def plot_data(self, times, figsz=(12, 5)):
"""
Plot the observations with its values u(x, t) at fixed locations for given time points
"""
n = len(times)
nrow = np.floor(np.sqrt(n)).astype('int')
ncol = np.ceil(np.sqrt(n)).astype('int')
fig, axes = plt.subplots(nrows=nrow,
ncols=ncol,
sharex=True,
sharey=True,
figsize=figsz)
sub_figs = [None] * len(axes.flat)
for i in range(n):
plt.axes(axes.flat[i])
dl.plot(self.Vh.mesh())
sub_figs[i] = plt.scatter(self.targets[:, 0],
self.targets[:, 1],
c=self.d.data[np.where(
np.isclose(self.d.times,
times[i]))[0][0]],
zorder=2)
# plt.xlim(0,1); plt.ylim(0,1)
# plt.gca().set_aspect('equal', 'box')
plt.title('Time: {:.1f} s'.format(times[i], ))
fig = common_colorbar(fig, axes, sub_figs)
return fig
def plot_soln(self, x, times, figsz=(12, 5)):
"""
Plot solution u(., times)
"""
n = len(times)
nrow = np.floor(np.sqrt(n)).astype('int')
ncol = np.ceil(np.sqrt(n)).astype('int')
fig, axes = plt.subplots(nrows=nrow,
ncols=ncol,
sharex=True,
sharey=True,
figsize=figsz)
sub_figs = [None] * len(axes.flat)
for i in range(n):
plt.axes(axes.flat[i])
sub_figs[i] = dl.plot(
vector2Function(x.data[list(x.times).index(times[i])],
self.Vh[STATE]))
plt.title('Time: {:.1f} s'.format(times[i], ))
fig = common_colorbar(fig, axes, sub_figs)
return fig
# obtain MAP as reconstruction of permittivity
try:
with open(os.path.join('./result',str(eit.gdim)+'d_EIT_MAP_dim'+str(eit.dim)+'.pckl'),'rb') as f:
ds=pickle.load(f)[0]
except:
ds=eit.get_MAP(lamb_decay=0.1,lamb=1e-3, method='kotre',maxiter=100)
# ds=eit.get_MAP(lamb_decay=0.2,lamb=1e-2, method='kotre', maxiter=100)
with open(os.path.join('./result',str(eit.gdim)+'d_EIT_MAP_dim'+str(eit.dim)+'.pckl'),'wb') as f:
pickle.dump([ds,n_el,bbox,meshsz,el_dist,step,anomaly],f)
# plot MAP results
if eit.gdim==2:
fig,axes = plt.subplots(nrows=1,ncols=2,sharex=True,sharey=False,figsize=(12,5))
sub_figs=[None]*2
sub_figs[0]=eit.plot(ax=axes.flat[0])
axes.flat[0].axis('equal')
axes.flat[0].set_title(r'True Conductivities')
sub_figs[1]=eit.plot(input=ds,ax=axes.flat[1])
axes.flat[1].axis('equal')
axes.flat[1].set_title(r'Reconstructed Conductivities (MAP)')
from util.common_colorbar import common_colorbar
fig=common_colorbar(fig,axes,sub_figs)
# plt.subplots_adjust(wspace=0.2, hspace=0)
# save plots
# fig.tight_layout(h_pad=1)
plt.savefig(os.path.join('./result',str(eit.gdim)+'d_EIT_MAP_dim'+str(eit.dim)+'.png'),bbox_inches='tight')
# plt.show()
else:
eit.plot()
eit.plot(input=ds)
