def main_deprecated():
df = _get_cks_data()
sel = _apply_cks_IV_metallicity_study_filters(df)
p17_df = df[sel]
vot = parse('../data/McQuillan_2014_ApJS_211_24_table1.vot')
m14 = vot.get_first_table()
m14_df = m14.to_table().to_pandas()
p17_df['id_kic'] = p17_df['id_kic'].astype(str)
m14_df['KIC'] = m14_df['KIC'].astype(str)
# # no matches. wat.
# mdf = p17_df.merge(m14_df, how='inner', left_on='id_kic', right_on='KIC')
q = NasaExoplanetArchive.query_criteria(table='koi',
select='kepoi_name,ra,dec',
order='kepoi_name')
q_df = q.to_pandas()
mdf0 = p17_df.merge(q_df,
how='inner',
left_on='id_koicand',
right_on='kepoi_name').drop_duplicates('id_koicand',
keep='first')
assert len(mdf0) == len(p17_df)
c_p17 = SkyCoord(ra=nparr(mdf0.ra) * u.deg, dec=nparr(mdf0.dec) * u.deg)
c_m14 = SkyCoord(ra=nparr(m14_df._RA) * u.deg,
dec=nparr(m14_df._DE) * u.deg)
idx_m14, idx_p17, d2d, _ = c_p17.search_around_sky(c_m14, 1 * u.arcsec)
def main():
df = _get_cks_data()
sel = _apply_cks_IV_metallicity_study_filters(df)
p17_df = df[sel]
vot = parse('../data/McQuillan_2013_ApJ_775L.vot')
m13 = vot.get_first_table()
m13_df = m13.to_table().to_pandas()
p17_df['id_kic'] = p17_df['id_kic'].astype(str)
m13_df['KIC'] = m13_df['KIC'].astype(str)
mdf = p17_df.merge(m13_df, how='inner', left_on='id_kic', right_on='KIC')
for scale in ['linear', 'log']:
f, ax = plt.subplots()
ax.scatter(mdf.giso_prad, mdf.Prot, s=2, c='k')
ax.set_xlabel('CKS Planet Size [R$_\oplus$]')
ax.set_ylabel('M13 Rotation Period [day]')
ax.set_xscale(scale)
ax.set_yscale(scale)
format_ax(ax)
outpath = f'../results/cks_rotation_period/cks_rp_vs_mcquillan_prot_{scale}.png'
f.savefig(outpath, dpi=300, bbox_inches='tight')
plt.close('all')
f, ax = plt.subplots()
ax.scatter(mdf.koi_period, mdf.Prot, s=2, c='k')
ax.set_xlabel('KOI Period [day]')
ax.set_ylabel('M13 Rotation Period [day]')
ax.set_xscale('log')
ax.set_yscale('log')
format_ax(ax)
outpath = '../results/cks_rotation_period/koi_period_vs_mcquillan_prot.png'
f.savefig(outpath, dpi=300, bbox_inches='tight')
plt.close('all')
f, ax = plt.subplots()
ax.scatter(mdf.koi_period, mdf.giso_prad, s=2, c='k')
ax.set_xlabel('KOI Period [day]')
ax.set_ylabel('CKS Planet Size [R$_\oplus$]')
ax.set_xscale('log')
ax.set_yscale('log')
format_ax(ax)
outpath = '../results/cks_rotation_period/koi_period_vs_giso_prad_M13_match.png'
f.savefig(outpath, dpi=300, bbox_inches='tight')
def make_old_short_period_plots():
