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get_Kepler_Mdwarf_planets.py
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get_Kepler_Mdwarf_planets.py
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from imports import *
from scipy.stats import gamma, skewnorm
from scipy.optimize import curve_fit
import mwdust, rvs
global KepMdwarffile, G, data_spanC
KepMdwarffile = '../GAIAMdwarfs/input_data/Keplertargets/KepMdwarfsv11_archiveplanetsv2.csv'
G = 6.67408e-11
# get stellar completeness parameters
d = np.genfromtxt('TPSfiles/nph-nstedAPI_clean.txt', skip_header=208, delimiter=',',
usecols=(0,49,64,65,66,67,68,69,70,71,72,73,74,75,76,77))
kepidC,data_spanC,cdpp1d5,cdpp2,cdpp2d5,cdpp3,cdpp3d5,cdpp4d5,cdpp5,cdpp6,cdpp7d5,cdpp9,cdpp10d5,cdpp12,cdpp12d5,cdpp15 = d.T
transit_durs = np.array([1.5,2,2.5,3,3.5,4.5,5,6,7.5,9,10.5,12,12.5,15])
cdpps = np.array([cdpp1d5,cdpp2,cdpp2d5,cdpp3,cdpp3d5,cdpp4d5,cdpp5,cdpp6,cdpp7d5,cdpp9,cdpp10d5,cdpp12,cdpp12d5,cdpp15]).T
def get_Kepler_Mdwarf_planets(fname):
'''
I have a list of Kepler M dwarfs with stellar parameters based on GAIA
distances. I also have a list of confirmed Kepler planets from the NASA
exoplanet archive. Match the two lists to get the empirical population of
Kepler M dwarf planets with high precision radii and updated stellar parameters.
'''
# get Kepler Mdwarfs parameters from GAIA
dG = np.loadtxt(KepMdwarffile, delimiter=',')
KepID,isMdwarf,badGAIA,bad2MASS,baddistpost,badTeff,ra_deg,dec_deg,GBPmag,e_GBPmag,GRPmag,e_GRPmag,Kepmag,Jmag,e_Jmag,Hmag,e_Hmag,Kmag,e_Kmag,parallax_mas,e_parallax,dist_pc,ehi_dist,elo_dist,mu,ehi_mu,elo_mu,AK,e_AK,BCK,e_BCK,MK,ehi_MK,elo_MK,Rs_RSun,ehi_Rs,elo_Rs,Teff_K,ehi_Teff,elo_Teff,Ms_MSun,ehi_Ms,elo_Ms,logg_dex,ehi_logg,elo_logg = dG.T
# get planet transits parameters from NASA exoplanet archive
dK = np.genfromtxt('Keplertargets/NASAarchive_confirmed_Keplerlowmassstars.csv',
delimiter=',', skip_header=66)
loc_rowid,kepid,kepler_name,koi_disposition,koi_score,koi_period,koi_period_err1,koi_period_err2,koi_time0,koi_time0_err1,koi_time0_err2,koi_impact,koi_impact_err1,koi_impact_err2,koi_duration,koi_duration_err1,koi_duration_err2,koi_depth,koi_depth_err1,koi_depth_err2,koi_ror,koi_ror_err1,koi_ror_err2,koi_prad,koi_prad_err1,koi_prad_err2,koi_incl,koi_incl_err1,koi_incl_err2,koi_dor,koi_dor_err1,koi_dor_err2,koi_limbdark_mod,koi_ldm_coeff4,koi_ldm_coeff3,koi_ldm_coeff2,koi_ldm_coeff1,koi_model_snr,koi_steff,koi_steff_err1,koi_steff_err2,koi_slogg,koi_slogg_err1,koi_slogg_err2,koi_smet,koi_smet_err1,koi_smet_err2,koi_srad,koi_srad_err1,koi_srad_err2,koi_kepmag,koi_jmag,koi_hmag,koi_kmag = dK.T
