def plot_instantaneous(self, legstr=""):
semilogx(self.E, legend = legstr)
xlabel("num_{mcs}")
ylabel("instantaneous energy")
def plot_instantaneous(self, legstr=""):
semilogx(self.E, legend=legstr)
xlabel("num_{mcs}")
ylabel("instantaneous energy")
if sim.ID == "bf_mc":
prec_bf_mc.append([sim.N, sim.energy])
if sim.ID == "is_mc":
prec_is_mc.append([sim.N, sim.energy])
exact = sim.exact_solution;
prec_gaulag = np.asarray(prec_gaulag)
prec_gauleg = np.asarray(prec_gauleg)
prec_is_mc = np.asarray(prec_is_mc)
prec_bf_mc = np.asarray(prec_bf_mc)
sort_data(prec_gaulag)
sort_data(prec_gauleg)
sort_data(prec_is_mc)
sort_data(prec_bf_mc)
figure()
semilogx(prec_gaulag[:,0], prec_gaulag[:,1])
hold("all")
semilogx(prec_gauleg[:,0], prec_gauleg[:,1])
semilogx(prec_bf_mc[:,0], prec_bf_mc[:,1])
semilogx(prec_is_mc[:,0], prec_is_mc[:,1])
semilogx([1e2, 1e10], [exact, exact], '--')
legend("Gauss Laguerre", "Gauss Legendre", "Brute force Monte Carlo", "Importance sampling Monte Carlo", "exact")
xlabel("Function evaluations")
ylabel("Calculated energy")
title("Convergence of the different methods")
hold("off");
print "Results with highest resolution for all methods"
print "Gauss Legendre: %.8f" % prec_gauleg[len(prec_gauleg)-1, 1], "Evaluations: %g" % prec_gauleg[len(prec_gauleg)-1, 0]
print "Gauss Laguerre: %.8f" % prec_gaulag[len(prec_gaulag)-1, 1], "Evaluations: %g" % prec_gaulag[len(prec_gaulag)-1, 0]
print "Brute force Monte Carlo: %.8f" % prec_bf_mc[len(prec_bf_mc)-1, 1], "Evaluations: %g" % prec_bf_mc[len(prec_bf_mc)-1, 0]
