ml = [] ui = [] suml1 = [] suml2 = [] suml3 = [] suml4 = [] suml5 = [] L = 0 while 1: # Step 1: run inital cases # set up runs ml.append(M**L*nx) if not(os.path.exists("L"+str(L))): os.mkdir("L"+str(L)) sums = mmc.preprocess(ml,N,L,frames,res,ui) suml1.append(N) suml2.append(sums[0,:]) suml3.append(sums[1,:]) suml4.append(sums[2,:]) suml5.append(sums[3,:]) # Step 2: estimate the variance using the inital number of samples Vl = mmc.variance(suml3,suml2,suml1) # Step 3: Calculate Optimal Nl, l=0,1,...,L Nl = np.ceil(2*e**(-2)*np.sqrt(Vl/ml)*np.sum(np.sqrt(Vl/ml))) # Step 4: Evaluate extra samples at each level as needed for the # new Nl for k in range(0,L+1):
res= 400 # plotting resolution frames = 10 ml = [] suml1 = [] suml2 = [] suml3 = [] suml4 = [] suml5 = [] while 1: # Step 1: run inital cases # set up runs ml.append(M**L*nx) os.mkdir("L"+str(L)) sums = mmc.preprocess(ml,N,L,frames,res) suml1.append(N) suml2.append(sums[0,:]) suml3.append(sums[1,:]) suml4.append(sums[2,:]) suml5.append(sums[3,:]) # Step 2: estimate the variance using the inital number of samples Vl = mmc.variance(suml3,suml2,suml1) # Step 3: Calculate Optimal Nl, l=0,1,...,L Nl = np.ceil(2*e**(-2)*np.sqrt(Vl/ml)*np.sum(np.sqrt(Vl/ml))) # Step 4: Evaluate extra samples at each level as needed for the # new Nl for k in range(0,L+1):
L = 4 frames = 10 Pl_mean = np.zeros((L+1,res), np.float) Pl1_mean= np.zeros((L+1,res), np.float) Pl_sigm = np.zeros((L+1,res), np.float) Pl1_sigm= np.zeros((L+1,res), np.float) levels = [] uii = [] mll = [] NN = 50 for m in range(0,L+1): mll.append(M**m*res) sums = mmc.preprocess(mll,NN,m,frames,res,uii) Pl_mean[m,:] = sums[2,:]/NN Pl_sigm[m,:] = sums[3,:]/(NN-1) - (1/(NN**2-NN))*(sums[2,:])**2 Pl1_mean[m,:] = sums[0,:]/NN Pl1_sigm[m,:] = sums[1,:]/(NN-1) - (1/(NN**2-NN))*(sums[0,:])**2 levels.append(m) figure(2) font = {'fontsize' : 20} subplot(2,1,1),plot(levels,np.log(np.abs(Pl_mean[:,0]))/np.log(M),'-s',levels[1:L+1],np.log(np.abs(Pl1_mean[1:L+1,0]))/np.log(M),'-*r',linewidth=2) legend(('$P_l$','$P_l-P_{l-1}$'),3) xlabel('$\ell$',font) ylabel('$log_M |mean|$',font) font = {'fontsize' : 20} subplot(2,1,2),plot(levels,np.log(Pl_sigm[:,0])/np.log(M),'-s',levels[1:L+1],np.log(Pl1_sigm[1:L+1,0])/np.log(M),'-*r',linewidth=2)
ml = [] ui = [] suml1 = [] suml2 = [] suml3 = [] suml4 = [] suml5 = [] while 1: # Step 1: run inital cases # set up runs ml.append(M**L * nx) if not (os.path.exists("L" + str(L))): os.mkdir("L" + str(L)) sums = mmc.preprocess(ml, N, L, frames, ui) suml1.append(N) suml2.append(sums[0, :]) suml3.append(sums[1, :]) suml4.append(sums[2, :]) suml5.append(sums[3, :]) # Step 2: estimate the variance using the inital number of samples Vl = mmc.variance(suml3, suml2, suml1) # Step 3: Calculate Optimal Nl, l=0,1,...,L Nl = np.ceil(2 * e**(-2) * np.sqrt(Vl / ml) * np.sum(np.sqrt(Vl / ml))) # Step 4: Evaluate extra samples at each level as needed for the # new Nl for k in range(0, L + 1):
res = 400 # plotting resolution frames = 10 ml = [] suml1 = [] suml2 = [] suml3 = [] suml4 = [] suml5 = [] while 1: # Step 1: run inital cases # set up runs ml.append(M**L * nx) os.mkdir("L" + str(L)) sums = mmc.preprocess(ml, N, L, frames, res) suml1.append(N) suml2.append(sums[0, :]) suml3.append(sums[1, :]) suml4.append(sums[2, :]) suml5.append(sums[3, :]) # Step 2: estimate the variance using the inital number of samples Vl = mmc.variance(suml3, suml2, suml1) # Step 3: Calculate Optimal Nl, l=0,1,...,L Nl = np.ceil(2 * e**(-2) * np.sqrt(Vl / ml) * np.sum(np.sqrt(Vl / ml))) # Step 4: Evaluate extra samples at each level as needed for the # new Nl for k in range(0, L + 1):