# ax[1].plot(x, y, c='0.25')
x = np.unique(nm_summ_short)
z = np.array(z)
ax[0].plot(x[x <= 1000], z[x <= 1000], c='0.25', ls='--')
ax[0].scatter(est2.model_runs,
est2.dofs.sum(),
zorder=10,
c='0.25',
s=75,
marker='*')
ax[0].set_xticks(np.arange(200, 1010, 200))
ax[0].set_xlim(0, 1000)
ax[0].set_ylim(50, 216)
ax[0].set_facecolor('0.98')
ax[0] = fp.add_labels(ax[0],
xlabel='Total Model Runs',
ylabel='Optimal\nDOFS',
labelpad=config.LABEL_PAD / 2)
# Plot number of model run contours (lines)
levels = [250, 750, 1250]
# locs = [(n/8, n/8),
# (n/2, n/2),
# (5*n/8, 5*n/8)]
locs = [(mr2n(l) / 2, mr2n(l) / 2) for l in levels]
# nm_summ[0, :] -= 1
# nm_summ[1:, 0] -= 1
cl = ax[1].contour(nm_summ, levels=levels, colors='white', linestyles='dotted')
ax[1].clabel(cl,
cl.levels,
inline=True,
# ax[1].plot(x, y, c='0.25')
x = np.unique(nm_summ_short)
z = np.array(z)
ax[0].plot(x[x <= 1000], z[x <= 1000], c='0.25', ls='--')
ax[0].scatter(est2.model_runs,
est2.dofs.sum(),
zorder=10,
c='0.25',
s=75,
marker='*')
ax[0].set_xticks(np.arange(200, 1010, 200))
ax[0].set_xlim(0, 1000)
ax[0].set_ylim(50, true.dofs.sum())
ax[0].set_facecolor('0.98')
ax[0] = fp.add_labels(ax[0],
xlabel='Total model runs',
ylabel='Optimal\nDOFS',
labelpad=config.LABEL_PAD / 2)
# Print information about this
print('-' * 25)
print(x[z >= 99])
print(z[z >= 99])
print('-' * 25)
print('\n')
# Plot number of model run contours (lines)
levels = [250, 750, 1250]
# locs = [(n/8, n/8),
# (n/2, n/2),
# (5*n/8, 5*n/8)]
locs = [(mr2n(l) / 2, mr2n(l) / 2) for l in levels]
est0 = inv.ReducedRankJacobian(k_est.values, xa.values, sa_vec.values,
y.values, y_base.values, so_vec.values)
est0.xa_abs = xa_abs * 1e3
est0.rf = RF
est0.edecomp()
est0.solve_inversion()
########################################
### BUILD REDUCED DIMENSION JACOBIAN ###
########################################
# Reduced dimension
fig, ax = fp.get_figax(aspect=4, rows=2, cols=1)
ax[0] = fp.add_labels(ax[0],
xlabel='Number of Native-Resolution Grid Cells',
ylabel='DOFS per Cluster')
est1_ms = est0.update_jacobian_rd(true.k,
true.xa_abs,
true.sa_vec,
clusters_plot,
n_cells=[2098],
n_cluster_size=[2098])
nstate_record_01 = [est1_ms.nstate]
dpc_record_01 = [est1_ms.dofs.sum() / est1_ms.nstate]
indices = np.arange(10, 160, 10)
for i in indices:
est1_ms = est0.update_jacobian_rd(true.k,
true.xa_abs,