plt.suptitle(
"BT-XXX Relative Synthethic Differences\nArtucus-like\nTeff=5200, logg=4.5, Fe/H= 0.0"
)
plt.xlabel("Wavelength(nm)")
plt.ylabel("Flux")
plt.legend()
plt.show()
# In[ ]:
# ARTUCUS 1000nm
artucus_1 = "/home/jneal/Phd/data/artucus/10097-10155_s-obs.fits"
data, hdr = fits.getdata(artucus_1, header=True)
artucus_1 = Spectrum(xaxis=get_wavelength(hdr) / 10, flux=data, header=hdr)
artucus_1 = artucus_1.normalize("linear")
# In[ ]:
#limits = [2100, 2200]
limits = [artucus_1.xaxis[0] - 2, artucus_1.xaxis[-1] + 2]
print(limits)
next_spec.wav_select(*limits)
next_spec = next_spec.normalize("exponential")
dusty_spec.wav_select(*limits)
dusty_spec = dusty_spec.normalize("exponential")
settl_spec.wav_select(*limits)
settl_spec = settl_spec.normalize("exponential")
cond_spec.wav_select(*limits)
cond_spec = cond_spec.normalize("exponential")
aces_spec.wav_select(*limits)
plt.plot(w_cond, f_cond / max(f_cond), label="BT-COND")
plt.plot(w_aces, f_aces / max(f_aces), label="PHOENIX ACES")
plt.plot(w_dusty_fits, f_dusty_fits / max(f_dusty_fits), label="Dusty fits")
plt.plot(w_next, f_next / max(f_next), label="NEXTGEN")
plt.title("Sun - 5800K")
plt.xlabel("Wavelength(nm)")
plt.ylabel("Flux")
plt.legend()
plt.show()
# In[ ]:
limits = [2116, 2120]
next_spec.wav_select(*limits)
#print(next_spec.xaxis, next_spec.flux)
next_spec = next_spec.normalize("exponential")
dusty_spec.wav_select(*limits)
dusty_spec = dusty_spec.normalize("exponential")
settl_spec.wav_select(*limits)
settl_spec = settl_spec.normalize("exponential")
cond_spec.wav_select(*limits)
cond_spec = cond_spec.normalize("exponential")
aces_spec.wav_select(*limits)
aces_spec = aces_spec.normalize("exponential")
dusty_fits_spec.wav_select(*limits)
dusty_fits_spec = dusty_fits_spec.normalize("exponential")
# In[ ]:
next_spec.plot(label="nextgen")
dusty_spec.plot(label="dusty_spec")
def compare_spectra(table, params):
"""Plot the min chi2 result against the observations."""
extractor = DBExtractor(table)
for ii, chi2_val in enumerate(chi2_names[0:-2]):
df = extractor.minimum_value_of(chi2_val)
df = df[["teff_1", "logg_1", "feh_1", "gamma",
"teff_2", "logg_2", "feh_2", "rv", chi2_val]]
params1 = [df["teff_1"].values[0], df["logg_1"].values[0], df["feh_1"].values[0]]
params2 = [df["teff_2"].values[0], df["logg_2"].values[0], df["feh_2"].values[0]]
params1 = [float(param1) for param1 in params1]
params2 = [float(param2) for param2 in params2]
gamma = df["gamma"].values
rv = df["rv"].values
from simulators.iam_module import iam_helper_function
obs_name, obs_params, output_prefix = iam_helper_function(params["star"], params["obsnum"], ii + 1)
obs_spec = load_spectrum(obs_name)
# Mask out bad portion of observed spectra
# obs_spec = spectrum_masking(obs_spec, params["star"], params["obsnum"], ii + 1)
# Barycentric correct spectrum
_obs_spec = barycorr_crires_spectrum(obs_spec, extra_offset=None)
normalization_limits = [obs_spec.xaxis[0] - 5, obs_spec.xaxis[-1] + 5]
# models
# print("params for models", params1, params2)
mod1 = load_starfish_spectrum(params1, limits=normalization_limits,
hdr=True, normalize=False, area_scale=True,
flux_rescale=True)
mod2 = load_starfish_spectrum(params2, limits=normalization_limits,
hdr=True, normalize=False, area_scale=True,