# match stellar parameters to confirmed Kepler stars
Nplanets = kepid.size
self = KepConfirmedMdwarfPlanets(fname, Nplanets)
self._initialize_arrays()
for i in range(Nplanets):
print float(i) / Nplanets
g = np.in1d(KepID, kepid[i])
print g.sum()
# stellar parameters (both pre (1) and post-GAIA (2))
self.KepIDs[i] = kepid[i]
self.isMdwarf[i] = isMdwarf[g] if g.sum() == 1 else np.nan
self.badGAIA[i] = badGAIA[g] if g.sum() == 1 else np.nan
self.bad2MASS[i] = bad2MASS[g] if g.sum() == 1 else np.nan
self.baddistpost[i] = baddistpost[g] if g.sum() == 1 else np.nan
self.badTeff[i] = badTeff[g] if g.sum() == 1 else np.nan
self.Jmags[i] = Jmag[g] if g.sum() == 1 else np.nan
self.e_Jmags[i] = e_Jmag[g] if g.sum() == 1 else np.nan
self.Hmags[i] = Hmag[g] if g.sum() == 1 else np.nan
self.e_Hmags[i] = e_Hmag[g] if g.sum() == 1 else np.nan
self.Kmags[i] = Kmag[g] if g.sum() == 1 else np.nan
self.e_Kmags[i] = e_Kmag[g] if g.sum() == 1 else np.nan
self.pars[i] = parallax_mas[g] if g.sum() == 1 else np.nan
self.e_pars[i] = e_parallax[g] if g.sum() == 1 else np.nan
self.mus[i] = mu[g] if g.sum() == 1 else np.nan
self.ehi_mus[i] = ehi_mu[g] if g.sum() == 1 else np.nan
self.elo_mus[i] = elo_mu[g] if g.sum() == 1 else np.nan
self.dists[i] = dist_pc[g] if g.sum() == 1 else np.nan
self.ehi_dists[i] = ehi_dist[g] if g.sum() == 1 else np.nan
self.elo_dists[i] = elo_dist[g] if g.sum() == 1 else np.nan
self.AKs[i] = AK[g] if g.sum() == 1 else np.nan
self.e_AKs[i] = e_AK[g] if g.sum() == 1 else np.nan
self.BCKs[i] = BCK[g] if g.sum() == 1 else np.nan
self.e_BCKs[i] = e_BCK[g] if g.sum() == 1 else np.nan
self.MKs[i] = MK[g] if g.sum() == 1 else np.nan
self.ehi_MKs[i] = ehi_MK[g] if g.sum() == 1 else np.nan
self.elo_MKs[i] = elo_MK[g] if g.sum() == 1 else np.nan
# pre-GAIA
self.Rss1[i] = koi_srad[i]
self.ehi_Rss1[i] = koi_srad_err1[i] if koi_srad_err1[i] > 0 else koi_srad[i]*.07
self.elo_Rss1[i] = abs(koi_srad_err2[i]) if koi_srad_err2[i] < 0 else koi_srad[i]*.08
self.Teffs1[i] = koi_steff[i]
self.ehi_Teffs1[i] = koi_steff_err1[i] if koi_steff_err1[i] > 0 else koi_steff[i]*.02
self.elo_Teffs1[i] = abs(koi_steff_err2[i]) if koi_steff_err2[i] < 0 else koi_steff[i]*.02
self.loggs1[i] = koi_slogg[i]
self.ehi_loggs1[i] = koi_slogg_err1[i] if koi_slogg_err1[i] > 0 else koi_slogg[i]*.009
self.elo_loggs1[i] = abs(koi_slogg_err2[i]) if koi_slogg_err2[i] < 0 else koi_slogg[i]*.006
_,_,samp_Rs = get_samples_from_percentiles(self.Rss1[i], self.ehi_Rss1[i],
self.elo_Rss1[i], Nsamp=1e3)
_,_,samp_logg = get_samples_from_percentiles(self.loggs1[i], self.ehi_loggs1[i],
self.elo_loggs1[i], Nsamp=1e3)
