def p_value_b(gamma_k, f, rs, nBDs, Tobs_R, robs_R, Mobs_R, heat_int_R, relT_R,
relM_R, relR_R, relA_R, steps=300):
"""
Return p-value for gamma_k @ (f, rs) under b hypothesis
"""
# Compute TS pdf
TS_k = np.zeros(steps)
# Load ATMO2020 model
path = "/home/mariacst/exoplanets/exoplanets/data/"
data = np.genfromtxt(path + "./ATMO_CEQ_vega_MIRI.txt", unpack=True)
points = np.transpose(data[0:2, :])
values = data[2]
for i in range(steps):
# Generate experiments under s+b hypothesis
robs, Tobs, Mobs, ages = mock_population_all(nBDs, relT_R, relM_R, relR_R,
relA_R, 0., gamma_k, rs)
# Predicted intrinsic temperatures
xi = np.transpose(np.asarray([ages, Mobs]))
Teff = griddata(points, values, xi)
heat_int = heat(Teff, np.ones(len(Teff))*R_jup.value)
# TS
TS_k[i] = TS(gamma_k, f, rs, Tobs, robs, Mobs, heat_int)
# observed TS
q_gamma_k_obs = TS(gamma_k, f, rs, Tobs_R, robs_R, Mobs_R, heat_int_R)
# return
return (100-percentileofscore(TS_k, q_gamma_k_obs, kind="strict"))
def UL(rs, f, nBDs, relT_R, relM_R, relR_R, relA_R, steps=300):
# Generate "real" observation assuming only background (no DM)
rho0=0.42
# Load ATMO2020 model
path = "/home/mariacst/exoplanets/exoplanets/data/"
data = np.genfromtxt(path + "./ATMO_CEQ_vega_MIRI.txt", unpack=True)
points = np.transpose(data[0:2, :])
values = data[2]
robs_R, Tobs_R, mass_R, ages_R = mock_population_all(nBDs, 0., 0.,
0., 0.,
0, 1., 1., rho0_true=rho0, v=None)
xi = np.transpose(np.asarray([ages_R, mass_R]))
Teff = griddata(points, values, xi)
heat_int_R = heat(Teff, np.ones(len(Teff))*R_jup.value)
import pdb
pdb.set_trace()
gamma_up = np.ones(len(rs))*10
for i in range(len(rs)):
if rs[i] > 7.:
gamma_k = np.linspace(0.4, 2.9, 35) # change this?
else:
gamma_k = np.linspace(0, 1.5, 35) # change this?
for g in gamma_k:
_p_sb = p_value_sb(g, f, rs[i], nBDs, Tobs_R, robs_R, mass_R,
heat_int_R, relT_R, relM_R, relR_R, relA_R,
steps=steps)
_p_b = p_value_b(g, f, rs[i], nBDs, Tobs_R, robs_R, mass_R, heat_int_R,
relT_R, relM_R, relR_R, relA_R, steps=steps)
try:
CL = _p_sb / _p_b
except ZeroDivisionError:
CL = 200.
if CL < 0.05:
print(rs[i], g)
gamma_up[i] = g
break
#return
return gamma_up
return lp + residual(p) #, robs, Tobs, rel_unc_Tobs, heat_int, mass)
# ------------------------------------------------------------------------
# Load theoretical cooling model
path = "./data/"
data = np.genfromtxt(path + "./ATMO_CEQ_vega_MIRI.txt", unpack=True)
points = np.transpose(data[0:2, :])
values = data[2]
# Generate mock observation
robs, Tobs, mass, ages = mock_population_all(nBDs,
relTobs,
relM,
relRobs,
relA,
f_true,
gamma_true,
rs_true,
rho0_true=rho0,
v=v)
## calculate predictic intrinsic heat flow for mock BDs
xi = np.transpose(np.asarray([ages, mass]))
Teff = griddata(points, values, xi)
heat_int = heat(Teff, np.ones(len(Teff)) * R_jup.value)
# ------------------ RECONSTRUCTION --------------------------------------
ndim = 3
nwalkers = 50
# first guess
p0 = [[0.9, 0.9, 20.] + 1e-4 * np.random.randn(ndim) for j in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)
#args=(r_obs, Tobs, rel_unc_T, heat_int, mass))