def generate_spectrum(planet):
"""
Function to loop over to generate spectra.
"""
#INSERT PLANET SELECTION RULES HERE
if planet["PlanetMass"] <= 17: #Planet masses in Earth Masses
#If Neptune or less, we'll just use the Exosims Flux Ratio for now at all wavelengths.
#Later this will also use PICASO once we learn how to use it better
planet_type = "Exosims"
planet_spectrum = np.repeat(planet['Flux Ratio'], len(model_wvs))
else:
planet_type = "Gas"
time1 = time.time()
#Generate the spectrum and downsample to intermediate resolution
atmospheric_parameters = spectrum.generate_picaso_inputs(planet,
planet_type,
clouds=True)
planet_spectrum = spectrum.simulate_spectrum(planet, model_wvs,
intermediate_R,
atmospheric_parameters)
time2 = time.time()
print('Spectrum took {0:.3f} s'.format((time2 - time1)))
return planet_type, planet_spectrum
# sim_F_lambda = tmp[0]
# sim_F_lambda_errs = tmp[1]
# sim_F_lambda_stellar = tmp[2]
# noise_components = fits.open("noise_components.fits")[0].data
######################## Plot Cloud vs. Clear ######################
# get the best SNR Planet
avg_snrs = np.mean(snrs, axis=1)
print(avg_snrs)
argsort_snrs = np.argsort(np.abs(avg_snrs - 6))
bestsnr = argsort_snrs[0] #np.argmax(avg_snrs)
# Generate the cloudy spectrum of this planet
planet = planet_table[rand_planets[bestsnr]]
atmospheric_parameters_clear = spectrum.generate_picaso_inputs(planet,
planet_type,
clouds=False)
planet_spectrum_clear = spectrum.simulate_spectrum(
planet, model_wvs, intermediate_R, atmospheric_parameters_clear)
# Generate noisy spectra for cloudy and clear
clear_F_lambda, clear_F_lambda_errs, _ = observation.simulate_observation(
tmt,
psi_blue,
planet_table[rand_planets[bestsnr]],
planet_spectrum_clear,
model_wvs,
intermediate_R,
inject_noise=True,
post_processing_gain=post_processing_gain)
cloudy_F_lambda, cloudy_F_lambda_errs, _ = observation.simulate_observation(
#The number of wavelengths to generate. Divide by two for nyquist in the d_wv.
#Multiply the final number by 2 just to be safe.
n_model_wv = int((model_wv_high - model_wv_low) / (dwv_c / 2)) * 2
#Generate the model wavelenths
model_wvs = np.linspace(model_wv_low, model_wv_high,
n_model_wv) #Choose some wavelengths
print("\n Starting to generate planet spectra")
for planet in planet_table[:n_planets_now]:
#INSERT PLANET SELECTION RULES HERE
planet_type = "Jupiter"
planet_types.append(planet_type)
#Generate the spectrum and downsample to intermediate resolution
atmospheric_parameters = spectrum.generate_picaso_inputs(
planet, planet_type)
planet_spectrum, polarized_spectrum = spectrum.simulate_spectrum(
planet,
model_wvs,
intermediate_R,
atmospheric_parameters,
package="picaso+pol")
# planet_spectrum = spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters,package="picaso")
planet_polarized_spectra.append(polarized_spectrum)
planet_spectra.append(planet_spectrum)
print("Done generating planet spectra")
print("\n Starting to simulate observations")
post_processing_gain = 1000
sim_F_lambda, sim_F_lambda_errs, sim_F_lambda_stellar, noise_components = observation.simulate_observation_set(