def plot_kc19_to_2rxs_age_vs_parallax(max_sep=12 * u.arcsec, overwrite=0):
#
# Compare KC2019 isochrone ages for things with matches to things without.
#
outpath = (
'../results/crossmatching/kc19_to_2rxs_age_vs_parallax_sep{}.png'.
format(max_sep.value))
if not os.path.exists(outpath) or overwrite:
kc19_match_df, df = match_kc19_to_2rxs(max_sep=max_sep)
plt.close('all')
f, ax = plt.subplots(figsize=(4, 3))
label = 'KC2019: {} sources'.format(len(df))
ax.scatter(df.age,
1 / (1e-3 * df.parallax),
label=label,
c='gray',
marker='o',
zorder=2,
rasterized=True,
s=1,
linewidths=0)
label = ('KC2019 & ROSAT match < {}": {} sources'.format(
int(max_sep.value), len(kc19_match_df)))
ax.scatter(kc19_match_df.age,
1 / (1e-3 * kc19_match_df.parallax),
label=label,
c='k',
marker='*',
zorder=3,
rasterized=True,
s=5,
linewidths=0)
ax.legend(loc='best', fontsize='xx-small')
format_ax(ax)
ax.set_xlabel('KC2019 log10(isochrone age)')
ax.set_ylabel('dist [pc] = 1/(parallax [arcsec])')
ax.set_yscale('log')
f.tight_layout(pad=0.2)
savefig(f, outpath)
else:
print('found {}'.format(outpath))
def plot_feros_spectra(spectrum_path, outpath=None, xlim=None):
hdul = fits.open(spectrum_path)
d = hdul[0].data
wav = d[0, 0]
flx = d[3, 0]
if isinstance(xlim, list):
xmin = xlim[0]
xmax = xlim[1]
sel = (wav > xmin) & (wav < xmax)
wav = wav[sel]
flx = flx[sel]
spec = Spectrum1D(spectral_axis=wav * u.AA,
flux=flx * u.dimensionless_unscaled)
f, ax = plt.subplots()
ax.plot(wav, flx, c='k', zorder=3)
if isinstance(xlim, list):
exclude_regions = []
if xmin < 6709.2 and xmax > 6710.2:
exclude_regions.append(SpectralRegion(6709.2 * u.AA,
6710.2 * u.AA))
if xmin < 6679 and xmax > 6681:
exclude_regions.append(SpectralRegion(6679 * u.AA, 6681 * u.AA))
cont_flx = fit_generic_continuum(
spec, exclude_regions=exclude_regions)(spec.spectral_axis)
ax.plot(wav, cont_flx, c='r', zorder=2)
ax.set_xlabel('wavelength [angstrom]')
ax.set_ylabel('relative flux')
if isinstance(xlim, list):
ax.set_xlim(xlim)
format_ax(ax)
savefig(f, outpath)
def test_veloce_continuum_fit():
specname = '20200131_837.01_Bouma_final_combined.fits'
spectrum_path = os.path.join(DATADIR, 'Veloce', specname)
flx_2d, wav_2d = read_veloce(spectrum_path, start=400, end=-400)
for o in VELOCE_ORDERS:
flx, wav = flx_2d[o, :], wav_2d[o, :]
wav, flx = wav[::-1], flx[::-1] # wavelength increasing order
cont_flx, cont_norm_spec = fit_continuum(flx, wav, instrument='Veloce')
#
# plot the results
#
outpath = os.path.join(
TESTOUTDIR,
specname.replace(
'.fits', '_order{}_continuum_fit.png'.format(str(o).zfill(2))))
plt.close('all')
f, axs = plt.subplots(nrows=2, ncols=1, figsize=(6, 4))
axs[0].plot(wav, flx, c='k', zorder=3, lw=1)
axs[0].plot(wav, cont_flx, c='r', zorder=2, lw=1)
sel = ((cont_flx > 0) & (cont_norm_spec.flux < 2) &
(cont_norm_spec.flux > -1))
axs[1].plot(cont_norm_spec.wavelength[sel],
cont_norm_spec.flux[sel],
c='k',
lw=1)
axs[0].set_ylabel('flux')
axs[1].set_ylabel('contnorm flux')
axs[1].set_xlabel('wavelength [angstrom]')
for ax in axs:
format_ax(ax)
savefig(f, outpath, writepdf=False)
def match_kc19_to_2rxs(max_sep=12 * u.arcsec):
"""
2RXS has RA/dec of ROSAT sources in J2000.