flux_rescale=True)
iam_grid_func = inherent_alpha_model(mod1.xaxis, mod1.flux, mod2.flux,
rvs=rv, gammas=gamma)
iam_grid_model = iam_grid_func(obs_spec.xaxis).squeeze()
iam_grid_model_full = iam_grid_func(mod1.xaxis).squeeze()
model_spec_full = Spectrum(flux=iam_grid_model_full, xaxis=mod1.xaxis)
model_spec = Spectrum(flux=iam_grid_model, xaxis=obs_spec.xaxis)
model_spec = model_spec.remove_nans()
model_spec = model_spec.normalize(method="exponential")
model_spec_full = model_spec_full.remove_nans()
model_spec_full = model_spec_full.normalize(method="exponential")
fig, ax = plt.subplots(1, 1)
plt.plot(obs_spec.xaxis, obs_spec.flux, label="Observation")
plt.plot(model_spec.xaxis, model_spec.flux, label="Minimum \chi^2 model")
plt.plot(model_spec_full.xaxis, model_spec_full.flux, "--", label="Model_full_res")
plt.legend()
fig.tight_layout()
name = "{0}-{1}_{2}_{3}_min_chi2_spectrum_comparison_{4}.png".format(
params["star"], params["obsnum"], params["chip"], chi2_val, params["suffix"])
plt.savefig(os.path.join(params["path"], "plots", name))
plt.close()
plt.plot(obs_spec.xaxis, obs_spec.flux, label="Observation")
plt.show()
def compare_spectra(table, params):
"""Plot the min chi2 result against the observations."""
extractor = DBExtractor(table)
gamma_df = extractor.simple_extraction(columns=["gamma"])
extreme_gammas = [min(gamma_df.gamma.values), max(gamma_df.gamma.values)]
for ii, chi2_val in enumerate(chi2_names[0:-2]):
df = extractor.ordered_extraction(
columns=["teff_1", "logg_1", "feh_1", "gamma", chi2_val],
order_by=chi2_val,
limit=1,
asc=True)
params1 = [
df["teff_1"].values[0], df["logg_1"].values[0],
df["feh_1"].values[0]
]
params1 = [float(param1) for param1 in params1]
gamma = df["gamma"].values
obs_name, obs_params, output_prefix = bhm_helper_function(
params["star"], params["obsnum"], ii + 1)
print(obs_name)
obs_spec = load_spectrum(obs_name)
# Mask out bad portion of observed spectra
# obs_spec = spectrum_masking(obs_spec, params["star"], params["obsnum"], ii + 1)
# Barycentric correct spectrum
_obs_spec = barycorr_crires_spectrum(obs_spec, extra_offset=None)
normalization_limits = [obs_spec.xaxis[0] - 5, obs_spec.xaxis[-1] + 5]
# models
# print("params for models", params1)
mod1 = load_starfish_spectrum(params1,
limits=normalization_limits,
hdr=True,
normalize=False,
area_scale=True,
flux_rescale=True)
bhm_grid_func = one_comp_model(mod1.xaxis, mod1.flux, gammas=gamma)
bhm_upper_gamma = one_comp_model(mod1.xaxis,
mod1.flux,
gammas=extreme_gammas[1])
bhm_lower_gamma = one_comp_model(mod1.xaxis,
mod1.flux,
gammas=extreme_gammas[0])
bhm_grid_model = bhm_grid_func(obs_spec.xaxis).squeeze()
bhm_grid_model_full = bhm_grid_func(mod1.xaxis).squeeze()
bhm_upper_gamma = bhm_upper_gamma(obs_spec.xaxis).squeeze()
bhm_lower_gamma = bhm_lower_gamma(obs_spec.xaxis).squeeze()
model_spec_full = Spectrum(flux=bhm_grid_model_full, xaxis=mod1.xaxis)
model_spec = Spectrum(flux=bhm_grid_model, xaxis=obs_spec.xaxis)
bhm_upper_gamma = Spectrum(flux=bhm_upper_gamma, xaxis=obs_spec.xaxis)
bhm_lower_gamma = Spectrum(flux=bhm_lower_gamma, xaxis=obs_spec.xaxis)
model_spec = model_spec.remove_nans()
model_spec = model_spec.normalize(method="exponential")
model_spec_full = model_spec_full.remove_nans()
