def loglike_h2och4(theta, y, fixed_parameters):
# retrieve the global variables
global pixel_bins
global transit_data
global err
# only 'y' changes on the fly
fixed = fixed_parameters
x = pixel_bins
yerr = err
_, model = transit_model_H2OCH4(x, theta, fixed, p0=p0)
sigma = yerr**2
return -0.5 * np.sum((y - model)**2 / sigma + np.log(sigma))
# pixel_bins = pixel_wavelengths
# test the model generation function
# this produces the 'true' transit spectrum
fixed_parameters = (fine_wavelengths, water_cross_sections, ch4_cross_sections,
nh3_cross_sections, hcn_cross_sections, h2_cross_sections,
g_planet, rad_star, R)
theta = (
rad_planet,
T,
log_f_h2o,
log_fch4,
)
# generate spectrum
pixel_wavelengths, pixel_transit_depth = transit_model_H2OCH4(
pixel_bins, theta, fixed_parameters)
# generate photon noise from a signal value
# signal = 1.22e9
# noise = (np.random.poisson(lam=signal, size=pixel_transit_depth.size) - signal)/signal
'''
generate noise instances!!!
'''
# convert error from parts per million to fractional
err = sampling_err * 1e-6
num_noise_inst = number_trials
noise_inst = []
while len(noise_inst) < num_noise_inst:
# increase noise by 0%
noise_inst.append(np.random.normal(scale=err * 1))
# generate spectrum
pixel_wavelengths_h2o, pixel_transit_depth_h2o = transit_model_H2O(pixel_bins,
theta_h2o,
fixed_h2o,
p0=p0)
pixel_wavelengths_ch4, pixel_transit_depth_ch4 = transit_model_CH4(pixel_bins,
theta_ch4,
fixed_ch4,
p0=p0)
pixel_wavelengths_null, pixel_transit_depth_null = transit_model_NULL(
pixel_bins, theta_null, fixed_null, p0=p0)
pixel_wavelengths_mix, pixel_transit_depth_mix = transit_model_H2OCH4(
pixel_bins, theta_h2och4, fixed_h2och4, p0=p0)
if np.array_equal(pixel_wavelengths_h2o, pixel_wavelengths_ch4):
pixel_wavelengths = pixel_wavelengths_h2o
if plot:
# compare the spectrums, to check
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(pixel_wavelengths_h2o,
pixel_transit_depth_h2o * 1e6,
'o',
label='H2O')
ax.plot(pixel_wavelengths_ch4,
pixel_transit_depth_ch4 * 1e6,
'o',
label='CH4')