def testKalmanp(self):
pModel = 4
qModel = 0
sampler = carmcmc.run_mcmc_carma(self.nSample, self.nBurnin,
self.xdata, self.ydata, self.dydata,
pModel, qModel, self.nWalkers, False,
self.nThin)
psampler = carmcmc.CarmaSample(np.array(self.xdata),
np.array(self.ydata),
np.array(self.dydata), sampler)
sigsqr = (psampler._samples["sigma"][0]**2)[0]
ma_coefs = carmcmc.vecD()
ma_coefs0 = psampler._samples["ma_coefs"][0]
if len(ma_coefs0) != pModel:
ma_coefs0 = np.append(ma_coefs0, np.zeros(pModel - qModel - 1))
ma_coefs.extend(ma_coefs0)
omega = carmcmc.vecC()
for i in range(psampler.p):
omega.append(psampler._samples["ar_roots"][0][i])
#import pdb; pdb.set_trace()
kfilter = carmcmc.KalmanFilterp(self.xdata, self.ydata, self.dydata,
sigsqr, omega, ma_coefs)
kfilter.Filter()
pred0 = kfilter.Predict(self.xdata[0]) # evaluate at data point
val0 = pred0.first
var0 = pred0.second
predN = kfilter.Predict(self.xdata[-1] + 1) # extrapolate
valN = predN.first
varN = predN.second
self.assertTrue(varN > var0)
def doit(args):
pModel = int(args[0])
x, y, dy = args[1]
nSample = 10000
nBurnin = 1000
nThin = 1
nWalkers = 10
# Should not have to do this...
xv = carmcmc.vecD()
xv.extend(x)
yv = carmcmc.vecD()
yv.extend(y)
dyv = carmcmc.vecD()
dyv.extend(dy)
if pModel == 1:
sampler = carmcmc.run_mcmc_car1(nSample, nBurnin, xv, yv, dyv, nWalkers, nThin)
samplep = carmcmc.CarSample1(x, y, dy, sampler)
else:
sampler = carmcmc.run_mcmc_carma(nSample, nBurnin, xv, yv, dyv, pModel, 0, nWalkers, False, nThin)
samplep = carmcmc.CarmaSample(x, y, dy, sampler)
dic = samplep.DIC()
print "DIC", pModel, dic
return samplep
def csample_from_files(datafile, chainfile, p, q):
data = np.loadtxt(datafile)
times, tind = np.unique(data[:, 0], return_index=True)
data = data[tind, :]
chain = np.loadtxt(chainfile)
assert chain.shape[1] == p + q + 5, 'dimension mismatch'
return cm.CarmaSample(data[:, 0],
data[:, 1],
data[:, 2],
None,
q=q,
trace=chain[:, :-2],
loglike=chain[:, -2],
logpost=chain[:, -1])
def testCarpq(self, pModel=3, qModel=2):
sampler = carmcmc.run_mcmc_carma(self.nSample, self.nBurnin,
self.xdata, self.ydata, self.dydata,
pModel, qModel, self.nWalkers, False,
self.nThin)
psampler = carmcmc.CarmaSample(np.array(self.xdata),
np.array(self.ydata),
np.array(self.dydata), sampler)
self.assertEqual(psampler.p, pModel + qModel)
psamples = np.array(sampler.getSamples())
ploglikes = np.array(sampler.GetLogLikes())
sample0 = carmcmc.vecD()
sample0.extend(psamples[0])
logprior0 = sampler.getLogPrior(sample0)
loglike0 = sampler.getLogDensity(sample0)
# OK, this is where I truly test that sampler is of class CARp and not CAR1
self.assertAlmostEqual(ploglikes[0], loglike0)
doShow=False)
ar_coef = np.poly(ar_roots)
true_psd = carmcmc.power_spectrum(freq,
np.sqrt(sigsqr),
ar_coef,
ma_coefs=ma_coefs)
plt.loglog(freq, true_psd, 'r', lw=2)
plt.xlabel('Frequency')
plt.ylabel('PSD, ZCARMA(5)')
plt.show()
fname = data_dir + 'carma_mcmc.dat'
Carma = carmcmc.CarmaSample(data[:, 0],
data[:, 1],
data[:, 2],
filename=fname,
q=p - 1)
Carma.assess_fit()
print "True values of MA coefs are:", ma_coefs
Carma.plot_1dpdf('ma_coefs', doShow=True)
Carma.posterior_summaries('ma_coefs')
print ''
print "True values of log_widths are", np.log(qpo_width)
Carma.posterior_summaries('log_width')
print ''
print "True values of log_centroids are", np.log(qpo_cent)
Carma.posterior_summaries('log_centroid')
psd_low, psd_high, psd_mid, freq = Carma.plot_power_spectrum(percentile=95.0,