model_spec_full = model_spec_full.normalize(method="exponential")
bhm_lower_gamma = bhm_lower_gamma.remove_nans()
bhm_lower_gamma = bhm_lower_gamma.normalize(method="exponential")
bhm_upper_gamma = bhm_upper_gamma.remove_nans()
bhm_upper_gamma = bhm_upper_gamma.normalize(method="exponential")
from mingle.utilities.chisqr import chi_squared
chisqr = chi_squared(obs_spec.flux, model_spec.flux)
print("Recomputed chi^2 = {0}".format(chisqr))
print("Database chi^2 = {0}".format(df[chi2_val]))
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
plt.plot(obs_spec.xaxis,
obs_spec.flux + 0.01,
label="0.05 + Observation, {}".format(obs_name))
plt.plot(model_spec.xaxis,
model_spec.flux,
label="Minimum \chi^2 model")
plt.plot(model_spec_full.xaxis,
model_spec_full.flux,
"--",
label="Model_full_res")
plt.plot(bhm_lower_gamma.xaxis,
bhm_lower_gamma.flux,
"-.",
label="gamma={}".format(extreme_gammas[0]))
plt.plot(bhm_upper_gamma.xaxis,
bhm_upper_gamma.flux,
":",
label="gamma={}".format(extreme_gammas[1]))
plt.title("bhm spectrum")
plt.legend()
fig.tight_layout()
name = "{0}-{1}_{2}_{3}_bhm_min_chi2_spectrum_comparison_{4}.png".format(
params["star"], params["obsnum"], params["chip"], chi2_val,
params["suffix"])
plt.savefig(os.path.join(params["path"], "plots", name))
plt.close()
plt.plot(obs_spec.xaxis, obs_spec.flux, label="Observation")
plt.show()
def main(star,
obsnum,
teff_1,
logg_1,
feh_1,
teff_2,
logg_2,
feh_2,
gamma,
rv,
plot_name=None):
fig, axis = plt.subplots(2, 2, figsize=(15, 8), squeeze=False)
for chip, ax in zip(range(1, 5), axis.flatten()):
# Get observation data
obs_name, params, output_prefix = iam_helper_function(
star, obsnum, chip)
# Load observed spectrum
obs_spec = load_spectrum(obs_name)
# Mask out bad portion of observed spectra
obs_spec = spectrum_masking(obs_spec, star, obsnum, chip)
# Barycentric correct spectrum
_obs_spec = barycorr_crires_spectrum(obs_spec, extra_offset=None)
# Determine Spectrum Errors
# error_off = False
# errors = spectrum_error(star, obsnum, chip, error_off=error_off)
# Create model with given parameters
host = load_starfish_spectrum([teff_1, logg_1, feh_1],
limits=[2110, 2165],
area_scale=True,
hdr=True)
if teff_2 is None:
assert (logg_2 is None) and (feh_2 is None) and (
rv == 0), "All must be None for bhm case."
companion = Spectrum(xaxis=host.xaxis,
flux=np.zeros_like(host.flux))
else:
companion = load_starfish_spectrum([teff_2, logg_2, feh_2],
limits=[2110, 2165],
area_scale=True,
hdr=True)
joint_model = inherent_alpha_model(host.xaxis,
host.flux,
companion.flux,
gammas=gamma,
rvs=rv)
model_spec = Spectrum(xaxis=host.xaxis,
flux=joint_model(host.xaxis).squeeze())
model_spec = model_spec.remove_nans()
model_spec = model_spec.normalize("exponential")
# plot
obs_spec.plot(axis=ax, label="{}-{}".format(star, obsnum))
model_spec.plot(axis=ax, linestyle="--", label="Chi-squared model")
ax.set_xlim([obs_spec.xmin() - 0.5, obs_spec.xmax() + 0.5])
ax.set_title("{} obs {} chip {}".format(star, obsnum, chip))
ax.legend()
plt.tight_layout()
if plot_name is None:
fig.show()
else:
if not (plot_name.endswith(".png") or plot_name.endswith(".pdf")):
raise ValueError("plot_name does not end with .pdf or .png")
fig.savefig(plot_name)
return 0