def simulate_noise(user_input):
observer = observe.OS_Simulator(user_input)
analyzer = analyze.Spectra_Analyzer(user_input)
sim_plot = plotter.Simulation_Plotter(user_input)
nu, ref_trans, bio_trans = load_npy(user_input["Save"]["Spectra"]["path"],
user_input["Save"]["Spectra"]["name"])
wav = (10000. / nu)
rbin_centers, rbin_mean_I = observer.calculate_bin(wav, ref_trans)
rbin_mean_error_I, rbin_mean_error_bar = observer.add_noise(rbin_mean_I)
bbin_centers, bbin_mean_I = observer.calculate_bin(wav, bio_trans)
bbin_mean_error_I, bbin_mean_error_bar = observer.add_noise(bbin_mean_I)
signal_diff = np.array(bbin_mean_I - rbin_mean_I)
signal_diff2 = np.array(bbin_mean_error_I - rbin_mean_I)
x, SNR = analyzer.spectra_SNR(bbin_centers, signal_diff2)
maxSNR = max(SNR)
print "Feature Max SNR: %s. " % maxSNR,
if maxSNR > 9:
print "Over 9 sigma"
elif maxSNR > 6:
print "Over 6 sigma"
elif maxSNR > 3:
print "Over 3 sigma"
else:
print "Not Detected"
def load_single(user_input):
nu, ref_trans = load_npy(user_input["Save"]["Spectra"]["path"],
user_input["Save"]["Spectra"]["name"])
sim_plot = plotter.Simulation_Plotter(user_input)
sim_plot.plot_xy(nu, ref_trans, "ref.")
sim_plot.show_plot()
def simulate(s, o, a):
s.Timer = simple_timer(4)
#create a base flat spectra based on planetary parameters
s.Surface_g, s.Base_TS_Value = s.load_astrophysical_properties()
# normalized pressure directly correspond to atmosphere layers.
s.normalized_pressure = s.load_atmosphere_pressure_layers()
# load mixing ration files and determine what molecules are added to the simulation
# acquire the mixing ratio at each pressure (need to be interpolated to normalized pressure)
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
# load molecular cross section for main constituent of the atmosphere
# will check first to see if the molecule is in the database
s.cross_db = s.check_molecules()
s.nu, s.normalized_cross_section = s.load_molecule_cross_section()
s.normalized_stellar_spectra = s.load_stellar_spectra()
s.Reference_Reflect_Signal = s.load_atmosphere_geometry_model()
s.user_input["Plotting"]["Figure"]["y_multiplier"] = 1
s.user_input["Plotting"]["Figure"][
"Title"] = "Simulated Example Reflection Spectra for Pure Water Atmosphere"
s.user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
s.user_input["Plotting"]["Figure"]["y_label"] = r"Flux Density (W/m^2)"
sim_plot = plotter.Simulation_Plotter(s.user_input)
plt_ref_1 = sim_plot.plot_xy(s.nu, s.normalized_stellar_spectra,
"Stellar Spectra")
plt_ref_2 = sim_plot.plot_xy(
s.nu, s.normalized_stellar_spectra * s.Reference_Reflect_Signal,
"Reflection Spectra")
sim_plot.set_legend([plt_ref_1, plt_ref_2])
if utils.to_bool(user_input["Save"]["Plot"]["save"]):
sim_plot.save_plot()
else:
sim_plot.show_plot()
def simulate_window(s, o, a):
s.Timer = simple_timer(4)
#create a base flat spectra based on planetary parameters
s.Surface_g, s.Base_TS_Value = s.load_astrophysical_properties()