# approach #1: just use Petigura+ 2018's filters
# approach #2: just use Petigura+ 2018's filters, + filter on gaia
# astrometric excess
# approach #3: just use Petigura+ 2018's filters, + filter on gaia
# astrometric excess, + a/Rstar<100 only, + only the innermost planets of
# systems.
do_approach1 = 0
do_approach2 = 0
do_approach3 = 1
approaches = np.array([do_approach1, do_approach2,
do_approach3]).astype(bool)
if not len(approaches[approaches]) == 1:
raise AssertionError('only one approach allowed')
make_all = 0
if make_all:
make_initial_plots = 1
make_stacked_histogram_plots = 1
make_sanity_check_scatters = 1
make_hr_diagram = 1
make_ks2sample_abyRstar = 1
make_ks2sample_turnoff = 1
make_turnoff_v_mainsequence_scatters = 1
else:
make_initial_plots = 1
make_stacked_histogram_plots = 0
make_sanity_check_scatters = 0
make_hr_diagram = 0
make_ks2sample_abyRstar = 0
make_ks2sample_turnoff = 0
make_turnoff_v_mainsequence_scatters = 0
if do_approach1:
df = _get_cks_data()
sel = _apply_cks_IV_metallicity_study_filters(df)
savdir = '../results/cks_age_plots_old_short_period_p18filters/'
savdir_append = '_old_short_period_p18filters'
if do_approach2:
df = _get_cks_data(merge_vs_gaia=True)
sel = _apply_cks_IV_filters_plus_gaia_astrom_excess(df)
savdir = '../results/cks_age_plots_old_short_period/'
savdir_append = '_old_short_period'
if do_approach3:
df = _get_cks_data(merge_vs_gaia=True)
sel = _apply_cks_IV_filters_plus_gaia_astrom_excess(df)
sel &= _take_innermost_filter(df)
savdir = '../results/cks_age_plots_old_short_period_onlyinnermost/'
savdir_append = '_old_short_period_onlyinnermost'
# remake the plots that got us interested in this
if make_initial_plots:
for xparam in ['koi_period', 'koi_dor', 'cks_VII_dor']:
for logy in [True, False]:
for logx in [True, False]:
plot_wellmeasuredparam(df,
sel,
xparam,
logx,
logy,
is_cks=True,
savdir_append=savdir_append)
plot_wellmeasuredparam(df,
sel,
xparam,
logx,
logy,
is_cks=True,
savdir_append=savdir_append)
if make_stacked_histogram_plots:
make_stacked_histograms(df[sel],
logtime=False,
xparam='koi_dor',
savdir=savdir)
make_stacked_histograms(df[sel],
logtime=False,
xparam='cks_VII_dor',
savdir=savdir)
make_stacked_histograms(df[sel],
logtime=False,
xparam='period',
savdir=savdir)
make_stacked_histograms(df[sel],
logtime=True,
xparam='koi_dor',
savdir=savdir)
make_stacked_histograms(df[sel],
logtime=True,
xparam='cks_VII_dor',
savdir=savdir)
make_stacked_histograms(df[sel],
logtime=True,
xparam='period',
savdir=savdir)
make_quartile_scatter(df[sel], xparam='koi_period', savdir=savdir)
make_quartile_scatter(df[sel], xparam='koi_dor', savdir=savdir)
make_quartile_scatter(df[sel], xparam='cks_VII_dor', savdir=savdir)
if make_sanity_check_scatters:
# a few sanity checks
plot_scatter(df,
sel,
'koi_period',
'cks_smet',
True,
False,
is_cks=True,
savdir=savdir,
ylim=[0.5, -0.5])
plot_scatter(df,
sel,
'koi_dor',
'cks_smet',
True,
False,
is_cks=True,
savdir=savdir,
ylim=[0.5, -0.5])
plot_scatter(df,
sel,
'cks_VII_dor',
'cks_smet',
True,
False,
is_cks=True,
savdir=savdir,
ylim=[0.5, -0.5])
plot_scatter(df,
sel,
'cks_smet',
'giso_slogage',
False,
False,
is_cks=True,
savdir=savdir,
xlim=[0.5, -0.5])
# do the rough calculation of "what %age of planets are at a/R<10 at
# >10 Gyr, and below it?
calculate_and_print_fractions(df, sel)
# what is the a/Rstar<3, smet < -0.3 object?
inds = (df['cks_smet'] < -0.3)
inds &= (df['koi_dor'] < 3)
print(df[inds])
# what are the a/Rstar<3, age > 10Gyr objects?
inds = (10**(df['giso_slogage']) > 12e9)
inds &= (df['koi_dor'] < 4)
print(df[inds])
