def plot_korbpost(outpath, truth=None,
xlabel='K$_\mathrm{orb}$ [m$\,$s$^{-1}$]'):
Nobs = [20, 30, 45, 90]
Korb_d = {}
from betty.paths import BETTYDIR
for N in Nobs:
pklpath = os.path.join(
BETTYDIR, f'test_HIP67522_rvspotorbit_Nobs{N}.pkl'
)
with open(pklpath, 'rb') as f:
d = pickle.load(f)
trace = d['trace']
samples = trace['K_orb']
Korb_d[N] = samples
# make plot
set_style()
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
for N in Nobs:
ls = ':' if N in [30, 90] else '-'
ax.hist(Korb_d[N], bins=np.arange(0,220,20),
weights=np.ones_like(Korb_d[N])*1/len(Korb_d[N]),
zorder=-1, histtype='step', alpha=1, ls=ls,
label='N$_\mathrm{obs}$='+f'{N}')
from matplotlib.lines import Line2D
handles, labels = ax.get_legend_handles_labels()
new_handles = []
for ix, h in enumerate(handles):
ls = ':' if ix in [1, 3] else '-'
new_handles.append(Line2D([], [], c=h.get_edgecolor(), ls=ls))
ax.legend(loc='best', fontsize='small', handles=new_handles, labels=labels)
if isinstance(xlabel, str):
ax.set_xlabel(xlabel)
if truth is not None:
ymin, ymax = ax.get_ylim()
ax.vlines(
truth, ymin, ymax, colors='k', alpha=0.8,
linestyles='--', zorder=-2, linewidths=1
)
ax.set_ylim((ymin, ymax))
ax.set_ylabel('Posterior probability')
savefig(f, outpath, writepdf=0)
def plot_spec_Prot_hist(Prot_source='M15', spec_source='cks'):
"""
Prot_source (str): M13 or M15
spec_source (str): always 'cks', because that's the only vsini source.
[nb. this is the vsini dataset from J. Winn, received 2 Aug 2020]
"""
if spec_source != 'cks':
raise NotImplementedError
mdf, cdf = _get_spec_vsini_phot_Prot_overlap(Prot_source=Prot_source)
sel = cdf['id_kic'].isin(arr(mdf['id_kic']))
scdf = cdf[~sel]
print(f'{len(cdf)} with vsini')
print(f'{len(mdf[(~pd.isnull(mdf.spec_Prot)) & (~pd.isnull(mdf.Prot))])} with vsini & M15 prot')
print(f'{len(scdf)} with vsini & and no M15 prot')
outdir = join(RESULTSDIR, 'spec_rot')
set_style()
f, ax = plt.subplots(figsize=(4,3))
bins = np.arange(0, 52.5, 2.5)
N_m = len(mdf)
N_n = len(scdf)
ax.hist(scdf.spec_Prot, bins=bins, cumulative=False, fill=False, density=False,
weights=np.ones(N_n)/N_n,
histtype='step', label=f'No M15 match ({len(scdf)})')
ax.hist(mdf.spec_Prot, bins=bins, cumulative=False, fill=False, density=False,
weights=np.ones(N_m)/N_m,
histtype='step', label=f'Has M15 match ({len(mdf)})')
# Create new legend handles but use the colors from the existing ones
handles, labels = ax.get_legend_handles_labels()
new_handles = [Line2D([], [], c=h.get_edgecolor()) for h in handles]
ax.legend(handles=new_handles, labels=labels, fontsize='small')
txt = (
r'$\mu_{T_{\mathrm{eff}}} \pm \sigma_{T_{\mathrm{eff}}}$ '+' for P$_{\mathrm{rot}}^{\mathrm{spec}} < 25$ d:\n'
'No M15: '+f'{int(scdf[scdf.spec_Prot<25].cks_steff.mean())} $\pm$ {int(scdf[scdf.spec_Prot<25].cks_steff.std())}\n'
'Has M15: '+f'{int(mdf[mdf.spec_Prot<25].VIIs_Teff.mean())} $\pm$ {int(mdf[mdf.spec_Prot<25].VIIs_Teff.std())}\n'
)
ax.text(0.95, 0.50, txt,
transform=ax.transAxes, ha='right', va='center',
fontsize='small')
ax.set_ylabel('Fraction per bin')
ax.set_xlabel('CKS-VII P$_{\mathrm{rot}}^{\mathrm{spec}}$ [day]')
outpath = join(
outdir,
f'spec_Prot_hist_prot{Prot_source}.png'
)
savefig(f, outpath, writepdf=0)
def plot_stsnr_vs_gyroage(Prot_source='M15', gyro_source='A19', cdpp_id='rms3'):
"""
Prot_source: M13 or M15
gyro_source: MH08 or A19
cdpp_id: rms3, rms6, or rms12
Makes a Berger+2020 style single-transit SNR vs age plot. The method is
identical to Berger+2020. CDPP is taken from Christiansen 2012.
Single-transit SNR is calculated by doing (Rp/R*)^2 * (1/CDPP). This was
cited as being from Petigura+2018. [.....]
"""
mdf = get_merged_gyroage_CKS(Prot_source=Prot_source,
gyro_source=gyro_source)
mdf = _add_Christiansen12_CDPP(mdf)
Rp = 1*u.Rearth
Rstar = arr(mdf.VIIs_R)*u.Rsun
Rp_Rs = (Rp/Rstar).cgs.value
mdf['stsnr'] = (
Rp_Rs**2
*
(1/arr(1e-6*mdf[cdpp_id]))
)
scols = ['gyroage_yr', 'stsnr']
sdf = mdf[scols]
outdir = join(
RESULTSDIR, f'singletransitSNR_{Prot_source}rot_{gyro_source}age'
)
if not os.path.exists(outdir):
os.mkdir(outdir)
set_style()
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(sdf.gyroage_yr, sdf.stsnr, s=2, c='k', zorder=3)
agelabel = (f'Gyro-Age [{Prot_source} '+'P$_\mathrm{rot}$ +'
+f'{gyro_source}; yr]')
ax.set_xlabel(agelabel, fontsize='small')
ax.set_ylabel(f'Single-transit SNR [C12 CDPP{cdpp_id.replace("rms","")}]',
fontsize='small')
ax.set_xscale('log'); ax.set_yscale('log')
outpath = join(outdir,
f'singletransitsnr_{cdpp_id}_rot{Prot_source}_gyro{gyro_source}.png')
savefig(f, outpath, writepdf=0)
def plot_spec_vs_phot_Prot(Prot_source='M15', spec_source='cks'):
"""
Prot_source (str): M13 or M15
spec_source (str): always 'cks', because that's the only vsini source.
[nb. this is the vsini dataset from J. Winn, received 2 Aug 2020]
"""
if spec_source != 'cks':
raise NotImplementedError
mdf, _ = _get_spec_vsini_phot_Prot_overlap(Prot_source=Prot_source)
outdir = join(RESULTSDIR, 'spec_rot')
if not os.path.exists(outdir):
os.mkdir(outdir)
set_style()
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.Prot, mdf.spec_Prot, s=2, c='k', zorder=3)
_, xmax = ax.get_xlim()
span = np.linspace(2, xmax, 100)
ax.plot(span, span, zorder=-1, lw=1, ls='--', c='lightgray',
label='$i=90^\circ$')
ax.plot(span, span/np.sin(np.deg2rad(45)), zorder=-1, lw=1, ls=':', c='lightgray',
label='$i=45^\circ$')
ax.legend(fontsize='small')
ax.set_xscale('log'); ax.set_yscale('log')
ax.set_xlabel(f'{Prot_source} phot '+'P$_\mathrm{rot}$ [day]')
ax.set_ylabel(f'CKS-VII spec '+'P$_\mathrm{rot}$ [day]')
outpath = join(outdir, f'cks-VII_specrot_vs_{Prot_source}_prot.png')
savefig(f, outpath, writepdf=0)
sel = (mdf.Prot < 10)
sdf = mdf[sel]
sdf['specProt/photProt'] = sdf.spec_Prot/sdf.Prot
sdf = sdf.sort_values(by='specProt/photProt', ascending=False)
selcols = ['VIIp_KOI', 'Prot', 'spec_Prot']
print(sdf[selcols][:20].to_string(index=False))
def plot_rp_vs_insol_scatter(active_targets=1, specialyoung=1, show_legend=1):
set_style()
#
# columns described at
# https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html
#
ea_tab = NasaExoplanetArchive.query_criteria(
table="exoplanets", select="*", cache=True
)
#
# get systems with finite ages (has a value, and +/- error bar)
#
has_age_value = ~ea_tab['st_age'].mask
has_age_errs = (~ea_tab['st_ageerr1'].mask) & (~ea_tab['st_ageerr2'].mask)
has_rp_value = ~ea_tab['pl_rade'].mask
has_rp_errs = (~ea_tab['pl_radeerr1'].mask) & (~ea_tab['pl_radeerr2'].mask)
rp_gt_0 = (ea_tab['pl_rade'] > 0)
transits = (ea_tab['pl_tranflag']==1)
sel = (
has_age_value & has_age_errs & has_rp_value & has_rp_errs & transits &
rp_gt_0
)
t = ea_tab[sel]
tyoung = t[(t['st_age'] < 0.1*u.Gyr) & (t['st_age'] > 0*u.Gyr)]
#
# read params
#
age = t['st_age']
age_perr = t['st_ageerr1']
age_merr = np.abs(t['st_ageerr2'])
age_errs = np.array([age_perr, age_merr]).reshape(2, len(age))
rp = t['pl_rade']
rp_perr = t['pl_radeerr1']
rp_merr = t['pl_radeerr2']
rp_errs = np.array([rp_perr, rp_merr]).reshape(2, len(age))
# rp /= 11.2089 # used jupiter radii
insol = t['pl_insol']
#
# plot age vs rp. (age is on y axis b/c it has the error bars, and I at
# least skimmed the footnotes of Hogg 2010)
#
fig,ax = plt.subplots(figsize=(4,3))
label = (
'Exoplanet Archive'
)
print(f'Mean age unc: {np.mean(age_errs):.2f} Gyr')
print(f'Median age unc: {np.median(age_errs):.2f} Gyr')
ax.scatter(insol, rp, color='darkgray', s=3, zorder=1, marker='o',
linewidth=0, label=label, alpha=1)
#
# targets
#
target_age = (np.array([3.5e7])*u.yr).to(u.Gyr)
target_rp = (np.array([0.82])*u.Rjup).to(u.Rearth)
target_rp_unc = (np.array([0.03, 0.09])*u.Rjup).to(u.Rearth)[:,None]
target_period = (np.array([8.3])*u.day)
from astropy import constants as const
Teff = 6047*u.K
Lstar = 4*np.pi*(1.022*u.Rsun)**2 * (const.sigma_sb * Teff**4)
target_insol = (
( Lstar/(1*u.Lsun) ).cgs *
((( 17.54*1.022*u.Rsun )/(1*u.AU)).cgs)**(-2)
)
# insol/insolEarth = (L*/Lsun) * (AU/a)^2
if active_targets:
label = (
'TOI$\,$837'
)
ax.plot(target_insol, target_rp, mew=0.5, markerfacecolor='yellow',
markersize=15, marker='*', color='k', lw=0, label=label,
zorder=10)
if specialyoung:
youngnames = tyoung['pl_hostname']
markertypes= ['o', 'v', 'X', 's', 'P', 'd']
zorders= [9, 8, 7, 6, 5, 4]
for ix, y, z in zip(range(len(zorders)), np.unique(youngnames), zorders):
s = (tyoung['pl_hostname'] == y)
ax.plot(tyoung[s]['pl_orbper'], tyoung[s]['pl_rade'], mew=0.5,
markerfacecolor='white', markersize=7,
marker=markertypes[ix], color='k', lw=0, label=y,
zorder=z)