# normalized pressure directly correspond to atmosphere layers.
s.normalized_pressure = s.load_atmosphere_pressure_layers()
# load mixing ration files and determine what molecules are added to the simulation
# acquire the mixing ratio at each pressure (need to be interpolated to normalized pressure)
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
# load molecular cross section for main constituent of the atmosphere
# will check first to see if the molecule is in the database
s.cross_db = s.check_molecules()
s.nu, s.normalized_cross_section = s.load_molecule_cross_section()
s.normalized_rayleigh = s.load_rayleigh_scattering()
print "load time", s.Timer.elapse()
s.Transit_Signal = s.load_atmosphere_geometry_model()
nu, trans = o.calculate_convolve(s.nu, s.Transit_Signal)
s.nu_window = a.spectra_window(nu, trans, "T", 0.3, 100., s.min_signal)
print "calc time", s.Timer.elapse()
user_input["Plotting"]["Figure"][
"Title"] = "Absorption and Atmospheric Window for Simulated Atmosphere of %s" % "_".join(
s.normalized_molecules)
user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
user_input["Plotting"]["Figure"]["y_label"] = r"Transit Signal (ppm)"
sim_plot = plotter.Simulation_Plotter(s.user_input)
sim_plot.plot_xy(nu, trans)
sim_plot.plot_window(s.nu_window, "k", 0.2)
sim_plot.show_plot()
return s.Transit_Signal
def simulate_noise(user_input):
observer = observe.OS_Simulator(user_input)
sim_plot = plotter.Simulation_Plotter(user_input)
nu, ref_trans = load_npy(user_input["Save"]["Spectra"]["path"],
user_input["Save"]["Spectra"]["name"])
wav = (10000. / nu)
trans = ref_trans
bin_centers, bin_mean_I = observer.calculate_bin(wav, trans)
bin_mean_error_I, bin_mean_error_bar = observer.add_noise(bin_mean_I)
sim_plot.plot_xy(wav, trans, "ref.", Dtype="um")
sim_plot.plot_bin(bin_centers, bin_mean_error_I, bin_mean_error_bar)
sim_plot.plot_bin(bin_centers, bin_mean_I, bin_mean_error_bar * 10**-6)
sim_plot.show_plot()
user_input["Simulation_Control"]["Mixing_Ratio_Name"] = "earth.txt"
user_input["Save"]["Intermediate_Data"][
"cross_section_savename"] = "%s_Cross_Section.npy" % Filename
user_input["Save"]["Window"][
"path"] = "../../output/Simple_Atmosphere_Window"
user_input["Save"]["Window"][
"name"] = "%s_Window_A1000_S100.txt" % Filename
user_input["Save"]["Plot"] = {}
user_input["Save"]["Plot"]["path"] = "../../output/Plot_Result"
user_input["Save"]["Plot"]["name"] = "%s_Plot.png" % Filename
simulation = theory.TS_Simulator(user_input)
observer = observe.OS_Simulator(user_input)
Raw_nu, Raw_TS = simulation.simulate_example()
nu, Trans = observer.calculate_convolve(Raw_nu, Raw_TS)
user_input["Plotting"]["Figure"][
"Title"] = "Transit Signal for Simulated Earth Atmosphere"
user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
user_input["Plotting"]["Figure"]["y_label"] = r"Transit Signal (ppm)"
sim_plot = plotter.Simulation_Plotter(user_input)
plt_ref_1 = sim_plot.plot_xy(Raw_nu, Raw_TS, "raw_spectra")
plt_ref_2 = sim_plot.plot_xy(nu, Trans, "convolved_spectra")
sim_plot.set_legend([plt_ref_1, plt_ref_2])
sim_plot.show_plot()
def simulate_NIST(s, o, a):
s.Timer = simple_timer(4)
#create a base flat spectra based on planetary parameters
s.Surface_g, s.Base_TS_Value = s.load_astrophysical_properties()
