def run_bls(self):
b = self.bls
r = self.bls()
self._rbls = array((b.bsde, b.tc, b.bper, b.duration, b.depth, sqrt(b.depth),
floor(diff(self.time[[0,-1]])[0]/b.bper)), dt_blsresult)
self._pv_bls = [b.bper, b.tc, sqrt(b.depth), as_from_rhop(2.5, b.bper), 0.1]
self.create_transit_arrays()
self.lcinfo['lnlike_constant'] = ll_normal_es(self.flux, ones_like(self.flux), self.flux_e)
self.period = b.bper
self.zero_epoch = b.tc
self.duration = b.duration
def compute_lc_model(self, pv):
_tc, _p, _b = pv[0], pv[1], pv[3]
_ld = pv[self.ldc_slice]
_a = as_from_rhop(pv[2], pv[1])
_i = m.acos(_b/_a)
k = np.sqrt(pv[self.k2_slice])
_k = k.mean()
kf = (k/_k)**2
z = of.z_circular(self.time, _tc, _p, _a, _i, 4)
flux = self.tmodel(z, _k, pv[self.ldc_slice])
return (kf*(flux-1.)+1.).reshape((self.npt,self.npb))
def compute_lc_model(self, pv):
_tc, _p, _b = pv[0], pv[1], pv[3]
_ld = pv[self.ldc_slice]
_a = as_from_rhop(pv[2], pv[1])
_i = m.acos(_b/_a)
self.ld[0::2] = 2.*np.sqrt(_ld[0::2])*_ld[1::2]
self.ld[1::2] = np.sqrt(_ld[0::2])*(1.-2.*_ld[1::2])
k = np.sqrt(pv[self.k2_slice])
_k = k.mean()
kf = (k/_k)**2
z = of.z_circular(self.time, _tc, _p, _a, _i, 4)
flux = self.tmodel(z, _k, self.ld)
return kf*(flux-1.)+1.
def run_bls(self):
b = self.bls
r = self.bls()
self._rbls = array(
(b.bsde, b.tc, b.bper, b.duration, b.depth, sqrt(b.depth),
floor(diff(self.time[[0, -1]])[0] / b.bper)), dt_blsresult)
self._pv_bls = [
b.bper, b.tc,
sqrt(b.depth),
as_from_rhop(2.5, b.bper), 0.1
]
self.create_transit_arrays()
self.lcinfo['lnlike_constant'] = ll_normal_es(self.flux,
ones_like(self.flux),
self.flux_e)
self.period = b.bper
self.zero_epoch = b.tc
self.duration = b.duration
def __call__(self, qmi=0.02,
qma = 0.25,
df = 0.0001,
nf = 10000,
nb = 1000,
fmin = 1./50.,
dur=0.2,
period_range=None,
plot=False,
win=49,
period_fit_prior=None,
t0_fit_prior=None):
self.bb = blsMOD.blsMOD(self.t,self.f, nf, fmin, df, nb, qmi, qma,period_range)
print("Computing bls...")
self.bb.compute()
self.pv_bls = [self.bb._best_period,self.bb._epoch,np.sqrt(self.bb._depth),as_from_rhop(2.5,self.bb._best_period),0.1]
self.fit_transit(pv_init=self.pv_bls,time=self.t,flux=self.f,period_fit_prior=period_fit_prior,t0_fit_prior=t0_fit_prior)
print("Per=",self.pv_min[0],"epoch=",self.pv_min[1])
if plot:
self.fig, self.ax = plt.subplots(nrows=4)
self.bb.plot_power_spectrum(inverse=True,ax=self.ax.flat[0])
self.bb.plot_folded(ax=self.ax.flat[1])
self.bb.plot_transits(ax=self.ax.flat[2])
if self.time is not None:
self.t_folded,self.f_folded = k2help.fold_transit_everest(self.t,self.f,t0=self.bb._epoch,period=self.bb._best_period,dur=dur)
self.ax.flat[3].plot(self.t_folded,self.f_folded,"k.",label="BLS",alpha=0.5)
self.t_folded,self.f_folded = k2help.fold_transit_everest(self.t,self.f,t0=self.pv_min[1],period=self.pv_min[0],dur=dur)
self.ax.flat[3].plot(self.t_folded,self.f_folded,"r.",label="Best Fit",markersize=5)
self.ax.flat[3].legend(loc="lower left",fontsize=8)
else:
self.t_folded,self.f_folded = self.star.get_folded_transit(t0=self.bb._epoch,period=self.bb._best_period,dur=dur,plot=False);
self.ax.flat[3].plot(self.t_folded,self.f_folded,"k.",label="BLS",alpha=0.5)
self.t_folded,self.f_folded = self.star.get_folded_transit(t0=self.pv_min[1],period=self.pv_min[0],dur=dur,plot=False);
self.ax.flat[3].plot(self.t_folded,self.f_folded,"r.",label="Best Fit",markersize=5)
self.ax.flat[3].legend(loc="lower left",fontsize=8)