# two extra systems...
N_uniq = len(np.unique(youngnames))
insol_67522 = (1.74) * (( (11.73*1.38*u.Rsun) / (1*u.AU) ).cgs)**(-2)
ax.plot(insol_67522, 10.02, mew=0.5, markerfacecolor='white',
markersize=7, marker=markertypes[N_uniq], color='k', lw=0,
label='HIP 67522', zorder=2)
# HD 63433 (TOI 1726, TIC 130181866) omitted -- 400 Myr is old!
# flip default legend order
if show_legend:
handles, labels = ax.get_legend_handles_labels()
leg = ax.legend(handles[::-1], labels[::-1], loc='upper right',
borderpad=0.3, handletextpad=0.5, fontsize=6,
framealpha=0)
leg.get_frame().set_linewidth(0.5)
ax.set_xlabel('Planet insolation [Earth flux]')
ax.set_ylabel('Planet size [Earth radii]')
#ax.set_xlim(list(ax.get_xlim())[::-1])
ax.set_xlim([1e5, 0.1])
format_ax(ax)
# ax.set_ylim([0.13, 85])
# ax.set_yscale('log')
ax.set_xscale('log')
# ax.tick_params(top=True, bottom=True, left=True, right=True, which='both')
# ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2g}'))
savstr = '_no_overplot' if not active_targets else '_toi837'
if show_legend:
savstr += '_yeslegend'
else:
savstr += '_nolegend'
outpath = (
'../results/rp_vs_insol_scatter/rp_vs_insol_scatter_{}{}.png'.
format(today_YYYYMMDD(), savstr)
)
savefig(fig, outpath, writepdf=1, dpi=400)
telfmts_default['harps'] = telfmts_default['h']
telfmts_default['LICK'] = telfmts_default['l']
telfmts_default['HIRES_RJ'] = telfmts_default['j']
telfmts_default['HIRES'] = telfmts_default['j']
telfmts_default['HIRES_RK'] = telfmts_default['k']
telfmts_default['APF'] = telfmts_default['a']
telfmts_default['HARPS'] = telfmts_default['h']
telfmts_default['HARPS-N'] = telfmts_default['harps-n']
telfmts_default['PFS'] = telfmts_default['pfs']
cmap = nipy_spectral
default_colors = [
'orange', 'purple', 'magenta', 'pink', 'green', 'grey', 'red'
]
from aesthetic.plot import set_style
set_style()
highlight_format = dict(marker='o',
ms=16,
mfc='none',
mew=2,
mec='gold',
zorder=99)
if not emcee.__version__ == "2.2.1":
raise AssertionError('radvel requires emcee v2')
class args_object(object):
"""
a minimal version of the "parser" object that lets you work with the
def plot_mass_vs_period_scatter(active_targets=1,
specialyoung=1,
show_legend=1):
set_style()
#
# columns described at
# https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html
#
ea_tab = NasaExoplanetArchive.query_criteria(table="exoplanets",
select="*",
cache=True)
#
# get systems with finite ages (has a value, and +/- error bar)
#
has_age_value = ~ea_tab['st_age'].mask
has_age_errs = (~ea_tab['st_ageerr1'].mask) & (~ea_tab['st_ageerr2'].mask)
has_rp_value = ~ea_tab['pl_rade'].mask
has_rp_errs = (~ea_tab['pl_radeerr1'].mask) & (~ea_tab['pl_radeerr2'].mask)
has_mp_value = ~ea_tab['pl_massj'].mask
has_mp_errs = (~ea_tab['pl_massjerr1'].mask) & (
~ea_tab['pl_massjerr2'].mask)
rp_gt_0 = (ea_tab['pl_rade'] > 0)
mp_gt_0 = (ea_tab['pl_massj'] > 0)
transits = (ea_tab['pl_tranflag'] == 1)
sel = (
has_age_value & has_age_errs & has_mp_value & mp_gt_0
# has_rp_value & has_rp_errs & transits & rp_gt_0
)
t = ea_tab[sel]
tyoung = t[(t['st_age'] < 0.1 * u.Gyr) & (t['st_age'] > 0 * u.Gyr)]
#
# read params
#
age = t['st_age']
age_perr = t['st_ageerr1']
age_merr = np.abs(t['st_ageerr2'])
age_errs = np.array([age_perr, age_merr]).reshape(2, len(age))
mp = t['pl_massj']
# rp /= 11.2089 # used jupiter radii
period = t['pl_orbper']
#
# plot age vs rp. (age is on y axis b/c it has the error bars, and I at
# least skimmed the footnotes of Hogg 2010)
#
fig, ax = plt.subplots(figsize=(4, 3))
label = ('Exoplanet Archive')
print(f'Mean age unc: {np.mean(age_errs):.2f} Gyr')
print(f'Median age unc: {np.median(age_errs):.2f} Gyr')
ax.scatter(period,
mp,
color='darkgray',
s=3,
zorder=1,
marker='o',
linewidth=0,
label=label,
alpha=1)
#
# targets
#
target_age = (np.array([3.5e7]) * u.yr).to(u.Gyr)
target_rp = (np.array([0.77]) * u.Rjup).to(u.Rearth)
target_rp_unc = (np.array([0.07, 0.09]) * u.Rjup).to(u.Rearth)[:, None]
target_period = (np.array([8.3]) * u.day)
if active_targets:
raise NotImplementedError('uber young planets dont have masses!')
label = ('TOI$\,$837')
ax.plot(target_period,
target_rp,
mew=0.5,
markerfacecolor='yellow',
markersize=15,
marker='*',
color='k',
lw=0,
label=label,
zorder=10)
if specialyoung:
youngnames = tyoung['pl_hostname']
markertypes = ['o', 'v', 'X', 's', 'P', 'd']
zorders = [9, 8, 7, 6, 5, 4]
for ix, y, z in zip(range(len(zorders)), np.unique(youngnames),
zorders):
s = (tyoung['pl_hostname'] == y)
ax.plot(tyoung[s]['pl_orbper'],
tyoung[s]['pl_rade'],
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[ix],
color='k',
lw=0,
label=y,
zorder=z)
# two extra systems...
N_uniq = len(np.unique(youngnames))
ax.plot(6.9596,
10.02,
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[N_uniq],
color='k',
lw=0,
label='HIP 67522',
zorder=2)
# HD 63433 (TOI 1726, TIC 130181866) omitted -- 400 Myr is old!
# flip default legend order
if show_legend:
handles, labels = ax.get_legend_handles_labels()
leg = ax.legend(handles[::-1],
labels[::-1],
loc='upper right',
borderpad=0.3,
handletextpad=0.5,
fontsize=6,
framealpha=0)
leg.get_frame().set_linewidth(0.5)
ax.set_xlabel('Orbital period [days]')
ax.set_ylabel('Planet mass [Jupiter masses]')
ax.set_xlim([0.1, 1100])
ax.set_ylim([0.001, 100])
format_ax(ax)
# ax.set_ylim([0.13, 85])
ax.set_yscale('log')
ax.set_xscale('log')
# ax.tick_params(top=True, bottom=True, left=True, right=True, which='both')
# ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2g}'))
savstr = '_no_overplot' if not active_targets else '_toi837'
if show_legend:
savstr += '_yeslegend'
else:
savstr += '_nolegend'
outpath = (
'../results/mass_vs_period_scatter/mass_vs_period_scatter_{}{}.png'.
format(today_YYYYMMDD(), savstr))
savefig(fig, outpath, writepdf=1, dpi=400)
def plot_S21_prot_vs_teff(koiflag, ylim=None):
# koiflag: 0 is confirmed. 1 is candidate.
set_style()
outdir = join(RESULTSDIR, f'S21_prot_vs_teff')
if not os.path.exists(outdir):
os.mkdir(outdir)
Prot_source = 'S21'
df = _get_Santos21_data()
sel = np.zeros(len(df)).astype(bool)
for f in koiflag:
sel |= (df.flag4 == f)
df = df[sel]
reference_clusters = ['Pleiades', 'Praesepe']
for scale in ['linear','log']:
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(df.Teff, df.Prot, s=2, c='k',
label=f'{Prot_source}', zorder=10)
for ix, c in enumerate(reference_clusters):
cdf = _get_Curtis20_data(c.lower())
# data
ax.scatter(cdf.Teff, cdf.Prot, s=1, c=f'C{ix}', label=c)
# interpolated model
InterpModel = (
PleiadesInterpModel if c == 'Pleiades' else PraesepeInterpModel
)
_teff = np.linspace(4500, 6400, 300)
_prot = InterpModel(_teff)
ax.plot(_teff, _prot, lw=1, c=f'C{ix}')
ax.legend(fontsize='x-small', loc='best')
ax.set_xlabel('T$_\mathrm{eff}$ [K]')
ax.set_ylabel(f'{Prot_source} '+'P$_\mathrm{rot}$ [day]')
ax.set_xlim(ax.get_xlim()[::-1])
ax.set_xlim([7000,4000])
if isinstance(ylim, list):
ax.set_ylim(ylim)
ax.set_xscale('linear'); ax.set_yscale(scale)
if koiflag == [0]:
titlestr = 'Confirmed KOIs only'
elif koiflag == [0,1]:
titlestr = 'Confirmed + candidate KOIs'
ax.set_title(titlestr)
kstr = '_'.join(np.array(koiflag).astype(str))
s = ''
if isinstance(ylim, list):
s += 'ylim'+'_'.join(np.array(ylim).astype(str))
outpath = join(
outdir, f'teff_vs_{Prot_source}_prot_{scale}_koiflag{kstr}{s}.png'
)
savefig(f, outpath, writepdf=0)
#
# print any KOIs below the cutoffs
#
sel = (df.Teff > 4500) & (df.Teff < 6400)
sdf = df[sel]
pleiades_model_prot = PleiadesInterpModel(sdf.Teff)
praesepe_model_prot = PraesepeInterpModel(sdf.Teff)
below_Pleiades = (sdf.Prot < pleiades_model_prot)
below_Praesepe = (sdf.Prot < praesepe_model_prot)
PRINT = 0
if scale == 'linear' and isinstance(ylim, list):
PRINT = 1
if PRINT:
print(42*'-')
print(f'Below Pleaides, KOI flags {kstr}. N={len(sdf[below_Pleiades])}')
print(sdf[below_Pleiades].sort_values(by='Prot'))
print(11*'#')
print(f'Below Praesepe, KOI flags {kstr}. N={len(sdf[below_Praesepe])}')
print(sdf[below_Praesepe].sort_values(by='Prot'))
print(42*'-')
sdf['is_below_Praesepe'] = below_Praesepe.astype(int)
sdf['is_below_Pleiades'] = below_Pleiades.astype(int)
outcsvpath = join(
DATADIR,
f'Santos21_koiflag{kstr}_below_Praesepe_in_Prot_Teff_Teff_btwn_4500_6400.csv'
)
if not os.path.exists(outcsvpath):
outdf = sdf[below_Praesepe].sort_values(by='Prot')
outdf.to_csv(outcsvpath, index=False)
print(f"Made {outcsvpath}")
else:
print(f"Found {outcsvpath} and did not overwrite.")
