parser.add_argument("starname", help="name of star")
parser.add_argument("--path", help="path to star files", default="./")
parser.add_argument("--png", help="save figure as a png file", action="store_true")
parser.add_argument("--eps", help="save figure as an eps file", action="store_true")
parser.add_argument("--pdf", help="save figure as a pdf file", action="store_true")
return parser
if __name__ == "__main__":
# commandline parser
parser = plot_spec_parser()
args = parser.parse_args()
# read in the observed data on the star
fstarname, file_path = get_full_starfile("m33_j013334.26+303327")
starobs = StarData(fstarname, path=file_path)
band_names = starobs.data["BAND"].get_band_names()
# get the model filenames
print("reading in the model spectra")
file_path = "/home/kgordon/Python_git/extstar_data/"
tlusty_models_fullpath = glob.glob("{}/Models/tlusty_*v10.dat".format(file_path))
tlusty_models_fullpath = tlusty_models_fullpath[0:10]
tlusty_models = [
tfile[tfile.rfind("/") + 1 : len(tfile)] for tfile in tlusty_models_fullpath
]
spectra_names = ["BAND", "STIS"]
# band_names = ['ACS_F814W', 'V', 'WFC3_F336W', 'WFC3_F160W',
# 'ACS_F475W', 'WFC3_F110W', 'WFC3_F275W']
font = {"size": fontsize}
mpl.rc("font", **font)
mpl.rc("lines", linewidth=1)
mpl.rc("axes", linewidth=2)
mpl.rc("xtick.major", width=2)
mpl.rc("xtick.minor", width=2)
mpl.rc("ytick.major", width=2)
mpl.rc("ytick.minor", width=2)
# setup the plot
fig, ax = plt.subplots(figsize=(13, 10))
# plot the bands and all spectra for this star
# plot all the spectra on the same plot
for k, cstarname in enumerate(starnames):
fstarname, file_path = get_full_starfile(cstarname)
starobs = StarData(fstarname, path=file_path)
starobs.plot(ax, norm_wave_range=[0.2, 0.3] * u.micron, yoffset=2**k)
# finish configuring the plot
ax.set_yscale("log")
ax.set_xscale("log")
ax.set_xlabel(r"$\lambda$ [$\mu m$]", fontsize=1.3 * fontsize)
ax.set_ylabel(r"$F(\lambda)$ [$ergs\ cm^{-2}\ s\ \AA$]",
fontsize=1.3 * fontsize)
ax.tick_params("both", length=10, width=2, which="major")
ax.tick_params("both", length=5, width=1, which="minor")
# use the whitespace better
fig.tight_layout()
new_samples[:, ndim+2] = (10**samples[:, 10])/new_samples[:, ndim]
per_params = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(new_samples, [16, 50, 84],
axis=0)))
return per_params
if __name__ == '__main__':
# commandline parser
parser = fit_model_parser()
args = parser.parse_args()
# get the full starfilename and path
fstarname, file_path = get_full_starfile(args.starname)
# get the observed reddened star data
reddened_star = StarData(fstarname, path=file_path)
band_names = reddened_star.data['BAND'].get_band_names()
spectra_names = reddened_star.data.keys()
# override for now
print('possible', spectra_names)
# spectra_names = ['BAND', 'STIS_Opt']
# spectra_names = ['BAND', 'STIS_Opt']
print('only using', spectra_names)
# override for now
# band_names = ['U', 'B', 'V']
# band_names = ['U', 'B', 'V', 'J', 'H', 'K']