def plot_currentmodels(self, nchains):
"""Return fig.
Plots the first nchains chains, no matter of outlier status.
"""
fig, ax = plt.subplots(figsize=(4, 6.5))
base = cm.get_cmap(name='rainbow')
color_list = base(np.linspace(0, 1, nchains))
for i, modfile in enumerate(self.modfiles[1][:nchains]):
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]
color = color_list[i]
vp, vs, h = Model.get_vp_vs_h(currentmodel, currentvpvs, self.mantle)
cvp, cvs, cdepth = Model.get_stepmodel_from_h(h=h, vs=vs, vp=vp)
label = 'c%d / %d' % (chainidx, vs.size-1)
ax.plot(cvs, cdepth, color=color, ls='-', lw=0.8,
alpha=0.7, label=label)
ax.invert_yaxis()
ax.set_xlabel('$V_S$ in km/s')
ax.set_ylabel('Depth in km')
# ax.set_xlim(self.priors['vs'])
ax.set_ylim(self.priors['z'][::-1])
ax.set_title('Current models')
ax.grid(color='gray', alpha=0.6, ls=':', lw=0.5)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
return fig
def plot_bestmodels(self):
"""Return fig.
Plot the best (fit) models ever discovered per each chain,
ignoring outliers.
"""
fig, ax = plt.subplots(figsize=(4, 6.5))
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)
vpvs = np.load(modfile.replace('models', 'vpvs')).T
misfits = np.load(modfile.replace('models', 'misfits')).T[-1]
bestmodel = models[np.argmin(misfits)]
bestvpvs = vpvs[np.argmin(misfits)]
bestmisfit = misfits[np.argmin(misfits)]
if bestmisfit < thebestmisfit:
thebestmisfit = bestmisfit
thebestmodel = bestmodel
thebestvpvs = bestvpvs
thebestchain = chainidx
vp, vs, h = Model.get_vp_vs_h(bestmodel, bestvpvs, self.mantle)
cvp, cvs, cdepth = Model.get_stepmodel_from_h(h=h, vs=vs, vp=vp)
ax.plot(cvs, cdepth, color='k', ls='-', lw=0.8, alpha=0.5)
# label = 'c%d' % thebestchain
# vp, vs, h = Model.get_vp_vs_h(thebestmodel, thebestvpvs, self.mantle)
# cvp, cvs, cdepth = Model.get_stepmodel_from_h(h=h, vs=vs, vp=vp)
# ax.plot(cvs, cdepth, color='red', ls='-', lw=1,
# alpha=0.8, label=label)
ax.invert_yaxis()
ax.set_xlabel('$V_S$ in km/s')
ax.set_ylabel('Depth in km')
# ax.set_xlim(self.priors['vs'])
ax.set_ylim(self.priors['z'][::-1])
ax.set_title('Best fit models from %d chains' %
(len(modfiles)-self.outliers.size))
ax.grid(color='gray', alpha=0.6, ls=':', lw=0.5)
# ax.legend(loc=3)
return fig