def plot_dilution_histogram(ap=None):
'''
Make the plot of log10(dilution [2px aperture]) vs log10(distance [pc]) for
T<16 mag, d<2kpc cluster members.
'''
df = pd.read_csv('../../data/cg18_cdips_table1_subset_with_dilution.csv')
sel = (df.parallax > 0.5) & (df.Tmag_pred < 16) # d < 2kpc, T<16
df = df[sel]
dil_n_px = np.array(df['dilution_ap{:.2f}'.format(ap)]) # y
dil_n_px[dil_n_px > 0.999] = 0.999
set_style()
plt.close('all')
fig, ax = plt.subplots(figsize=(3, 3))
ax.set_xscale('log')
ax.set_xlabel('(Target flux)/(Total flux in aperture)'.format(ap))
ax.set_ylabel('Relative probability')
ax.set_xlim((10**(-2.05), 1.1))
ax.tick_params(which='both', direction='in', zorder=0)
xmin, xmax = 10**(-3), 10**1
log_dil_n_px_bins = np.linspace(np.log10(xmin), np.log10(xmax), 17)
x = 10**log_dil_n_px_bins
y = np.histogram(np.log10(dil_n_px), log_dil_n_px_bins)[0]
x = np.array(list(zip(x[:-1], x[1:]))).flatten()
y = np.array(list(zip(y, y))).flatten()
ax.plot(x, y, lw=1, color='black')
inds = (x <= 0.1)
ax.fill_between(x[inds],
y[inds],
np.zeros_like(y[inds]),
facecolor='none',
hatch='/',
edgecolor='gray',
lw=0)
frac_not_ok = np.sum(y[inds]) / np.sum(y)
nonrecov_str = r'$\approx$' + '{:d}%\ntoo crowded'.format(
int(100 * frac_not_ok))
recov_str = r'$\approx$' + '{:d}%\nrecoverable'.format(
int(round(100 * (1 - frac_not_ok))))
t = ax.text(10**(-0.5),
11500,
recov_str,
verticalalignment='center',
horizontalalignment='center',
fontsize='medium')
t.set_bbox(dict(facecolor='white', alpha=1, edgecolor='gray'))
t = ax.text(10**(-1.5),
11500,
nonrecov_str,
verticalalignment='center',
horizontalalignment='center',
fontsize='medium')
t.set_bbox(dict(facecolor='white', alpha=1, edgecolor='gray'))
#ax.set_xticklabels([])
ax.set_yticklabels([])
ax.yaxis.set_major_locator(plt.MaxNLocator(5))
format_ax(ax)
# ax.yaxis.set_ticks_position('both')
# ax.xaxis.set_ticks_position('both')
# ax.get_yaxis().set_tick_params(which='both', direction='in')
# ax.get_xaxis().set_tick_params(which='both', direction='in')
# ax.xaxis.set_tick_params(labelsize='small')
# ax.yaxis.set_tick_params(labelsize='small')
# ax.tick_params(which='both', direction='in', zorder=0)
# ax.spines['right'].set_visible(False)
# ax.spines['top'].set_visible(False)
ax.set_ylim((0, max(ax.get_ylim())))
outname = ('../../results/dilution_histogram/'
'dil_Tmaglt16_dlt2kpc_cg18_ap{:.2f}px.png'.format(ap))
savefig(fig, outname)
def plot_cks_rp_vs_prot(Prot_source='M15'):
"""
Prot_source: M13 or M15
Make scatter plots of {plantet radius, orbital period} against
rotation periods in a few different projections.
"""
mdf, fp18_df = get_merged_rot_CKS(Prot_source=Prot_source)
Prot_key = 'Prot' if Prot_source in ['M13','M15','S21'] else 'spec_Prot'
set_style()
outdir = join(RESULTSDIR, f'cks_{Prot_source}_rotation_period')
if not os.path.exists(outdir):
os.mkdir(outdir)
for scale in ['linear','log']:
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.VIIp_Rp, mdf[Prot_key], s=2, c='k', zorder=3)
ax.set_xlabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_ylabel(f'{Prot_source} '+'P$_\mathrm{rot}$ [day]')
ax.set_xscale(scale); ax.set_yscale(scale)
outpath = join(outdir, f'cks-VII_rp_vs_{Prot_source}_prot_{scale}.png')
savefig(f, outpath, writepdf=0)
for scale in ['linear','log']:
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.VIIs_Teff, mdf[Prot_key], s=2, c='k',
label=f'CKS-VII $\otimes$ {Prot_source}', zorder=10)
for ix, c in enumerate(['Pleiades', 'Praesepe']):
cdf = _get_Curtis20_data(c.lower())
ax.scatter(cdf.Teff, cdf.Prot, s=1, c=f'C{ix}', label=c)
ax.legend(fontsize='x-small', loc='best')
ax.set_xlabel('CKS-VII T$_\mathrm{eff}$ [K]')
ax.set_ylabel(f'{Prot_source} '+'P$_\mathrm{rot}$ [day]')
ax.set_xlim(ax.get_xlim()[::-1])
ax.set_xlim([7000,4000])
ax.set_xscale('linear'); ax.set_yscale(scale)
outpath = join(outdir, f'cks-VII_teff_vs_{Prot_source}_prot_{scale}.png')
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.VIIp_Per, mdf[Prot_key], s=2, c='k')
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel(f'{Prot_source} '+'P$_\mathrm{rot}$ [day]')
ax.set_xscale('log'); ax.set_yscale('log')
outpath = join(outdir, f'cks-VII_period_vs_{Prot_source}_prot.png')
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
sel = ~pd.isnull(mdf[Prot_key])
N_withProt = len(mdf[sel])
N_noProt = len(fp18_df) - N_withProt
ax.scatter(mdf[sel].VIIp_Per, mdf[sel].VIIp_Rp, s=1.5, c='k', zorder=3,
label=f'Yes {Prot_source} '+'P$_\mathrm{rot}$ '+f'({N_withProt})')
ax.scatter(fp18_df.VIIp_Per, fp18_df.VIIp_Rp, s=1.5, c='darkgray', zorder=2,
label=f'No {Prot_source} '+'P$_\mathrm{rot}$ '+f'({N_noProt})')
ax.legend(fontsize='small')
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_xscale('log'); ax.set_yscale('log')
outpath = join(outdir, f'cks-VII_period_vs_cks-VII_prad_{Prot_source}_match.png')
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
sel = ~pd.isnull(mdf[Prot_key])
N_withProt = len(mdf[sel])
N_noProt = len(fp18_df) - N_withProt
hasrot = (fp18_df['VIIp_SimbadName'].isin(mdf[sel]['VIIp_SimbadName']))
norot = ~hasrot
ax.scatter(fp18_df[hasrot].VIIp_Per, fp18_df[hasrot].VIIp_Rp, s=1.5, c='darkgray',
zorder=2,
label=f'Yes {Prot_source} '+'P$_\mathrm{rot}$ '+f'({N_withProt})')
ax.scatter(fp18_df[norot].VIIp_Per, fp18_df[norot].VIIp_Rp, s=1.5, c='k',
zorder=3,
label=f'No {Prot_source} '+'P$_\mathrm{rot}$ '+f'({N_noProt})')
ax.legend(fontsize='small')
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_xscale('log'); ax.set_yscale('log')
outpath = join(
outdir,
f'cks-VII_period_vs_cks-VII_prad_{Prot_source}_match_inverted.png'
)
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4.3,3))
sel = ~pd.isnull(mdf[Prot_key])
N_withProt = len(mdf[sel])
N_noProt = len(fp18_df) - N_withProt
l = f'Yes {Prot_source}'+' P$_\mathrm{rot}$ '+f'({N_withProt})'
norm = mpl.colors.Normalize(vmin=5, vmax=25)
cax = ax.scatter(mdf[sel].VIIp_Per, mdf[sel].VIIp_Rp, s=7,
c=mdf[sel][Prot_key],
cmap='plasma_r', zorder=3, label=l, norm=norm,
edgecolors='k', linewidths=0.2)
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_xscale('log'); ax.set_yscale('log')
cbar = f.colorbar(cax, extend='both')
cbar.set_label(f'{Prot_source}'+' P$_\mathrm{rot}$ [day]')
cbar.minorticks_off()
outpath = join(outdir, f'cks-VII_period_vs_cks-VII_prad_{Prot_source}-Protcolors.png')
savefig(f, outpath, writepdf=0)
def plot_TIC268_nbhd_small(outdir=RESULTSDIR):
set_style()
df = get_cdips_catalog(ver=0.4)
kc19_sel = ((df.cluster.str.contains('NGC_2516')) &
(df.reference.str.contains('Kounkel_2019')))
cg18_sel = ((df.cluster.str.contains('NGC_2516')) &
(df.reference.str.contains('CantatGaudin_2018')))
target_df = df[df.source_id == 5489726768531119616] # TIC 2683...
