# posterior prob
def lnprob(theta, x, y, yerr):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
try:
return lp + lnlike(theta, x, y, yerr)
except:
print theta
raise
if __name__ == "__main__":
# Load real data
x, y, yerr = load("/Users/angusr/angusr/data2/Q3_public/kplr010295224-2009350155506_llc.fits")
# shorten data
l = 300.
x = x[:l]
y = y[:l]
yerr = yerr[:l]
# # median normalise
# yerr /= np.median(y)
# y = y/np.median(y) -1
# normalise so range is 2 - no idea if this is the right thing to do...
yerr = 2*yerr/(max(y)-min(y))
y = 2*y/(max(y)-min(y))
y = y-np.median(y)
# Load target list with ACF periods
data = np.genfromtxt('/Users/angusr/Python/george/targets.txt').T
KIDs = data[0]
# p_init = data[1]
save_results = np.zeros((len(KIDs), 8))
for k, KID in enumerate(KIDs):
# p_init = p_init[k]
print k, KID
# Load real quarter 3 data
x, y, yerr = load(
"/Users/angusr/angusr/data2/Q3_public/kplr0%s-2009350155506_llc.fits"
% int(KID))
r = .4 # range of periods to try
s = 30. # number of periods to try
b = .2 # prior boundaries
# compute acf
p_init = autocorrelation(x, y)
# compute lomb scargle periodogram
pgram(y, r, p_init, highres=True)
# normalise so range is 2 - no idea if this is the right thing to do...
yerr = 2 * yerr / (max(y) - min(y))
y = 2 * y / (max(y) - min(y))
pl.plot(xs, predict(xs, x, y, yerr, theta, P)[0], 'r-')
pl.xlabel('time (days)')
pl.savefig('%sresult' % name)
savedata = np.empty(len(theta) + 1)
savedata[:len(theta)] = theta
savedata[-1] = like
np.savetxt('%sresult.txt' % name, savedata)
return like
if __name__ == "__main__":
# Load real data
x, y, yerr = load(
"/Users/angusr/angusr/data2/Q3_public/kplr010295224-2009350155506_llc.fits"
)
# shorten data
l = 550.
x = x[:l]
y = y[:l]
yerr = yerr[:l]
# normalise so range is 2 - no idea if this is the right thing to do...
yerr = 2 * yerr / (max(y) - min(y))
y = 2 * y / (max(y) - min(y))
y = y - np.median(y)
# theta, P = [0., .2, .2, 1.], 1.7 # initial
# theta, P = [-2., -2., -1.2, 6.], 1.7 # better initialisation
cadence = 0.02043365
# Load target list with ACF periods
data = np.genfromtxt('/Users/angusr/Python/george/targets.txt').T
KIDs = data[0]
# p_init = data[1]
save_results = np.zeros((len(KIDs), 8))
for k, KID in enumerate(KIDs):
# p_init = p_init[k]
print k, KID
# Load real quarter 3 data
x, y, yerr = load("/Users/angusr/angusr/data2/Q3_public/kplr0%s-2009350155506_llc.fits" %int(KID))
r = .4 # range of periods to try
s = 30. # number of periods to try
b = .2 # prior boundaries
# compute acf
p_init = autocorrelation(x, y)
# compute lomb scargle periodogram
pgram(y, r, p_init, highres = True)
# normalise so range is 2 - no idea if this is the right thing to do...
yerr = 2*yerr/(max(y)-min(y))
y = 2*y/(max(y)-min(y))
y = y-np.median(y)