Cut on the ROSAT detection likelihood to be >=9. Following the
Boller et al (2016) abstract, this is "conservative".
Cut on the "Sflag' screening flag (0=good, not equal to 0 = screening flag
set).
https://ui.adsabs.harvard.edu/abs/2016A%26A...588A.103B/abstract
"""
csvpath = '../data/kc19_to_2rxs_within_{}_arcsec.csv'.format(max_sep.value)
if not os.path.exists(csvpath):
#
# Get 2RXS sources. From Boller+2016, they were taken with the PSPC between
# June 1990 and Aug 1991.
#
with fits.open("../data/Boller_2016_2RXS_ROSAT_sources.fits") as hdul:
t = Table(hdul[1].data)
sel = (t['ExiML'] >= 9) & (t['Sflag'] == 0)
t = t[sel]
rosat_coord = SkyCoord(nparr(t['_RAJ2000']),
nparr(t['_DEJ2000']),
frame='icrs',
unit=(u.deg, u.deg),
equinox=Time(2000.0, format='jyear'),
obstime=Time(1991.0, format='jyear'))
#
# Get Kounkel & Covey 2019 sources with gaia info crossmatched. Set an
# annoyingly high 1" absolute tolerance on the ra/dec match between what I
# got from gaiadr2.source table, and what was listed in KC19 table 1. Use
# the gaiadr2.source ra/dec (in this case, "_x", not "_y"). rtol is set as
# what it needed to be to pass. Set the correct equinox and estimate of
# observing time by the Gaia satellite, and include the proper motions.
#
df = pd.read_csv('../data/kounkel_table1_sourceinfo.csv')
for k_x, k_y in zip(['ra_x', 'dec_x'], ['ra_y', 'dec_y']):
np.testing.assert_allclose(nparr(df[k_x]),
nparr(df[k_y]),
rtol=3e-2,
atol=1 / 3600)
kc19_coord = SkyCoord(nparr(df.ra_x),
nparr(df.dec_x),
frame='icrs',
unit=(u.deg, u.deg),
pm_ra_cosdec=nparr(df.pmra) * u.mas / u.yr,
pm_dec=nparr(df.pmdec) * u.mas / u.yr,
equinox=Time(2015.5, format='jyear'),
obstime=Time(2015.5, format='jyear'))
kc19_coord_rosat_epoch = kc19_coord.apply_space_motion(
new_obstime=Time(1991.0, format='jyear'))
#
# For each ROSAT coordinate, get the nearest KC2019 match. Ensure they are
# each in the same equinox system (J2000.0). Examine the separation
# distribution of the match.
#
idx, d2d, _ = rosat_coord.match_to_catalog_sky(
kc19_coord_rosat_epoch.transform_to(rosat_coord))
sel = (d2d < 1000 * u.arcsec) # ~1/3rd of a degree
bins = np.logspace(-2, 3, 11)
plt.close('all')
f, ax = plt.subplots(figsize=(4, 3))
ax.hist(d2d[sel].to(u.arcsec).value,
bins=bins,
cumulative=False,
color='black',
fill=False,
linewidth=0.5)
format_ax(ax)
ax.set_xlabel('2RXS to nearest KC19 source [arcsec]')
ax.set_ylabel('Number per bin')
ax.set_xscale('log')
ax.set_yscale('log')
f.tight_layout(pad=0.2)
outpath = '../results/crossmatching/kc19_to_2rxs_separation.png'
savefig(f, outpath)
#
# The above shows a hint of a bump at like ~10 arcsec. This is roughly the
# level of positional uncertainty expected from ROSAT (Ayres+2004,
# https://ui.adsabs.harvard.edu/abs/2004ApJ...608..957A/abstract). 6" was
# the design specification accuracy. Seems like 12" (2x the expected
# positional uncertainty) is a decent place to set the cut. It will give
# ~1.5k matches.
#
sep_constraint = (d2d < max_sep)
rosat_matches = t[sep_constraint]
kc19_match_df = df.iloc[idx[sep_constraint]]
kc19_match_df['has_2RXS_match'] = 1
kc19_match_df['2RXS_match_dist_arcsec'] = (d2d[sep_constraint].to(
u.arcsec).value)
kc19_match_df['2RXS_name'] = rosat_matches['_2RXS']
kc19_match_df['2RXS_ExiML'] = rosat_matches['ExiML']
kc19_match_df.to_csv(csvpath, index=False)
print('made {}'.format(csvpath))
else:
kc19_match_df = pd.read_csv(csvpath)
df = pd.read_csv('../data/kounkel_table1_sourceinfo.csv')
return kc19_match_df, df
def get_toi837_li_equivalent_width():
spectrum_path = '../data/spectra/TOI-837_FEROS.fits'
plotpath = '../results/TOI_837/feros_spectrum_get_li_equivalent_width.png'