# normalized pressure directly correspond to atmosphere layers.
s.normalized_pressure = s.load_atmosphere_pressure_layers()
# load mixing ration files and determine what molecules are added to the simulation
# acquire the mixing ratio at each pressure (need to be interpolated to normalized pressure)
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
# load molecular cross section for main constituent of the atmosphere
# will check first to see if the molecule is in the database
s.cross_db = s.check_molecules()
s.nu, s.normalized_cross_section = s.load_molecule_cross_section()
print "load time", s.Timer.elapse()
# load rayleigh scattering
s.Rayleigh_enable = utils.to_bool(
s.user_input["Atmosphere_Effects"]["Rayleigh"]["enable"])
if s.Rayleigh_enable:
s.normalized_rayleigh = s.load_rayleigh_scattering()
user_input["Plotting"]["Figure"][
"Title"] = "Simulated Earth-like Exoplanet Atmosphere Comparison Study"
user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
user_input["Plotting"]["Figure"]["y_label"] = r"Transit Signal (ppm)"
sim_plot = plotter.Simulation_Plotter(s.user_input)
# calculate theoretical transmission spectra
s.Reference_Transit_Signal = s.load_atmosphere_geometry_model()
nu, ref_trans = o.calculate_convolve(s.nu, s.Reference_Transit_Signal)
# plotting
plt_legend = []
plt_ref = sim_plot.plot_xy(nu, ref_trans, "ref.", "r")
plt_legend.append(plt_ref)
cloud_deck = 1000 # 100mbar
cloud_amount = 0.5
s.Cloudy_Transit_Signal = s.load_atmosphere_geometry_model_with_cloud(
cloud_deck, cloud_amount)
nu, clo_trans = o.calculate_convolve(s.nu, s.Cloudy_Transit_Signal)
plt_ref = sim_plot.plot_xy(nu, clo_trans, "%s" % cloud_amount)
plt_legend.append(plt_ref)
"""
cloud_deck = 10000 # 100mbar
cloud_amount = 0.1
# cloud absorption
for cloud_amount in [0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10]:
s.Cloudy_Transit_Signal = s.load_atmosphere_geometry_model_with_cloud(cloud_deck,cloud_amount)
nu,clo_trans = o.calculate_convolve(s.nu, s.Cloudy_Transit_Signal)
plt_ref = sim_plot.plot_xy(nu,clo_trans,"%s"%cloud_amount)
plt_legend.append(plt_ref)
"""
"""
# cloud deck
for cloud_deck in [100000,10000,1000,100,10,1,0.1,0.01,0.001]:
s.Cloudy_Transit_Signal = s.load_atmosphere_geometry_model_with_cloud(cloud_deck,cloud_amount)
nu,clo_trans = o.calculate_convolve(s.nu, s.Cloudy_Transit_Signal)
plt_ref = sim_plot.plot_xy(nu,clo_trans,"%s"%cloud_deck)
plt_legend.append(plt_ref)
"""
# comparison study
"""
for cloud_amount in [0.01,0.1,1]:
for cloud_deck in [1000,1]:
s.Cloudy_Transit_Signal = s.load_atmosphere_geometry_model_with_cloud(cloud_deck,cloud_amount)
nu,clo_trans = o.calculate_convolve(s.nu, s.Cloudy_Transit_Signal)
plt_ref = sim_plot.plot_xy(nu,clo_trans,"D%s_A%s"%(cloud_deck,cloud_amount))
plt_legend.append(plt_ref)
"""
s.nu_window = a.spectra_window(nu, ref_trans, "T", 0.3, 100., s.min_signal)
sim_plot.plot_window(s.nu_window, "k", 0.2)
sim_plot.set_legend(plt_legend)
sim_plot.show_plot()
return s
def simulate_NIST(s, o, a):
s.Timer = simple_timer(4)
#create a base flat spectra based on planetary parameters
s.Surface_g, s.Base_TS_Value = s.load_astrophysical_properties()
# normalized pressure directly correspond to atmosphere layers.
s.normalized_pressure = s.load_atmosphere_pressure_layers()
# load mixing ration files and determine what molecules are added to the simulation
# acquire the mixing ratio at each pressure (need to be interpolated to normalized pressure)
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
# load molecular cross section for main constituent of the atmosphere
# will check first to see if the molecule is in the database
s.cross_db = s.check_molecules()
s.nu, s.normalized_cross_section = s.load_molecule_cross_section()
# load biosignature molecules
bio_enable = utils.to_bool(
s.user_input["Atmosphere_Effects"]["Bio_Molecule"]["enable"])
if bio_enable == True:
data_type = s.user_input["Atmosphere_Effects"]["Bio_Molecule"][
"data_type"]
bio_molecule = s.user_input["Atmosphere_Effects"]["Bio_Molecule"][
"molecule"]
bio_abundance = utils.to_float(
s.user_input["Atmosphere_Effects"]["Bio_Molecule"]["abundance"])
s.is_smile = utils.to_bool(
s.user_input["Atmosphere_Effects"]["Bio_Molecule"]["is_smile"])