kc19_df = df[kc19_sel]
cg18_df = df[cg18_sel]
kc19_df = kc19_df[~(kc19_df.source_id.isin(cg18_df.source_id))]
##########
# NGC 2516 rough
bounds = {
'parallax_lower': 1.5,
'parallax_upper': 4.0,
'ra_lower': 108,
'ra_upper': 132,
'dec_lower': -76,
'dec_upper': -45
}
groupname = 'customngc2516'
nbhd_df = query_neighborhood(bounds,
groupname,
n_max=6000,
overwrite=False,
manual_gmag_limit=17)
sel_nbhd = ((~nbhd_df.source_id.isin(kc19_df.source_id))
& (~nbhd_df.source_id.isin(cg18_df.source_id)))
from copy import deepcopy
orig_nbhd_df = deepcopy(nbhd_df)
nbhd_df = nbhd_df[sel_nbhd]
print(f'Got {len(nbhd_df)} neighbors')
print(f'Got {len(cg18_df)} in core')
print(f'Got {len(kc19_df)} in corona')
##########
plt.close('all')
f, axs = plt.subplots(figsize=(4, 3), ncols=2)
xv, yv = 'ra', 'dec'
axs[0].scatter(nbhd_df[xv],
nbhd_df[yv],
c='gray',
alpha=0.5,
zorder=2,
s=7,
rasterized=True,
linewidths=0,
label='Field',
marker='.')
axs[0].scatter(kc19_df[xv],
kc19_df[yv],
c='lightskyblue',
alpha=0.9,
zorder=3,
s=7,
rasterized=True,
linewidths=0.15,
label='Corona',
marker='.',
edgecolors='k')
axs[0].scatter(cg18_df[xv],
cg18_df[yv],
c='k',
alpha=0.9,
zorder=4,
s=7,
rasterized=True,
label='Core',
marker='.')
axs[0].plot(target_df[xv],
target_df[yv],
alpha=1,
mew=0.5,
zorder=8,
label='TOI 1937',
markerfacecolor='lightskyblue',
markersize=14,
marker='*',
color='black',
lw=0)
axs[0].set_xlabel(r'$\alpha$ [deg]')
axs[0].set_ylabel(r'$\delta$ [deg]')
axs[0].set_xlim([108, 132])
axs[0].set_ylim([-76, -45])
##########
get_yval = (lambda _df: np.array(_df['phot_g_mean_mag'] + 5 * np.log10(_df[
'parallax'] / 1e3) + 5))
get_xval = (lambda _df: np.array(_df['phot_bp_mean_mag'] - _df[
'phot_rp_mean_mag']))
axs[1].scatter(get_xval(nbhd_df),
get_yval(nbhd_df),
c='gray',
alpha=0.8,
zorder=2,
s=7,
rasterized=True,
linewidths=0,
label='Field',
marker='.')
axs[1].scatter(get_xval(kc19_df),
get_yval(kc19_df),
c='lightskyblue',
alpha=1,
zorder=3,
s=7,
rasterized=True,
linewidths=0.15,
label='Corona',
marker='.',
edgecolors='k')
axs[1].scatter(get_xval(cg18_df),
get_yval(cg18_df),
c='k',
alpha=0.9,
zorder=4,
s=7,
rasterized=True,
linewidths=0,
label='Core',
marker='.')
axs[1].plot(get_xval(target_df),
get_yval(target_df),
alpha=1,
mew=0.5,
zorder=8,
label='TOI 1937',
markerfacecolor='lightskyblue',
markersize=14,
marker='*',
color='black',
lw=0)
axs[1].set_ylim(axs[1].get_ylim()[::-1])
axs[1].set_xlabel('Bp - Rp [mag]')
axs[1].set_ylabel('Absolute G [mag]', labelpad=-6)
##########
words = ['Field', 'Corona', 'Core', 'TOI1937'][::-1]
colors = ['gray', 'lightskyblue', 'k', 'lightskyblue'][::-1]
rainbow_text(0.98, 0.02, words, colors, size='medium', ax=axs[0])
f.tight_layout(w_pad=2)
outpath = os.path.join(outdir, 'small_ngc2516.png')
savefig(f, outpath)
def plot_rp_vs_period_scatter(active_targets=1,
specialyoung=1,
show_legend=1,
showcandidates=0,
deemph837=0):
set_style()
#
# columns described at
# https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html
#
ea_tab = NasaExoplanetArchive.query_criteria(table="exoplanets",
select="*",
cache=True)
#
# get systems with finite ages (has a value, and +/- error bar)
#
has_age_value = ~ea_tab['st_age'].mask
has_age_errs = (~ea_tab['st_ageerr1'].mask) & (~ea_tab['st_ageerr2'].mask)
has_rp_value = ~ea_tab['pl_rade'].mask
has_rp_errs = (~ea_tab['pl_radeerr1'].mask) & (~ea_tab['pl_radeerr2'].mask)
rp_gt_0 = (ea_tab['pl_rade'] > 0)
transits = (ea_tab['pl_tranflag'] == 1)
sel = (has_age_value & has_age_errs & has_rp_value & has_rp_errs & transits
& rp_gt_0)
t = ea_tab[sel]
tyoung = t[(t['st_age'] < 0.1 * u.Gyr) & (t['st_age'] > 0 * u.Gyr)]
#
# read params
#
age = t['st_age']
age_perr = t['st_ageerr1']
age_merr = np.abs(t['st_ageerr2'])
age_errs = np.array([age_perr, age_merr]).reshape(2, len(age))
rp = t['pl_rade']
rp_perr = t['pl_radeerr1']
rp_merr = t['pl_radeerr2']
rp_errs = np.array([rp_perr, rp_merr]).reshape(2, len(age))
# rp /= 11.2089 # used jupiter radii
period = t['pl_orbper']
#
# plot age vs rp. (age is on y axis b/c it has the error bars, and I at
# least skimmed the footnotes of Hogg 2010)
#
fig, ax = plt.subplots(figsize=(4, 3))
label = ('Exoplanet Archive')
print(f'Mean age unc: {np.mean(age_errs):.2f} Gyr')
print(f'Median age unc: {np.median(age_errs):.2f} Gyr')
ax.scatter(period,
rp,
color='darkgray',
s=3,
zorder=1,
marker='o',
linewidth=0,
label=label,
alpha=1)
#
# targets
#
target_age = (np.array([3.5e7]) * u.yr).to(u.Gyr)
target_rp = (np.array([0.77]) * u.Rjup).to(u.Rearth)
target_rp_unc = (np.array([0.07, 0.09]) * u.Rjup).to(u.Rearth)[:, None]
target_period = (np.array([8.3]) * u.day)
if active_targets:
label = ('TOI$\,$837')
mfc = 'yellow' if not deemph837 else 'white'
ms = 15 if not deemph837 else 8
ax.plot(target_period,
target_rp,
mew=0.5,
markerfacecolor=mfc,
markersize=ms,
marker='*',
color='k',
lw=0,
label=label,
zorder=10)
if specialyoung:
youngnames = tyoung['pl_hostname']
markertypes = ['o', 'v', 'X', 's', 'P', 'd']
zorders = [9, 8, 7, 6, 5, 4]
for ix, y, z in zip(range(len(zorders)), np.unique(youngnames),
zorders):
s = (tyoung['pl_hostname'] == y)
ax.plot(tyoung[s]['pl_orbper'],
tyoung[s]['pl_rade'],
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[ix],
color='k',
lw=0,
label=y,
zorder=z)
# two extra systems...
N_uniq = len(np.unique(youngnames))
ax.plot(6.9596,
10.02,
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[N_uniq],
color='k',
lw=0,
label='HIP 67522',
zorder=2)
# HD 63433 (TOI 1726, TIC 130181866) omitted -- 400 Myr is old!
if showcandidates:
from cdips_followup.manage_candidates import get_candidate_params
vdf, sdf, _age, _rp, _rp_unc, _period = (get_candidate_params(
isvalidated=0, ismanualsubset=1))
l = 'New Planet Candidates' if not deemph837 else 'Active Targets'
ax.plot(_period,
_rp,
mew=0.5,
markerfacecolor='lightskyblue',
markersize=8,
marker='*',
color='k',
lw=0,
label=l,
zorder=1)
# flip default legend order
if show_legend:
handles, labels = ax.get_legend_handles_labels()
leg = ax.legend(handles[::-1],
labels[::-1],
loc='upper right',
borderpad=0.3,
handletextpad=0.5,
fontsize=6,
framealpha=0)
leg.get_frame().set_linewidth(0.5)
ax.set_xlabel('Orbital period [days]')
ax.set_ylabel('Planet size [Earth radii]')
ax.set_xlim([0.1, 1100])
format_ax(ax)
ax.set_xscale('log')
savstr = '_no_overplot' if not active_targets else '_toi837'
if show_legend:
savstr += '_yeslegend'
else:
savstr += '_nolegend'
if showcandidates:
savstr += '_showcandidates'
if deemph837:
savstr += '_deemph837'
outpath = (
'../results/rp_vs_period_scatter/rp_vs_period_scatter_{}{}.png'.format(
today_YYYYMMDD(), savstr))
savefig(fig, outpath, writepdf=1, dpi=400)
def plot_cdf_rp_agecut_KS_test(agecut=1e9, Prot_source='M15',
gyro_source='A19'):
"""
Prot_source (str): M13 or M15
gyro_source (str): MH08 or A19
agecut (float): Age cut in years
Plot CDF, do KS test, calculate p-values.
"""
mdf = get_merged_gyroage_CKS(Prot_source=Prot_source,
gyro_source=gyro_source)
odf = mdf[(mdf.gyroage_yr > agecut) & (mdf.VIIp_Rp < 6)] # old
ydf = mdf[(mdf.gyroage_yr <= agecut) & (mdf.VIIp_Rp < 6)] # young
N_o = len(odf)
N_y = len(ydf)
#
# make the plot!
#
outdir = join(
RESULTSDIR, f'cdf_rp_agecut_KS_test'
)
if not os.path.exists(outdir):
os.mkdir(outdir)
set_style()
f, ax = plt.subplots(figsize=(4,3))
bins = np.linspace(mdf.VIIp_Rp.min(), mdf.VIIp_Rp.max(), 1000)
cnt, bin_edges = np.histogram(arr(odf.VIIp_Rp), bins=bins, normed=True)
cdf = np.cumsum(cnt)
ax.plot(bin_edges[:-1], cdf/cdf[-1],
label=f'Age > {agecut/1e9:.1f} Gyr ({N_o})')
cnt, bin_edges = np.histogram(arr(ydf.VIIp_Rp), bins=bins, normed=True)
cdf = np.cumsum(cnt)
ax.plot(bin_edges[:-1], cdf/cdf[-1],
label=f'Age < {agecut/1e9:.1f} Gyr ({N_y})')
ax.set_xscale('log')
from scipy.stats import ks_2samp
D, p_value = ks_2samp(arr(odf.VIIp_Rp), arr(ydf.VIIp_Rp))
txt = f'D={D:.2e}, p={p_value:.2e}'
ax.text(0.05, 0.95, txt,
transform=ax.transAxes, ha='left', va='top',
fontsize='small')
ax.legend(fontsize='small', loc='lower right')
ax.set_xlabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
outpath = join(
outdir,
f'cdf_Rp_KS_cut{agecut/1e9:.1f}_prot{Prot_source}_gyro{gyro_source}.png'
)
savefig(f, outpath, writepdf=0)
def plot_hist_rp_agecut(agecut=1e9, Prot_source='M15', gyro_source='A19',
fix_ylim=None):
"""
Prot_source (str): M13 or M15
gyro_source (str): MH08 or A19
agecut (float): Age cut in years
Plot histogram. $$$?
