def __init__(self, p, teff=None, feh=None, logg=None, bv=None):
self.p = p
self.bv = bv
if not self.bv:
self.bv = teff2bv(teff, logg, feh)
self.teff = teff
self.feh = feh
self.logg = logg
def p_vs_t(self, pars, pars2, model, col):
# convert teff data to colours
if col==True:
self.teff = teff_bv.teff2bv(self.teff, self.logg, self.feh)
self.t_err = np.ones_like(self.teff)*0.01
# remove subgiants and kraft break with kraft toggle
period, p_err, age, teff, t_err, a_errp, a_errm, logg, logg_errp,\
logg_errm = self.kraft_sub(kraft=False)
# Remove zeros
zero = teff > 0
# age masks
a_lim = [0, 1, 2, 3, 4, 5, 8, max(age)]
# bin according to age
for i in range(len(a_lim)-1):
a = (a_lim[i] < age)*(age < a_lim[i+1])*zero
# Plot data
pl.clf()
pl.errorbar(teff[a], period[a], xerr=t_err[a], \
yerr=p_err[a], color='k', fmt='o', mec='k', \
capsize=0, markersize=5, ecolor='0.8', zorder = 4, \
label='$%s < \mathrm{Age} <%s \mathrm{Gyr}$'%(a_lim[i], a_lim[i+1]))
# Add Isochrones
xs, ys, t_ref = plotting.iso_calc(self, a, pars, age, model, col)
pl.plot(xs, ys, color = ocols[i], linestyle='--', linewidth = 2, \
label = '$\mathrm{Age} = %.1f$\,$\mathrm{Gyr}$ \
$\mathrm{(Barnes~2007)}$' % t_ref, zorder = 1)
if pars != pars2:
xs, ys, t_ref = plotting.iso_calc(self, a, pars2, age, model, col)
pl.plot(xs, ys, color = ocols[i], linestyle='-', linewidth = 2, \
label = '$\mathrm{Age} = %.1f$\,$\mathrm{Gyr}$ \
$\mathrm{This~work}$' % t_ref, zorder = 1)
# a = a_lim[i]
# xs, ys, t_ref = plotting.iso_calc(self, a, pars, age, model, col)
# pl.plot(xs, ys, color = ocols[i], linestyle = '--', linewidth = 2, zorder = 2)
# a = a_lim[i+1]
# xs, ys, t_ref = plotting.iso_calc(self, a, pars, age, model, col)
# pl.plot(xs, ys, color = ocols[i], linestyle = '--', linewidth = 2, zorder = 3)
pl.xlabel("$\mathrm{B-V}$")
if col==False: pl.xlabel("$\mathrm{T_{eff (K)}}$")
pl.ylabel("$\mathrm{P_{rot} (days)}$")
# pl.xlim(pl.gca().get_xlim()[::-1])
pl.xlim(.2, .8)
if col==False: pl.xlim(7000, 5000)
pl.ylim(0, 70)
pl.legend(loc='upper left')
pl.savefig("p_vs_t%s"%i)
c_err = .01
# remove subs
g = data[9] > 4.