# what exactly is the difference between a 14 gyro and 10 gyro isochrone
# age?
if make_hr_diagram:
plot_hr(df,
sel,
'giso_slogage',
is_cks=True,
savdir=savdir,
yaxis='abskmag',
xscale='log')
plot_hr(df,
sel,
'giso_slogage',
is_cks=True,
savdir=savdir,
yaxis='abskmag',
xscale='linear')
plot_hr(df,
sel,
'giso_slogage',
is_cks=True,
savdir=savdir,
yaxis='logg',
xscale='log')
plot_hr(df,
sel,
'giso_slogage',
is_cks=True,
savdir=savdir,
yaxis='logg',
xscale='linear')
plot_hr(df,
sel,
'giso_slogage',
is_cks=True,
savdir=savdir,
yaxis='logg',
savstr='turnoffcut',
xscale='linear')
# CDFs and two-sample KS to compare the a/Rstar distributions of old and
# young planets. What is the p-value?
if make_ks2sample_abyRstar:
agecuts = [7e9, 8e9, 9e9, 10e9, 11e9, 12e9]
for agecut in agecuts:
plot_ks2sample_abyRstar_old_v_young(df[sel],
agecut,
savdir=savdir,
abyRstar_str='koi_dor')
plot_ks2sample_abyRstar_old_v_young(df[sel],
agecut,
savdir=savdir,
abyRstar_str='cks_VII_dor')
if make_ks2sample_turnoff:
_, _ = (plot_ks2sample_abyRstar_turnoff_v_mainsequence(
df[sel], savdir=savdir, abyRstar_str='koi_dor'))
df_turnedoff, df_onMS = (
plot_ks2sample_abyRstar_turnoff_v_mainsequence(
df[sel], savdir=savdir, abyRstar_str='cks_VII_dor'))
if make_turnoff_v_mainsequence_scatters and make_ks2sample_turnoff:
turnoff_v_mainsequence_scatters(df_turnedoff,
df_onMS,
savdir=savdir,
xparam='koi_period')
turnoff_v_mainsequence_scatters(df_turnedoff,
df_onMS,
savdir=savdir,
xparam='koi_dor')
turnoff_v_mainsequence_scatters(df_turnedoff,
df_onMS,
savdir=savdir,
xparam='cks_VII_dor')
rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
rc('text', usetex=True)
from astropy.table import Table
from astropy.io import ascii
from astropy.coordinates import SkyCoord
import astropy.units as u
df = pd.read_csv('../data/Winn_2017_ajaa93e3t1_ascii.txt', comment='#',
delimiter='\t')
df.rename(index=str, columns={'$v\sin i$':'vsini',
'${T}_{\mathrm{eff}}$':'Teff'}, inplace=True)
# ok. Winn's table only gives 54 stars, but his paper talks about 156. what's
# up? Ah -- these are all the stars with v > 4km/s, which were selected because
# of the large fractional uncertainties otherwise.
from cks_age_exploration import _get_cks_data
cks = _get_cks_data()
d = pd.merge(df, cks, how='left', left_on='KIC', right_on='id_kic')
# 84 planets orbiting the 54 stars.
# 3 of the planets have a/Rstar < 5
# 17 of them have a/Rstar < 10.
# NOTE: it's somewhat shocking that the state of the rotation period literature
# is that there are only 54 STARS with reliable Prot & vsini, with v > 4km/s.
# WTF...
print('saved %s' % savname)
savname = (
'../results/cks_age_plots_old_short_period/' +
'hill_radius_pairs_vs_age_aByRstar_cut_{:d}.png'.format(aByRstar_cut))
f.savefig(savname, bbox_inches='tight', dpi=350)
print('saved %s' % savname)
if __name__ == '__main__':
make_fig1_and_split = False
make_fig6_weiss17 = False
make_stackedhist_innermost = False
make_scatter_p2byp1_vs_p1 = False
df = _get_cks_data()
sel = _apply_cks_VI_metallicity_study_filters(df)
pairs = get_all_planet_pairs(df, sel)
pairs = compute_pair_separations_hill_radii(pairs)
systems = get_system_innermost_sma_by_Rstar(df, sel)
if make_fig1_and_split:
# How does the average number of transiting planets per stellar system
# change with metallicity?
make_weiss18_VI_fig1(df, sel)
split_weiss18_fig1_high_low_met(df, sel)
if make_fig6_weiss17:
# Does average separation between planet pairs in multis (in units of
# mutual hill radii increase for the oldest multis?
make_weiss17_V_fig6(pairs)