"""
mdf = get_merged_gyroage_CKS(Prot_source=Prot_source,
gyro_source=gyro_source)
odf = mdf[(mdf.gyroage_yr > agecut) & (mdf.VIIp_Rp < 6)] # old
ydf = mdf[(mdf.gyroage_yr <= agecut) & (mdf.VIIp_Rp < 6)] # young
N_o = len(odf)
N_y = len(ydf)
# Following Fulton et al 2017, we define a “super-Earth” as a
# planet with a radius of 1–1.75 Re, and a “sub-Neptune” as
# having a radius of 1.75–4.0 Re.
# old: number of superNeptunes / number of super Earths
o_NsN = len(odf[(odf.VIIp_Rp > 1.75) & (odf.VIIp_Rp <= 4.00)])
o_NsE = len(odf[(odf.VIIp_Rp > 1.00) & (odf.VIIp_Rp <= 1.75)])
unc_o_NsN = np.sqrt(len(odf[(odf.VIIp_Rp > 1.75) & (odf.VIIp_Rp <= 4.00)]))
unc_o_NsE = np.sqrt(len(odf[(odf.VIIp_Rp > 1.00) & (odf.VIIp_Rp <= 1.75)]))
o_NsN_div_NsE = o_NsN / o_NsE
unc_o_div = o_NsN_div_NsE * np.sqrt( (unc_o_NsN/o_NsN)**2 + (unc_o_NsE/o_NsE)**2 )
# ditto for young
y_NsN = len(ydf[(ydf.VIIp_Rp > 1.75) & (ydf.VIIp_Rp <= 4.00)])
y_NsE = len(ydf[(ydf.VIIp_Rp > 1.00) & (ydf.VIIp_Rp <= 1.75)])
unc_y_NsN = np.sqrt(len(ydf[(ydf.VIIp_Rp > 1.75) & (ydf.VIIp_Rp <= 4.00)]))
unc_y_NsE = np.sqrt(len(ydf[(ydf.VIIp_Rp > 1.00) & (ydf.VIIp_Rp <= 1.75)]))
y_NsN_div_NsE = y_NsN / y_NsE
unc_y_div = y_NsN_div_NsE * np.sqrt( (unc_y_NsN/y_NsN)**2 + (unc_y_NsE/y_NsE)**2 )
txt = (
'N$_{\mathrm{subNeptune}}$/N$_{\mathrm{superEarth}}$:'
+
f'\nOld: {o_NsN_div_NsE:.2f} $\pm$ {unc_o_div:.2f}'
+
f'\nYoung: {y_NsN_div_NsE:.2f} $\pm$ {unc_y_div:.2f}'
)
#
# make the plot!
#
outdir = join(
RESULTSDIR, f'hist_rp_agecut'
)
if not os.path.exists(outdir):
os.mkdir(outdir)
set_style()
f, ax = plt.subplots(figsize=(4,3))
bins = np.arange(0, 6.25, 0.25)
ax.hist(odf.VIIp_Rp, bins=bins, cumulative=False, fill=False, density=False,
weights=np.ones(N_o)/N_o,
histtype='step', label=f'Age > {agecut/1e9:.1f} Gyr ({N_o})')
ax.hist(ydf.VIIp_Rp, bins=bins, cumulative=False, fill=False, density=False,
weights=np.ones(N_y)/N_y,
histtype='step', label=f'Age < {agecut/1e9:.1f} Gyr ({N_y})')
if fix_ylim:
ax.set_ylim([0,0.28])
ymin, ymax = ax.get_ylim()
ax.vlines(
1.74, ymin, ymax, colors='darkgray', alpha=1,
linestyles='--', zorder=-2, linewidths=0.5
)
ax.set_ylim((ymin, ymax))
# Create new legend handles but use the colors from the existing ones
handles, labels = ax.get_legend_handles_labels()
new_handles = [Line2D([], [], c=h.get_edgecolor()) for h in handles]
ax.legend(handles=new_handles, labels=labels, fontsize='small')
ax.text(0.95, 0.50, txt,
transform=ax.transAxes, ha='right', va='center',
fontsize='small')
ax.set_ylabel('Fraction per bin')
ax.set_xlabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
outpath = join(
outdir,
f'hist_Rp_cut{agecut/1e9:.1f}_prot{Prot_source}_gyro{gyro_source}.png'
)
savefig(f, outpath, writepdf=0)
def calc_semiamplitude_period_recovery(N_samples,
gammadot_limit,
delta_time,
t_observed,
do_serial=0):
np.random.seed(42)
log_semiamplitude = np.random.uniform(np.log(1e1),
np.log(1e7),
size=N_samples)
log_period = np.random.uniform(np.log(1e0), np.log(1e15), size=N_samples)
cosi = np.random.uniform(0, 1, N_samples)
i = np.arccos(cosi)
sini = np.sin(i)
phase = np.random.uniform(0, 1, N_samples)
t_start = t_observed.min()
t0s = t_start + phase * np.exp(log_period)
outpath = os.path.join(rdir,
f'semiamplitude_period_recovery_{N_samples}.csv')
if not os.path.exists(outpath):
if do_serial:
slopes, detectables = [], []
ix = 0
for logK, logP, t0, si in zip(log_semiamplitude, log_period, t0s,
sini):
if ix % 10 == 0:
print(f'{ix}/{N_samples}')
task = (logK, logP, t0, si, t_observed, gammadot_limit)
slope, isdetectable = rv_injection_worker(task)
slopes.append(slope)
detectables.append(isdetectable)
ix += 1
else:
print(f'{datetime.now().isoformat()}: Beginning injection workers')
tasks = [(logK, logP, t0, si, t_observed, gammadot_limit)
for logK, logP, t0, si in zip(log_semiamplitude,
log_period, t0s, sini)]
nworkers = mp.cpu_count()
maxworkertasks = 1000
pool = mp.Pool(nworkers, maxtasksperchild=maxworkertasks)
result = pool.map(rv_injection_worker, tasks)
pool.close()
pool.join()
print(f'{datetime.now().isoformat()}: Finished injection workers')
out = np.array(result, dtype=np.dtype('float,bool'))
out.dtype.names = ['slopes', 'detectables']
slopes = out['slopes']
detectables = out['detectables']
df = pd.DataFrame({
'logK': log_semiamplitude,
'logP': log_period,
'phase': phase,
'slope': slopes,
'detectable': detectables
})
df.to_csv(outpath, index=False)
print(f'saved {outpath}')
else:
df = pd.read_csv(outpath)
#
# make plots; convert to desired mass vs semimajor axis contours
#
from aesthetic.plot import set_style, savefig
set_style()
f, ax = plt.subplots()
ax.scatter(df.logP, df.logK, c=df.detectable.astype(int), s=0.2)
ax.set_xlabel('logP')
ax.set_ylabel('logK')
figpath = os.path.join(rdir, 'logP_vs_logK_detectable_check.png')
savefig(f, figpath, writepdf=False, dpi=300)
# NOTE: assuming log[k (m/s)]
# NOTE: this is the Mstar >> Mcomp approximation. If this doesn't hold, it's an
# implicit equation (the "binary mass function"). So in the large semimajor
# axis regime, your resulting converted contours are slightly wrong.
e = 0
sini = 1
Mstar = MSTAR * u.Msun
msini_msun = sini * ((
(np.exp(np.array(df.logK)) /
(28.4329)) * (1 - e**2)**(1 / 2) * (Mstar.to(u.Msun).value)**(2 / 3) *
((np.exp(np.array(df.logP)) * u.day).to(u.yr).value)**(1 / 3)) *
u.Mjup).to(u.Msun).value
log10msini = np.log10(msini_msun)
per1 = np.exp(np.array(df.logP)) * u.day
sma1 = ((per1**2 * const.G * (Mstar + msini_msun * u.Msun) /
(4 * np.pi**2))**(1 / 3)).to(u.au).value
log10sma1 = np.log10(sma1)
plt.close('all')
f, ax = plt.subplots()
ax.scatter(log10sma1, log10msini, c=df.detectable.astype(int), s=0.2)
ax.set_xlabel('log$_{10}$(semi-major axis [AU])')
ax.set_ylabel('log$_{10}$(M$\sin i$ [M$_\odot$])')
ax.set_xlim([-2, 5])
ax.set_ylim([np.log10(0.001), np.log10(2)])
figpath = os.path.join(rdir, 'log10sma_vs_log10msini_detectable_check.png')
savefig(f, figpath, writepdf=False, dpi=300)
def plot_rp_vs_age_scatter(active_targets=0,
split_toi_ctoi=0,
hjs_only=0,
ismanualsubset=0,
isvalidated=0,
ispublictalk=0):
#
# columns described at
# https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html
#
ea_tab = NasaExoplanetArchive.query_criteria(table="exoplanets",
select="*",
cache=True)
#
# get systems with finite ages (has a value, and +/- error bar)
#
has_age_value = ~ea_tab['st_age'].mask
has_age_errs = (~ea_tab['st_ageerr1'].mask) & (~ea_tab['st_ageerr2'].mask)
has_rp_value = ~ea_tab['pl_rade'].mask
has_rp_errs = (~ea_tab['pl_radeerr1'].mask) & (~ea_tab['pl_radeerr2'].mask)
transits = (ea_tab['pl_tranflag'] == 1)
sel = (has_age_value & has_age_errs & has_rp_value & has_rp_errs
& transits)
t = ea_tab[sel]
if hjs_only:
is_hj = (t['pl_orbper'] < 10 * u.day) & (t['pl_rade'] > 7)
t = t[is_hj]
#
# read params
#
age = t['st_age']
age_perr = t['st_ageerr1']
age_merr = np.abs(t['st_ageerr2'])
age_errs = np.array([age_perr, age_merr]).reshape(2, len(age))
rp = t['pl_rade']
rp_perr = t['pl_radeerr1']
rp_merr = t['pl_radeerr2']
rp_errs = np.array([rp_perr, rp_merr]).reshape(2, len(age))
#
# plot age vs rp. (age is on y axis b/c it has the error bars, and I at
# least skimmed the footnotes of Hogg 2010)
#
set_style()
f, ax = plt.subplots(figsize=(4, 3))
label = ('Exoplanet Archive, ' +
r"$\langle \sigma_{{\mathrm{{age}}}} \rangle$ = " +
"{:.1f} Gyr".format(np.median(age_errs)))
color = 'C1'
if hjs_only:
label = ('Known hot Jupiters')
rp /= 11.2089 # used jupiter radii
color = 'C1'
if ispublictalk:
label = 'Known'
color = 'lightgray'
ax.scatter(age,
rp,
color=color,
s=3,
zorder=1,
marker='o',
linewidth=0,
label=label,
alpha=1)
#
# targets
#
from cdips_followup.manage_candidates import get_candidate_params
vdf, sdf, target_age, target_rp, target_rp_unc, target_period = (
get_candidate_params(isvalidated=isvalidated,
ismanualsubset=ismanualsubset))
if active_targets:
label = (r'Active targets, $\langle \sigma_{{\mathrm{{age}}}} \rangle$'
'< 0.1 Gyr')
if hjs_only:
label = ('Possible hot Jupiters')
istoi = ~(sdf.toi == '--')
if split_toi_ctoi:
ax.scatter(target_age[istoi],
target_rp[istoi],
color='C0',
s=10,
zorder=3,
marker='s',
linewidth=0)
ax.scatter(target_age[~istoi],
target_rp[~istoi],
color='C0',
s=25,
zorder=3,
marker='*',
linewidth=0,
label=label)
elif hjs_only:
hj = (target_rp > 7) & (target_rp < 29)
ax.scatter(target_age[hj],
target_rp[hj] / 11.2089,
color='C0',
s=35,
zorder=3,
marker='*',
linewidth=0,
label=label)
else:
if isvalidated:
if len(vdf) > 1:
raise NotImplementedError('might want to just assign ages')
val_age = 4e7 / 1e9
val_rp = np.array(vdf.rp)
ax.plot(val_age,
val_rp,
mew=0.5,
markerfacecolor='yellow',
markersize=12,
marker='*',
color='k',
lw=0,
label='Validated',
zorder=4)
ax.scatter(target_age,
target_rp,
s=40,
color='C0',
zorder=3,
marker='*',
linewidth=0,
label="Potential")
else:
ax.scatter(target_age,
target_rp,
color='C0',
s=25,
zorder=3,
marker='*',
linewidth=0,
label=label)
sel = (~pd.isnull(target_age)) & (~pd.isnull(target_rp))
print('{} targets w/ finite ages and radii'.format(len(
target_age[sel])))
print('{} targets w/ finite ages and radii that are TOIs'.format(
len(target_age[sel & istoi])))
print('{} targets w/ finite ages and radii that are not TOIs'.format(
len(target_age[sel & ~istoi])))
target_rp_rel_unc = target_rp_unc / target_rp
##########
if hjs_only:
# HACK: force to 10% relative uncertainty (eep)
sel = target_rp_rel_unc > 0.1
target_rp_unc[sel] = target_rp[sel] * 0.1 * (np.random.uniform(
0.8, 1.2, size=len(target_rp[sel])))
else:
# HACK: for the one very small object...
sel = target_rp_rel_unc > 0.33
target_rp_unc[sel] = target_rp[sel] * 0.33
##########
if hjs_only:
pass
#ax.errorbar(target_age[hj], target_rp[hj], yerr=target_rp_unc[hj],
# elinewidth=0.3, ecolor='k', capsize=0, capthick=0,
# linewidth=0, fmt='*', ms=0, zorder=2, color='black',
# alpha=0.5)
else:
if not ispublictalk:
ax.errorbar(target_age,
target_rp,
yerr=target_rp_unc,
elinewidth=0.3,
ecolor='k',
capsize=0,
capthick=0,
linewidth=0,
fmt='*',
ms=0,
zorder=2,
color='black',
alpha=0.5)
# flip default legend order
handles, labels = ax.get_legend_handles_labels()
if not hjs_only:
leg = ax.legend(handles[::-1],
labels[::-1],
loc='lower left',
borderpad=0.3,
handletextpad=0.3,
fontsize='small',
framealpha=1)
else:
leg = ax.legend(handles[::-1],
labels[::-1],
loc='upper right',
borderpad=0.3,
handletextpad=0.3,
fontsize='small',
framealpha=0)
leg.get_frame().set_linewidth(0.5)
ax.set_xlabel('Planet age [billion years]')
ax.set_ylabel('Planet size [Earth radii]')
if hjs_only:
ax.set_ylabel('Planet size [Jupiter radii]')
ax.set_xlim([6e-3, 17])
if not hjs_only:
ax.set_ylim([0.13, 85])
ax.set_yscale('log')
if hjs_only:
ax.set_ylim([0.1, 3])
ax.set_yscale('linear')
ax.set_xscale('log')
if hjs_only:
ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.2g}'))
else:
ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2g}'))
f.tight_layout()
savstr = '_no_overplot' if not active_targets else '_active_targets'
if split_toi_ctoi:
savstr += '_split_ctoi'
if hjs_only:
savstr += '_onlyhjs'
if ispublictalk:
savstr += '_ispublic'
if isvalidated:
savstr += '_hasvalidated'
outpath = (
'../results/rp_vs_age_scatter/rp_vs_age_scatter_{}{}.png'.format(
today_YYYYMMDD(), savstr))
f.savefig(outpath, bbox_inches='tight', dpi=450)
f.savefig(outpath.replace('.png', '.pdf'), bbox_inches='tight')
print('made {}'.format(outpath))
def get_simulated_rvs(sigma_obs, N_obs, start_time, end_time, xoparams, xo_df,
makeplot=True):
#
# a GP model is not defined without data. so _time, _rv, and _rv_err are
# fake data, got by multiplying the photometry by the expected spot-induced
# RV amplitude.
#
eps = 1e-5
_time = np.ascontiguousarray(xo_df.time, dtype=np.float64)
_rv = np.ascontiguousarray(xo_df.rv, dtype=np.float64)
_rv_err = np.ones_like(_rv)*30
t_train = np.linspace(_time.min(), _time.max(), int(1e4))
t_true = np.linspace(start_time.jd, end_time.jd, int(1e4))
t_long = np.linspace(_time.min(), end_time.jd, int(1e4))
np.random.seed(42)
t_obs = np.sort(np.random.uniform(start_time.jd, end_time.jd, N_obs))
assert np.all(np.diff(t_obs) > 0)
with pm.Model() as model:
mean = pm.Normal('mean', mu=np.mean(_rv), sd=np.std(_rv))
upper_amp = 13.8 # 14 works
log_amp = pm.Bound(pm.Normal, lower=10, upper=upper_amp)(
'log_amp', mu=xoparams['log_amp'], sd=10
)
P_rot = pm.Normal('P_rot', mu=xoparams['P_rot'], sd=0.01)
log_Q0 = pm.Bound(pm.Normal, upper=7, lower=0.7)(
'log_Q0', mu=xoparams['log_Q0'], sd=2
)
log_deltaQ = pm.Normal('log_deltaQ', mu=xoparams['log_deltaQ'], sd=0.1)
mix = pm.Uniform('mix', lower=1e-4, upper=1)
kernel = xo.gp.terms.RotationTerm(
log_amp=log_amp, period=P_rot, log_Q0=log_Q0, log_deltaQ=log_deltaQ, mix=mix
)
gp = xo.gp.GP(kernel, _time, _rv_err**2, mean=mean)
gp.marginal('gp', observed=_rv)
pm.Deterministic('pred_train', gp.predict(t_train))
pm.Deterministic('pred_long', gp.predict(t_long))
pm.Deterministic('pred_obs', gp.predict(t_obs))
pm.Deterministic('pred_true', gp.predict(t_true))
map_soln = xo.optimize(start=model.test_point)
# gp model doesn't really work because i don't understand it. period etc
# are fine; scale is wonky.
rv_train = map_soln['pred_train']
rv_long = map_soln['pred_long']
rv_obs_spot = map_soln['pred_obs']
rv_true_spot = map_soln['pred_true']
print(map_soln)
orbit = xo.orbits.KeplerianOrbit(
period=xoparams['period'], b=xoparams['b'], t0=xoparams['t0'],
ecc=xoparams['ecc'], omega=xoparams['omega'],
m_star=xoparams['m_star'], r_star=xoparams['r_star']
)
np.random.seed(42)
rv_true_kep = orbit.get_radial_velocity(t_true, K=xoparams['K_orb']).eval()
rv_obs_kep = orbit.get_radial_velocity(t_obs, K=xoparams['K_orb']).eval()
rv_obs_noise = np.random.normal(loc=0, scale=sigma_obs, size=len(t_obs))
rv_true = rv_true_spot + rv_true_kep
rv_obs = rv_obs_spot + rv_obs_kep + rv_obs_noise
rv_err_obs = np.ones_like(t_obs)*sigma_obs
if makeplot:
####################
# plot #1: training data
from cdips_followup.paths import RESULTSDIR
set_style()
plt.close('all')
f, ax = plt.subplots(figsize=(8,3))
ax.scatter(_time, _rv, s=0.5, c='k', label='train data', zorder=3)
ax.plot(t_train, rv_train, label='gp fit', lw=0.3, zorder=1)
ax.legend()
ax.set_xlabel('time')
ax.set_ylabel('RV [m/s]')
figpath = os.path.join(RESULTSDIR, 'HIP_67522',
f'training_data_short_Nobs{N_obs}.png')
savefig(f, figpath, writepdf=0)
####################
# plot #2: training + predict
set_style()
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(_time, _rv, s=1, c='k', label='train data', zorder=3)
ax.plot(t_long, rv_long, label='gp fit', lw=0.5, zorder=1)
ax.legend()
ax.set_xlabel('time')
ax.set_ylabel('RV [m/s]')
figpath = os.path.join(RESULTSDIR, 'HIP_67522',
f'training_data_long_Nobs{N_obs}.png')
savefig(f, figpath, writepdf=0)
####################
# plot #3: generated data
plt.close('all')
t0 = t_true.min()
f, axs = plt.subplots(nrows=3, figsize=(4,3), sharex=True)
axs[0].plot(t_true-t0, rv_true_spot, c='C0', lw=1)
axs[1].plot(t_true-t0, rv_true_kep, c='C1', lw=1)
axs[2].errorbar(t_obs-t0, rv_obs, yerr=sigma_obs, fmt='none', ecolor='k',
alpha=1, elinewidth=1, capsize=1, zorder=12)
axs[2].plot(t_true-t0, rv_true, c='C2', lw=1)
axs[2].plot(t_true-t0, rv_true_kep, c='C1', lw=1)
props = dict(boxstyle='square', facecolor='white', alpha=0.95, pad=0.1,
linewidth=0)
axs[0].text(0.03, 0.05, 'Star', ha='left', va='bottom',
transform=axs[0].transAxes, bbox=props, zorder=2,
color='C0')
axs[1].text(0.03, 0.05, 'Planet', ha='left', va='bottom',
transform=axs[1].transAxes, bbox=props, zorder=2,
color='C1')
axs[2].text(0.03, 0.05, 'Sum', ha='left', va='bottom',
transform=axs[2].transAxes, bbox=props, zorder=15,
color='C2')
axs[2].set_xlabel('Days from 2021-Apr-28')
axs[0].set_ylim([-600, 600])
axs[1].set_ylim([-120, 120])
axs[2].set_ylim([-600, 600])
f.text(-0.02,0.5, 'Radial velocity [m/s]', va='center', rotation=90)
figpath = os.path.join(RESULTSDIR, 'HIP_67522', f'sim_spot_Nobs{N_obs}.png')
savefig(f, figpath, writepdf=0)
obs_df = pd.DataFrame({
't_obs':t_obs,
'rv_obs':rv_obs,
'rv_err_obs':rv_err_obs
})
true_df = pd.DataFrame({
'rv_true_spot':rv_true_spot,
'rv_true_kep':rv_true_kep,
'rv_true':rv_true,
't_true':t_true
})
return obs_df, true_df
def plot_Prot_vs_eps(includefit=False, xscale=None):