samp_Ms = rvs.kg2Msun(10**samp_logg * rvs.Rsun2m(samp_Rs)**2 * 1e-2 / G)
v = np.percentile(samp_Ms, (16,50,84))
self.Mss1[i], self.ehi_Mss1[i], self.elo_Mss1[i] = v[1], v[2]-v[1], v[1]-v[0]
self.FeHs1[i] = koi_smet[i]
self.ehi_FeHs1[i] = koi_smet_err1[i]
self.elo_FeHs1[i] = abs(koi_smet_err1[i])
# post-GAIA
self.Rss2[i] = Rs_RSun[g] if g.sum() == 1 else np.nan
self.ehi_Rss2[i] = ehi_Rs[g] if g.sum() == 1 else np.nan
self.elo_Rss2[i] = elo_Rs[g] if g.sum() == 1 else np.nan
self.Teffs2[i] = Teff_K[g] if g.sum() == 1 else np.nan
self.ehi_Teffs2[i] = ehi_Teff[g] if g.sum() == 1 else np.nan
self.elo_Teffs2[i] = elo_Teff[g] if g.sum() == 1 else np.nan
self.Mss2[i] = Ms_MSun[g] if g.sum() == 1 else np.nan
self.ehi_Mss2[i] = ehi_Ms[g] if g.sum() == 1 else np.nan
self.elo_Mss2[i] = elo_Ms[g] if g.sum() == 1 else np.nan
self.loggs2[i] = logg_dex[g] if g.sum() == 1 else np.nan
self.ehi_loggs2[i] = ehi_logg[g] if g.sum() == 1 else np.nan
self.elo_loggs2[i] = elo_logg[g] if g.sum() == 1 else np.nan
# planet parameters
self.Ps[i] = koi_period[i]
self.e_Ps[i] = np.abs([koi_period_err1[i], koi_period_err2[i]]).mean()
self.T0s[i] = koi_time0[i]
self.e_T0s[i] = np.abs([koi_time0_err1[i], koi_time0_err2[i]]).mean()
self.Ds[i] = koi_duration[i]
self.e_Ds[i] = np.abs([koi_duration_err1[i], koi_duration_err2[i]]).mean()
self.Zs[i] = koi_depth[i]
self.e_Zs[i] = np.abs([koi_depth_err1[i], koi_depth_err2[i]]).mean()
self.aRs[i] = koi_dor[i]
self.e_aRs[i] = np.abs([koi_dor_err1[i], koi_dor_err2[i]]).mean()
self.rpRs[i] = koi_ror[i]
self.ehi_rpRs[i] = koi_ror_err1[i]
self.elo_rpRs[i] = abs(koi_ror_err2[i])
self.bs[i] = koi_impact[i]
self.ehi_bs[i] = koi_impact_err1[i]
self.elo_bs[i] = abs(koi_impact_err2[i])
# completeness parameters
self.CDPPs[i] = get_fitted_cdpp(self.KepIDs[i], self.Ds[i])
self.data_spans[i] = data_spanC[kepidC == self.KepIDs[i]]
self.SNRtransits[i] = self.Zs[i] / self.CDPPs[i] * np.sqrt(get_Ntransits(self.KepIDs[i],
self.Ps[i]))
# computed planet parameters
if self.isMdwarf[i]:
rps1, smas1, Teqs1, Fs1 = sample_planet_params(self, i, postGAIA=False)
self.rps1[i], self.ehi_rps1[i], self.elo_rps1[i] = rps1
self.smas1[i], self.ehi_smas1[i], self.elo_smas1[i] = smas1
self.Teqs1[i], self.ehi_Teqs1[i], self.elo_Teqs1[i] = Teqs1
self.Fs1[i], self.ehi_Fs1[i], self.elo_Fs1[i] = Fs1
rps2, smas2, Teqs2, Fs2 = sample_planet_params(self, i, postGAIA=True)
self.rps2[i], self.ehi_rps2[i], self.elo_rps2[i] = rps2
self.smas2[i], self.ehi_smas2[i], self.elo_smas2[i] = smas2
self.Teqs2[i], self.ehi_Teqs2[i], self.elo_Teqs2[i] = Teqs2
self.Fs2[i], self.ehi_Fs2[i], self.elo_Fs2[i] = Fs2
# save Kepler M dwarf planet population
self._pickleobject()
