import numpy as np
import matplotlib.pyplot as plt
import pyfits
import glob
from scipy.signal.spectral import lombscargle
import scipy.signal as sps
from scaling_relations import nu_max, delta_nu
from astero import astero
from rc_params import plot_params
reb, rfb = plot_params()
from colours import plot_colours
ocols = plot_colours()
def testing():
x = np.arange(0, 10, .1)
y = np.sin(2*x + np.pi/4.)
yerr = np.ones_like(y)*.01
plt.clf()
plt.plot(x, y)
ys, A = fit_single_sine_err(x, y, yerr, 2)
plt.plot(x, ys, 'r')
plt.show()
print find_phase(A)/np.pi
# calculate the periodogram for plotting
def pgram(x, y, peak1):
fs = np.linspace(5, 45, 10000) # c/d
ws = 2*np.pi*fs # lombscargle uses angular frequencies
pgram = lombscargle(x, y, ws)
plt.clf()
plt.subplot(2, 1, 1)
import numpy as np
import matplotlib.pyplot as plt
import pyfits
import glob
import scipy.signal as sps
from astero_modelling import gen_freqs_nm_dn
import kplr
client = kplr.API()
from astero_modelling import model
from sin_tests import fit_sine_err
from rc_params import plot_params
reb, fbt = plot_params()
from colours import plot_colours
ocols = plot_colours()
# Attempting to perform asteroseismology on kepler data
def download(KID):
# load data, median normalise and join together
lc_files = glob.glob("/Users/angusr/.kplr/data/lightcurves/%s/*_slc.fits"
% str(KID).zfill(9))
# check to see whether you've already downloaded data...
print len(lc_files), ' files'
if len(lc_files) == 0:
print 'downloading..'
star = client.star("%s" % KID)
lcs = star.get_light_curves(short_cadence=True, fetch=True)
lc_files = glob.glob("/Users/angusr/.kplr/data/lightcurves/%s/*_slc.fits"
% str(KID).zfill(9))
print lc_files
import numpy as np
import matplotlib.pyplot as plt
import george
from george.kernels import ExpSquaredKernel, ExpSine2Kernel, WhiteKernel
from colors import plot_colors
ocols = plot_colors()
from rc_params import plot_params
reb = plot_params()
from BGdata import BetaGem
BG = BetaGem()
from HD82074 import HD
HD = HD()
def sampling(t, xs, ys, rv_err):
# try calculating different rms
ppd = 1./(max(t) - min(t)) * 1000. # points per day
ppt = np.array([24, 24*2, 24*3, 24*4, 24*5, 24*10,
24*20, 24*30, 24*60]) # pph, pphh, pptm, ppm
rms = []
for pts in ppt[::-1]:
l = int(ppd/pts)
x, y, yerr = xs[::l], ys[::l], np.ones_like(ys[::l])*np.mean(rv_err)
print np.std(y)
rms.append(np.sqrt(sum((y)**2)/len(y)))
return ppt, rms
def prior_sample(t, rv, rv_err, theta, i):
# sample from the prior
k = theta[0] * ExpSquaredKernel(theta[1]) * \
ExpSine2Kernel(theta[2], theta[3])
import numpy as np
import matplotlib.pyplot as plt
import idlsave
from rc_params import plot_params
from colors import plot_colors
params = plot_params()
ocols = plot_colors()
# load star names
DIR = '/Users/angusr/Python/Subgiants'
stars = np.genfromtxt('%s/data/star_names.txt' % DIR)
rv_files = glob.glob('%s/data/vst*.dat' % DIR)
plt.clf()
for i, star in enumerate(stars):
data = idlsave.read("%s/data/vst%s.dat" % (DIR, int(star)))
t = data.cf3['JD']
rv = data.cf3['MNVEL']
rv_err = data.cf3['ERRVEL']
plt.subplot(len(stars), 1, i+1)
plt.errorbar(t, rv, yerr=rv_err, capsize=0, ecolor='.8', fmt='k.')
plt.savefig("%s/figures/all_stars" % DIR)
import numpy as np
import matplotlib.pyplot as plt
from rc_params import plot_params
pp = plot_params()
from astropy import constants
from YREC import YREC
yrec = YREC()
L_sun = 3.846e26
R_sun = 6.955e8
def radius_sb(L, T):
L *= L_sun
sb = 5.67e-8
return np.sqrt(L / (sb * T**4)) / R_sun
# for a given mass and teff, what is the radius?
def rad(M, T):
# load isochrones
iso_M, iso_L, iso_T = yrec.mass, 10**yrec.logL, yrec.teff
# find closest mass, closest teff and corresponding l
m = min(iso_M, key=lambda x:abs(x-M))
teff = min(iso_T, key=lambda x:abs(x-T))
l = iso_L[iso_M==m] # warning: this is often degenerate!
return radius_sb(l[0], teff), m, teff, l
if __name__ == "__main__":
