def maxlike(theta, x, y, yerr, P, name):
print 'initial values = ', theta, P
print 'initial nll = ', neglnlike(theta, x, y, yerr, P)
result = fmin(neglnlike, theta, args = (x, y, yerr, P))
print 'final values = ', result, P
like = neglnlike(result, x, y, yerr, P)
print 'final likelihood = ', like
# savedata = np.empty(len(result)+2)
# savedata[:len(result)] = result
# savedata[-2] = P
# savedata[-1] = like
# np.savetxt('results/ml_result%s.txt'%name, savedata)
return -like, result
def global_max(x, y, yerr, theta, Periods, P, r, s, b, save):
print "Initial parameters = (exp)", theta
start = time.clock()
print "Initial lnlike = ", -neglnlike(theta, x, y, yerr, P), "\n"
elapsed = (time.clock() - start)
print 'time =', elapsed
L = np.zeros_like(Periods)
results = np.zeros((len(L), 4))
for i, p in enumerate(Periods):
L[i], results[i, :] = maxlike(theta, x, y, yerr, p, i)
iL = L == max(L)
mlp = Periods[iL]
mlresult = results[iL]
print 'max likelihood period = ', mlp
print 'mlresult = ', mlresult[0]
print 'L', L
# plot data
# pl.clf()
# pl.errorbar(x, y, yerr=yerr, fmt='k.', capsize=0, ecolor='0.5', zorder=2)
# xs = np.linspace(min(x), max(x), 1000)
# pl.plot(xs, predict(xs, x, y, yerr, mlresult[0], mlp)[0], color='#339999', linestyle = '-',\
# zorder=1, linewidth='2')
# pl.xlabel('$\mathrm{Time~(days)}$')
# pl.ylabel('$\mathrm{Normalised~Flux}$')
# pl.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
# pl.savefig('/Users/angusr/Python/george/data/%sml_data%s' %(int(KID), save))
# plot GPeriodogram
pl.clf()
area = sp.integrate.trapz(np.exp(L))
pl.plot(Periods, (np.exp(L)) / area, 'k-')
pl.xlabel('Period')
pl.ylabel('Likelihood')
tdata = np.genfromtxt(
'/Users/angusr/angusr/Suz_simulations/final_table.txt',
skip_header=1).T
truemin = tdata[19][KID]
truemax = tdata[20][KID]
true = .5 * (truemin + truemax)
pl.title('Period = %s, true = %s, init = %s' % (mlp, true, P))
pl.savefig('/Users/angusr/Python/george/likelihood/%shr_ml_likelihood%s' %
(int(KID), save))
# set period prior boundaries
bm = mlp - b * mlp
bp = mlp + b * mlp
return L, mlp, bm, bp, mlresult[0]
def global_max(x, y, yerr, theta, Periods, P, r, s, b, save):
print "Initial parameters = (exp)", theta
start = time.clock()
print "Initial lnlike = ", -neglnlike(theta, x, y, yerr, P),"\n"
elapsed = (time.clock() - start)
print 'time =', elapsed
L = np.zeros_like(Periods)
results = np.zeros((len(L), 4))
for i, p in enumerate(Periods):
L[i], results[i,:] = maxlike(theta, x, y, yerr, p, i)
iL = L==max(L)
mlp = Periods[iL]
mlresult = results[iL]
print 'max likelihood period = ', mlp
print 'mlresult = ', mlresult[0]
print 'L', L
# plot data
# pl.clf()
# pl.errorbar(x, y, yerr=yerr, fmt='k.', capsize=0, ecolor='0.5', zorder=2)
# xs = np.linspace(min(x), max(x), 1000)
# pl.plot(xs, predict(xs, x, y, yerr, mlresult[0], mlp)[0], color='#339999', linestyle = '-',\
# zorder=1, linewidth='2')
# pl.xlabel('$\mathrm{Time~(days)}$')
# pl.ylabel('$\mathrm{Normalised~Flux}$')
# pl.