08**2, min(0.6**2, 2 * pr.k2.med)))
lpf.set_prior('q1', 'NP', pr.q1.med, pr.q1.err)
lpf.set_prior('q2', 'NP', pr.q2.med, pr.q2.err)
# TODO: The limb darkening table has been computed for TESS. Needs to be made flexible.
if self.ts.teff is not None:
ldcs = Table.read(Path(__file__).parent / "data/ldc_table.fits").to_pandas()
ip = interp1d(ldcs.teff, ldcs[['q1', 'q2']].T)
q1, q2 = ip(clip(self.ts.teff, 2000., 12000.))
lpf.set_prior('q1', 'NP', q1, 1e-5)
lpf.set_prior('q2', 'NP', q2, 1e-5)
if initialize_only:
return
else:
lpf.optimize_global(niter=de_niter, npop=npop, use_tqdm=self.use_tqdm, plot_convergence=False)
lpf.sample_mcmc(mcmc_niter, repeats=mcmc_repeats, use_tqdm=self.use_tqdm, leave=False)
df = lpf.posterior_samples(derived_parameters=True)
df = pd.DataFrame((df.median(), df.std()), index='med err'.split())
pv = lpf.posterior_samples(derived_parameters=False).median().values
self.phase = fold(self.time, pv[1], pv[0], 0.5) * pv[1] - 0.5 * pv[1]
self.fmod = lpf.flux_model(pv)
self.ftra = lpf.transit_model(pv)
self.fbase = lpf.baseline(pv)
# Calculate the per-orbit log likelihood differences
# --------------------------------------------------
ues = unique(epochs)
lnl = zeros(ues.size)
err = 10 ** pv[7]
def lnlike_normal(o, m, e):
npt = o.size
return -npt * log(e) - 0.5 * npt * log(2. * pi) - 0.5 * sum((o - m) ** 2 / e ** 2)
for i, e in enumerate(ues):
m = epochs == e
lnl[i] = lnlike_normal(self.fobs[m], self.fmod[m], err) - lnlike_normal(self.fobs[m], 1.0, err)
self.parameters = df
self.dll_epochs = ues
self.dll_values = lnl
self.zero_epoch = df.tc.med
self.period = df.p.med
self.duration = df.t14.med
self.depth = df.k2.med
if self.mode == 'all':
self.delta_bic = self.ts.dbic = delta_bic(lnl.sum(), 0, 9, self.time.size)
self.ts.update_ephemeris(self.zero_epoch, self.period, self.duration, self.depth)
def plot_gb_transits(self,
method='de',
pv: ndarray = None,
remove_baseline: bool = True,
figsize: tuple = (14, 2),
axes=None,
ncol: int = 4,
xlim: tuple = None,
ylim: tuple = None,
nsamples: int = 200):
if pv is None:
if method == 'de':
if self.de is None:
raise ValueError(
"The global optimizer hasn't been initialized.")
pvp = None
pv = self.de.minimum_location
elif method == 'mcmc':
if self.sampler is None:
raise ValueError("The sampler hasn't been initialized.")
df = self.posterior_samples(derived_parameters=False)
pvp = permutation(df.values)[:nsamples, :]
pv = median(pvp, 0)
else:
if pv.ndim == 1:
pvp = None
pv = pv
else:
pvp = permutation(pv)[:nsamples, :]
pv = median(pvp, 0)
if pvp is None:
if remove_baseline:
fobs = self.ofluxa / squeeze(self.baseline(pv))
fmodel = squeeze(self.transit_model(pv))
fbasel = ones_like(self.ofluxa)
else:
fobs = self.ofluxa
fmodel = squeeze(self.flux_model(pv))
fbasel = squeeze(self.baseline(pv))
fmodel_limits = None
else:
if remove_baseline:
fobs = self.ofluxa / squeeze(self.baseline(pv))
fmodels = percentile(self.transit_model(pvp),
[50, 16, 84, 2.5, 97.5], 0)
fbasel = ones_like(self.ofluxa)
else:
fobs = self.ofluxa
fmodels = percentile(self.flux_model(pvp),
[50, 16, 84, 2.5, 97.5], 0)
fbasel = median(self.baseline(pvp), 0)
fmodel = fmodels[0]
fmodel_limits = fmodels[1:]
tcids = [
self.ps.names.index(f'tc_{i + 1}') for i in range(self.nplanets)
]
prids = [
self.ps.names.index(f'p_{i + 1}') for i in range(self.nplanets)