#
# fit out the continuum to get the continuum normalized flux, over the
# window of 6670 angstrom to 6713 angstrom.
#
xlim = [6670, 6713]
hdul = fits.open(spectrum_path)
d = hdul[0].data
wav = d[0, 0]
flx = d[3, 0]
if isinstance(xlim, list):
xmin = xlim[0]
xmax = xlim[1]
sel = (wav > xmin) & (wav < xmax)
wav = wav[sel]
flx = flx[sel]
spec = Spectrum1D(spectral_axis=wav * u.AA,
flux=flx * u.dimensionless_unscaled)
if isinstance(xlim, list):
exclude_regions = []
if xmin < 6709.2 and xmax > 6710.2:
exclude_regions.append(SpectralRegion(6709.2 * u.AA,
6710.2 * u.AA))
if xmin < 6679 and xmax > 6681:
exclude_regions.append(SpectralRegion(6679 * u.AA, 6681 * u.AA))
cont_flx = (fit_generic_continuum(spec, exclude_regions=exclude_regions)(
spec.spectral_axis))
cont_norm_spec = spec / cont_flx
#
# to fit gaussians, look at 1-flux.
#
full_spec = Spectrum1D(spectral_axis=cont_norm_spec.wavelength,
flux=(1 - cont_norm_spec.flux))
#
# get the Li EW
#
region = SpectralRegion(6708.5 * u.AA, 6711.5 * u.AA)
li_equiv_width = equivalent_width(cont_norm_spec, regions=region)
li_centroid = centroid(full_spec, region)
#
# fit a gaussian too, and get ITS equiv width
# https://specutils.readthedocs.io/en/stable/fitting.html
#
g_init = models.Gaussian1D(amplitude=0.2 * u.dimensionless_unscaled,
mean=6709.7 * u.AA,
stddev=0.5 * u.AA)
g_fit = fit_lines(full_spec, g_init, window=(region.lower, region.upper))
y_fit = g_fit(full_spec.wavelength)
fitted_spec = Spectrum1D(spectral_axis=full_spec.wavelength,
flux=(1 - y_fit) * u.dimensionless_unscaled)
fitted_li_equiv_width = equivalent_width(fitted_spec, regions=region)
#
# print bestfit params
#
print(42 * '=')
print('got Li equiv width of {}'.format(li_equiv_width))
print('got fitted Li equiv width of {}'.format(fitted_li_equiv_width))
print('got Li centroid of {}'.format(li_centroid))
print('fit gaussian1d params are\n{}'.format(repr(g_fit)))
print(42 * '=')
#
# plot the results
#
f, axs = plt.subplots(nrows=4, ncols=1, figsize=(6, 8))
axs[0].plot(wav, flx, c='k', zorder=3)
axs[0].plot(wav, cont_flx, c='r', zorder=2)
axs[1].plot(cont_norm_spec.wavelength, cont_norm_spec.flux, c='k')
axs[2].plot(cont_norm_spec.wavelength, cont_norm_spec.flux, c='k')
axs[3].plot(full_spec.wavelength, full_spec.flux, c='k')
axs[3].plot(full_spec.wavelength, y_fit, c='g')
txt = ('gaussian1d\namplitude:{:.3f}\nmean:{:.3f}\nstd:{:.3f}\nEW:{:.3f}'.
format(g_fit.amplitude.value, g_fit.mean.value, g_fit.stddev.value,
fitted_li_equiv_width))
axs[3].text(0.95,
0.95,
txt,
ha='right',
va='top',
transform=axs[3].transAxes,
fontsize='xx-small')
axs[0].set_ylabel('flux')
axs[1].set_ylabel('contnorm flux')
axs[2].set_ylabel('contnorm flux [zoom]')
axs[3].set_ylabel('1 - (contnorm flux)')
if isinstance(xlim, list):
for ax in axs:
ax.set_xlim(xlim)
axs[2].set_xlim([6708.5, 6711.5])
axs[3].set_xlim([6708.5, 6711.5])
axs[-1].set_xlabel('wavelength [angstrom]')
for ax in axs:
format_ax(ax)
outpath = '../results/TOI_837/toi837_li_equivalent_width_routine.png'
savefig(f, outpath)