class KepConfirmedMdwarfPlanets:
def __init__(self, fname, Nplanets):
self.fname_out = fname
self.Nplanets = int(Nplanets)
def _initialize_arrays(self):
N = self.Nplanets
# stellar parameters (both pre (1) and post-GAIA (2))
self.KepIDs, self.isMdwarf = np.zeros(N), np.zeros(N, dtype=bool)
self.badGAIA, self.bad2MASS = np.zeros(N, dtype=bool), np.zeros(N, dtype=bool)
self.baddistpost, self.badTeff = np.zeros(N, dtype=bool), np.zeros(N, dtype=bool)
self.Jmags, self.e_Jmags = np.zeros(N), np.zeros(N)
self.Hmags, self.e_Hmags = np.zeros(N), np.zeros(N)
self.Kmags, self.e_Kmags = np.zeros(N), np.zeros(N)
self.pars, self.e_pars = np.zeros(N), np.zeros(N)
self.mus, self.ehi_mus, self.elo_mus=np.zeros(N),np.zeros(N),np.zeros(N)
self.dists, self.ehi_dists, self.elo_dists = np.zeros(N), np.zeros(N), \
np.zeros(N)
self.AKs, self.e_AKs = np.zeros(N), np.zeros(N)
self.BCKs, self.e_BCKs = np.zeros(N), np.zeros(N)
self.MKs, self.ehi_MKs, self.elo_MKs=np.zeros(N),np.zeros(N),np.zeros(N)
self.Rss1, self.ehi_Rss1, self.elo_Rss1=np.zeros(N),np.zeros(N),np.zeros(N)
self.Mss1, self.ehi_Mss1, self.elo_Mss1=np.zeros(N),np.zeros(N),np.zeros(N)
self.Teffs1, self.ehi_Teffs1, self.elo_Teffs1 = np.zeros(N), np.zeros(N), \
np.zeros(N)
self.loggs1, self.ehi_loggs1, self.elo_loggs1 = np.zeros(N), np.zeros(N), \
np.zeros(N)
self.FeHs1, self.ehi_FeHs1, self.elo_FeHs1=np.zeros(N),np.zeros(N),np.zeros(N)
self.Rss2, self.ehi_Rss2, self.elo_Rss2=np.zeros(N),np.zeros(N),np.zeros(N)
self.Mss2, self.ehi_Mss2, self.elo_Mss2=np.zeros(N),np.zeros(N),np.zeros(N)
self.Teffs2, self.ehi_Teffs2, self.elo_Teffs2 = np.zeros(N), np.zeros(N), \
np.zeros(N)
self.loggs2, self.ehi_loggs2, self.elo_loggs2 = np.zeros(N), np.zeros(N), \
np.zeros(N)
# planet parameters (both pre (1) and post-GAIA (2))
self.Ps, self.e_Ps = np.zeros(N), np.zeros(N)
self.T0s, self.e_T0s = np.zeros(N), np.zeros(N)
self.Ds, self.e_Ds = np.zeros(N), np.zeros(N)
self.Zs, self.e_Zs = np.zeros(N), np.zeros(N)
self.aRs, self.e_aRs = np.zeros(N), np.zeros(N)
self.rpRs, self.ehi_rpRs, self.elo_rpRs = np.zeros(N), np.zeros(N), \
np.zeros(N)
self.bs, self.ehi_bs, self.elo_bs=np.zeros(N),np.zeros(N),np.zeros(N)
self.rps1 = np.repeat(np.nan,N)
self.ehi_rps1 = np.repeat(np.nan,N)
self.elo_rps1 = np.repeat(np.nan,N)
self.smas1 = np.repeat(np.nan,N)
self.ehi_smas1 = np.repeat(np.nan,N)
self.elo_smas1 = np.repeat(np.nan,N)
self.Teqs1 = np.repeat(np.nan,N)
self.ehi_Teqs1 = np.repeat(np.nan,N)
self.elo_Teqs1 = np.repeat(np.nan,N)
self.Fs1 = np.repeat(np.nan,N)
self.ehi_Fs1 = np.repeat(np.nan,N)
self.elo_Fs1 = np.repeat(np.nan,N)
self.rps2 = np.repeat(np.nan,N)
self.ehi_rps2 = np.repeat(np.nan,N)