s.nu, s.bio_cross_section = s.load_bio_molecule_cross_section(
bio_molecule, data_type)
s.bio_normalized_cross_section = np.concatenate(
[s.normalized_cross_section, s.bio_cross_section], axis=0)
# modify the molecular abundance after adding biosignatures
# scale heights untouched still since effect is small
s.bio_normalized_abundance = []
for i, abundance in enumerate(s.normalized_abundance):
s.bio_normalized_abundance.append([])
for j in abundance:
s.bio_normalized_abundance[i].append(j * (1 - bio_abundance))
s.bio_normalized_abundance[i].append(bio_abundance)
s.bio_normalized_molecules = np.concatenate(
[s.normalized_molecules, [bio_molecule]], axis=0)
print "load time", s.Timer.elapse()
# load rayleigh scattering
s.Rayleigh_enable = utils.to_bool(
s.user_input["Atmosphere_Effects"]["Rayleigh"]["enable"])
if s.Rayleigh_enable:
s.normalized_rayleigh = s.load_rayleigh_scattering()
s.Overlay_enable = utils.to_bool(
s.user_input["Atmosphere_Effects"]["Overlay"]["enable"])
if s.Overlay_enable:
o_nu, o_xsec = s.load_overlay_effects(dtype="HITRAN")
s.normalized_overlay = s.interpolate_overlay_effects(o_nu, o_xsec)
# calculate theoretical transmission spectra
s.Reference_Transit_Signal = s.load_atmosphere_geometry_model()
s.Bio_Transit_Signal = s.load_atmosphere_geometry_model(bio=bio_enable)
# calculate observed transmission spectra
nu, ref_trans = o.calculate_convolve(s.nu, s.Reference_Transit_Signal)
nu, bio_trans = o.calculate_convolve(s.nu, s.Bio_Transit_Signal)
# analyze the spectra
s.nu_window = a.spectra_window(nu, ref_trans, "T", 0.3, 100., s.min_signal)
user_input["Plotting"]["Figure"][
"Title"] = "Transit Signal and Atmospheric Window for Simulated Earth Atmosphere with traces of %s at %s ppm" % (
bio_molecule, bio_abundance * 10**6)
user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
user_input["Plotting"]["Figure"]["y_label"] = r"Transit Signal (ppm)"
sim_plot = plotter.Simulation_Plotter(s.user_input)
plt_ref_1 = sim_plot.plot_xy(nu, bio_trans, "with_bio")
plt_ref_2 = sim_plot.plot_xy(nu, ref_trans, "ref.")
sim_plot.plot_window(s.nu_window, "k", 0.2)
sim_plot.set_legend([plt_ref_1, plt_ref_2])
if utils.to_bool(user_input["Save"]["Plot"]["save"]):
sim_plot.save_plot()
else:
sim_plot.show_plot()
return s
def simulate_CIA(s):
s.Timer = simple_timer(4)
#create a base flat spectra based on planetary parameters
s.Surface_g, s.Base_TS_Value = s.load_astrophysical_properties()
# normalized pressure directly correspond to atmosphere layers.
s.normalized_pressure = s.load_atmosphere_pressure_layers()
# load mixing ration files and determine what molecules are added to the simulation
# acquire the mixing ratio at each pressure (need to be interpolated to normalized pressure)
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
s.normalized_molecules, s.MR_Pressure, s.molecule_MR = s.load_mixing_ratio(
)
s.normalized_abundance = s.interpolate_mixing_ratio()
# load temperature pressure profile
s.TP_Pressure, s.TP_Temperature = s.load_TP_profile()
s.normalized_temperature = s.interpolate_TP_profile()
# calculate the scale height for each layer of the atmosphere
s.normalized_scale_height = s.calculate_scale_height()
s.cross_db = s.check_molecules()
s.nu, s.normalized_cross_section = s.load_molecule_cross_section()
s.normalized_rayleigh = s.load_rayleigh_scattering()
CIA_Enable = utils.to_bool(
s.user_input["Atmosphere_Effects"]["CIA"]["enable"])
if CIA_Enable == True:
s.CIA_File, s.CIA_Data = s.load_CIA(["H2"])
s.normalized_CIA = s.interpolate_CIA()
s.Transit_Signal_CIA = s.load_atmosphere_geometry_model(CIA=True)
s.Transit_Signal = s.load_atmosphere_geometry_model()
user_input["Plotting"]["Figure"][
"Title"] = r"Transit Signal and Atmospheric Window for Simulated Atmosphere of %s" % "_".join(
s.normalized_molecules)
user_input["Plotting"]["Figure"]["x_label"] = r'Wavelength ($\mu m$)'
user_input["Plotting"]["Figure"]["y_label"] = r"Transit Signal (ppm)"
sim_plot = plotter.Simulation_Plotter(s.user_input)
plt_ref_1 = sim_plot.plot_xy(s.nu, s.Transit_Signal, "Molecular")
plt_ref_2 = sim_plot.plot_xy(s.nu, s.Transit_Signal_CIA, "Molecular+CIA")
sim_plot.set_legend([plt_ref_1, plt_ref_2])
sim_plot.show_plot()