#
# make the plot!
#
set_style()
f, ax = plt.subplots(figsize=(4, 3))
Porb_1937 = 0.947
Prot_1937 = 6.5
MpMs_1937 = ((1.79 * u.Mjup) / (1.045 * u.Msun)).cgs.value
Rs_a_1937 = (1 / 4.26) # from MIT report
eps_1937 = MpMs_1937 * Rs_a_1937**5
ax.scatter(df['epsilon'],
df['P_rot'],
zorder=2,
c='k',
alpha=0.9,
s=9,
linewidths=0,
label='HJs (phot P$_\mathrm{rot}$)')
# ax.scatter(
# d['Per'][sel1], d['SPer'][sel1], zorder=2, c='gray', alpha=0.9, s=9,
# linewidths=0,
# label='HJs ($\mathrm{R}_{\star} \geq 1.2 \mathrm{R}_{\odot}$)'
# )
ax.plot(eps_1937,
Prot_1937,
mew=0.5,
zorder=3,
markerfacecolor='yellow',
markersize=18,
marker='*',
color='k',
lw=0,
label='TOI 1937b')
# if includefit:
# x = np.hstack([d['Per'][sel0], Porb_1937])
# y = np.hstack([d['SPer'][sel0], Prot_1937])
# slope, intercept, rvalue, pvalue, stderr = linregress(x, y)
# label = f'Slope={slope:.1f}$\pm${stderr:.1f}, p={pvalue:.1e}'
# ax.plot(x, intercept + slope*x, label=label)
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), handletextpad=0.1)
ax.set_xlabel('$\epsilon \equiv (M_\mathrm{p}/M_{\star})(R_{\star}/a)^5$')
ax.set_ylabel('Stellar spin period [days]')
# ax.set_xlim([0.5, 4.2])
ax.set_ylim([2, 28])
extrastr = '_withfit' if includefit else '_nofit'
if isinstance(xscale, str):
ax.set_xscale(xscale)
extrastr += f'_{xscale}'
figpath = (f'../results/Prot_vs_eps/Prot_vs_eps_snrcut{extrastr}.png')
savefig(f, figpath)
def plot_cks_rp_vs_gyroage(Prot_source='M15', gyro_source='A19'):
"""
Prot_source: M13 or M15
gyro_source: MH08 or A19 or SL20
Make scatter plots of {plantet radius, orbital period} against
gyrochronology ages in a few different projections.
"""
mdf = get_merged_gyroage_CKS(Prot_source=Prot_source,
gyro_source=gyro_source)
outdir = join(RESULTSDIR, f'{Prot_source}rot_{gyro_source}gyroage')
if not os.path.exists(outdir):
os.mkdir(outdir)
set_style()
agelabel = (f'Gyro-Age [{Prot_source} '+'P$_\mathrm{rot}$ +'
+f'{gyro_source}; yr]')
for scale in ['linear','log']:
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.VIIp_Rp, mdf.gyroage_yr, s=2, c='k', zorder=3)
ax.set_xlabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_ylabel(agelabel)
ax.set_xscale(scale); ax.set_yscale('log')
ylim = ax.get_ylim()
ymax = 1.5e10
ax.set_ylim([1e8, ymax])
print(f'WRN! {len(mdf[mdf.gyroage_yr > ymax])} older than max plotted gyroage')
outpath = join(outdir, f'cks-VII_rp_vs_gyroage_{scale}.png')
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4,3))
ax.scatter(mdf.VIIp_Per, mdf.gyroage_yr, s=2, c='k')
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel(agelabel)
ax.set_xscale('log'); ax.set_yscale('log')
ylim = ax.get_ylim()
ymax = 1.5e10
ax.set_ylim([ylim[0], ymax])
outpath = join(outdir, 'cks-VII_period_vs_gyroage.png')
savefig(f, outpath, writepdf=0)
plt.close('all')
f, ax = plt.subplots(figsize=(4.3,3))
Prot_key = 'Prot' if Prot_source in ['M13','M15','S21'] else 'spec_Prot'
sel = ~pd.isnull(mdf[Prot_key])
norm = mpl.colors.Normalize(vmin=8.5, vmax=10)
cax = ax.scatter(mdf[sel].VIIp_Per, mdf[sel].VIIp_Rp, s=7,
c=np.log10(mdf[sel].gyroage_yr),
cmap='plasma_r', zorder=3, norm=norm,
edgecolors='k', linewidths=0.2)
ax.set_xlabel('CKS-VII P$_\mathrm{orb}$ [day]')
ax.set_ylabel('CKS-VII R$_\mathrm{p}$ [R$_\oplus$]')
ax.set_xscale('log'); ax.set_yscale('log')
cbar = f.colorbar(cax, extend='both')
cbar.set_label('log$_{10}$'+agelabel, fontsize='small')
cbar.minorticks_off()
outpath = join(outdir, 'cks-VII_period_vs_cks-VII_prad_gyroagecolors.png')
savefig(f, outpath, writepdf=0)
def SpadaLanzafame20_gyro(BmV=None, Prot=None, makeplot=True):
"""
Spada & Lanzafame 2020 give gyrochrones in B-V (and mass) vs age space.
Will need to interpolate between them on a 2D grid.
"""
sl20path = os.path.join(DATADIR, 'Spada_Lanzafame_2020_BmV_table.csv')
df = pd.read_csv(sl20path, sep='&')
from scipy.interpolate import interp2d, interp1d
from scipy.optimize import newton
df = df.sort_values(by='BmV')
BmV_arr = np.array(df.BmV).astype(float)
age_gyr = np.array(df.columns[1:-1]).astype(float)
Prot_array = np.array(df[[
c for c in df.columns if (c != 'BmV' and c != '4.57')
]]).astype(float)
fn = interp2d(age_gyr,
BmV_arr,
Prot_array,
kind='cubic',
bounds_error=False,
fill_value=0)
N_pts = 100
BmV_new = np.linspace(min(BmV_arr), max(BmV_arr), N_pts)
age_new = np.linspace(min(age_gyr), max(age_gyr), 2 * N_pts)
Prot_new = fn(age_new, BmV_new)
if makeplot:
from aesthetic.plot import set_style, savefig
set_style()
fig, ax = plt.subplots(figsize=(4, 3))
X, Y = np.meshgrid(BmV_new, age_new)
#cmap = plt.cm.viridis
cmap = plt.cm.Paired
norm = mpl.colors.Normalize(vmin=np.nanmin(Prot_new),
vmax=np.nanmax(Prot_new))
im = ax.pcolormesh(X,
Y,
Prot_new.T,
cmap=cmap,
norm=norm,
shading='flat') # vs 'gouraud'
ax.set_xlabel('B-V')
ax.set_ylabel('Age [Gyr]')
cbar = fig.colorbar(im,
orientation='vertical',
extend='min',
label='Prot [day]')
cbar.ax.tick_params(labelsize=6, direction='out')
outpath = os.path.join(RESULTSDIR, 'SpadaLanzafame2020',
'interpolation_check.png')
savefig(fig, outpath, writepdf=False)
age_gyrs = []
for _prot, _bmv in zip(Prot, BmV):
nearest_BmV, idx = find_nearest(BmV_new,
_bmv,
verbose=True,
return_index=True)
# now it's a 1-dimensional optimization problem
fn_1d = interp1d(age_new,
Prot_new[idx, :],
kind='quadratic',
bounds_error=False,
fill_value=0)
interp_fn2 = lambda x: fn_1d(x) - _prot
age_init_gyr = 2
try:
root_age_gyr = newton(interp_fn2, age_init_gyr)
except:
if _prot > max(Prot_new[idx, :]):
root_age_gyr = 10
else:
root_age_gyr = 0
age_gyrs.append(root_age_gyr)
t_yr = np.array(age_gyrs) * 1e9
return t_yr
def plot_TIC268_nbhd_full(outdir=RESULTSDIR):
set_style()
df = get_cdips_catalog(ver=0.4)
kc19_sel = ((df.cluster.str.contains('NGC_2516')) &
(df.reference.str.contains('Kounkel_2019')))
cg18_sel = ((df.cluster.str.contains('NGC_2516')) &
(df.reference.str.contains('CantatGaudin_2018')))
target_df = df[df.source_id == 5489726768531119616] # TIC 2683...
kc19_df = df[kc19_sel]
cg18_df = df[cg18_sel]
##########
plt.close('all')
params = ['ra', 'dec', 'parallax', 'pmra', 'pmdec']
nparams = len(params)
qlimd = {
'ra': 0,
'dec': 0,
'parallax': 0,
'pmra': 0,
'pmdec': 0,
'radial_velocity': 0
} # whether to limit axis by quantile
ldict = {
'ra': r'$\alpha$ [deg]',
'dec': r'$\delta$ [deg]',
'parallax': r'$\pi$ [mas]',
'pmra': r'$\mu_{{\alpha}} \cos\delta$ [mas/yr]',
'pmdec': r'$\mu_{{\delta}}$ [mas/yr]',
'radial_velocity': 'RV [km/s]'
}
f, axs = plt.subplots(figsize=(6, 6), nrows=nparams - 1, ncols=nparams - 1)
for i in range(nparams):
for j in range(nparams):
print(i, j)
if j == nparams - 1 or i == nparams - 1:
continue
if j > i:
axs[i, j].set_axis_off()
continue
xv = params[j]
yv = params[i + 1]
print(i, j, xv, yv)
axs[i, j].scatter(kc19_df[xv],
kc19_df[yv],
c='gray',
alpha=0.9,
zorder=2,
s=5,
rasterized=True,
linewidths=0,
label='Core',
marker='.')