p1 = data[1][g]
p_err1 = data[2][g]
a1 = data[3][g]
a_errp1 = data[4][g]
a_errm1 = data[5][g]
a_err1 = .5 * (a_errp1 + a_errm1)
t1 = data[12][g]
t_err1 = data[13][g]
logg1 = data[9][g]
logg_err1 = data[10][g]
bv1 = teff2bv(t1, logg1, np.ones_like(t1)*-.02, t_err1, logg_err1, \
np.ones_like(t1)*.001, error=False)
bv_err1 = np.ones_like(bv1)*c_err
# add clusters
data = np.genfromtxt("/Users/angusr/Python/Gyro/data/clusters.txt").T
bv2 = data[0]
bv_err2 = data[1]
p2 = data[2]
p_err2 = data[3]
a2 = data[4]
a_err2 = data[5]
a_errp2 = data[5]
a_errm2 = data[5]
logg2 = data[6]
logg_err2 = data[7]
uncertainty_hist(df)
# a, b, n, c = .7725, .601, .5189, .4 # Barnes
a, b, n, c = .407, .325, .566, .495 # MH
par = [a, b, n, c]
id_starname = []
for koi in my.KOI.values:
id_starname.append("K{}".format(str(koi).zfill(5)))
my["id_starname"] = id_starname
new = pd.merge(my, df, on="id_starname", how="inner")
# new = pd.merge(my, df, on="id_kic", how="inner")
bvs = tbv.teff2bv(new.teff.values, new.logg.values, new.iso_smet.values)
new["bv"] = bvs
m = bvs > c
new = new.iloc[m]
cks_age = new.iso_sage.values
cks_age_errp = new.iso_sage_err1.values
cks_age_errm = new.iso_sage_err2.values
lncks_age = new.iso_slogage.values
lncks_age_errp = new.iso_slogage_err1.values
lncks_age_errm = new.iso_slogage_err2.values
my_age = gyro_age(par, new.period.values, new.bv.values)
plt.clf()
plt.errorbar(cks_age,
plt.hist(periods)
plt.savefig("phist")
teff, teff_errp, teff_errm, logg, logg_errp, logg_errm, feh, feh_errp, \
feh_errm = get_kic_properties(kois)
periods = np.array(periods)
m = periods > 1
print(len(periods), "periods")
teff, teff_errp, teff_errm, logg, logg_errp, logg_errm, feh, feh_errp, \
feh_errm = teff[m], teff_errp[m], teff_errm[m], logg[m], \
logg_errp[m], logg_errm[m], feh[m], feh_errp[m], feh_errm[m]
kois, periods = np.array(kois)[m], periods[m]
errp, errm = errp[m], errm[m]
make_data_file(kois, periods, errp, errm, teff, teff_errp, teff_errm, logg,
logg_errp, logg_errm, feh, feh_errp, feh_errm)
kois, periods, errp, errm, teff, teff_errp, teff_err, logg, logg_errp, \
logg_errm, feh, feh_errp, feh_errm = read_data_file()
make_tex_table(kois, periods, errp, errm, teff, teff_errp, teff_err, logg,
logg_errp, logg_errm, feh, feh_errp, feh_errm)
plt.clf()
plt.errorbar(tbv.teff2bv(teff, logg, feh),
periods,
yerr=[errp, errm],
fmt="k.",
alpha=.5)
plt.savefig("p_vs_bv")
# matched, tgas_inds, rot_inds = match(tgas["kepid"], rot["KID"])
matched, tgas_inds, rot_inds = match(tgas["kepid"], kid)
# Create data arrays.
periods = np.array(rot["Prot"])[rot_inds]
teff = np.array(tgas["teff"])[tgas_inds]
logg = np.array(tgas["logg"])[tgas_inds]
feh = np.array(tgas["feh"])[tgas_inds]
pmra = np.array(tgas["pmra"])[tgas_inds]
ra = np.array(tgas["ra"])[tgas_inds]
pmdec = np.array(tgas["pmdec"])[tgas_inds]
dec = np.array(tgas["dec"])[tgas_inds]
parallax = np.array(tgas["parallax"])[tgas_inds]
# calculate B-V color and rotation-age
bv = teff2bv(teff, logg, feh)
age = bn.age(periods, bv)
# calculate the z position in Kpc
c = SkyCoord(ra=ra * u.degree,
dec=dec * u.degree,
distance=1. / parallax * u.kpc)
z = c.cartesian.z
y = c.cartesian.y
x = c.cartesian.x
# calculate velocities
mas2deg = lambda x: x / 60 / 60 / 1e3
w = gd.CartesianPhaseSpacePosition(
pos=[ra * u.deg, dec * u.deg, 1. / parallax * u.kpc],
vel=[
