def plot_bestdatafits(self):
"""Plot best data fits from each chain and ever best,
ignoring outliers."""
targets = Targets.JointTarget(targets=self.targets)
fig, ax = targets.plot_obsdata(mod=False)
thebestmodel = np.nan
thebestmisfit = 1e15
thebestchain = np.nan
modfiles = self.modfiles[1]
for i, modfile in enumerate(modfiles):
chainidx, _, _ = self._return_c_p_t(modfile)
if chainidx in self.outliers:
continue
models = np.load(modfile)
misfits = np.load(modfile.replace('models', 'misfits')).T[-1]
bestmodel = models[np.argmin(misfits)]
bestmisfit = misfits[np.argmin(misfits)]
if bestmisfit < thebestmisfit:
thebestmisfit = bestmisfit
thebestmodel = bestmodel
thebestchain = chainidx
vp, vs, h = Model.get_vp_vs_h(bestmodel, self.priors['vpvs'])
rho = vp * 0.32 + 0.77
for n, target in enumerate(targets.targets):
xmod, ymod = target.moddata.plugin.run_model(h=h,
vp=vp,
vs=vs,
rho=rho)
if len(targets.targets) > 1:
ax[n].plot(xmod, ymod, color='k', alpha=0.5, lw=0.7)
else:
ax.plot(xmod, ymod, color='k', alpha=0.5, lw=0.7)
if len(targets.targets) > 1:
ax[0].set_title('Best data fits from %d chains' %
(len(modfiles) - self.outliers.size))
# idx = len(targets.targets) - 1
han, lab = ax[0].get_legend_handles_labels()
handles, labels = self._unique_legend(han, lab)
ax[0].legend().set_visible(False)
else:
ax.set_title('Best data fits from %d chains' %
(len(modfiles) - self.outliers.size))
han, lab = ax.get_legend_handles_labels()
handles, labels = self._unique_legend(han, lab)
ax.legend().set_visible(False)
fig.legend(handles,
labels,
loc='center left',
bbox_to_anchor=(0.92, 0.5))
return fig
def plot_currentdatafits(self, nchains):
"""Plot the first nchains chains, no matter of outlier status.
"""
base = cm.get_cmap(name='rainbow')
color_list = base(np.linspace(0, 1, nchains))
targets = Targets.JointTarget(targets=self.targets)
fig, ax = targets.plot_obsdata(mod=False)
for i, modfile in enumerate(self.modfiles[1][:nchains]):
color = color_list[i]
chainidx, _, _ = self._return_c_p_t(modfile)
models = np.load(modfile)
vpvs = np.load(modfile.replace('models', 'vpvs')).T
currentvpvs = vpvs[-1]
currentmodel = models[-1]
vp, vs, h = Model.get_vp_vs_h(currentmodel, currentvpvs, self.mantle)
rho = vp * 0.32 + 0.77
jmisfit = 0
for n, target in enumerate(targets.targets):
xmod, ymod = target.moddata.plugin.run_model(
h=h, vp=vp, vs=vs, rho=rho)
yobs = target.obsdata.y
misfit = target.valuation.get_rms(yobs, ymod)
jmisfit += misfit
label = ''
if len(targets.targets) > 1:
if ((len(targets.targets) - 1) - n) < 1e-2:
label = 'c%d / %.3f' % (chainidx, jmisfit)
ax[n].plot(xmod, ymod, color=color, alpha=0.7, lw=0.8,
label=label)
else:
label = 'c%d / %.3f' % (chainidx, jmisfit)
ax.plot(xmod, ymod, color=color, alpha=0.5, lw=0.7,
label=label)
if len(targets.targets) > 1:
ax[0].set_title('Current data fits')
idx = len(targets.targets) - 1
han, lab = ax[idx].get_legend_handles_labels()
handles, labels = self._unique_legend(han, lab)
ax[0].legend().set_visible(False)
else:
ax.set_title('Current data fits')
han, lab = ax.get_legend_handles_labels()
handles, labels = self._unique_legend(han, lab)
ax.legend().set_visible(False)
fig.legend(handles, labels, loc='center left',
bbox_to_anchor=(0.92, 0.5))
return fig
#
# ----------------------------------------------------------- DEFINE TARGETS
#
# Only pass x and y observed data to the Targets object which is matching
# the data type. You can chose for SWD any combination of Rayleigh, Love, group
# and phase velocity. Default is the fundamendal mode, but this can be updated.
# For RF chose P or S. You can also use user defined targets or replace the
# forward modeling plugin wih your own module.
target1 = Targets.RayleighDispersionPhase(xsw, ysw)
target2 = Targets.PReceiverFunction(xrf, yrf)
target2.moddata.plugin.set_modelparams(gauss=1., water=0.01, p=6.4)
# Join the targets. targets must be a list instance with all targets
# you want to use for MCMC Bayesian inversion.
targets = Targets.JointTarget(targets=[target1, target2])
#
# --------------------------------------------------- Quick parameter update
#
# "priors" and "initparams" from config.ini are python dictionaries. You could
# also simply define the dictionaries directly in the script, if you don't want
# to use a config.ini file. Or update the dictionaries as follows, e.g. if you
# have station specific values, etc.
# See docs/bayhunter.pdf for explanation of parameters
priors.update({
'mohoest': (38, 4), # optional, moho estimate (mean, std)
'rfnoise_corr': 0.9,
'swdnoise_corr': 0.
# 'rfnoise_sigma': np.std(yrf_err), # fixed to true value