axs[i, j].scatter(cg18_df[xv],
cg18_df[yv],
c='k',
alpha=0.9,
zorder=3,
s=5,
rasterized=True,
linewidths=0,
label='Corona')
axs[i, j].plot(target_df[xv],
target_df[yv],
alpha=1,
mew=0.5,
zorder=8,
label='TOI 1937',
markerfacecolor='yellow',
markersize=14,
marker='*',
color='black',
lw=0)
# set the axis limits as needed
if qlimd[xv]:
xlim = (np.nanpercentile(kc19_df[xv], 25),
np.nanpercentile(kc19_df[xv], 75))
axs[i, j].set_xlim(xlim)
if qlimd[yv]:
ylim = (np.nanpercentile(kc19_df[yv], 25),
np.nanpercentile(kc19_df[yv], 75))
axs[i, j].set_ylim(ylim)
# fix labels
if j == 0:
axs[i, j].set_ylabel(ldict[yv])
if not i == nparams - 2:
# hide xtick labels
labels = [
item.get_text()
for item in axs[i, j].get_xticklabels()
]
empty_string_labels = [''] * len(labels)
axs[i, j].set_xticklabels(empty_string_labels)
if i == nparams - 2:
axs[i, j].set_xlabel(ldict[xv])
if not j == 0:
# hide ytick labels
labels = [
item.get_text()
for item in axs[i, j].get_yticklabels()
]
empty_string_labels = [''] * len(labels)
axs[i, j].set_yticklabels(empty_string_labels)
if (not (j == 0)) and (not (i == nparams - 2)):
# hide ytick labels
labels = [
item.get_text() for item in axs[i, j].get_yticklabels()
]
empty_string_labels = [''] * len(labels)
axs[i, j].set_yticklabels(empty_string_labels)
# hide xtick labels
labels = [
item.get_text() for item in axs[i, j].get_xticklabels()
]
empty_string_labels = [''] * len(labels)
axs[i, j].set_xticklabels(empty_string_labels)
# axs[2,2].legend(loc='best', handletextpad=0.1, fontsize='medium', framealpha=0.7)
# leg = axs[2,2].legend(bbox_to_anchor=(0.8,0.8), loc="upper right",
# handletextpad=0.1, fontsize='medium',
# bbox_transform=f.transFigure)
for ax in axs.flatten():
format_ax(ax)
f.tight_layout(h_pad=0.1, w_pad=0.1)
outpath = os.path.join(outdir, 'full_kinematics.png')
savefig(f, outpath)
def plot_mass_vs_age_scatter(active_targets=1,
specialyoung=1,
showcandidates=0,
show_legend=0):
set_style()
#
# columns described at
# https://exoplanetarchive.ipac.caltech.edu/docs/API_exoplanet_columns.html
#
ea_tab = NasaExoplanetArchive.query_criteria(table="exoplanets",
select="*",
cache=True)
#
# get systems with finite ages (has a value, and +/- error bar)
#
has_age_value = ~ea_tab['st_age'].mask
has_age_errs = (~ea_tab['st_ageerr1'].mask) & (~ea_tab['st_ageerr2'].mask)
has_rp_value = ~ea_tab['pl_rade'].mask
has_rp_errs = (~ea_tab['pl_radeerr1'].mask) & (~ea_tab['pl_radeerr2'].mask)
rp_gt_0 = (ea_tab['pl_rade'] > 0)
m_gt_0 = (ea_tab['pl_bmassj'] > 0) & (np.abs(ea_tab['pl_bmassjerr1']) > 0)
has_m_errs = (~ea_tab['pl_bmassjerr1'].mask) & (
~ea_tab['pl_bmassjerr2'].mask)
transits = (ea_tab['pl_tranflag'] == 1)
sel = (has_age_value & has_age_errs & has_rp_value & has_rp_errs & transits
& rp_gt_0 & m_gt_0 & has_m_errs)
t = ea_tab[sel]
tyoung = t[(t['st_age'] < 0.1 * u.Gyr) & (t['st_age'] > 0 * u.Gyr)]
#
# read params
#
age = t['st_age']
age_perr = t['st_ageerr1']
age_merr = np.abs(t['st_ageerr2'])
age_errs = np.array([age_perr, age_merr]).reshape(2, len(age))
rp = t['pl_rade']
rp_perr = t['pl_radeerr1']
rp_merr = t['pl_radeerr2']
rp_errs = np.array([rp_perr, rp_merr]).reshape(2, len(age))
# rp /= 11.2089 # used jupiter radii
mass = t['pl_bmassj']
##########################################
fig, ax = plt.subplots(figsize=(4, 3))
label = ('Exoplanet Archive')
print(f'Mean age unc: {np.mean(age_errs):.2f} Gyr')
print(f'Median age unc: {np.median(age_errs):.2f} Gyr')
print(42 * '-')
print('Ages below 700 Myr:')
_t = t[(t['st_age'] < 0.7 * u.Gyr) & (t['st_age'] > 0 * u.Gyr)]
_t.sort('st_age')
print(_t['st_age', 'pl_hostname', 'pl_rade', 'pl_orbper'])
print(42 * '-')
ax.scatter(age,
mass,
color='darkgray',
s=3,
zorder=1,
marker='o',
linewidth=0,
label=label,
alpha=1)
#
# targets
#
target_age = (np.array([3.5e7]) * u.yr).to(u.Gyr)
target_rp = (np.array([0.77]) * u.Rjup).to(u.Rearth)
target_rp_unc = (np.array([0.07, 0.09]) * u.Rjup).to(u.Rearth)[:, None]
if active_targets:
label = ('TOI$\,$837')
ax.plot(target_age,
target_mass,
mew=0.5,
markerfacecolor='yellow',
markersize=15,
marker='*',
color='k',
lw=0,
label=label,
zorder=3)
if specialyoung:
youngnames = tyoung['pl_hostname']
markertypes = ['o', 'v', 'X', 's', 'P', 'd']
for ix, y in enumerate(np.unique(youngnames)):
s = (tyoung['pl_hostname'] == y)
ax.plot(tyoung[s]['st_age'],
tyoung[s]['pl_rade'],
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[ix],
color='k',
lw=0,
label=y,
zorder=2)
# two extra systems...
N_uniq = len(np.unique(youngnames))
ax.plot(1.5e7 / 1e9,
10.02,
mew=0.5,
markerfacecolor='white',
markersize=7,
marker=markertypes[N_uniq],
color='k',
lw=0,
label='HIP 67522',
zorder=2)
# HD 63433 (TOI 1726, TIC 130181866) omitted -- 400 Myr is old!
if showcandidates:
from cdips_followup.manage_candidates import get_candidate_params
vdf, sdf, _age, _rp, _rp_unc, _period = (get_candidate_params(
isvalidated=0, ismanualsubset=1))
ax.plot(_age,
_rp,
mew=0.5,
markerfacecolor='lightskyblue',
markersize=8,
marker='*',
color='k',
lw=0,
label='New Planet Candidates',
zorder=1)
# flip default legend order
if show_legend:
handles, labels = ax.get_legend_handles_labels()
leg = ax.legend(handles[::-1],
labels[::-1],
loc='upper right',
borderpad=0.3,
handletextpad=0.5,
fontsize=6,
framealpha=0)
leg.get_frame().set_linewidth(0.5)
ax.set_xlabel('Age [billion years]')
ax.set_ylabel(r'Planet mass [M$_\mathrm{Jup}$]')
ax.set_xlim([6e-3, 17])
ax.set_ylim([0.001, 1e2])
format_ax(ax)
ax.set_xscale('log')
ax.set_yscale('log')
savstr = '_no_overplot' if not active_targets else '_toi837'
if showcandidates:
savstr += '_showcandidates'
if show_legend:
savstr += '_yeslegend'
else:
savstr += '_nolegend'
outpath = (
'../results/mass_vs_age_scatter/mass_vs_age_scatter_{}{}.png'.format(
today_YYYYMMDD(), savstr))
savefig(fig, outpath, writepdf=1, dpi=400)
def plot_Porb_Prot(snr_cutoff=2, includefit=False):
sel = (((d['SPer'] / d['E_SPer']) > snr_cutoff) & (d['SPer'] < 30))
sel0 = (((d['SPer'] / d['E_SPer']) > snr_cutoff)
& (d['R_'] < 1.2)
& (d['SPer'] < 30))
sel1 = (((d['SPer'] / d['E_SPer']) > snr_cutoff)
& (d['R_'] >= 1.2)
& (d['SPer'] < 30))
sel2 = (((d['SPer'] / d['E_SPer']) > snr_cutoff)
& (d['R_'] < 1.2)
& (d['SPer'] < 8))
sel3 = (((d['SPer'] / d['E_SPer']) > snr_cutoff)
& (d['R_'] < 1.2)
& (d['Per'] < 1))
print(d['ID'][sel2])
print(d['ID'][sel3])
#
# make the plot!
#
set_style()
f, ax = plt.subplots(figsize=(4, 3))
Porb_1937 = 0.947
Prot_1937 = 6.5
ax.scatter(d['Per'][sel0],
d['SPer'][sel0],
zorder=2,
c='k',
alpha=0.9,
s=9,
linewidths=0,
label='HJs ($\mathrm{R}_{\star} < 1.2 \mathrm{R}_{\odot}$)')
ax.scatter(d['Per'][sel1],
d['SPer'][sel1],
zorder=2,
c='gray',
alpha=0.9,
s=9,
linewidths=0,
label='HJs ($\mathrm{R}_{\star} \geq 1.2 \mathrm{R}_{\odot}$)')
ax.plot(Porb_1937,
Prot_1937,
mew=0.5,
zorder=3,
markerfacecolor='yellow',
markersize=18,
marker='*',
color='k',
lw=0,
label='TOI 1937b (1.1$\mathrm{R}_{\odot}$)')
if includefit:
x = np.hstack([d['Per'][sel0], Porb_1937])
y = np.hstack([d['SPer'][sel0], Prot_1937])
slope, intercept, rvalue, pvalue, stderr = linregress(x, y)
label = f'Slope={slope:.1f}$\pm${stderr:.1f}, p={pvalue:.1e}'
ax.plot(x, intercept + slope * x, label=label)
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), handletextpad=0.1)
ax.set_xlabel('Planet orbital period [days]')
ax.set_ylabel('Stellar spin period [days]')
ax.set_xlim([0.5, 4.2])
ax.set_ylim([2, 28])
extrastr = '_withfit' if includefit else '_nofit'
figpath = (
f'../results/Porb_Prot/Porb_Prot_snrcut{snr_cutoff}{extrastr}.png')
savefig(f, figpath)
