'vmin': 0,
'vmax': 1,
'cbar_kwargs': avker_cbar_kwargs,
'fig_kwargs': small_fig_kwargs,
'map_kwargs': small_map_kwargs
}
fig2a, ax, c = true.plot_state('dofs',
clusters_plot,
title=title,
**avker_kwargs)
ax.text(0.025,
0.05,
'DOFS = %d' % round(np.trace(true.a)),
fontsize=config.LABEL_FONTSIZE * config.SCALE,
transform=ax.transAxes)
fp.save_fig(fig2a, plots, 'fig2a_true_averaging_kernel')
# Initial estimate averaging kernel
title = 'Initial estimate averaging\nkernel sensitivities'
avker_cbar_kwargs = {'title': r'$\partial\hat{x}_i/\partial x_i$'}
avker_kwargs['cbar_kwargs'] = avker_cbar_kwargs
fig2b, ax, c = est0.plot_state('dofs',
clusters_plot,
title=title,
**avker_kwargs)
ax.text(0.025,
0.05,
'DOFS = %d' % round(np.trace(est0.a)),
fontsize=config.LABEL_FONTSIZE * config.SCALE,
transform=ax.transAxes)
fp.save_fig(fig2b, plots, 'fig2b_est0_averaging_kernel')
fontsize=(config.TICK_FONTSIZE - 2) * config.SCALE,
loc='center')
table.scale(1, 3)
for cell in table._cells:
table._cells[cell].set_alpha(0.1)
ax[1].add_table(table)
plt.subplots_adjust(hspace=0.4)
# print(cols)
ax[0] = fp.add_title(ax[0],
title='DOFS per Cluster Accumulation in Multiscale Grids')
fp.save_fig(fig, plots, 'rd_scheme')
# Second update
est2_ms = est1_ms.update_jacobian_rd(true.k,
true.xa_abs,
true.sa_vec,
clusters_plot,
n_cells=[60],
dofs_e=est0.dofs)
print(est2_ms.dofs.sum())
# Save outputs
np.savetxt(join(inputs, 'state_vector_rd.csv'),
est2_ms.state_vector,
delimiter=',')
np.savetxt(join(inputs, 'k_rd.csv'), est2_ms.k, delimiter=',')
'y': 1,
'pad':
(1.5 * config.TITLE_PAD + 1 * config.SUBTITLE_FONTSIZE * config.SCALE)
}
## Original dimension
fig1a, ax = est0.plot_multiscale_grid(clusters_plot,
colors='0.5',
zorder=3,
linewidths=0.5,
fig_kwargs=small_fig_kwargs,
map_kwargs=small_map_kwargs)
ax = flow_chart_settings(ax)
ax = fp.add_title(ax, 'Native resolution', **title_kwargs)
ax = fp.add_subtitle(ax, r'dimension $n$, rank $> k$')
fp.save_fig(fig1a, loc=plots, name='fig1a_dimn_rankn')
## Reduced rank
true.evec_sum = true.evecs[:, :3].sum(axis=1)
fig1b, ax, c = true.plot_state('evec_sum',
clusters_plot,
title='',
cbar=False,
cmap='RdBu_r',
vmin=-0.1,
vmax=0.1,
default_value=0,
fig_kwargs=small_fig_kwargs,
map_kwargs=small_map_kwargs)
ax = flow_chart_settings(ax)
ax = fp.add_subtitle(ax,
ax[0].plot(nstate_record, dpc_record, color=fp.color(0))
for i in range(len(state_thresholds) - 1):
ax[0].axvspan(state_thresholds[i],
state_thresholds[i + 1],
color=fp.color(2 * i),
alpha=0.2,
zorder=-100)
ax[0].set_ylim(0.3, 0.5)
ax1 = ax[0].twinx()
ax1.plot(nstate_record, dofs_record, color=fp.color(0), ls=':')
ax[0].set_xlim(0, est0.nstate)
fp.save_fig(fig, plots, 'rd_scheme_test')
# Save outputs
np.savetxt(join(inputs, 'state_vector_rd.csv'),
est2_ms.state_vector,
delimiter=',')
np.savetxt(join(inputs, 'k_rd.csv'), est2_ms.k, delimiter=',')
np.savetxt(join(inputs, 'sa_vec_rd.csv'), est2_ms.sa_vec, delimiter=',')
np.savetxt(join(inputs, 'xa_rd.csv'), est2_ms.xa, delimiter=',')
np.savetxt(join(inputs, 'xa_abs_rd.csv'), est2_ms.xa_abs, delimiter=',')
np.savetxt(join(inputs, 'xhat_long_rd.csv'), est2_ms.xhat_long, delimiter=',')
np.savetxt(join(inputs, 'dofs_long_rd.csv'), est2_ms.dofs_long, delimiter=',')
np.savetxt(join(inputs, 'shat_err_long_rd.csv'),
est2_ms.shat_err_long,
delimiter=',')