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return ' '.join(text.split())
'''Plot showing the phase ratio fits for the best model.'''
2, Float.T(), default=(15., 7.5), help='Width and length of the figure in [cm]')
optional=True, help='Plot fits for models up to this misfit value.')
default='misfit', help='Choice of value to color, options: "misfit" (default), ' '"dist" or any source parameter name.')
default=10, help='Stride value N to choose every Nth model from the solution ' 'ensemble.')
default=10, help='Font size of title [pt]')
cm = environ.get_plot_collection_manager() mpl_init(fontsize=self.font_size)
environ.setup_modelling() ds = environ.get_dataset() history = environ.get_history(subset='harvest')
scolor = { 'misfit': S(''' The synthetic markers are colored according to their respective global (non-bootstrapped) misfit value. Red indicates better fit, blue worse.'''),
'dist': S(''' The synthetic markers are colored according to their Mahalanobis distance from the mean solution.''')
}.get(self.color_parameter, S(''' The synthetic markers are colored according to source parameter "%s".''' % self.color_parameter))
cm.create_group_mpl( self, self.draw_figures(ds, history), title=u'Fits Phase Ratios', section='fits', feather_icon='activity', description=u''' Observed (black markers) and synthetic waveform amplitude phase ratio estimates (spectral average ratio; colored markers) at different stations for every Nth model in the bootstrap solution ensemble (N=%i).
%s
The frequency range used to estimate spectral averages is not shown (see config file). Optimal solutions show good agreement between black and colored markers. ''' % (self.istride_ensemble, scolor))
problem = history.problem
for target in problem.targets: target.set_dataset(ds)
targets = [ t for t in problem.targets if isinstance(t, PhaseRatioTarget)]
tpaths = sorted(set(t.path for t in targets))
for tpath in tpaths: for (item, fig) in self.draw_figure(ds, history, tpath): yield item, fig
problem = history.problem color_parameter = self.color_parameter misfit_cutoff = self.misfit_cutoff fontsize = self.font_size targets = [ t for t in problem.targets if isinstance(t, PhaseRatioTarget) and t.path == tpath]
gms = history.get_sorted_misfits(chain=0)[::-1] models = history.get_sorted_models(chain=0)[::-1]
if misfit_cutoff is not None: ibest = gms < misfit_cutoff gms = gms[ibest] models = models[ibest]
gms = gms[::self.istride_ensemble] models = models[::self.istride_ensemble]
nmodels = models.shape[0] if color_parameter == 'dist': mx = num.mean(models, axis=0) cov = num.cov(models.T) mdists = core.mahalanobis_distance(models, mx, cov) icolor = meta.ordersort(mdists)
elif color_parameter == 'misfit': iorder = num.arange(nmodels) icolor = iorder
elif color_parameter in problem.parameter_names: ind = problem.name_to_index(color_parameter) icolor = problem.extract(models, ind)
from matplotlib import colors cmap = cm.ScalarMappable( norm=colors.Normalize(vmin=num.min(icolor), vmax=num.max(icolor)), cmap=plt.get_cmap('coolwarm'))
imodel_to_color = [] for imodel in range(nmodels): imodel_to_color.append(cmap.to_rgba(icolor[imodel]))
data = [] for imodel in range(nmodels): model = models[imodel, :]
# source = problem.get_source(model) results = problem.evaluate(model, targets=targets)
for target, result in zip(targets, results): if isinstance(result, gf.SeismosizerError): continue
if not isinstance(target, PhaseRatioTarget): continue
a_obs = result.a_obs b_obs = result.b_obs a_syn = result.a_syn b_syn = result.b_syn
r_obs = a_obs / (a_obs + b_obs) r_syn = a_syn / (a_syn + b_syn)
data.append(('.'.join(target.codes), imodel, r_obs, r_syn))
fontsize = self.font_size
item = PlotItem( name='fig_%s' % tpath)
item.attributes['targets'] = [ t.string_id() for t in targets]
fig = plt.figure(figsize=self.size_inch)
labelpos = mpl_margins( fig, nw=1, nh=1, left=7., right=1., bottom=10., top=3, units=fontsize)
axes = fig.add_subplot(1, 1, 1) labelpos(axes, 2.5, 2.0)
labels = sorted(set(x[0] for x in data))
ntargets = len(labels) string_id_to_itarget = dict((x, i) for (i, x) in enumerate(labels))
itargets = num.array([string_id_to_itarget[x[0]] for x in data])
imodels = num.array([x[1] for x in data], dtype=num.int).T r_obs, r_syn = num.array([x[2:] for x in data]).T
r_obs_median = num.zeros(ntargets) for itarget in range(ntargets): r_obs_median[itarget] = num.median(r_obs[itargets == itarget])
iorder = meta.ordersort(r_obs_median)
for imodel in range(nmodels): mask = imodels == imodel axes.plot( iorder[itargets[mask]], r_obs[mask], '_', ms=20., zorder=-10, alpha=0.5, color='black') axes.plot( iorder[itargets[mask]], r_syn[mask], '_', ms=10., alpha=0.5, color=imodel_to_color[imodel])
axes.set_yscale('log') axes.set_ylabel('Ratio')
axes.set_xticks( iorder[num.arange(ntargets)]) axes.set_xticklabels(labels, rotation='vertical')
fig.suptitle(tpath, fontsize=self.font_size_title)
yield item, fig
FitsPhaseRatioPlot, ] |