def plot_rfcorr(self, rf='prf'):
from BayHunter import SynthObs
p2models, p2noise, p2misfits, p2vpvs = self._get_posterior_data(
['models', 'noise', 'misfits', 'vpvs'], final=True)
fig, axes = plt.subplots(2, sharex=True, sharey=True)
ind = self.refs.index(rf)
best = np.argmin(p2misfits.T[ind])
model = p2models[best]
vpvs = p2vpvs[best]
target = self.targets[ind]
x, y = target.obsdata.x, target.obsdata.y
vp, vs, h = Model.get_vp_vs_h(model, vpvs, self.mantle)
rho = vp * 0.32 + 0.77
_, ymod = target.moddata.plugin.run_model(
h=h, vp=vp, vs=vs, rho=rho)
yobs = target.obsdata.y
yresiduals = yobs - ymod
# axes[0].set_title('Residuals [dobs-g(m)] obtained with best fitting model m')
axes[0].plot(x, yresiduals, color='k', lw=0.7, label='residuals')
corr, sigma = p2noise[best][2*ind:2*(ind+1)]
yerr = SynthObs.compute_gaussnoise(y, corr=corr, sigma=sigma)
# axes[1].set_title('One Realization of random noise from inferred CeRF')
axes[1].plot(x, yerr, color='k', lw=0.7, label='noise realization')
axes[1].set_xlabel('Time in s')
axes[0].legend(loc=4)
axes[1].legend(loc=4)
axes[0].grid(color='gray', ls=':', lw=0.5)
axes[1].grid(color='gray', ls=':', lw=0.5)
axes[0].set_xlim([x[0], x[-1]])
return fig
#
# Load priors and initparams from config.ini or simply create dictionaries.
initfile = 'config.ini'
priors, initparams = utils.load_params(initfile)
# Load observed data (synthetic test data)
xsw, _ysw = np.loadtxt('observed/st3_rdispph.dat', usecols=[0, 1]).T
xrf, _yrf = np.loadtxt('observed/st3_prf.dat', usecols=[0, 1]).T
# add noise to create observed data
# order of noise values (correlation, amplitude):
# noise = [corr1, sigma1, corr2, sigma2] for 2 targets
noise = [0.0, 0.012, 0.90, 0.005]
ysw_err = SynthObs.compute_expnoise(_ysw, corr=noise[0], sigma=noise[1])
ysw = _ysw + ysw_err
yrf_err = SynthObs.compute_gaussnoise(_yrf, corr=noise[2], sigma=noise[3])
yrf = _yrf + yrf_err
#
# ------------------------------------------- get refernece model for BayWatch
#
# Create truemodel only if you wish to have reference values in plots
# and BayWatch. You ONLY need to assign the values in truemodel that you
# wish to have visible.
dep, vs = np.loadtxt('observed/st3_mod.dat', usecols=[0, 2], skiprows=1).T
pdep = np.concatenate((np.repeat(dep, 2)[1:], [150]))
pvs = np.repeat(vs, 2)
truenoise = np.concatenate((
[noise[0]],
[np.std(ysw_err)], # target 1