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)