phi_rot = abs(2 * np.pi - sol_phase)
# NUMERIC
true_params = {
'log_orbital_period': np.log(p_orbit),
'log_rotation_period': np.log(p_rotation),
'logit_cos_inc': logit(np.cos(inclination)),
'logit_cos_obl': logit(np.cos(obliquity)),
'logit_phi_orb': logit(phi_orb, low=0, high=2 * np.pi),
'logit_obl_orientation': logit(phi_rot, low=0, high=2 * np.pi)
}
truth = IlluminationMapPosterior(times,
np.zeros_like(times),
measurement_std,
nside=nside)
truth.fix_params(true_params)
p = np.concatenate([np.zeros(truth.nparams), one_point_map])
numeric_lightcurve = truth.lightcurve(p)
run_times_analytic = np.array([])
run_times_numeric = np.array([])
nside_resolutions = np.array([1, 2, 4, 8])
print("Test:")
for i in np.nditer(nside_resolutions):
nside = i
whitenoise_relative_amp = 0.2
length_scale = 30 * np.pi / 2
albedo_mean = 0.5
albedo_std = 0.2
while True:
measurement_std,
nside=sim_nside)
true_params = {
'log_orbital_period': np.log(p_orbit),
'logit_cos_inc': logit(np.cos(inclination)),
'logit_cos_obl': logit(np.cos(obliquity)),
'logit_phi_orb': logit(phi_orb, low=0, high=2 * np.pi),
'logit_obl_orientation': logit(phi_rot, low=0, high=2 * np.pi),
'mu': 0.5,
'log_sigma': np.log(0.25),
'logit_wn_rel_amp': logit(0.02),
'logit_spatial_scale': logit(30. * np.pi / 180),
'log_error_scale': np.log(1.)
}
truth_1.fix_params(true_params)
truth_2.fix_params(true_params)
truth_3.fix_params(true_params)
truth_4.fix_params(true_params)
p = np.concatenate([[np.log(day)], sim_map])
true_lightcurve_1 = truth_1.lightcurve(p)
true_lightcurve_2 = truth_2.lightcurve(p)
true_lightcurve_3 = truth_3.lightcurve(p)
true_lightcurve_4 = truth_4.lightcurve(p)
true_posterior_1 = IlluminationMapPosterior(epoch_1,
true_lightcurve_1,
measurement_std,
nside=sim_nside)
true_posterior_2 = IlluminationMapPosterior(epoch_2,
# Posterior parameters
truth = IlluminationMapPosterior(times, np.zeros_like(times), measurement_std, nside=sim_nside)
# Parameters for the gaussian process and the maps; same as those used in sim_map.py
# We fix all parameters except for the rotation period, and assume other parameters are known
true_params = {
'log_orbital_period':np.log(p_orbit),
'logit_cos_inc':logit(np.cos(inclination)),
'logit_cos_obl':logit(np.cos(obliquity)),
'logit_phi_orb':logit(phi_orb, low=0, high=2*np.pi),
'logit_obl_orientation':logit(phi_rot, low=0, high=2*np.pi),
'mu':0.5,
'log_sigma':np.log(0.25),
'logit_wn_rel_amp':logit(0.02),
'logit_spatial_scale':logit(30. * np.pi/180),
'log_error_scale': np.log(1.)}
truth.fix_params(true_params) # fixing the parameters with the measurements
p = np.concatenate([[np.log(day)], sim_map]) # create an array with the map and the rotation period
# Generate and save a lightcurve
true_lightcurve = truth.lightcurve(p) # lightcurve from known parameters, map and period
obs_lightcurve = true_lightcurve.copy() # copy the lightcurve and add noise (below)
obs_lightcurve += truth.sigma_reflectance * np.random.randn(len(true_lightcurve))
np.savetxt('obs_lightcurve.csv', obs_lightcurve, delimiter=',') # save the lightcurve with noise as a csv file
# Posterior from lightcurve
logpost = IlluminationMapPosterior(times, obs_lightcurve, # posterior from lightcurve and parameters
measurement_std,
nside=sim_nside)
fix = true_params.copy()
logpost.fix_params(fix) # fix the parameters to the true parameters
measurement_std,
nside=sim_nside)
true_params = {
'log_orbital_period': np.log(p_orbit),
'logit_cos_inc': logit(np.cos(inclination)),
'logit_cos_obl': logit(np.cos(obliquity)),
'logit_phi_orb': logit(phi_orb, low=0, high=2 * np.pi),
'logit_obl_orientation': logit(phi_rot, low=0, high=2 * np.pi),
'mu': 0.5,
'log_sigma': np.log(0.25),
'logit_wn_rel_amp': logit(0.02),
'logit_spatial_scale': logit(30. * np.pi / 180),
'log_error_scale': np.log(1.)
}
truth.fix_params(true_params) # fix the known parameters
p = np.concatenate([[np.log(day)],
sim_map]) # create a function with the map and the period
# Generate a lightcurve from the parameters
true_lightcurve = truth.lightcurve(
p) # lightcurve from parameters, map and period
obs_lightcurve = true_lightcurve.copy() # copy the lightcurve and add noise
obs_lightcurve += truth.sigma_reflectance * np.random.randn(
len(true_lightcurve))
np.savetxt('obs_lightcurve.csv', obs_lightcurve,
delimiter=',') # save the observed lightcurve as a text file
logpost = IlluminationMapPosterior(
times,
obs_lightcurve, # posterior from parameters and times of observation