self.elo_rps2 = np.repeat(np.nan,N)
self.smas2 = np.repeat(np.nan,N)
self.ehi_smas2 = np.repeat(np.nan,N)
self.elo_smas2 = np.repeat(np.nan,N)
self.Teqs2 = np.repeat(np.nan,N)
self.ehi_Teqs2 = np.repeat(np.nan,N)
self.elo_Teqs2 = np.repeat(np.nan,N)
self.Fs2 = np.repeat(np.nan,N)
self.ehi_Fs2 = np.repeat(np.nan,N)
self.elo_Fs2 = np.repeat(np.nan,N)
# sensitivity calculations
self.CDPPs = np.zeros(N)
self.data_spans = np.zeros(N)
self.SNRtransits = np.zeros(N)
def _pickleobject(self):
fObj = open(self.fname_out, 'wb')
pickle.dump(self, fObj)
fObj.close()
def loadpickle(fname):
fObj = open(fname, 'rb')
self = pickle.load(fObj)
fObj.close()
return self
def resample_PDF(pdf, Nsamp, sig=1e-3):
pdf_resamp = np.random.choice(pdf, int(Nsamp)) + np.random.randn(int(Nsamp))*sig
return pdf_resamp
def sample_planet_params(self, index, postGAIA=True):
'''sample distribution of planet parameters from observables and stellar pdfs'''
# get stellar parameters PDFs either from derived from GAIA distances
# or from original Kepler parameters (approximate distributions as skewnormal)
g = int(index)
print self.KepIDs[g]
if postGAIA:
path = '../GAIAMdwarfs/Gaia-DR2-distances_custom/DistancePosteriors/'
try:
samp_Rs,samp_Teff,samp_Ms = np.loadtxt('%s/KepID_allpost_%i'%(path,self.KepIDs[g]),
delimiter=',', usecols=(9,10,11)).T
except IOError:
samp_Rs,samp_Teff,samp_Ms = np.zeros(1000),np.zeros(1000),np.zeros(1000)
if np.all(np.isnan(samp_Rs)) or np.all(np.isnan(samp_Teff)) or np.all(np.isnan(samp_Ms)):
samp_Rs,samp_Teff,samp_Ms = np.zeros(1000),np.zeros(1000),np.zeros(1000)
samp_Rs = resample_PDF(samp_Rs[np.isfinite(samp_Rs)], samp_Rs.size, sig=1e-3)
samp_Teff = resample_PDF(samp_Teff[np.isfinite(samp_Teff)], samp_Teff.size, sig=5)
samp_Ms = resample_PDF(samp_Ms[np.isfinite(samp_Ms)], samp_Ms.size, sig=1e-3)
else:
_,_,samp_Rs = get_samples_from_percentiles(self.Rss1[g], self.ehi_Rss1[g],
self.elo_Rss1[g], Nsamp=1e3)
_,_,samp_Teff = get_samples_from_percentiles(self.Teffs1[g], self.ehi_Teffs1[g],
self.elo_Teffs1[g], Nsamp=1e3)
_,_,samp_Ms = get_samples_from_percentiles(self.Mss1[g], self.ehi_Mss1[g],
self.elo_Mss1[g], Nsamp=1e3)
# sample rp/Rs distribution from point estimates
_,_,samp_rpRs = get_samples_from_percentiles(self.rpRs[g], self.ehi_rpRs[g],
self.elo_rpRs[g],
Nsamp=samp_Rs.size)
# compute planet radius PDF
samp_rp = rvs.m2Rearth(rvs.Rsun2m(samp_rpRs * samp_Rs))
v = np.percentile(samp_rp, (16,50,84))
rps = v[1], v[2]-v[1], v[1]-v[0]
# compute semi-major axis PDF
samp_Ps = np.random.normal(self.Ps[g], self.e_Ps[g], samp_Ms.size)
samp_as = rvs.semimajoraxis(samp_Ps, samp_Ms, 0)
