# In[ ]:
w_aces, f_aces = load_phoenix_aces(
"data/lte05200-1.50-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits")
aces_spec_15 = Spectrum(xaxis=vac2air(w_aces * 10) / 10, flux=f_aces)
(aces_spec_15 - aces_spec).plot(label="Teff=5200")
plt.title("Difference between 1.5 and 4.5 logg for phoenix aces")
plt.show()
# In[ ]:
# Full Spectrum
plt.figure(figsize=(15, 10))
dusty_spec.plot(label="BT-DUSTY")
settl_spec.plot(label="BT-SETTL")
cond_spec.plot(label="BT-COND")
aces_spec.plot(linestyle="--", label="PHOENIX ACES")
next_spec.plot(linestyle=":", label="NEXTGEN")
#plt.plot(w_dusty_fits, f_dusty_fits/max(f_dusty_fits), label="Dusty fits")
plt.title("HD30501 host - 5200K")
plt.xlabel("Wavelength(nm)")
plt.ylabel("Flux")
plt.legend()
plt.show()
# In[ ]:
# COMPARISON TO PHOENIX ACES
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
#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")
cond_spec.plot(label="cond")
aces_spec.plot(label="aces")
dusty_fits_spec.plot(label="dusty_fits")
settl_spec.plot(label="settl")
plt.legend()
plt.show()
# In[ ]:
next_spec.plot(label="nextgen")
plt.legend()
plt.show()
dusty_spec.plot(label="dusty_spec")
plt.legend()
# Manually load Phoenix spectra #phoenix_path = simulators... phoenix_path = "/home/jneal/Phd/data/PHOENIX-ALL/PHOENIX/" phoenix_1 = "Z-0.0/lte06000-4.50-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits" phoenix_2 = "Z-0.0/lte05800-4.50-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits" wav = fits.getdata(phoenix_path + "WAVE_PHOENIX-ACES-AGSS-COND-2011.fits") phoenix1 = fits.getdata(phoenix_path + phoenix_1) phoenix2 = fits.getdata(phoenix_path + phoenix_2) spec1 = Spectrum(xaxis=wav/10, flux=phoenix1) spec2 = Spectrum(xaxis=wav/10, flux=phoenix2) spec1.wav_select(2000,2200) spec2.wav_select(2000,2200) spec1.plot(label="1") spec2.plot(label="2") plt.legend() plt.show() # In[4]: from Convolution.IP_multi_Convolution import convolve_spectrum # Convolve to 50000 conv1 = convolve_spectrum(spec1, [2100, 2150], 50000, numProcs=4) conv2 = convolve_spectrum(spec2, [2100, 2150], 50000, numProcs=4)