def minfun(pv, period, zero_epoch):
if any(pv < 0): return inf
dummy = []
for j in range(4):
dummy.append(-ll_normal_es(
self.flux, self.sine_model(pv, j * 2 * period, zero_epoch),
self.flux_e))
return np.nanmin(
dummy
) #-ll_normal_es(self.flux, self.sine_model(pv, 2*period, zero_epoch), self.flux_e)
def per_orbit_likelihoods(self):
pv = self._pv_trf
lnl = array([ll_normal_es(f, self.transit_model(pv, t), e) / t.size
for t,f,e in zip(self.times, self.fluxes, self.flux_es)])
self._tr_lnlike_po = lnl
self._tr_lnlike_med = lmed = median(lnl)
self._tr_lnlike_std = lstd = lnl.std()
self._tr_lnlike_max = lmax = (lnl.max() - lmed) / lstd
self._tr_lnlike_min = lmin = (lnl.min() - lmed) / lstd
self._rpol = array((lmed, lstd, lmax, lmin), dt_poresult)
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 per_orbit_likelihoods(self):
pv = self._pv_trf
lnl = array([
ll_normal_es(f, self.transit_model(pv, t), e) / t.size
for t, f, e in zip(self.times, self.fluxes, self.flux_es)
])
self._tr_lnlike_po = lnl
self._tr_lnlike_med = lmed = median(lnl)
self._tr_lnlike_std = lstd = lnl.std()
self._tr_lnlike_max = lmax = (lnl.max() - lmed) / lstd
self._tr_lnlike_min = lmin = (lnl.min() - lmed) / lstd
self._rpol = array((lmed, lstd, lmax, lmin), dt_poresult)
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 eclipse_likelihood(self, f, shift, time=None):
return ll_normal_es(self.flux, (1-f)+f*self.eclipse_model(shift, time), self.flux_e)
def minfun(pv, period, zero_epoch):
if any(pv<0): return inf
dummy = []
for j in range(4):
dummy.append(-ll_normal_es(self.flux, self.sine_model(pv, j*2*period, zero_epoch), self.flux_e))
return np.nanmin(dummy)#-ll_normal_es(self.flux, self.sine_model(pv, 2*period, zero_epoch), self.flux_e)
def minfun(pv, time, flux, flux_e):
if any(pv<=0) or (pv[3] <= 1) or (pv[4] > 1): return inf
return -ll_normal_es(flux, self.transit_model(pv, time), flux_e)
def minfun(pv):
if any(pv<=0) or (pv[3] <= 1) or (pv[4] > 1) or not (0.75<pv[0]<80) or abs(pv[0]-pbls) > 1.: return inf
return -ll_normal_es(self.flux, self.transit_model(pv), self.flux_e)
def eclipse_likelihood(self, f, shift, time=None):
return ll_normal_es(self.flux,
(1 - f) + f * self.eclipse_model(shift, time),
self.flux_e)
def minfun(pv, time, flux, flux_e):
if any(pv <= 0) or (pv[3] <= 1) or (pv[4] > 1): return inf
return -ll_normal_es(flux, self.transit_model(pv, time), flux_e)
def minfun(pv):
if any(pv <= 0) or (pv[3] <= 1) or (pv[4] > 1) or not (
0.75 < pv[0] < 80) or abs(pv[0] - pbls) > 1.:
return inf
return -ll_normal_es(self.flux, self.transit_model(pv),
self.flux_e)