v = np.percentile(samp_as, (16,50,84))
smas = v[1], v[2]-v[1], v[1]-v[0]
# compute equilibrium T PDF (Bond albedo=0)
samp_Teq = samp_Teff * np.sqrt(.5*rvs.Rsun2m(samp_Rs)/rvs.AU2m(samp_as))
v = np.percentile(samp_Teq, (16,50,84))
Teqs = v[1], v[2]-v[1], v[1]-v[0]
# compute insolation
samp_F = samp_Rs**2 * (samp_Teff/5778.)**4 / samp_as**2
v = np.percentile(samp_F, (16,50,84))
Fs = v[1], v[2]-v[1], v[1]-v[0]
return rps, smas, Teqs, Fs
def get_fitted_cdpp(KepID, duration):
# get cdpps for this star
g = kepidC == KepID
if g.sum() == 0:
return np.nan
else:
cdpp_arr = cdpps[g].reshape(transit_durs.size)
if np.any(np.isnan(cdpp_arr)): return np.nan
# fit cubic function to cdpp(t)
func = np.poly1d(np.polyfit(transit_durs, cdpp_arr, 3))
##plt.plot(transit_durs, cdpp_arr, '.', transit_durs, func(transit_durs), '-')
##plt.show()
return func(duration)
def get_Ntransits(KepID, P):
# get data span for this star
g = kepidC == KepID
if g.sum() == 0:
return np.nan
else:
data_span = float(data_spanC[g])
return int(np.round(data_span / P))
def Gamma_CDF_func(x, k, theta):
'''
k = shape parameter (sometimes called a)
l = location parameter
theta = scale parameter (related to the rate b=1/theta)
'''
return gamma.cdf(x, k, loc=1., scale=theta)
def Skewnorm_CDF_func(x, a, mu, sig):
'''
a = skewness (gaussian if a==0)
mu = mean of gaussian
sig = std dev of gaussian
'''
return skewnorm.cdf(x, a, loc=mu, scale=sig)
def get_samples_from_percentiles(val, ehi, elo, Nsamp=1e3, add_p5_p95=True, pltt=False):
'''Given the 16, 50, and 84 percentiles of a parameter's distribution,
fit a Skew normal CDF and sample it.'''
# get percentiles
p16, med, p84 = float(val-elo), float(val), float(val+ehi)
assert p16 < med
assert med < p84
# add approximate percentiles to help with fitting the wings
# otherwise the resulting fitting distritubions tend to
if add_p5_p95:
p5_approx = med-2*(med-p16)
p95_approx = med+2*(p84-med)
xin = [p5_approx,p16,med,p84,p95_approx]
yin = [.05,.16,.5,.84,.95]
else:
xin, yin = [p16,med,p84], [.16,.5,.84]
# make initial parameter guess
a, mu, sig = (p16-p84)/med, med, np.mean([p16,p84])
p0 = a,mu,sig
popt,pcov = curve_fit(Skewnorm_CDF_func, xin, yin, p0=p0,
sigma=np.repeat(.01,len(yin)), absolute_sigma=False)
# sample the fitted pdf
samples = skewnorm.rvs(*popt, size=int(Nsamp))
# plot distribution if desired
if pltt:
plt.hist(samples, bins=30, normed=True, label='Sampled parameter posterior')
plt.plot(np.sort(samples), skewnorm.pdf(np.sort(samples), *popt),
'-', label='Skew-normal fit: a=%.3f, m=%.3f, s=%.3f'%tuple(popt))
plt.xlabel('Parameter values'), plt.legend(loc='upper right')
plt.show()
return p0, popt, samples