sa = otlhs.SimulatedAnnealingLHS(lhsDesign, geomProfile, c2)
tic = time.time()
result = sa.generate()
toc = time.time()
dt1 = toc - tic
print ("time=%f" % dt1)
print ("dimension=%d, size=%d,sa=%s" % (dimension, size, sa))
print str(result.getOptimalValue()) + " c2=" + str(result.getC2()) + " phiP=" + str(
result.getPhiP()
) + " minDist=" + str(result.getMinDist())
# plot design
fig = PyPlotDesign(result.getOptimalDesign(), bounds, 10, 10, plot_kwargs={"color": "blue", "marker": "o", "ms": 6})
plt.suptitle("LHS design of size=%d - Optimization of %s criterion using geometric SA" % (size, c2.getName()))
fig.savefig("lhs_sa_geom_%d.png" % size)
plt.close(fig)
crit = result.drawHistoryCriterion()
proba = result.drawHistoryProbability()
temp = result.drawHistoryTemperature()
pp = PdfPages("small_OTLHS.pdf")
# Criterion
fig = View(crit, plot_kwargs={"color": "blue"}).getFigure()
fig.savefig("crit_sa_geom.png")
pp.savefig(fig)
plt.close(fig)
# Proba
fig = View(proba, plot_kwargs={"marker": "o", "ms": 0.6}, axes_kwargs={"ylim": [-0.05, 1.05]}).getFigure()
fig.savefig("lhs_c2_proba.png")
