help="save figure as an eps file",
action="store_true")
parser.add_argument("--pdf",
help="save figure as a pdf file",
action="store_true")
args = parser.parse_args()
# read in the observed data for both stars
redstarobs = StarData('DAT_files/%s.dat' % args.redstarname,
path=args.path)
compstarobs = StarData('DAT_files/%s.dat' % args.compstarname,
path=args.path)
# calculate the extinction curve
extdata = ExtData()
extdata.calc_elv(redstarobs, compstarobs)
# plotting setup for easier to read plots
fontsize = 18
font = {'size': fontsize}
matplotlib.rc('font', **font)
matplotlib.rc('lines', linewidth=1)
matplotlib.rc('axes', linewidth=2)
matplotlib.rc('xtick.major', width=2)
matplotlib.rc('xtick.minor', width=2)
matplotlib.rc('ytick.major', width=2)
matplotlib.rc('ytick.minor', width=2)
# setup the plot
fig, ax = plt.subplots(figsize=(10, 13))
# intrinsic sed
modsed = modinfo.stellar_sed(fit_params[0:3], velocity=args.velocity)
# dust_extinguished sed
ext_modsed = modinfo.dust_extinguished_sed(fit_params[3:10], modsed)
# hi_abs sed
hi_ext_modsed = modinfo.hi_abs_sed(fit_params[10:12], [args.velocity, 0.0],
ext_modsed)
# create a StarData object for the best fit SED
modsed_stardata = modinfo.SED_to_StarData(modsed)
# create an extincion curve and save it
extdata = ExtData()
extdata.calc_elv(reddened_star, modsed_stardata)
col_info = {'av': fit_params[3],
'rv': fit_params[4]}
extdata.save_ext_data(args.starname + '_ext.fits',
column_info=col_info)
# plot the SEDs
norm_model = np.average(hi_ext_modsed['BAND'])
norm_data = np.average(reddened_star.data['BAND'].fluxes)
# plotting setup for easier to read plots
fontsize = 18
font = {'size': fontsize}
mpl.rc('font', **font)
mpl.rc('lines', linewidth=1)
mpl.rc('axes', linewidth=2)