Python View.savefig 예제들

예제 #1

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()))

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")
pp.savefig(fig)
plt.close(fig)
# Temperature

예제 #2

파일: validate_SA_big.py 프로젝트: aurelieladier/openturns

design = sa.generate()
result = sa.getResult()
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()))
crit = result.drawHistoryCriterion()
proba = result.drawHistoryProbability()
temp = result.drawHistoryTemperature()

pp = PdfPages('large_OTLHS.pdf')

# Criterion
fig = View(crit, plot_kwargs={'color':'blue'}).getFigure()
fig.savefig("otlhs_c2_crit_big.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_big.png")
pp.savefig(fig)
plt.close(fig)
# Temperature
fig = View(temp).getFigure()
pp.savefig(fig)
plt.close(fig)

minDist = ot.SpaceFillingMinDist()
sa = ot.SimulatedAnnealingLHS(lhsDesign, geomProfile, minDist)
tic = time.time()

예제 #3

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()))
crit = result.drawHistoryCriterion()
proba = result.drawHistoryProbability()
temp = result.drawHistoryTemperature()

pp = PdfPages('large_OTLHS.pdf')

# Criterion
fig = View(crit, plot_kwargs={'color': 'blue'}).getFigure()
fig.savefig("otlhs_c2_crit_big.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_big.png")
pp.savefig(fig)
plt.close(fig)
# Temperature

예제 #4

파일: validate_MC_small.py 프로젝트: jschueller/otlhs

    mc = otlhs.MonteCarloLHS(lhsDesign, nSimu, c2)
    tic = time.time()
    result = mc.generate()
    toc = time.time()
    print("%d %f %f" % (nSimu, result.getOptimalValue(), toc - tic))

pp = PdfPages("small_mc_OTLHS.pdf")
# plot criterion & save it
crit = result.drawHistoryCriterion()
fig = View(crit, plot_kwargs={"color": "blue"}).getFigure()
pp.savefig(fig)
plt.close(fig)
# plot design
fig = PyPlotDesign(result.getOptimalDesign(), bounds, size, size, plot_kwargs={"color": "blue", "marker": "o", "ms": 6})
plt.suptitle("LHS design of size=%d - Optimization of %s criterion using %d MC sample" % (size, c2.getName(), nSimu))
fig.savefig("lhs_mc_c2_%d.png" % size)
plt.close(fig)

minDist = otlhs.SpaceFillingMinDist()

# Factory: lhs generates
lhsDesign = otlhs.LHSDesign(bounds, size)
mc = otlhs.MonteCarloLHS(lhsDesign, nSimu, minDist)
tic = time.time()
result = mc.generate()
toc = time.time()
print("cpu time=%f" % (toc - tic))
print("dimension=%d, size=%d,mc=%s" % (dimension, size, mc))
print(
    "optimal value="
    + str(result.getOptimalValue())

예제 #5

파일: validate_SA_small.py 프로젝트: aurelieladier/openturns

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()))

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")
pp.savefig(fig)
plt.close(fig)
# Temperature
fig = View(temp).getFigure()
pp.savefig(fig)
plt.close(fig)

linearProfile = ot.LinearProfile(10.0, 50000)
minDist = ot.SpaceFillingMinDist()
sa = ot.SimulatedAnnealingLHS(lhsDesign, linearProfile, minDist)