import scipy.optimize as op
from generateData import x, y, yerr, np, plt, m_true, b_true, f_true
def lnlike(theta, x, y, yerr):
m, b, lnf = theta
model = m * x + b
inv_sigma2 = 1.0 / (yerr**2 + model**2 * np.exp(2 * lnf))
return -0.5 * (np.sum((y - model)**2 * inv_sigma2 - np.log(inv_sigma2)))
nll = lambda *args: -lnlike(*args)
# plt.show()
# result = op.minimize(nll, [m_true,b_true, np.log(f_true)], args=(x, y, yerr))
result = op.minimize(nll, [2, 8, np.log(1)], args=(x, y, yerr))
m_ml, b_ml, lnf_ml = result["x"]
print("""Maximum likelihood result:
m = {0} (truth: {1})
b = {2} (truth: {3})
f = {4} (truth: {5})
""".format(m_ml, m_true, b_ml, b_true, np.exp(lnf_ml), f_true))
y_ml = m_ml * x + b_ml
plt.plot(x, y_ml, 'b--')
# import pdb; pdb.set_trace()
# plt.show()
def lnlike(theta, x, y, yerr):
m, b, lnf = theta
model = m * x + b
inv_sigma2 = 1.0 / (yerr**2 + model**2 * np.exp(2 * lnf))
return -0.5 * (np.sum((y - model)**2 * inv_sigma2 - np.log(inv_sigma2)))
print("Done.")
plt.clf()
plt.close()
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(8, 9))
axes[0].plot(sampler.chain[:, :, 0].T, color="k", alpha=0.4)
axes[0].yaxis.set_major_locator(MaxNLocator(5))
axes[0].axhline(m_true, color="#888888", lw=2)
axes[0].set_ylabel("$m$")
axes[1].plot(sampler.chain[:, :, 1].T, color="k", alpha=0.4)
axes[1].yaxis.set_major_locator(MaxNLocator(5))
axes[1].axhline(b_true, color="#888888", lw=2)
axes[1].set_ylabel("$b$")
axes[2].plot(np.exp(sampler.chain[:, :, 2]).T, color="k", alpha=0.4)
axes[2].yaxis.set_major_locator(MaxNLocator(5))
axes[2].axhline(f_true, color="#888888", lw=2)
axes[2].set_ylabel("$f$")
axes[2].set_xlabel("step number")
fig.tight_layout(h_pad=0.0)
import corner
# Make the triangle plot.
burnin = 1000
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
f = open('MCMC_samples', 'w')
np.save(f, samples)
f.close()
exit()