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%pylab inline
import emcee
import triangle
import pandas as pd
import seaborn as sns
from astroML.decorators import pickle_results
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sns.set_context("paper", font_scale=2.0, rc={"lines.linewidth": 2.5})
sns.set(style="ticks")
Read in the data. We want "VG02_pos2" or "VG02_pos3"
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df = pd.read_csv('../data/cln_20130218_cary5000.csv', index_col=0)
df.head()
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df = df[df.index > 1250.0]
Import all the local models, saved locally as etalon.py. See the paper for derivations of these equations.
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from etalon import *
np.random.seed(78704) #My old zip code
Determine the individual measurement uncertainty by taking the standard deviation of four mean-substracted reference measurements.
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arr = np.vstack([df.VG05-df.VG05.mean(axis=0),
df.VG05_post-df.VG05_post.mean(axis=0),
df.VG06-df.VG06.mean(axis=0),
df.VG06_post-df.VG06_post.mean(axis=0)])
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arr = np.vstack([df.VG05-df.VG05.mean(axis=0),
df.VG05_post-df.VG05_post.mean(axis=0),
df.VG06-df.VG06.mean(axis=0),
df.VG06_post-df.VG06_post.mean(axis=0)])
print arr.shape
arr_norm = arr - arr.mean(axis=0)
er = np.std(arr_norm, axis=0)/100.0
print er.shape
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# Introduce the Real data
x = df.index.values
N = len(x)
# Define T_DSP for the model
T_DSP = T_gap_Si(x, 0.0)
n1 = sellmeier_Si(x)
# Define uncertainty
yerr = er# 0.0002*np.ones(N)
iid_cov = np.diag(yerr ** 2)
# Select the spectrum of interest
# Normalize the spectrum by measured DSP Si wafer.
y = df.VG02_pos3/(0.25*(df.VG05+df.VG05+df.VG06+df.VG06_post))
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plot(x, er)
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np.median(er)
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#np.savetxt("hetroscedastic.txt",er, delimiter=',', header='x, y')
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errorbar(x,y, yerr=er)
plt.ylim(0.98, 1.01)
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Define the likelihood.
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def lnlike(d, f, lna, lns):
a, s = np.exp(lna), np.exp(lns)
off_diag_terms = a**2 * np.exp(-0.5 * (x[:, None] - x[None, :])**2 / s**2)
C = iid_cov + off_diag_terms
sgn, logdet = np.linalg.slogdet(C)
if sgn <= 0:
return -np.inf
r = y - T_gap_Si_withFF_fast(x, d, f, n1)/T_DSP
return -0.5 * (np.dot(r, np.linalg.solve(C, r)) + logdet)
Define the prior.
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def lnprior(d, f, lna, lns):
if not (0 < d < 100 and 0.0 < f < 1.0 and -12 < lna < -2 and 0 < lns < 10):
return -np.inf
return 0.0
Combine likelihood and prior to obtain the posterior.
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def lnprob(p):
lp = lnprior(*p)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(*p)
Set up emcee.
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@pickle_results('SiGaps_13_VG02_noGap-sampler.pkl')
def hammer_time(ndim, nwalkers, d_Guess, f_Guess, a_Guess, s_Guess, nburnins, ntrials):
# Initialize the walkers
p0 = np.array([d_Guess, f_Guess, np.log(a_Guess), np.log(s_Guess)])
pos = [p0 + 1.0e-2*p0 * np.random.randn(ndim) for i in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)
pos, lp, state = sampler.run_mcmc(pos, nburnins)
sampler.reset()
pos, lp, state = sampler.run_mcmc(pos, ntrials)
return sampler
Set up the initial conditions.
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np.random.seed(78704)
ndim, nwalkers = 4, 32
d_Guess = 15.0
f_Guess = 0.95
a_Guess = 0.0016
s_Guess = 500.0
nburnins = 300
ntrials = 9000
Run the burn-in phase. Run the full MCMC. Pickle the results.
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sampler = hammer_time(ndim, nwalkers, d_Guess, f_Guess, a_Guess, s_Guess, nburnins, ntrials)
Linearize $a$ and $s$ for easy inspection of the values.
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chain = sampler.chain
samples_lin = copy(sampler.flatchain)
samples_lin[:, 2:] = np.exp(samples_lin[:, 2:])
Inspect the chain.
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fig, axes = plt.subplots(4, 1, figsize=(5, 6), sharex=True)
fig.subplots_adjust(left=0.1, bottom=0.1, right=0.96, top=0.98,
wspace=0.0, hspace=0.05)
[a.plot(np.arange(chain.shape[1]), chain[:, :, i].T, "k", alpha=0.5)
for i, a in enumerate(axes)]
[a.set_ylabel("${0}$".format(l)) for a, l in zip(axes, ["d", "f", "\ln a", "\ln s"])]
axes[-1].set_xlim(0, chain.shape[1])
axes[-1].set_xlabel("iteration");
Make a triangle corner plot.
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fig = triangle.corner(samples_lin,
labels=map("${0}$".format, ["d", "f", "a", "s"]),
quantiles=[0.16, 0.84])
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fig = triangle.corner(samples_lin[:,0:2],
labels=map("${0}$".format, ["d", "f"]),
quantiles=[0.16, 0.84])
plt.savefig("VG02p3_corner.pdf")
Calculate confidence intervals.
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d_mcmc, f_mcmc, a_mcmc, s_mcmc = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(samples_lin, [16, 50, 84],
axis=0)))
d_mcmc, f_mcmc, a_mcmc, s_mcmc
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print "{:.0f}^{{+{:.0f}}}_{{-{:.0f}}}".format(*d_mcmc)
print "{:.3f}^{{+{:.3f}}}_{{-{:.3f}}}".format(*f_mcmc)
Overlay draws from the Gaussian Process.
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plt.figure(figsize=(6,3))
for d, f, a, s in samples_lin[np.random.randint(len(samples_lin), size=60)]:
off_diag_terms = a**2 * np.exp(-0.5 * (x[:, None] - x[None, :])**2 / s**2)
C = iid_cov + off_diag_terms
fit = T_gap_Si_withFF_fast(x, d, f, n1)/T_DSP
vec = np.random.multivariate_normal(fit, C)
plt.plot(x, vec,"-b", alpha=0.06)
plt.step(x, y,color="k", label='Measurement')
plt.errorbar(x, y,yerr=er, color="k")
fit = T_gap_Si_withFF_fast(x, d_mcmc[0], f_mcmc[0], n1)/T_DSP
fit_label = 'Model with $d={:.0f}$ nm, $f={:.3f}$'.format(d_mcmc[0], f_mcmc[0])
plt.plot(x, fit, '--', color=sns.xkcd_rgb["pale red"], alpha=1.0, label=fit_label)
plt.plot([-10, -9], [-10, -9],"-b", alpha=0.45, label='Draws from GP')
plt.plot([0, 5000], [1.0, 1.0], '-.k', alpha=0.5)
plt.fill_between([1200, 1250], 2.0, 0.0, hatch='\\', alpha=0.4, color='k', label='Si absorption cutoff')
plt.xlabel('$\lambda$ (nm)');
plt.ylabel('$T_{gap}$');
plt.xlim(1200, 2501);
plt.ylim(0.96, 1.019);
plt.legend(loc='lower right')
plt.savefig("VG02_noGap.pdf", bbox_inches='tight')
The end.
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