def test_get_fitdata():
data_path = pkg_resources.resource_filename("measure_extinction", "data/")
# read in the observed data of the stars
redstar = StarData("hd229238.dat", path=data_path)
compstar = StarData("hd204172.dat", path=data_path)
# calculate the extinction curve
ext = ExtData()
ext.calc_elx(redstar, compstar)
# once wavelenth units saved, update FITS file and use this line instead
# of the 4 lines above
# ext = ExtData(filename=data_path + "hd283809_hd064802_ext.fits")
wave, y, unc = ext.get_fitdata(
["BAND", "IUE"], remove_uvwind_region=True, remove_lya_region=True
)
# fitting routines often cannot handle units, make sure none are present
for cursrc in ext.waves.keys():
assert isinstance(wave, u.Quantity)
assert not isinstance(y, u.Quantity)
assert not isinstance(unc, u.Quantity)
def calc_extinction(redstarname, compstarname, path):
# read in the observed data for both stars
redstarobs = StarData("%s.dat" % redstarname.lower(), path=path)
compstarobs = StarData("%s.dat" % compstarname.lower(), path=path)
# calculate the extinction curve
extdata = ExtData()
extdata.calc_elx(redstarobs, compstarobs)
extdata.save(path + "%s_%s_ext.fits" %
(redstarname.lower(), compstarname.lower()))
def test_calc_AV_RV():
# get the location of the data files
data_path = pkg_resources.resource_filename("measure_extinction", "data/")
# read in the observed data of the stars
redstar = StarData("hd229238.dat", path=data_path)
compstar = StarData("hd204172.dat", path=data_path)
# calculate the extinction curve
ext = ExtData()
ext.calc_elx(redstar, compstar)
# calculate A(V)
ext.calc_AV()
np.testing.assert_almost_equal(ext.columns["AV"], 2.5626900237367805)
# calculate R(V)
ext.calc_RV()
np.testing.assert_almost_equal(ext.columns["RV"], 2.614989769244703)
def test_calc_ext():
# get the location of the data files
data_path = pkg_resources.resource_filename("measure_extinction", "data/")
# read in the observed data of the stars
redstar = StarData("hd229238.dat", path=data_path)
compstar = StarData("hd204172.dat", path=data_path)
# calculate the extinction curve
ext = ExtData()
ext.calc_elx(redstar, compstar)
# test that the quantities have units (or not as appropriate)
for cursrc in ext.waves.keys():
assert isinstance(ext.waves[cursrc], u.Quantity)
assert not isinstance(ext.exts[cursrc], u.Quantity)
assert not isinstance(ext.uncs[cursrc], u.Quantity)
assert not isinstance(ext.npts[cursrc], u.Quantity)
# check that the wavelengths can be converted to microns
for cursrc in ext.waves.keys():
twave = ext.waves[cursrc].to(u.micron)
assert twave.unit == u.micron
if __name__ == "__main__":
# commandline parser
parser = argparse.ArgumentParser()
parser.add_argument("redstarname", help="name of reddened star")
parser.add_argument("compstarname", help="name of comparision star")
parser.add_argument(
"--path",
help="base path to observed data",
default="/home/kgordon/Python_git/extstar_data/",
)
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)
# output filebase
filebase = "fits/%s_%s" % (args.redstarname, args.compstarname)
# calculate the extinction curve
extdata = ExtData()
extdata.calc_elx(redstarobs, compstarobs)
# save the extinction curve
out_fname = "fits/%s_%s_ext.fits" % (args.redstarname, args.compstarname)
extdata.save(out_fname)
# 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_elx(reddened_star, modsed_stardata, rel_band=args.relband)
col_info = {"av": fit_params[3], "rv": fit_params[4]}
extdata.save(out_basename + "_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)
mpl.rc("xtick.major", width=2)
mpl.rc("xtick.minor", width=2)