]
t0s = pv[tcids]
prs = pv[prids]
tcs = array([t.mean() for t in self.times[self._stess:]])
tds = array([
abs(fold(tcs, prs[i], t0s[i], 0.5) - 0.5)
for i in range(self.nplanets)
])
pids = argmin(tds, 0)
nlc = self.nlc - self._stess
nrow = int(ceil(nlc / ncol))
if axes is None:
fig, axs = subplots(nrow,
ncol,
figsize=figsize,
sharex='all',
sharey='all',
squeeze=False)
else:
fig, axs = None, axes
[ax.autoscale(enable=True, axis='x', tight=True) for ax in axs.flat]
etess = self._stess
for iax, i in enumerate(range(self.nlc - etess)):
ax = axs.flat[iax]
sl = self.lcslices[etess + i]
t = self.times[etess + i]
e = epoch(t.mean(), t0s[pids[i]], prs[pids[i]])
tc = t0s[pids[i]] + e * prs[pids[i]]
tt = 24 * (t - tc)
if fmodel_limits is not None:
ax.fill_between(tt,
fmodel_limits[2, sl],
fmodel_limits[3, sl],
facecolor='blue',
alpha=0.15)
ax.fill_between(tt,
fmodel_limits[0, sl],
fmodel_limits[1, sl],
facecolor='darkblue',
alpha=0.25)
ax.plot(tt, fobs[sl], 'k.', alpha=0.2)
ax.plot(tt, fmodel[sl], 'k')
setp(axs, xlim=xlim, ylim=ylim)
setp(axs[-1, :], xlabel='Time - T$_c$ [h]')
setp(axs[:, 0], ylabel='Normalised flux')
fig.tight_layout()
return fig
def plot_gb_transits(self, solution: str = 'de', pv: ndarray = None, figsize: tuple = None, axes=None, ncol: int = 4,
xlim: tuple = None, ylim: tuple = None, remove_baseline: bool = True, n_samples: int = 1500):
solution = solution.lower()
samples = None
if pv is None:
if solution == 'local':
pv = self._local_minimization.x
elif solution in ('de', 'global'):
solution = 'global'
pv = self.de.minimum_location
elif solution in ('mcmc', 'mc'):
solution = 'mcmc'
samples = self.posterior_samples(derived_parameters=False)
samples = permutation(samples.values)[:n_samples]
pv = median(samples, 0)
else:
raise NotImplementedError("'solution' should be either 'local', 'global', or 'mcmc'")
nlc = self.nlc - self._stess
nrow = int(ceil(nlc / ncol))
if axes is None:
fig, axs = subplots(nrow, ncol, figsize=figsize, sharex='all', sharey='all', squeeze=False)
else:
fig, axs = None, axes
[ax.autoscale(enable=True, axis='x', tight=True) for ax in axs.flat]
if remove_baseline:
if solution == 'mcmc':
fbasel = median(self.baseline(samples), axis=0)
fmodel, fmodm, fmodp = percentile(self.transit_model(samples), [50, 0.5, 99.5], axis=0)
else:
fbasel = squeeze(self.baseline(pv))
fmodel, fmodm, fmodp = squeeze(self.transit_model(pv)), None, None
fobs = self.ofluxa / fbasel
else:
if solution == 'mcmc':
fbasel = median(self.baseline(samples), axis=0)
fmodel, fmodm, fmodp = percentile(self.flux_model(samples), [50, 1, 99], axis=0)
else:
fbasel = squeeze(self.baseline(pv))
fmodel, fmodm, fmodp = squeeze(self.flux_model(pv)), None, None
fobs = self.ofluxa
etess = self._stess
t0, p = pv[[0, 1]]
for i in range(nlc):
ax = axs.flat[i]
sl = self.lcslices[etess + i]
t = self.times[etess + i]
e = epoch(t.mean(), t0, p)
tc = t0 + e * p
tt = 24 * (t - tc)
ax.plot(tt, fobs[sl], 'k.', alpha=0.2)
ax.plot(tt, fmodel[sl], 'k')
if solution == 'mcmc':
ax.fill_between(tt, fmodm[sl], fmodp[sl], zorder=-100, alpha=0.2, fc='k')
if not remove_baseline:
ax.plot(tt, fbasel[sl], 'k--', alpha=0.2)
setp(axs, xlim=xlim, ylim=ylim)
setp(axs[-1, :], xlabel='Time - T$_c$ [h]')
setp(axs[:, 0], ylabel='Normalised flux')
if fig is not None:
fig.tight_layout()
return fig