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
# pl.savefig('/Users/angusr/Python/george/data/%sml_data%s' %(int(KID), save))
# print 'plotting likelihoods'
# pl.clf()
# area = sp.integrate.trapz(np.exp(L))
# pl.plot(Periods, (np.exp(L))/area, 'k-')
# pl.xlabel('Period')
# pl.ylabel('Likelihood')
# tdata = np.genfromtxt('/Users/angusr/angusr/Suz_simulations/final_table.txt', skip_header=1).T
# truemin = tdata[19][KID]
# truemax = tdata[20][KID]
# true = .5*(truemin+truemax)
# pl.title('Period = %s, true = %s, init = %s' %(mlp, true, P))
# pl.savefig('/Users/angusr/Python/george/likelihood/%sml_likelihood%s' %(int(KID), save))
# set period prior boundaries
bm = mlp - b*mlp
bp = mlp + b*mlp
return L, mlp, bm, bp, mlresult[0]
def global_max(x, y, yerr, theta, Periods, P, r, s, b, save):
print "Initial parameters = (exp)", theta
start = time.clock()
print "Initial lnlike = ", -neglnlike(theta, x, y, yerr, P), "\n"
elapsed = (time.clock() - start)
print 'time =', elapsed
L = np.zeros_like(Periods)
results = np.zeros((len(L), 4))
for i, p in enumerate(Periods):
L[i], results[i, :] = maxlike(theta, x, y, yerr, p, i)
iL = L == max(L)
mlp = Periods[iL]
mlresult = results[iL]
print 'max likelihood period = ', mlp
print 'mlresult = ', mlresult[0]
# plot data
pl.clf()
pl.errorbar(x, y, yerr=yerr, fmt='k.', capsize=0, ecolor='0.5', zorder=2)
xs = np.linspace(min(x), max(x), 1000)
pl.plot(xs, predict(xs, x, y, yerr, mlresult[0], mlp)[0], color='#339999', linestyle = '-',\
zorder=1, linewidth='2')
pl.xlabel('$\mathrm{Time~(days)}$')
pl.ylabel('$\mathrm{Normalised~Flux}$')
pl.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
pl.savefig('%sml_data%s' % (int(KID), save))
# plot GPeriodogram
pl.clf()
area = sp.integrate.trapz(np.exp(L))
pl.plot(Periods, (np.exp(L)) / area, 'k-')
pl.xlabel('Period')
pl.ylabel('Likelihood')
pl.title('Period = %s' % mlp)
pl.savefig('%sml_likelihood%s' % (int(KID), save))
# set period prior boundaries
bm = mlp - b * mlp
bp = mlp + b * mlp
return L, mlp, bm, bp, mlresult[0]
def global_max(x, y, yerr, theta, Periods, P, r, s, b, save):
print "Initial parameters = (exp)", theta
start = time.clock()
print "Initial lnlike = ", -neglnlike(theta, x, y, yerr, P),"\n"
elapsed = (time.clock() - start)
print 'time =', elapsed
L = np.zeros_like(Periods)
results = np.zeros((len(L), 4))
for i, p in enumerate(Periods):
L[i], results[i,:] = maxlike(theta, x, y, yerr, p, i)
iL = L==max(L)
mlp = Periods[iL]
mlresult = results[iL]
print 'max likelihood period = ', mlp
print 'mlresult = ', mlresult[0]
# plot data
pl.clf()
pl.errorbar(x, y, yerr=yerr, fmt='k.', capsize=0, ecolor='0.5', zorder=2)
xs = np.linspace(min(x), max(x), 1000)
pl.plot(xs, predict(xs, x, y, yerr, mlresult[0], mlp)[0], color='#339999', linestyle = '-',\
zorder=1, linewidth='2')
pl.xlabel('$\mathrm{Time~(days)}$')
pl.ylabel('$\mathrm{Normalised~Flux}$')
pl.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
pl.savefig('%sml_data%s' %(int(KID), save))
# plot GPeriodogram
pl.clf()
area = sp.integrate.trapz(np.exp(L))
pl.plot(Periods, (np.exp(L))/area, 'k-')
pl.xlabel('Period')
pl.ylabel('Likelihood')
pl.title('Period = %s' %mlp)
pl.savefig('%sml_likelihood%s' %(int(KID), save))
# set period prior boundaries
bm = mlp - b*mlp
bp = mlp + b*mlp
return L, mlp, bm, bp, mlresult[0]
