m = [m0, m1, m2, m3, mlp] print 'm = ', m print 'r0, r1 = ', r0, r1 # load lightcurve strKIDs = [] [strKIDs.append(str(KID).zfill(4)) for i in range(nstars)] data = np.genfromtxt("/Users/angusr/angusr/Suz_simulations/final/lightcurve_%s.txt" \ %strKIDs[KID]).T x = data[0] y = data[1] yerr = y*2e-5 # one part per million #FIXME: this is made up! # 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) print 'subsample and truncate' x_sub, y_sub, yerr_sub = subs(x, y, yerr, mlp, 500.) pl.clf() pl.plot(x_sub, y_sub, 'k.') xs = np.linspace(min(x_sub), max(x_sub), 100) pl.plot(xs, predict(xs, x_sub, y_sub, yerr_sub, m, m[4])[0], 'r-') pl.savefig('test') # better initialisation m = [-5, m[1], 1.5, 9., np.log(m[-1])] MCMC(m, x_sub, y_sub, yerr_sub, np.log(r0), np.log(r1), "4")
y = tbdata["PDCSAP_FLUX"] yerr = tbdata["PDCSAP_FLUX_ERR"] q = tbdata["SAP_QUALITY"] # remove nans and bad flags n = np.isfinite(x)*np.isfinite(y)*np.isfinite(yerr)*(q==0) x = x[n] y = y[n] yerr = yerr[n] # 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) # subsample and truncate x_sub, y_sub, yerr_sub = subs(x, y, yerr, 1., 500, 100) # A, l2, l1, s P = np.log(.7) theta = [-2., -2., -1.2, 1., P] # plot data and prediction pl.clf() pl.errorbar(x_sub, y_sub, yerr=yerr_sub, fmt='k.', capsize=0) xs = np.linspace(min(x_sub), max(x_sub), 100) pl.plot(xs, predict(xs, x_sub, y_sub, yerr_sub, theta, P)[0], color='.7') pl.savefig('single_binary_data') # Compute initial likelihood print 'initial lnlike = ', lnlike(theta, x, y, yerr)